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1. Upper-atmosphere responses to the 2022 Hunga Tonga–Hunga Ha′apai volcanic eruption via acoustic gravity waves and air–sea interaction

Author

Q. Li, J. Xu, A. R. Gusman, H. Liu, W. Yuan, W. Liu, Y. Zhu, and X. Liu

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

A multi-group of strong atmospheric waves (wave packet nos. 1–5) over China associated with the 2022 Hunga Tonga–Hunga Ha′apai (HTHH) volcano eruptions were observed in the mesopause region using a ground-based airglow imager network. The horizontal phase speed of wave packet nos. 1 and 2 is approximately 309 and 236 m s−1, respectively, which is consistent with Lamb wave L0 mode and L1 mode from theoretical predictions. The amplitude of the Lamb wave L1 mode is larger than that of the L0 mode. The wave fronts of Lamb wave L0 and L1 below the lower thermosphere are vertical, while the wave fronts of L0 mode tilt forward above the lower atmosphere, exhibiting internal wave characteristics which show good agreement with the theoretical results. Two types of tsunamis were simulated; one type of tsunami is induced by the atmospheric-pressure wave (TIAPW), and the other type of tsunami is directly induced by the Tonga volcano eruption (TITVE). From backward ray-tracing analysis, the TIAPW and TITVE were likely the sources of wave packet nos. 3 and 4–5, respectively. The scale of tsunamis near the coast is very consistent with the atmospheric AGWs observed by the airglow network. The atmospheric gravity waves (AGWs) triggered by TITVE propagate nearly 3000 km inland with the support of a duct. The atmospheric-pressure wave can directly affect the upper atmosphere and can also be coupled with the upper atmosphere through the indirect way of generating a tsunami and, subsequently, tsunami-generating AGWs, which will provide a new understanding of the coupling between ocean and atmosphere.

Published
2024
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2. National forest carbon harvesting and allocation dataset for the period 2003 to 2018

Author

D. Wang, P. Ren, X. Xia, L. Fan, Z. Qin, X. Chen, and W. Yuan

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Forest harvesting is one of the anthropogenic activities that most significantly affect the carbon budget of forests. However, the absence of explicit spatial information on harvested carbon poses a huge challenge in assessing forest-harvesting impacts, as well as the forest carbon budget. This study utilized provincial-level statistical data on wood harvest, the tree cover loss (TCL) dataset, and a satellite-based vegetation index to develop a Long-term harvEst and Allocation of Forest Biomass (LEAF) dataset. The aim was to provide the spatial location of forest harvesting with a spatial resolution of 30 m and to quantify the post-harvest carbon dynamics. The validations against the surveyed forest harvesting in 133 cities and counties indicated a good performance of the LEAF dataset in capturing the spatial variation of harvested carbon, with a coefficient of determination (R2) of 0.83 between the identified and surveyed harvested carbon. The linear regression slope was up to 0.99. Averaged from 2003 to 2018, forest harvesting removed 68.3 ± 9.3 Mt C yr−1, of which more than 80 % was from selective logging. Of the harvested carbon, 19.6 ± 4.0 %, 2.1 ± 1.1 %, 35.5 ± 12.6 % 6.2 ± 0.3 %, 17.5 ± 0.9 %, and 19.1 ± 9.8 % entered the fuelwood, paper and paperboard, wood-based panels, solid wooden furniture, structural constructions, and residue pools, respectively. Direct combustion of fuelwood was the primary source of carbon emissions after wood harvest. However, carbon can be stored in wood products for a long time, and by 2100, almost 40 % of the carbon harvested during the study period will still be retained. This dataset is expected to provide a foundation and reference for estimating the forestry and national carbon budgets. The 30 m × 30 m harvested-carbon dataset from forests in China can be downloaded at https://doi.org/10.6084/m9.figshare.23641164.v2 (Wang et al., 2023).

Published
2024
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3. Measurement report: Brown carbon aerosol in polluted urban air of the North China Plain – day–night differences in the chromophores and optical properties

Author

Y. Gong, R.-J. Huang, L. Yang, T. Wang, W. Yuan, W. Xu, W. Cao, Y. Wang, and Y. Li

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Brown carbon (BrC) aerosol is light-absorbing organic carbon that affects radiative forcing and atmospheric photochemistry. The BrC chromophoric composition and its linkage to optical properties at the molecular level, however, are still not well characterized. In this study, we investigate the day–night differences in the chromophoric composition (38 species) and optical properties of water-soluble and water-insoluble BrC fractions (WS-BrC and WIS-BrC) in aerosol samples collected in Shijiazhuang, one of the most polluted cities in China. We found that the light absorption contribution of WS-BrC to total BrC at 365 nm was higher during the day (62±8 %) than during the night (47±26 %), which is in line with the difference in chromophoric polarity between daytime (more polar nitrated aromatics) and nighttime (more less-polar polycyclic aromatic hydrocarbons, PAHs). The high polarity and water solubility of BrC in the daytime suggests the enhanced contribution of secondary formation to BrC during the day. There was a decrease in the mass absorption efficiency of BrC from nighttime to daytime (2.88±0.24 vs. 2.58±0.14 for WS-BrC and 1.43±0.83 vs. 1.02±0.49 m2 g C−1 for WIS-BrC, respectively). Large polycyclic aromatic hydrocarbons (PAHs) with four- to six-ring PAHs and nitrophenols contributed to 76.7 % of the total light absorption between 300–420 nm at nighttime, while nitrocatechols and two- to three-ring oxygenated PAHs accounted for 52.6 % of the total light absorption during the day. The total mass concentrations of the identified chromophores showed larger day–night difference during the low-pollution period (day-to-night ratio of 4.3) than during the high-pollution period (day-to-night ratio of 1.8). The large day–night difference in BrC composition and absorption, therefore, should be considered when estimating the sources, atmospheric processes, and impacts of BrC.

Published
2023
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4. High-resolution distribution maps of single-season rice in China from 2017 to 2022

Author

R. Shen, B. Pan, Q. Peng, J. Dong, X. Chen, X. Zhang, T. Ye, J. Huang, and W. Yuan

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. However, there is no high-resolution map of rice covering all of China. This study developed a new rice-mapping method by combining optical and synthetic aperture radar (SAR) images in cloudy areas based on the time-weighted dynamic time warping (TWDTW) method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data. On average, the user's, producer's, and overall accuracy values over all investigated provincial administrative regions were 73.08 %, 82.81 %, and 85.23 %, respectively. Compared with the statistical data from 2017 to 2019, the distribution maps explained 83 % of the spatial variation of county-level planting areas on average. The distribution maps can be obtained at https://doi.org/10.57760/sciencedb.06963 (Shen et al., 2023).

Published
2023
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5. Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

Author

A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, Z. Ahmad, J. Ahmed, B. Aimard, F. Akbar, B. Ali-Mohammadzadeh, K. Allison, S. Alonso Monsalve, M. AlRashed, C. Alt, A. Alton, R. Alvarez, P. Amedo, J. Anderson, C. Andreopoulos, M. Andreotti, M. Andrews, F. Andrianala, S. Andringa, N. Anfimov, A. Ankowski, M. Antoniassi, M. Antonova, A. Antoshkin, S. Antusch, A. Aranda-Fernandez, L. Arellano, L. O. Arnold, M. A. Arroyave, J. Asaadi, L. Asquith, A. Aurisano, V. Aushev, D. Autiero, V. Ayala Lara, M. Ayala-Torres, F. Azfar, A. Back, H. Back, J. J. Back, C. Backhouse, I. Bagaturia, L. Bagby, N. Balashov, S. Balasubramanian, P. Baldi, B. Baller, B. Bambah, F. Barao, G. Barenboim, G. Barker, W. Barkhouse, C. Barnes, G. Barr, J. Barranco Monarca, A. Barros, N. Barros, J. L. Barrow, A. Basharina-Freshville, A. Bashyal, V. Basque, C. Batchelor, J. Battat, F. Battisti, F. Bay, M. C. Q. Bazetto, J. L. Bazo Alba, J. F. Beacom, E. Bechetoille, B. Behera, E. Belchior Batista das Chagas, L. Bellantoni, G. Bellettini, V. Bellini, O. Beltramello, N. Benekos, C. Benitez Montiel, F. Bento Neves, J. Berger, S. Berkman, P. Bernardini, R. M. Berner, A. Bersani, S. Bertolucci, M. Betancourt, A. Betancur Rodríguez, A. Bevan, Y. Bezawada, A. T. Bezerra, T. J. Bezerra, A. Bhardwaj, V. Bhatnagar, M. Bhattacharjee, D. Bhattarai, S. Bhuller, B. Bhuyan, S. Biagi, J. Bian, M. Biassoni, K. Biery, B. Bilki, M. Bishai, A. Bitadze, A. Blake, F. D. M. Blaszczyk, G. C. Blazey, E. Blucher, J. Boissevain, S. Bolognesi, T. Bolton, L. Bomben, M. Bonesini, C. Bonilla-Diaz, F. Bonini, A. Booth, F. Boran, S. Bordoni, A. Borkum, N. Bostan, P. Bour, D. Boyden, J. Bracinik, D. Braga, D. Brailsford, A. Branca, A. Brandt, J. Bremer, C. Brew, S. J. Brice, C. Brizzolari, C. Bromberg, J. Brooke, A. Bross, G. Brunetti, M. Brunetti, N. Buchanan, H. Budd, I. Butorov, I. Cagnoli, T. Cai, D. Caiulo, R. Calabrese, P. Calafiura, J. Calcutt, M. Calin, S. Calvez, E. Calvo, A. Caminata, A. Campos Benitez, D. Caratelli, D. Carber, J. M. Carceller, G. Carini, B. Carlus, M. F. Carneiro, P. Carniti, I. Caro Terrazas, H. Carranza, T. Carroll, J. F. Castaño Forero, A. Castillo, C. Castromonte, E. Catano-Mur, C. Cattadori, F. Cavalier, G. Cavallaro, F. Cavanna, S. Centro, G. Cerati, A. Cervelli, A. Cervera Villanueva, M. Chalifour, A. Chappell, E. Chardonnet, N. Charitonidis, A. Chatterjee, S. Chattopadhyay, M. S. Chavarry Neyra, H. Chen, M. Chen, Y. Chen, Z. Chen, Z. Chen-Wishart, Y. Cheon, D. Cherdack, C. Chi, S. Childress, R. Chirco, A. Chiriacescu, K. Cho, S. Choate, D. Chokheli, P. S. Chong, A. Christensen, D. Christian, G. Christodoulou, A. Chukanov, M. Chung, E. Church, V. Cicero, P. Clarke, G. Cline, T. E. Coan, A. G. Cocco, J. Coelho, J. Collot, N. Colton, E. Conley, R. Conley, J. Conrad, M. Convery, S. Copello, P. Cova, L. Cremaldi, L. Cremonesi, J. I. Crespo-Anadón, M. Crisler, E. Cristaldo, J. Crnkovic, R. Cross, A. Cudd, C. Cuesta, Y. Cui, D. Cussans, J. Dai, O. Dalager, H. Da Motta, L. Da Silva Peres, C. David, Q. David, G. S. Davies, S. Davini, J. Dawson, K. De, S. De, P. Debbins, I. De Bonis, M. Decowski, A. De Gouvea, P. C. De Holanda, I. L. De Icaza Astiz, A. Deisting, P. De Jong, A. Delbart, V. De Leo, D. Delepine, M. Delgado, A. Dell’Acqua, N. Delmonte, P. De Lurgio, J. R. De Mello Neto, D. M. DeMuth, S. Dennis, C. Densham, G. W. Deptuch, A. De Roeck, V. De Romeri, G. De Souza, R. Devi, R. Dharmapalan, M. Dias, J. Diaz, F. Díaz, F. Di Capua, A. Di Domenico, S. Di Domizio, L. Di Giulio, P. Ding, L. Di Noto, G. Dirkx, C. Distefano, R. Diurba, M. Diwan, Z. Djurcic, D. Doering, S. Dolan, F. Dolek, M. Dolinski, L. Domine, Y. Donon, D. Douglas, A. Dragone, G. Drake, F. Drielsma, L. Duarte, D. Duchesneau, K. Duffy, P. Dunne, B. Dutta, H. Duyang, O. Dvornikov, D. Dwyer, A. Dyshkant, M. Eads, A. Earle, D. Edmunds, J. Eisch, L. Emberger, S. Emery, P. Englezos, A. Ereditato, T. Erjavec, C. Escobar, L. Escudero Sanchez, G. Eurin, J. J. Evans, E. Ewart, A. C. Ezeribe, K. Fahey, A. Falcone, M. Fani’, C. Farnese, Y. Farzan, D. Fedoseev, J. Felix, Y. Feng, E. Fernandez-Martinez, P. Fernandez Menendez, F. Ferraro, L. Fields, P. Filip, F. Filthaut, R. Fine, G. Fiorillo, M. Fiorini, V. Fischer, R. S. Fitzpatrick, W. Flanagan, B. Fleming, R. Flight, S. Fogarty, W. Foreman, J. Fowler, W. Fox, J. Franc, K. Francis, D. Franco, J. Freeman, J. Freestone, J. Fried, A. Friedland, S. Fuess, I. K. Furic, K. Furman, A. P. Furmanski, A. Gabrielli, A. Gago, H. Gallagher, A. Gallas, A. Gallego-Ros, N. Gallice, V. Galymov, E. Gamberini, T. Gamble, F. Ganacim, R. Gandhi, S. Ganguly, F. Gao, S. Gao, D. Garcia-Gamez, M. Á. García-Peris, S. Gardiner, D. Gastler, J. Gauvreau, P. Gauzzi, G. Ge, N. Geffroy, B. Gelli, A. Gendotti, S. Gent, Z. Ghorbani-Moghaddam, P. Giammaria, T. Giammaria, N. Giangiacomi, D. Gibin, I. Gil-Botella, S. Gilligan, C. Girerd, A. Giri, D. Gnani, O. Gogota, M. Gold, S. Gollapinni, K. Gollwitzer, R. A. Gomes, L. Gomez Bermeo, L. S. Gomez Fajardo, F. Gonnella, D. González Caamaño, D. Gonzalez-Diaz, M. Gonzalez-Lopez, M. C. Goodman, O. Goodwin, S. Goswami, C. Gotti, E. Goudzovski, C. Grace, R. Gran, E. Granados, P. Granger, C. Grant, D. Gratieri, P. Green, S. Green, S. Greenberg, L. Greenler, J. Greer, J. Grenard, C. Griffith, M. Groh, J. Grudzinski, K. Grzelak, W. Gu, E. Guardincerri, V. Guarino, M. Guarise, R. Guenette, E. Guerard, M. Guerzoni, D. Guffanti, A. Guglielmi, B. Guo, A. Gupta, V. Gupta, K. Guthikonda, P. Guzowski, M. M. Guzzo, S. Gwon, C. Ha, K. Haaf, A. Habig, H. Hadavand, R. Haenni, A. Hahn, J. Haiston, P. Hamacher-Baumann, T. Hamernik, P. Hamilton, J. Han, D. A. Harris, J. Hartnell, T. Hartnett, J. Harton, T. Hasegawa, C. Hasnip, R. Hatcher, K. W. Hatfield, A. Hatzikoutelis, C. Hayes, K. Hayrapetyan, J. Hays, E. Hazen, M. He, A. Heavey, K. M. Heeger, J. Heise, S. Henry, M. Hernandez Morquecho, K. Herner, J. Hewes, C. Hilgenberg, T. Hill, S. J. Hillier, A. Himmel, E. Hinkle, L. R. Hirsch, J. Ho, J. Hoff, A. Holin, E. Hoppe, G. A. Horton-Smith, M. Hostert, A. Hourlier, B. Howard, R. Howell, I. Hristova, M. S. Hronek, J. Huang, Z. Hulcher, G. Iles, N. Ilic, A. M. Iliescu, R. Illingworth, G. Ingratta, A. Ioannisian, B. Irwin, L. Isenhower, R. Itay, C. M. Jackson, V. Jain, E. James, W. Jang, B. Jargowsky, F. Jediny, D. Jena, Y. Jeong, C. Jesús-Valls, X. Ji, J. Jiang, L. Jiang, S. Jiménez, A. Jipa, F. Joaquim, W. Johnson, N. Johnston, B. Jones, M. Judah, C. Jung, T. Junk, Y. Jwa, M. Kabirnezhad, A. Kaboth, I. Kadenko, I. Kakorin, A. Kalitkina, D. Kalra, F. Kamiya, D. M. Kaplan, G. Karagiorgi, G. Karaman, A. Karcher, M. Karolak, Y. Karyotakis, S. Kasai, S. P. Kasetti, L. Kashur, N. Kazaryan, E. Kearns, P. Keener, K. J. Kelly, E. Kemp, O. Kemularia, W. Ketchum, S. H. Kettell, M. Khabibullin, A. Khotjantsev, A. Khvedelidze, D. Kim, B. King, B. Kirby, M. Kirby, J. Klein, A. Klustova, T. Kobilarcik, K. Koehler, L. W. Koerner, D. H. Koh, S. Kohn, P. P. Koller, L. Kolupaeva, D. Korablev, M. Kordosky, T. Kosc, U. Kose, V. Kostelecky, K. Kothekar, R. Kralik, L. Kreczko, F. Krennrich, I. Kreslo, W. Kropp, T. Kroupova, S. Kubota, Y. Kudenko, V. A. Kudryavtsev, S. Kuhlmann, S. Kulagin, J. Kumar, P. Kumar, P. Kunze, R. Kuravi, N. Kurita, C. Kuruppu, V. Kus, T. Kutter, J. Kvasnicka, D. Kwak, A. Lambert, B. Land, C. E. Lane, K. Lang, T. Langford, M. Langstaff, J. Larkin, P. Lasorak, D. Last, A. Laundrie, G. Laurenti, A. Lawrence, I. Lazanu, R. LaZur, M. Lazzaroni, T. Le, S. Leardini, J. Learned, P. LeBrun, T. LeCompte, C. Lee, S. Lee, G. Lehmann Miotto, R. Lehnert, M. Leigui de Oliveira, M. Leitner, L. M. Lepin, S. Li, Y. Li, H. Liao, C. Lin, Q. Lin, S. Lin, R. A. Lineros, J. Ling, A. Lister, B. R. Littlejohn, J. Liu, Y. Liu, S. Lockwitz, T. Loew, M. Lokajicek, I. Lomidze, K. Long, T. Lord, J. LoSecco, W. C. Louis, X. Lu, K. Luk, B. Lunday, X. Luo, E. Luppi, T. Lux, V. P. Luzio, J. Maalmi, D. MacFarlane, A. Machado, P. Machado, C. Macias, J. Macier, A. Maddalena, A. Madera, P. Madigan, S. Magill, K. Mahn, A. Maio, A. Major, J. A. Maloney, G. Mandrioli, R. C. Mandujano, J. C. Maneira, L. Manenti, S. Manly, A. Mann, K. Manolopoulos, M. Manrique Plata, V. N. Manyam, M. Marchan, A. Marchionni, W. Marciano, D. Marfatia, C. Mariani, J. Maricic, R. Marie, F. Marinho, A. D. Marino, T. Markiewicz, D. Marsden, M. Marshak, C. Marshall, J. Marshall, J. Marteau, J. Martín-Albo, N. Martinez, D. A. Martinez Caicedo, P. Martínez Miravé, S. Martynenko, V. Mascagna, K. Mason, A. Mastbaum, F. Matichard, S. Matsuno, J. Matthews, C. Mauger, N. Mauri, K. Mavrokoridis, I. Mawby, R. Mazza, A. Mazzacane, E. Mazzucato, T. McAskill, E. McCluskey, N. McConkey, K. S. McFarland, C. McGrew, A. McNab, A. Mefodiev, P. Mehta, P. Melas, O. Mena, H. Mendez, P. Mendez, D. P. Méndez, A. Menegolli, G. Meng, M. Messier, W. Metcalf, M. Mewes, H. Meyer, T. Miao, G. Michna, V. Mikola, R. Milincic, G. Miller, W. Miller, J. Mills, O. Mineev, A. Minotti, O. G. Miranda, S. Miryala, C. Mishra, S. Mishra, A. Mislivec, M. Mitchell, D. Mladenov, I. Mocioiu, K. Moffat, N. Moggi, R. Mohanta, T. A. Mohayai, N. Mokhov, J. A. Molina, L. Molina Bueno, E. Montagna, A. Montanari, C. Montanari, D. Montanari, D. Montanino, L. M. Montaño Zetina, S. Moon, M. Mooney, A. F. Moor, D. Moreno, D. Moretti, C. Morris, C. Mossey, M. Mote, E. Motuk, C. A. Moura, J. Mousseau, G. Mouster, W. Mu, L. Mualem, J. Mueller, M. Muether, S. Mufson, F. Muheim, A. Muir, M. Mulhearn, D. Munford, H. Muramatsu, M. Murphy, S. Murphy, J. Musser, J. Nachtman, Y. Nagai, S. Nagu, M. Nalbandyan, R. Nandakumar, D. Naples, S. Narita, A. Nath, A. Navrer-Agasson, N. Nayak, M. Nebot-Guinot, K. Negishi, J. K. Nelson, J. Nesbit, M. Nessi, D. Newbold, M. Newcomer, H. Newton, R. Nichol, F. Nicolas-Arnaldos, A. Nikolica, E. Niner, K. Nishimura, A. Norman, A. Norrick, R. Northrop, P. Novella, J. A. Nowak, M. Oberling, J. Ochoa-Ricoux, A. Olivier, A. Olshevskiy, Y. Onel, Y. Onishchuk, J. Ott, L. Pagani, G. Palacio, O. Palamara, S. Palestini, J. M. Paley, M. Pallavicini, C. Palomares, W. Panduro Vazquez, E. Pantic, V. Paolone, V. Papadimitriou, R. Papaleo, A. Papanestis, S. Paramesvaran, S. Parke, E. Parozzi, Z. Parsa, M. Parvu, S. Pascoli, L. Pasqualini, J. Pasternak, J. Pater, C. Patrick, L. Patrizii, R. B. Patterson, S. Patton, T. Patzak, A. Paudel, B. Paulos, L. Paulucci, Z. Pavlovic, G. Pawloski, D. Payne, V. Pec, S. J. Peeters, A. Pena Perez, E. Pennacchio, A. Penzo, O. L. Peres, J. Perry, D. Pershey, G. Pessina, G. Petrillo, C. Petta, R. Petti, V. Pia, F. Piastra, L. Pickering, F. Pietropaolo, V. L. Pimentel, G. Pinaroli, K. Plows, R. Plunkett, F. Pompa, X. Pons, N. Poonthottathil, F. Poppi, S. Pordes, J. Porter, S. Porzio, M. Potekhin, R. Potenza, B. V. Potukuchi, J. Pozimski, M. Pozzato, S. Prakash, T. Prakash, M. Prest, S. Prince, F. Psihas, D. Pugnere, X. Qian, J. Raaf, V. Radeka, J. Rademacker, B. Radics, A. Rafique, E. Raguzin, M. Rai, M. Rajaoalisoa, I. Rakhno, A. Rakotonandrasana, L. Rakotondravohitra, R. Rameika, M. Ramirez Delgado, B. Ramson, A. Rappoldi, G. Raselli, P. Ratoff, S. Raut, H. Razafinime, R. Razakamiandra, E. M. Rea, J. S. Real, B. Rebel, R. Rechenmacher, M. Reggiani-Guzzo, J. Reichenbacher, S. D. Reitzner, H. Rejeb Sfar, A. Renshaw, S. Rescia, F. Resnati, M. Ribas, S. Riboldi, C. Riccio, G. Riccobene, L. C. Rice, J. S. Ricol, A. Rigamonti, Y. Rigaut, E. V. Rincón, H. Ritchie-Yates, D. Rivera, A. Robert, J. Rocabado Rocha, L. Rochester, M. Roda, P. Rodrigues, J. V. Rodrigues da Silva Leite, M. J. Rodriguez Alonso, J. Rodriguez Rondon, S. Rosauro-Alcaraz, P. Rosier, B. Roskovec, M. Rossella, M. Rossi, J. Rout, P. Roy, A. Rubbia, C. Rubbia, B. Russell, D. Ruterbories, A. Rybnikov, A. Saa-Hernandez, R. Saakyan, S. Sacerdoti, N. Sahu, P. Sala, N. Samios, O. Samoylov, M. Sanchez, V. Sandberg, D. A. Sanders, D. Sankey, N. Saoulidou, P. Sapienza, C. Sarasty, I. Sarcevic, G. Savage, V. Savinov, A. Scaramelli, A. Scarff, A. Scarpelli, T. Schefke, H. Schellman, S. Schifano, P. Schlabach, D. Schmitz, A. W. Schneider, K. Scholberg, A. Schukraft, E. Segreto, A. Selyunin, C. R. Senise, J. Sensenig, D. Sgalaberna, M. Shaevitz, S. Shafaq, F. Shaker, M. Shamma, R. Sharankova, H. R. Sharma, R. Sharma, R. K. Sharma, K. Shaw, T. Shaw, K. Shchablo, C. Shepherd-Themistocleous, A. Sheshukov, S. Shin, I. Shoemaker, D. Shooltz, R. Shrock, H. Siegel, L. Simard, J. Sinclair, G. Sinev, J. Singh, L. Singh, P. Singh, V. Singh, R. Sipos, F. Sippach, G. Sirri, A. Sitraka, K. Siyeon, K. Skarpaas, E. Smith, P. Smith, J. Smolik, M. Smy, E. Snider, P. Snopok, D. Snowden-Ifft, M. Soares Nunes, H. Sobel, M. Soderberg, S. Sokolov, C. J. Solano Salinas, S. Söldner-Rembold, S. Soleti, N. Solomey, V. Solovov, W. E. Sondheim, M. Sorel, A. Sotnikov, J. Soto-Oton, F. Soto Ugaldi, A. Sousa, K. Soustruznik, F. Spagliardi, M. Spanu, J. Spitz, N. J. C. Spooner, K. Spurgeon, M. Stancari, L. Stanco, C. Stanford, R. Stein, H. Steiner, A. F. Steklain Lisbôa, J. Stewart, B. Stillwell, J. Stock, F. Stocker, T. Stokes, M. Strait, T. Strauss, L. Strigari, A. Stuart, J. G. Suarez, J. Suárez Sunción, H. Sullivan, A. Surdo, V. Susic, L. Suter, C. Sutera, Y. Suvorov, R. Svoboda, B. Szczerbinska, A. M. Szelc, N. Talukdar, H. Tanaka, S. Tang, B. Tapia Oregui, A. Tapper, S. Tariq, E. Tarpara, N. Tata, E. Tatar, R. Tayloe, A. Teklu, P. Tennessen, M. Tenti, K. Terao, C. A. Ternes, F. Terranova, G. Testera, T. Thakore, A. Thea, C. Thorn, S. Timm, V. Tishchenko, L. Tomassetti, A. Tonazzo, D. Torbunov, M. Torti, M. Tortola, F. Tortorici, N. Tosi, D. Totani, M. Toups, C. Touramanis, R. Travaglini, J. Trevor, S. Trilov, W. H. Trzaska, Y. Tsai, Z. Tsamalaidze, K. Tsang, N. Tsverava, S. Z. Tu, S. Tufanli, C. Tull, J. Tyler, E. Tyley, M. Tzanov, L. Uboldi, M. A. Uchida, J. Urheim, T. Usher, S. Uzunyan, M. R. Vagins, P. Vahle, S. Valder, G. D. Valdiviesso, E. Valencia, R. Valentim, Z. Vallari, E. Vallazza, J. W. Valle, S. Vallecorsa, R. Van Berg, R. G. Van de Water, D. Vanegas Forero, D. Vannerom, F. Varanini, D. Vargas Oliva, G. Varner, J. Vasel, S. Vasina, G. Vasseur, N. Vaughan, K. Vaziri, S. Ventura, A. Verdugo, S. Vergani, M. A. Vermeulen, M. Verzocchi, M. Vicenzi, H. Vieira de Souza, C. Vignoli, C. Vilela, B. Viren, T. Vrba, T. Wachala, A. V. Waldron, M. Wallbank, C. Wallis, T. Walton, H. Wang, J. Wang, L. Wang, M. H. Wang, X. Wang, Y. Wang, K. Warburton, D. Warner, M. Wascko, D. Waters, A. Watson, K. Wawrowska, P. Weatherly, A. Weber, M. Weber, H. Wei, A. Weinstein, D. Wenman, M. Wetstein, A. White, L. H. Whitehead, D. Whittington, M. J. Wilking, A. Wilkinson, C. Wilkinson, Z. Williams, F. Wilson, R. J. Wilson, W. Wisniewski, J. Wolcott, T. Wongjirad, A. Wood, K. Wood, E. Worcester, M. Worcester, K. Wresilo, C. Wret, W. Wu, Y. Xiao, B. Yaeggy, E. Yandel, G. Yang, K. Yang, T. Yang, A. Yankelevich, N. Yershov, K. Yonehara, Y. Yoon, T. Young, B. Yu, H. Yu, J. Yu, Y. Yu, W. Yuan, R. Zaki, J. Zalesak, L. Zambelli, B. Zamorano, A. Zani, L. Zazueta, G. Zeller, J. Zennamo, K. Zeug, C. Zhang, S. Zhang, Y. Zhang, M. Zhao, E. Zhivun, G. Zhu, E. D. Zimmerman, S. Zucchelli, J. Zuklin, V. Zutshi, R. Zwaska, and DUNE Collaboration

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 $$\pm 0.6$$ ± 0.6 % and 84.1 $$\pm 0.6$$ ± 0.6 %, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.

Published
2023
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6. A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests

Author

X. Yang, X. Chen, J. Ren, W. Yuan, L. Liu, J. Liu, D. Chen, Y. Xiao, Q. Song, Y. Du, S. Wu, L. Fan, X. Dai, Y. Wang, and Y. Su

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

The quantification of large-scale leaf-age-dependent leaf area index has been lacking in tropical and subtropical evergreen broadleaved forests (TEFs), despite the recognized importance of leaf age in influencing leaf photosynthetic capacity in this biome. Here, we simplified the canopy leaves of TEFs into three age cohorts (i.e., young, mature, and old, with different photosynthesis capacities; i.e., Vc,max) and proposed a novel neighbor-based approach to develop the first gridded dataset of a monthly leaf-age-dependent leaf area index (LAI) product (referred to as Lad-LAI) at 0.25∘ spatial resolution over the continental scale during 2001–2018 from satellite observations of sun-induced chlorophyll fluorescence (SIF) that was reconstructed from MODIS and TROPOMI (the TROPOspheric Monitoring Instrument). The new Lad-LAI products show good performance in capturing the seasonality of three LAI cohorts, i.e., young (LAIyoung; the Pearson correlation coefficient of R=0.36), mature (LAImature; R=0.77), and old (LAIold; R=0.59) leaves at eight camera-based observation sites (four in South America, three in subtropical Asia, and one in the Democratic Republic of the Congo (DRC)) and can also represent their interannual dynamics, validated only at the Barro Colorado site, with R being equal to 0.54, 0.64, and 0.49 for LAIyoung, LAImature, and LAIold, respectively. Additionally, the abrupt drops in LAIold are mostly consistent with the seasonal litterfall peaks at 53 in situ measurements across the whole tropical region (R=0.82). The LAI seasonality of young and mature leaves also agrees well with the seasonal dynamics of the enhanced vegetation index (EVI; R=0.61), which is a proxy for photosynthetically effective leaves. Spatially, the gridded Lad-LAI data capture a dry-season green-up of canopy leaves across the wet Amazonian areas, where mean annual precipitation exceeds 2000 mm yr−1, consistent with previous satellite-based analyses. The spatial patterns clustered from the three LAI cohorts also coincide with those clustered from climatic variables over the whole TEF region. Herein, we provide the average seasonality of three LAI cohorts as the main dataset and their time series as a supplementary dataset. These Lad-LAI products are available at https://doi.org/10.6084/m9.figshare.21700955.v4 (Yang et al., 2022).

Published
2023
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7. Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups

Author

Y. Chen, X. Chen, M. Xue, C. Yang, W. Zheng, J. Cao, W. Yan, and W. Yuan

Subjects
Technology, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350
Abstract

The Budyko framework is a simple and effective tool for estimating the water balance of watersheds. Quantification of the watershed-characteristic-related parameter (Pw) is critical for accurate water balance simulations with the Budyko framework. However, there is no universal method for calculating Pw as the interactions between hydrologic, climatic, and watershed characteristic factors differ greatly across watersheds. To fill this research gap, this study introduced the principle of hydrologically similar groups into the Budyko framework for quantifying the Pw of watersheds in similar environments. We first classified the 366 selected watersheds worldwide into six hydrologically similar groups based on watershed attributes, including climate, soil, and vegetation. Results show that soil moisture (SM) and fractional vegetation cover (FVC) are two controlling factors of the Pw in each group. The SM exhibits a power-law relationship with the Pw values, with increasing SM leading to higher Pw values in dry watersheds (SM ≤ 20 mm) and lower Pw values in humid watersheds (SM > 20 mm). Additionally, the FVC shows to be linearly correlated with the Pw values in most hydrologically similar groups, except in that group with moist soil and no strong rainfall seasonality (SM >20 mm and seasonal index (SI) ≤ 0.4). Multiple non-linear regression models between Pw and the controlling factors (SM and FVC) were developed to individually estimate the Pw of six hydrologically similar groups. Cross-validations using the bootstrap sampling method (R2=0.63) and validations of time-series Global Runoff Data Centre (GRDC) data (R2=0.89) both indicate that the proposed models perform satisfactorily in estimating the Pw parameter in the Budyko framework. Overall, this study is a new attempt to quantify the unknown Pw in the Budyko framework using the method for hydrologically similar groups. The results will be helpful in improving the applicability of the Budyko framework for estimating the annual runoff of watersheds in diverse climates and with different characteristics.

Published
2023
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8. Harmonising the land-use flux estimates of global models and national inventories for 2000–2020

Author

G. Grassi, C. Schwingshackl, T. Gasser, R. A. Houghton, S. Sitch, J. G. Canadell, A. Cescatti, P. Ciais, S. Federici, P. Friedlingstein, W. A. Kurz, M. J. Sanz Sanchez, R. Abad Viñas, R. Alkama, S. Bultan, G. Ceccherini, S. Falk, E. Kato, D. Kennedy, J. Knauer, A. Korosuo, J. Melo, M. J. McGrath, J. E. M. S. Nabel, B. Poulter, A. A. Romanovskaya, S. Rossi, H. Tian, A. P. Walker, W. Yuan, X. Yue, and J. Pongratz

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO2 yr−1) in global land-use CO2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO2 yr−1 for the period 2000–2020, and NGHGIs, which estimate a sink of −1.9 GtCO2 yr−1 in the same period. Most of the gap is found on forest land (3.5 GtCO2 yr−1), with differences also for deforestation (2.4 GtCO2 yr−1), for fluxes from other land uses (1.0 GtCO2 yr−1) and to a lesser extent for fluxes from organic soils (0.2 GtCO2 yr−1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (−6.4 GtCO2 yr−1) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO2 yr−1) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement. Data from this study are openly available online via the Zenodo portal (Grassi et al., 2023) at https://doi.org/10.5281/zenodo.7650360.

Published
2023
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9. Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Author

A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, A. Aduszkiewicz, J. Aguilar, Z. Ahmad, J. Ahmed, B. Aimard, B. Ali-Mohammadzadeh, T. Alion, K. Allison, S. Alonso Monsalve, M. AlRashed, C. Alt, A. Alton, R. Alvarez, P. Amedo, J. Anderson, C. Andreopoulos, M. Andreotti, M. Andrews, F. Andrianala, S. Andringa, N. Anfimov, A. Ankowski, M. Antoniassi, M. Antonova, A. Antoshkin, S. Antusch, A. Aranda-Fernandez, L. Arellano, L. O. Arnold, M. A. Arroyave, J. Asaadi, L. Asquith, A. Aurisano, V. Aushev, D. Autiero, V. Ayala Lara, M. Ayala-Torres, F. Azfar, M. Babicz, A. Back, H. Back, J. J. Back, C. Backhouse, I. Bagaturia, L. Bagby, N. Balashov, S. Balasubramanian, P. Baldi, B. Baller, B. Bambah, F. Barao, G. Barenboim, G. Barker, W. Barkhouse, C. Barnes, G. Barr, J. Barranco Monarca, A. Barros, N. Barros, J. L. Barrow, A. Basharina-Freshville, A. Bashyal, V. Basque, C. Batchelor, E. Batista das Chagas, J. Battat, F. Battisti, F. Bay, M. C. Q. Bazetto, J. Bazo Alba, J. F. Beacom, E. Bechetoille, B. Behera, C. Beigbeder, L. Bellantoni, G. Bellettini, V. Bellini, O. Beltramello, N. Benekos, C. Benitez Montiel, F. Bento Neves, J. Berger, S. Berkman, P. Bernardini, R. M. Berner, A. Bersani, S. Bertolucci, M. Betancourt, A. Betancur Rodríguez, A. Bevan, Y. Bezawada, T. S. Bezerra, A. Bhardwaj, V. Bhatnagar, M. Bhattacharjee, D. Bhattarai, S. Bhuller, B. Bhuyan, S. Biagi, J. Bian, M. Biassoni, K. Biery, B. Bilki, M. Bishai, A. Bitadze, A. Blake, F. Blaszczyk, G. Blazey, E. Blucher, J. Boissevain, S. Bolognesi, T. Bolton, L. Bomben, M. Bonesini, M. Bongrand, C. Bonilla-Diaz, F. Bonini, A. Booth, F. Boran, S. Bordoni, A. Borkum, N. Bostan, P. Bour, C. Bourgeois, D. Boyden, J. Bracinik, D. Braga, D. Brailsford, A. Branca, A. Brandt, J. Bremer, D. Breton, C. Brew, S. J. Brice, C. Brizzolari, C. Bromberg, J. Brooke, A. Bross, G. Brunetti, M. Brunetti, N. Buchanan, H. Budd, I. Butorov, I. Cagnoli, T. Cai, D. Caiulo, R. Calabrese, P. Calafiura, J. Calcutt, M. Calin, S. Calvez, E. Calvo, A. Caminata, M. Campanelli, D. Caratelli, D. Carber, J. Carceller, G. Carini, B. Carlus, M. F. Carneiro, P. Carniti, I. Caro Terrazas, H. Carranza, T. Carroll, J. F. Castaño Forero, A. Castillo, C. Castromonte, E. Catano-Mur, C. Cattadori, F. Cavalier, G. Cavallaro, F. Cavanna, S. Centro, G. Cerati, A. Cervelli, A. Cervera Villanueva, M. Chalifour, A. Chappell, E. Chardonnet, N. Charitonidis, A. Chatterjee, S. Chattopadhyay, M. Chavarry Neyra, H. Chen, M. Chen, Y. Chen, Z. Chen, Z. Chen-Wishart, Y. Cheon, D. Cherdack, C. Chi, S. Childress, R. Chirco, A. Chiriacescu, G. Chisnall, K. Cho, S. Choate, D. Chokheli, P. S. Chong, A. Christensen, D. Christian, G. Christodoulou, A. Chukanov, M. Chung, E. Church, V. Cicero, P. Clarke, G. Cline, T. E. Coan, A. G. Cocco, J. Coelho, N. Colton, E. Conley, R. Conley, J. Conrad, M. Convery, S. Copello, P. Cova, L. Cremaldi, L. Cremonesi, J. I. Crespo-Anadón, M. Crisler, E. Cristaldo, J. Crnkovic, R. Cross, A. Cudd, C. Cuesta, Y. Cui, D. Cussans, O. Dalager, H. Da Motta, L. Da Silva Peres, C. David, Q. David, G. S. Davies, S. Davini, J. Dawson, K. De, S. De, P. Debbins, I. De Bonis, M. Decowski, A. De Gouvea, P. C. De Holanda, I. L. De Icaza Astiz, A. Deisting, P. De Jong, A. Delbart, D. Delepine, M. Delgado, A. Dell’Acqua, N. Delmonte, P. De Lurgio, J. R. De Mello Neto, D. M. DeMuth, S. Dennis, C. Densham, G. W. Deptuch, A. De Roeck, V. De Romeri, G. De Souza, R. Devi, R. Dharmapalan, M. Dias, F. Diaz, J. Diaz, S. Di Domizio, L. Di Giulio, P. Ding, L. Di Noto, G. Dirkx, C. Distefano, R. Diurba, M. Diwan, Z. Djurcic, D. Doering, S. Dolan, F. Dolek, M. Dolinski, L. Domine, Y. Donon, D. Douglas, D. Douillet, A. Dragone, G. Drake, F. Drielsma, L. Duarte, D. Duchesneau, K. Duffy, P. Dunne, B. Dutta, H. Duyang, O. Dvornikov, D. Dwyer, A. Dyshkant, M. Eads, A. Earle, D. Edmunds, J. Eisch, L. Emberger, S. Emery, P. Englezos, A. Ereditato, T. Erjavec, C. Escobar, G. Eurin, J. J. Evans, E. Ewart, A. C. Ezeribe, K. Fahey, A. Falcone, M. Fani’, C. Farnese, Y. Farzan, D. Fedoseev, J. Felix, Y. Feng, E. Fernandez-Martinez, P. Fernandez Menendez, M. Fernandez Morales, F. Ferraro, L. Fields, P. Filip, F. Filthaut, M. Fiorini, V. Fischer, R. S. Fitzpatrick, W. Flanagan, B. Fleming, R. Flight, S. Fogarty, W. Foreman, J. Fowler, W. Fox, J. Franc, K. Francis, D. Franco, J. Freeman, J. Freestone, J. Fried, A. Friedland, F. Fuentes Robayo, S. Fuess, I. K. Furic, K. Furman, A. P. Furmanski, A. Gabrielli, A. Gago, H. Gallagher, A. Gallas, A. Gallego-Ros, N. Gallice, V. Galymov, E. Gamberini, T. Gamble, F. Ganacim, R. Gandhi, R. Gandrajula, F. Gao, S. Gao, D. Garcia-Gamez, M. Á. García-Peris, S. Gardiner, D. Gastler, J. Gauvreau, G. Ge, N. Geffroy, B. Gelli, A. Gendotti, S. Gent, Z. Ghorbani-Moghaddam, P. Giammaria, T. Giammaria, N. Giangiacomi, D. Gibin, I. Gil-Botella, S. Gilligan, C. Girerd, A. Giri, D. Gnani, O. Gogota, M. Gold, S. Gollapinni, K. Gollwitzer, R. A. Gomes, L. Gomez Bermeo, L. S. Gomez Fajardo, F. Gonnella, D. Gonzalez-Diaz, M. Gonzalez-Lopez, M. C. Goodman, O. Goodwin, S. Goswami, C. Gotti, E. Goudzovski, C. Grace, R. Gran, E. Granados, P. Granger, A. Grant, C. Grant, D. Gratieri, P. Green, L. Greenler, J. Greer, J. Grenard, C. Griffith, M. Groh, J. Grudzinski, K. Grzelak, W. Gu, E. Guardincerri, V. Guarino, M. Guarise, R. Guenette, E. Guerard, M. Guerzoni, D. Guffanti, A. Guglielmi, B. Guo, A. Gupta, V. Gupta, K. Guthikonda, R. Gutierrez, P. Guzowski, M. M. Guzzo, S. Gwon, C. Ha, K. Haaf, A. Habig, H. Hadavand, R. Haenni, A. Hahn, J. Haiston, P. Hamacher-Baumann, T. Hamernik, P. Hamilton, J. Han, D. A. Harris, J. Hartnell, T. Hartnett, J. Harton, T. Hasegawa, C. Hasnip, R. Hatcher, K. W. Hatfield, A. Hatzikoutelis, C. Hayes, K. Hayrapetyan, J. Hays, E. Hazen, M. He, A. Heavey, K. M. Heeger, J. Heise, S. Henry, M. Hernandez Morquecho, K. Herner, J. Hewes, C. Hilgenberg, T. Hill, S. J. Hillier, A. Himmel, E. Hinkle, L. R. Hirsch, J. Ho, J. Hoff, A. Holin, E. Hoppe, G. A. Horton-Smith, M. Hostert, A. Hourlier, B. Howard, R. Howell, J. Hoyos, I. Hristova, M. S. Hronek, J. Huang, Z. Hulcher, G. Iles, N. Ilic, A. M. Iliescu, R. Illingworth, G. Ingratta, A. Ioannisian, B. Irwin, L. Isenhower, R. Itay, C. M. Jackson, V. Jain, E. James, W. Jang, B. Jargowsky, F. Jediny, D. Jena, Y. Jeong, C. Jesús-Valls, X. Ji, L. Jiang, S. Jiménez, A. Jipa, R. Johnson, W. Johnson, N. Johnston, B. Jones, S. Jones, M. Judah, C. Jung, T. Junk, Y. Jwa, M. Kabirnezhad, A. Kaboth, I. Kadenko, I. Kakorin, A. Kalitkina, D. Kalra, F. Kamiya, N. Kaneshige, D. M. Kaplan, G. Karagiorgi, G. Karaman, A. Karcher, M. Karolak, Y. Karyotakis, S. Kasai, S. P. Kasetti, L. Kashur, N. Kazaryan, E. Kearns, P. Keener, K. J. Kelly, E. Kemp, O. Kemularia, W. Ketchum, S. H. Kettell, M. Khabibullin, A. Khotjantsev, A. Khvedelidze, D. Kim, B. King, B. Kirby, M. Kirby, J. Klein, A. Klustova, T. Kobilarcik, K. Koehler, L. W. Koerner, D. H. Koh, S. Kohn, P. P. Koller, L. Kolupaeva, D. Korablev, M. Kordosky, T. Kosc, U. Kose, V. Kostelecky, K. Kothekar, R. Kralik, L. Kreczko, F. Krennrich, I. Kreslo, W. Kropp, T. Kroupova, S. Kubota, Y. Kudenko, V. A. Kudryavtsev, S. Kulagin, J. Kumar, P. Kumar, P. Kunze, N. Kurita, C. Kuruppu, V. Kus, T. Kutter, J. Kvasnicka, D. Kwak, A. Lambert, B. Land, C. E. Lane, K. Lang, T. Langford, M. Langstaff, J. Larkin, P. Lasorak, D. Last, A. Laundrie, G. Laurenti, A. Lawrence, I. Lazanu, R. LaZur, M. Lazzaroni, T. Le, S. Leardini, J. Learned, P. LeBrun, T. LeCompte, C. Lee, S. Lee, G. Lehmann Miotto, R. Lehnert, M. Leigui de Oliveira, M. Leitner, L. M. Lepin, S. Li, Y. Li, H. Liao, C. Lin, Q. Lin, S. Lin, R. A. Lineros, J. Ling, A. Lister, B. R. Littlejohn, J. Liu, Y. Liu, S. Lockwitz, T. Loew, M. Lokajicek, I. Lomidze, K. Long, T. Lord, J. LoSecco, W. C. Louis, X. Lu, K. Luk, B. Lunday, X. Luo, E. Luppi, T. Lux, V. P. Luzio, J. Maalmi, D. MacFarlane, A. Machado, P. Machado, C. Macias, J. Macier, A. Maddalena, A. Madera, P. Madigan, S. Magill, K. Mahn, A. Maio, A. Major, J. A. Maloney, G. Mandrioli, R. C. Mandujano, J. C. Maneira, L. Manenti, S. Manly, A. Mann, K. Manolopoulos, M. Manrique Plata, V. N. Manyam, L. Manzanillas, M. Marchan, A. Marchionni, W. Marciano, D. Marfatia, C. Mariani, J. Maricic, R. Marie, F. Marinho, A. D. Marino, D. Marsden, M. Marshak, C. Marshall, J. Marshall, J. Marteau, J. Martin-Albo, N. Martinez, D. A. Martinez Caicedo, P. Martínez Miravé, S. Martynenko, V. Mascagna, K. Mason, A. Mastbaum, F. Matichard, S. Matsuno, J. Matthews, C. Mauger, N. Mauri, K. Mavrokoridis, I. Mawby, R. Mazza, A. Mazzacane, E. Mazzucato, T. McAskill, E. McCluskey, N. McConkey, K. S. McFarland, C. McGrew, A. McNab, A. Mefodiev, P. Mehta, P. Melas, O. Mena, H. Mendez, P. Mendez, D. P. Méndez, A. Menegolli, G. Meng, M. Messier, W. Metcalf, T. Mettler, M. Mewes, H. Meyer, T. Miao, G. Michna, T. Miedema, V. Mikola, R. Milincic, G. Miller, W. Miller, J. Mills, O. Mineev, A. Minotti, O. G. Miranda, S. Miryala, C. Mishra, S. Mishra, A. Mislivec, M. Mitchell, D. Mladenov, I. Mocioiu, K. Moffat, N. Moggi, R. Mohanta, T. A. Mohayai, N. Mokhov, J. A. Molina, L. Molina Bueno, E. Montagna, A. Montanari, C. Montanari, D. Montanari, L. M. Montano Zetina, S. Moon, M. Mooney, A. F. Moor, D. Moreno, D. Moretti, C. Morris, C. Mossey, M. Mote, E. Motuk, C. A. Moura, J. Mousseau, G. Mouster, W. Mu, L. Mualem, J. Mueller, M. Muether, S. Mufson, F. Muheim, A. Muir, M. Mulhearn, D. Munford, H. Muramatsu, S. Murphy, J. Musser, J. Nachtman, S. Nagu, M. Nalbandyan, R. Nandakumar, D. Naples, S. Narita, A. Nath, A. Navrer-Agasson, N. Nayak, M. Nebot-Guinot, K. Negishi, J. K. Nelson, J. Nesbit, M. Nessi, D. Newbold, M. Newcomer, H. Newton, R. Nichol, F. Nicolas-Arnaldos, A. Nikolica, E. Niner, K. Nishimura, A. Norman, A. Norrick, R. Northrop, P. Novella, J. A. Nowak, M. Oberling, J. Ochoa-Ricoux, A. Olivier, A. Olshevskiy, Y. Onel, Y. Onishchuk, J. Ott, L. Pagani, G. Palacio, O. Palamara, S. Palestini, J. M. Paley, M. Pallavicini, C. Palomares, W. Panduro Vazquez, E. Pantic, V. Paolone, V. Papadimitriou, R. Papaleo, A. Papanestis, S. Paramesvaran, S. Parke, E. Parozzi, Z. Parsa, M. Parvu, S. Pascoli, L. Pasqualini, J. Pasternak, J. Pater, C. Patrick, L. Patrizii, R. B. Patterson, S. Patton, T. Patzak, A. Paudel, B. Paulos, L. Paulucci, Z. Pavlovic, G. Pawloski, D. Payne, V. Pec, S. J. Peeters, A. Pena Perez, E. Pennacchio, A. Penzo, O. L. Peres, J. Perry, D. Pershey, G. Pessina, G. Petrillo, C. Petta, R. Petti, V. Pia, F. Piastra, L. Pickering, F. Pietropaolo, V. L. Pimentel, G. Pinaroli, K. Plows, R. Plunkett, R. Poling, F. Pompa, X. Pons, N. Poonthottathil, F. Poppi, S. Pordes, J. Porter, M. Potekhin, R. Potenza, B. V. Potukuchi, J. Pozimski, M. Pozzato, S. Prakash, T. Prakash, M. Prest, S. Prince, F. Psihas, D. Pugnere, X. Qian, J. Raaf, V. Radeka, J. Rademacker, B. Radics, A. Rafique, E. Raguzin, M. Rai, M. Rajaoalisoa, I. Rakhno, A. Rakotonandrasana, L. Rakotondravohitra, R. Rameika, M. Ramirez Delgado, B. Ramson, A. Rappoldi, G. Raselli, P. Ratoff, S. Raut, R. Razakamiandra, E. Rea, J. Real, B. Rebel, R. Rechenmacher, M. Reggiani-Guzzo, J. Reichenbacher, S. D. Reitzner, H. Rejeb Sfar, A. Renshaw, S. Rescia, F. Resnati, A. Reynolds, M. Ribas, S. Riboldi, C. Riccio, G. Riccobene, L. C. Rice, J. Ricol, A. Rigamonti, Y. Rigaut, E. V. Rincón, H. Ritchie-Yates, D. Rivera, A. Robert, L. Rochester, M. Roda, P. Rodrigues, M. J. Rodriguez Alonso, E. Rodriguez Bonilla, J. Rodriguez Rondon, S. Rosauro-Alcaraz, M. Rosenberg, P. Rosier, B. Roskovec, M. Rossella, M. Rossi, J. Rout, P. Roy, A. Rubbia, C. Rubbia, B. Russell, D. Ruterbories, A. Rybnikov, A. Saa-Hernandez, R. Saakyan, S. Sacerdoti, T. Safford, N. Sahu, P. Sala, N. Samios, O. Samoylov, M. Sanchez, V. Sandberg, D. A. Sanders, D. Sankey, S. Santana, M. Santos-Maldonado, N. Saoulidou, P. Sapienza, C. Sarasty, I. Sarcevic, G. Savage, V. Savinov, A. Scaramelli, A. Scarff, A. Scarpelli, T. Schefke, H. Schellman, S. Schifano, P. Schlabach, D. Schmitz, A. W. Schneider, K. Scholberg, A. Schukraft, E. Segreto, A. Selyunin, C. R. Senise, J. Sensenig, A. Sergi, D. Sgalaberna, M. Shaevitz, S. Shafaq, F. Shaker, M. Shamma, R. Sharankova, H. R. Sharma, R. Sharma, R. K. Sharma, T. Shaw, K. Shchablo, C. Shepherd-Themistocleous, A. Sheshukov, S. Shin, I. Shoemaker, D. Shooltz, R. Shrock, H. Siegel, L. Simard, J. Sinclair, G. Sinev, J. Singh, L. Singh, P. Singh, V. Singh, R. Sipos, F. Sippach, G. Sirri, A. Sitraka, K. Siyeon, K. Skarpaas, A. Smith, E. Smith, P. Smith, J. Smolik, M. Smy, E. Snider, P. Snopok, D. Snowden-Ifft, M. Soares Nunes, H. Sobel, M. Soderberg, S. Sokolov, C. J. Solano Salinas, S. Söldner-Rembold, S. Soleti, N. Solomey, V. Solovov, W. E. Sondheim, M. Sorel, A. Sotnikov, J. Soto-Oton, F. Soto Ugaldi, A. Sousa, K. Soustruznik, F. Spagliardi, M. Spanu, J. Spitz, N. J. C. Spooner, K. Spurgeon, M. Stancari, L. Stanco, C. Stanford, D. Stefan, R. Stein, H. Steiner, A. F. Steklain Lisbôa, J. Stewart, B. Stillwell, J. Stock, F. Stocker, T. Stokes, M. Strait, T. Strauss, L. Strigari, A. Stuart, J. G. Suarez, J. Suárez Sunción, R. Sulej, H. Sullivan, D. Summers, A. Surdo, V. Susic, L. Suter, C. Sutera, R. Svoboda, B. Szczerbinska, A. M. Szelc, H. Tanaka, S. Tang, A. Tapia, B. Tapia Oregui, A. Tapper, S. Tariq, E. Tarpara, N. Tata, E. Tatar, R. Tayloe, A. Teklu, P. Tennessen, M. Tenti, K. Terao, C. A. Ternes, F. Terranova, G. Testera, T. Thakore, A. Thea, J. L. Thompson, C. Thorn, S. Timm, V. Tishchenko, L. Tomassetti, A. Tonazzo, D. Torbunov, M. Torti, M. Tortola, F. Tortorici, N. Tosi, D. Totani, M. Toups, C. Touramanis, R. Travaglini, J. Trevor, S. Trilov, W. H. Trzaska, Y. Tsai, Z. Tsamalaidze, K. Tsang, N. Tsverava, S. Tufanli, C. Tull, E. Tyley, M. Tzanov, L. Uboldi, M. A. Uchida, J. Urheim, T. Usher, S. Uzunyan, M. R. Vagins, P. Vahle, S. Valder, G. A. Valdiviesso, E. Valencia, R. Valentim, Z. Vallari, E. Vallazza, J. W. Valle, S. Vallecorsa, R. Van Berg, R. G. Van de Water, D. Vanegas Forero, D. Vannerom, F. Varanini, D. Vargas, G. Varner, J. Vasel, S. Vasina, G. Vasseur, N. Vaughan, K. Vaziri, S. Ventura, A. Verdugo, S. Vergani, M. A. Vermeulen, M. Verzocchi, M. Vicenzi, H. Vieira de Souza, C. Vignoli, C. Vilela, B. Viren, T. Vrba, T. Wachala, A. V. Waldron, M. Wallbank, C. Wallis, H. Wang, J. Wang, L. Wang, M. H. Wang, X. Wang, Y. Wang, K. Warburton, D. Warner, M. Wascko, D. Waters, A. Watson, K. Wawrowska, P. Weatherly, A. Weber, M. Weber, H. Wei, A. Weinstein, D. Wenman, M. Wetstein, A. White, L. H. Whitehead, D. Whittington, M. J. Wilking, A. Wilkinson, C. Wilkinson, Z. Williams, F. Wilson, R. J. Wilson, W. Wisniewski, J. Wolcott, T. Wongjirad, A. Wood, K. Wood, E. Worcester, M. Worcester, K. Wresilo, C. Wret, W. Wu, Y. Xiao, F. Xie, B. Yaeggy, E. Yandel, G. Yang, K. Yang, T. Yang, A. Yankelevich, N. Yershov, K. Yonehara, Y. Yoon, T. Young, B. Yu, H. Yu, J. Yu, Y. Yu, W. Yuan, R. Zaki, J. Zalesak, L. Zambelli, B. Zamorano, A. Zani, L. Zazueta, G. Zeller, J. Zennamo, K. Zeug, C. Zhang, S. Zhang, Y. Zhang, M. Zhao, E. Zhivun, G. Zhu, E. D. Zimmerman, S. Zucchelli, J. Zuklin, V. Zutshi, and R. Zwaska

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.

Published
2022
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10. How do gravity waves triggered by a typhoon propagate from the troposphere to the upper atmosphere?

Author

Q. Li, J. Xu, H. Liu, X. Liu, and W. Yuan

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Gravity waves (GWs) strongly affect atmospheric dynamics and photochemistry and the coupling between the troposphere, stratosphere, mesosphere, and thermosphere. In addition, GWs generated by strong disturbances in the troposphere (e.g. thunderstorms and typhoons) can affect the atmosphere of Earth from the troposphere to the thermosphere. However, the fundamental process of GW propagation from the troposphere to the thermosphere is poorly understood because it is challenging to constrain this process using observations. Moreover, GWs tend to dissipate rapidly in the thermosphere because the molecular diffusion increases exponentially with height. In this study, a double-layer airglow network was used to capture concentric GWs (CGWs) over China that were excited by Typhoon Chaba (2016). We used ERA5 reanalysis data and Multi-functional Transport Satellite-1R observations to quantitatively describe the propagation processes of typhoon-generated CGWs from the troposphere, through the stratosphere and mesosphere, to the thermosphere. We found that the CGWs in the mesopause region were generated directly by the typhoon in the troposphere. However, the backward-ray-tracing analysis suggested that CGWs in the thermosphere originated from the secondary waves generated by the dissipation of the CGW and/or nonlinear processes in the mesopause region.

Published
2022
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11. Estimating the lateral transfer of organic carbon through the European river network using a land surface model

Author

H. Zhang, R. Lauerwald, P. Regnier, P. Ciais, K. Van Oost, V. Naipal, B. Guenet, and W. Yuan

Subjects
Science, Geology, QE1-996.5, Dynamic and structural geology, QE500-639.5
Abstract

Lateral carbon transport from soils to the ocean through rivers has been acknowledged as a key component of the global carbon cycle, but it is still neglected in most global land surface models (LSMs). Fluvial transport of dissolved organic carbon (DOC) and CO2 has been implemented in the ORCHIDEE LSM, while erosion-induced delivery of sediment and particulate organic carbon (POC) from land to river was implemented in another version of the model. Based on these two developments, we take the final step towards the full representation of biospheric carbon transport through the land–river continuum. The newly developed model, called ORCHIDEE-Clateral, simulates the complete lateral transport of water, sediment, POC, DOC, and CO2 from land to sea through the river network, the deposition of sediment and POC in the river channel and floodplains, and the decomposition of POC and DOC in transit. We parameterized and evaluated ORCHIDEE-Clateral using observation data in Europe. The model explains 94 %, 75 %, and 83 % of the spatial variations of observed riverine water discharges, bankfull water flows, and riverine sediment discharges in Europe, respectively. The simulated long-term average total organic carbon concentrations and DOC concentrations in river flows are comparable to the observations in major European rivers, although our model generally overestimates the seasonal variation of riverine organic carbon concentrations. Application of ORCHIDEE-Clateral for Europe reveals that the lateral carbon transfer affects land carbon dynamics in multiple ways, and omission of this process in LSMs may lead to an overestimation of 4.5 % in the simulated annual net terrestrial carbon uptake over Europe. Overall, this study presents a useful tool for simulating large-scale lateral carbon transfer and for predicting the feedbacks between lateral carbon transfer and future climate and land use changes.

Published
2022
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12. Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

Author

A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, A. Aduszkiewicz, J. Aguilar, Z. Ahmad, J. Ahmed, B. Aimard, B. Ali-Mohammadzadeh, T. Alion, K. Allison, S. Alonso Monsalve, M. AlRashed, C. Alt, A. Alton, R. Alvarez, P. Amedo, J. Anderson, C. Andreopoulos, M. Andreotti, M. Andrews, F. Andrianala, S. Andringa, N. Anfimov, A. Ankowski, M. Antoniassi, M. Antonova, A. Antoshkin, S. Antusch, A. Aranda-Fernandez, L. Arellano, L. O. Arnold, M. A. Arroyave, J. Asaadi, L. Asquith, A. Aurisano, V. Aushev, D. Autiero, V. Ayala Lara, M. Ayala-Torres, F. Azfar, A. Back, H. Back, J. J. Back, C. Backhouse, I. Bagaturia, L. Bagby, N. Balashov, S. Balasubramanian, P. Baldi, B. Baller, B. Bambah, F. Barao, G. Barenboim, P. Barham Alzas, G. Barker, W. Barkhouse, C. Barnes, G. Barr, J. Barranco Monarca, A. Barros, N. Barros, J. L. Barrow, A. Basharina-Freshville, A. Bashyal, V. Basque, C. Batchelor, E. Batista das Chagas, J. B. R. Battat, F. Battisti, F. Bay, M. C. Q. Bazetto, J. L. L. Bazo Alba, J. F. Beacom, E. Bechetoille, B. Behera, C. Beigbeder, L. Bellantoni, G. Bellettini, V. Bellini, O. Beltramello, N. Benekos, C. Benitez Montiel, F. Bento Neves, J. Berger, S. Berkman, P. Bernardini, R. M. Berner, A. Bersani, S. Bertolucci, M. Betancourt, A. Betancur Rodríguez, A. Bevan, Y. Bezawada, T. J. C. Bezerra, A. Bhardwaj, V. Bhatnagar, M. Bhattacharjee, D. Bhattarai, S. Bhuller, B. Bhuyan, S. Biagi, J. Bian, M. Biassoni, K. Biery, B. Bilki, M. Bishai, A. Bitadze, A. Blake, F. Blaszczyk, G. C. Blazey, E. Blucher, J. Boissevain, S. Bolognesi, T. Bolton, L. Bomben, M. Bonesini, M. Bongrand, C. Bonilla-Diaz, F. Bonini, A. Booth, F. Boran, S. Bordoni, A. Borkum, N. Bostan, P. Bour, C. Bourgeois, D. Boyden, J. Bracinik, D. Braga, D. Brailsford, A. Branca, A. Brandt, J. Bremer, D. Breton, C. Brew, S. J. Brice, C. Brizzolari, C. Bromberg, J. Brooke, A. Bross, G. Brunetti, M. Brunetti, N. Buchanan, H. Budd, I. Butorov, I. Cagnoli, T. Cai, D. Caiulo, R. Calabrese, P. Calafiura, J. Calcutt, M. Calin, S. Calvez, E. Calvo, A. Caminata, M. Campanelli, D. Caratelli, D. Carber, J. C. Carceller, G. Carini, B. Carlus, M. F. Carneiro, P. Carniti, I. Caro Terrazas, H. Carranza, T. Carroll, J. F. Castaño Forero, A. Castillo, C. Castromonte, E. Catano-Mur, C. Cattadori, F. Cavalier, G. Cavallaro, F. Cavanna, S. Centro, G. Cerati, A. Cervelli, A. Cervera Villanueva, M. Chalifour, A. Chappell, E. Chardonnet, N. Charitonidis, A. Chatterjee, S. Chattopadhyay, M. S. S. Chavarry Neyra, H. Chen, M. Chen, Y. Chen, Z. Chen, Z. Chen-Wishart, Y. Cheon, D. Cherdack, C. Chi, S. Childress, R. Chirco, A. Chiriacescu, G. Chisnall, K. Cho, S. Choate, D. Chokheli, P. S. Chong, A. Christensen, D. Christian, G. Christodoulou, A. Chukanov, M. Chung, E. Church, V. Cicero, P. Clarke, G. Cline, T. E. Coan, A. G. Cocco, J. A. B. Coelho, N. Colton, E. Conley, R. Conley, J. Conrad, M. Convery, S. Copello, P. Cova, L. Cremaldi, L. Cremonesi, J. I. Crespo-Anadón, M. Crisler, E. Cristaldo, J. Crnkovic, R. Cross, A. Cudd, C. Cuesta, Y. Cui, D. Cussans, O. Dalager, H. da Motta, L. Da Silva Peres, C. David, Q. David, G. S. Davies, S. Davini, J. Dawson, K. De, S. De, P. Debbins, I. De Bonis, M. P. Decowski, A. De Gouvêa, P. C. De Holanda, I. L. De Icaza Astiz, A. Deisting, P. De Jong, A. Delbart, D. Delepine, M. Delgado, A. Dell’Acqua, N. Delmonte, P. De Lurgio, J. R. T. de Mello Neto, D. M. DeMuth, S. Dennis, C. Densham, G. W. Deptuch, A. De Roeck, V. De Romeri, G. De Souza, R. Devi, R. Dharmapalan, M. Dias, F. Diaz, J. S. Díaz, S. Di Domizio, L. Di Giulio, P. Ding, L. Di Noto, G. Dirkx, C. Distefano, R. Diurba, M. Diwan, Z. Djurcic, D. Doering, S. Dolan, F. Dolek, M. Dolinski, L. Domine, Y. Donon, D. Douglas, D. Douillet, A. Dragone, G. Drake, F. Drielsma, L. Duarte, D. Duchesneau, K. Duffy, P. Dunne, B. Dutta, H. Duyang, O. Dvornikov, D. Dwyer, A. Dyshkant, M. Eads, A. Earle, D. Edmunds, J. Eisch, L. Emberger, S. Emery, P. Englezos, A. Ereditato, T. Erjavec, C. Escobar, G. Eurin, J. J. Evans, E. Ewart, A. C. Ezeribe, K. Fahey, A. Falcone, M. Fani’, C. Farnese, Y. Farzan, D. Fedoseev, J. Felix, Y. Feng, E. Fernandez-Martinez, P. Fernandez Menendez, M. Fernandez Morales, F. Ferraro, L. Fields, P. Filip, F. Filthaut, M. Fiorini, V. Fischer, R. S. Fitzpatrick, W. Flanagan, B. Fleming, R. Flight, S. Fogarty, W. Foreman, J. Fowler, W. Fox, J. Franc, K. Francis, D. Franco, J. Freeman, J. Freestone, J. Fried, A. Friedland, F. Fuentes Robayo, S. Fuess, I. K. Furic, K. Furman, A. P. Furmanski, A. Gabrielli, A. Gago, H. Gallagher, A. Gallas, A. Gallego-Ros, N. Gallice, V. Galymov, E. Gamberini, T. Gamble, F. Ganacim, R. Gandhi, R. Gandrajula, F. Gao, S. Gao, D. Garcia-Gamez, M. Á. García-Peris, S. Gardiner, D. Gastler, J. Gauvreau, G. Ge, N. Geffroy, B. Gelli, A. Gendotti, S. Gent, Z. Ghorbani-Moghaddam, P. Giammaria, T. Giammaria, N. Giangiacomi, D. Gibin, I. Gil-Botella, S. Gilligan, C. Girerd, A. K. Giri, D. Gnani, O. Gogota, M. Gold, S. Gollapinni, K. Gollwitzer, R. A. Gomes, L. V. Gomez Bermeo, L. S. Gomez Fajardo, F. Gonnella, D. Gonzalez-Diaz, M. Gonzalez-Lopez, M. C. Goodman, O. Goodwin, S. Goswami, C. Gotti, E. Goudzovski, C. Grace, R. Gran, E. Granados, P. Granger, A. Grant, C. Grant, D. Gratieri, P. Green, L. Greenler, J. Greer, J. Grenard, W. C. Griffith, M. Groh, J. Grudzinski, K. Grzelak, W. Gu, E. Guardincerri, V. Guarino, M. Guarise, R. Guenette, E. Guerard, M. Guerzoni, D. Guffanti, A. Guglielmi, B. Guo, A. Gupta, V. Gupta, K. K. Guthikonda, R. Gutierrez, P. Guzowski, M. M. Guzzo, S. Gwon, C. Ha, K. Haaf, A. Habig, H. Hadavand, R. Haenni, A. Hahn, J. Haiston, P. Hamacher-Baumann, T. Hamernik, P. Hamilton, J. Han, D. A. Harris, J. Hartnell, T. Hartnett, J. Harton, T. Hasegawa, C. Hasnip, R. Hatcher, K. W. Hatfield, A. Hatzikoutelis, C. Hayes, K. Hayrapetyan, J. Hays, E. Hazen, M. He, A. Heavey, K. M. Heeger, J. Heise, S. Henry, M. A. Hernandez Morquecho, K. Herner, J. Hewes, C. Hilgenberg, T. Hill, S. J. Hillier, A. Himmel, E. Hinkle, L. R. Hirsch, J. Ho, J. Hoff, A. Holin, E. Hoppe, G. A. Horton-Smith, M. Hostert, A. Hourlier, B. Howard, R. Howell, J. Hoyos, I. Hristova, M. S. Hronek, J. Huang, Z. Hulcher, G. Iles, N. Ilic, A. M. Iliescu, R. Illingworth, G. Ingratta, A. Ioannisian, B. Irwin, L. Isenhower, R. Itay, C. M. Jackson, V. Jain, E. James, W. Jang, B. Jargowsky, F. Jediny, D. Jena, Y. S. Jeong, C. Jesús-Valls, X. Ji, L. Jiang, S. Jiménez, A. Jipa, R. Johnson, W. Johnson, N. Johnston, B. Jones, S. Jones, M. Judah, C. K. Jung, T. Junk, Y. Jwa, M. Kabirnezhad, A. Kaboth, I. Kadenko, I. Kakorin, A. Kalitkina, D. Kalra, F. Kamiya, N. Kaneshige, D. M. Kaplan, G. Karagiorgi, G. Karaman, A. Karcher, M. Karolak, Y. Karyotakis, S. Kasai, S. P. Kasetti, L. Kashur, N. Kazaryan, E. Kearns, P. Keener, K. J. Kelly, E. Kemp, O. Kemularia, W. Ketchum, S. H. Kettell, M. Khabibullin, A. Khotjantsev, A. Khvedelidze, D. Kim, B. King, B. Kirby, M. Kirby, J. Klein, A. Klustova, T. Kobilarcik, K. Koehler, L. W. Koerner, D. H. Koh, S. Kohn, P. P. Koller, L. Kolupaeva, D. Korablev, M. Kordosky, T. Kosc, U. Kose, V. A. Kostelecký, K. Kothekar, R. Kralik, L. Kreczko, F. Krennrich, I. Kreslo, W. Kropp, T. Kroupova, S. Kubota, Y. Kudenko, V. A. Kudryavtsev, S. Kulagin, J. Kumar, P. Kumar, P. Kunze, N. Kurita, C. Kuruppu, V. Kus, T. Kutter, J. Kvasnicka, D. Kwak, A. Lambert, B. Land, C. E. Lane, K. Lang, T. Langford, M. Langstaff, J. Larkin, P. Lasorak, D. Last, A. Laundrie, G. Laurenti, A. Lawrence, I. Lazanu, R. LaZur, M. Lazzaroni, T. Le, S. Leardini, J. Learned, P. LeBrun, T. LeCompte, C. Lee, S. Y. Lee, G. Lehmann Miotto, R. Lehnert, M. A. Leigui de Oliveira, M. Leitner, L. M. Lepin, S. W. Li, Y. Li, H. Liao, C. S. Lin, Q. Lin, S. Lin, R. A. Lineros, J. Ling, A. Lister, B. R. Littlejohn, J. Liu, Y. Liu, S. Lockwitz, T. Loew, M. Lokajicek, I. Lomidze, K. Long, T. Lord, J. M. LoSecco, W. C. Louis, X.-G. Lu, K. B. Luk, B. Lunday, X. Luo, E. Luppi, T. Lux, V. P. Luzio, J. Maalmi, D. MacFarlane, A. A. Machado, P. Machado, C. T. Macias, J. R. Macier, A. Maddalena, A. Madera, P. Madigan, S. Magill, K. Mahn, A. Maio, A. Major, J. A. Maloney, G. Mandrioli, R. C. Mandujano, J. Maneira, L. Manenti, S. Manly, A. Mann, K. Manolopoulos, M. Manrique Plata, V. N. Manyam, L. Manzanillas, M. Marchan, A. Marchionni, W. Marciano, D. Marfatia, C. Mariani, J. Maricic, R. Marie, F. Marinho, A. D. Marino, D. Marsden, M. Marshak, C. Marshall, J. Marshall, J. Marteau, J. Martín-Albo, N. Martinez, D. A. Martinez Caicedo, P. Martínez Miravé, S. Martynenko, V. Mascagna, K. Mason, A. Mastbaum, F. Matichard, S. Matsuno, J. Matthews, C. Mauger, N. Mauri, K. Mavrokoridis, I. Mawby, R. Mazza, A. Mazzacane, E. Mazzucato, T. McAskill, E. McCluskey, N. McConkey, K. S. McFarland, C. McGrew, A. McNab, A. Mefodiev, P. Mehta, P. Melas, O. Mena, H. Mendez, P. Mendez, D. P. Méndez, A. Menegolli, G. Meng, M. D. Messier, W. Metcalf, T. Mettler, M. Mewes, H. Meyer, T. Miao, G. Michna, T. Miedema, V. Mikola, R. Milincic, G. Miller, W. Miller, J. Mills, O. Mineev, A. Minotti, O. G. Miranda, S. Miryala, C. S. Mishra, S. R. Mishra, A. Mislivec, M. Mitchell, D. Mladenov, I. Mocioiu, K. Moffat, N. Moggi, R. Mohanta, T. A. Mohayai, N. Mokhov, J. Molina, L. Molina Bueno, E. Montagna, A. Montanari, C. Montanari, D. Montanari, L. M. Montañno Zetina, S. H. Moon, M. Mooney, A. F. Moor, D. Moreno, D. Moretti, C. Morris, C. Mossey, M. Mote, E. Motuk, C. A. Moura, J. Mousseau, G. Mouster, W. Mu, L. Mualem, J. Mueller, M. Muether, S. Mufson, F. Muheim, A. Muir, M. Mulhearn, D. Munford, H. Muramatsu, S. Murphy, J. Musser, J. Nachtman, S. Nagu, M. Nalbandyan, R. Nandakumar, D. Naples, S. Narita, A. Nath, A. Navrer-Agasson, N. Nayak, M. Nebot-Guinot, K. Negishi, J. K. Nelson, J. Nesbit, M. Nessi, D. Newbold, M. Newcomer, H. Newton, R. Nichol, F. Nicolas-Arnaldos, A. Nikolica, E. Niner, K. Nishimura, A. Norman, A. Norrick, R. Northrop, P. Novella, J. A. Nowak, M. Oberling, J. Ochoa-Ricoux, A. Olivier, A. Olshevskiy, Y. Onel, Y. Onishchuk, J. Ott, L. Pagani, G. Palacio, O. Palamara, S. Palestini, J. M. Paley, M. Pallavicini, C. Palomares, W. Panduro Vazquez, E. Pantic, V. Paolone, V. Papadimitriou, R. Papaleo, A. Papanestis, S. Paramesvaran, S. Parke, E. Parozzi, Z. Parsa, M. Parvu, S. Pascoli, L. Pasqualini, J. Pasternak, J. Pater, C. Patrick, L. Patrizii, R. B. Patterson, S. J. Patton, T. Patzak, A. Paudel, B. Paulos, L. Paulucci, Z. Pavlovic, G. Pawloski, D. Payne, V. Pec, S. J. M. Peeters, A. Pena Perez, E. Pennacchio, A. Penzo, O. L. G. Peres, J. Perry, D. Pershey, G. Pessina, G. Petrillo, C. Petta, R. Petti, V. Pia, F. Piastra, L. Pickering, F. Pietropaolo, V. L. Pimentel, G. Pinaroli, K. Plows, R. Plunkett, R. Poling, F. Pompa, X. Pons, N. Poonthottathil, F. Poppi, S. Pordes, J. Porter, M. Potekhin, R. Potenza, B. V. K. S. Potukuchi, J. Pozimski, M. Pozzato, S. Prakash, T. Prakash, M. Prest, S. Prince, F. Psihas, D. Pugnere, X. Qian, J. L. Raaf, V. Radeka, J. Rademacker, B. Radics, A. Rafique, E. Raguzin, M. Rai, M. Rajaoalisoa, I. Rakhno, A. Rakotonandrasana, L. Rakotondravohitra, R. Rameika, M. A. Ramirez Delgado, B. Ramson, A. Rappoldi, G. Raselli, P. Ratoff, S. Raut, R. F. Razakamiandra, E. M. Rea, J. S. Real, B. Rebel, R. Rechenmacher, M. Reggiani-Guzzo, J. Reichenbacher, S. D. Reitzner, H. Rejeb Sfar, A. Renshaw, S. Rescia, F. Resnati, M. Ribas, S. Riboldi, C. Riccio, G. Riccobene, L. C. J. Rice, J. S. Ricol, A. Rigamonti, Y. Rigaut, E. V. Rincón, H. Ritchie-Yates, D. Rivera, A. Robert, L. Rochester, M. Roda, P. Rodrigues, M. J. Rodriguez Alonso, E. Rodriguez Bonilla, J. Rodriguez Rondon, S. Rosauro-Alcaraz, M. Rosenberg, P. Rosier, B. Roskovec, M. Rossella, M. Rossi, J. Rout, P. Roy, A. Rubbia, C. Rubbia, B. Russell, D. Ruterbories, A. Rybnikov, A. Saa-Hernandez, R. Saakyan, S. Sacerdoti, T. Safford, N. Sahu, K. Sakashita, P. Sala, N. Samios, O. Samoylov, M. C. Sanchez, V. Sandberg, D. A. Sanders, D. Sankey, S. Santana, M. Santos-Maldonado, N. Saoulidou, P. Sapienza, C. Sarasty, I. Sarcevic, G. Savage, V. Savinov, A. Scaramelli, A. Scarff, A. Scarpelli, T. Schefke, H. Schellman, S. Schifano, P. Schlabach, D. Schmitz, A. W. Schneider, K. Scholberg, A. Schukraft, E. Segreto, A. Selyunin, C. R. Senise, J. Sensenig, A. Sergi, D. Sgalaberna, M. H. Shaevitz, S. Shafaq, F. Shaker, M. Shamma, R. Sharankova, H. R. Sharma, R. Sharma, R. K. Sharma, T. Shaw, K. Shchablo, C. Shepherd-Themistocleous, A. Sheshukov, S. Shin, I. Shoemaker, D. Shooltz, R. Shrock, H. Siegel, L. Simard, J. Sinclair, G. Sinev, J. Singh, L. Singh, P. Singh, V. Singh, R. Sipos, F. W. Sippach, G. Sirri, A. Sitraka, K. Siyeon, K. Skarpaas, A. Smith, E. Smith, P. Smith, J. Smolik, M. Smy, E. Snider, P. Snopok, D. Snowden-Ifft, M. Soares Nunes, H. Sobel, M. Soderberg, S. Sokolov, C. J. Solano Salinas, S. Söldner-Rembold, S. R. Soleti, N. Solomey, V. Solovov, W. E. Sondheim, M. Sorel, A. Sotnikov, J. Soto-Oton, F. A. Soto Ugaldi, A. Sousa, K. Soustruznik, F. Spagliardi, M. Spanu, J. Spitz, N. J. C. Spooner, K. Spurgeon, M. Stancari, L. Stanco, C. Stanford, R. Stein, H. M. Steiner, A. F. Steklain Lisbôa, J. Stewart, B. Stillwell, J. Stock, F. Stocker, T. Stokes, M. Strait, T. Strauss, L. Strigari, A. Stuart, J. G. Suarez, J. M. Suárez Sunción, H. Sullivan, D. Summers, A. Surdo, V. Susic, L. Suter, C. M. Sutera, R. Svoboda, B. Szczerbinska, A. M. Szelc, H. Tanaka, S. Tang, A. Tapia, B. Tapia Oregui, A. Tapper, S. Tariq, E. Tarpara, N. Tata, E. Tatar, R. Tayloe, A. M. Teklu, P. Tennessen, M. Tenti, K. Terao, C. A. Ternes, F. Terranova, G. Testera, T. Thakore, A. Thea, J. L. Thompson, C. Thorn, S. C. Timm, V. Tishchenko, L. Tomassetti, A. Tonazzo, D. Torbunov, M. Torti, M. Tortola, F. Tortorici, N. Tosi, D. Totani, M. Toups, C. Touramanis, R. Travaglini, J. Trevor, S. Trilov, W. H. Trzaska, Y. Tsai, Y.-T. Tsai, Z. Tsamalaidze, K. V. Tsang, N. Tsverava, S. Tufanli, C. Tull, E. Tyley, M. Tzanov, L. Uboldi, M. A. Uchida, J. Urheim, T. Usher, S. Uzunyan, M. R. Vagins, P. Vahle, S. Valder, G. D. A. Valdiviesso, E. Valencia, R. Valentim, Z. Vallari, E. Vallazza, J. W. F. Valle, S. Vallecorsa, R. Van Berg, R. G. Van de Water, D. Vanegas Forero, D. Vannerom, F. Varanini, D. Vargas Oliva, G. Varner, J. Vasel, S. Vasina, G. Vasseur, N. Vaughan, K. Vaziri, S. Ventura, A. Verdugo, S. Vergani, M. A. Vermeulen, M. Verzocchi, M. Vicenzi, H. Vieira de Souza, C. Vignoli, C. Vilela, B. Viren, T. Vrba, T. Wachala, A. V. Waldron, M. Wallbank, C. Wallis, H. Wang, J. Wang, L. Wang, M. H. L. S. Wang, X. Wang, Y. Wang, K. Warburton, D. Warner, M. O. Wascko, D. Waters, A. Watson, K. Wawrowska, P. Weatherly, A. Weber, M. Weber, H. Wei, A. Weinstein, D. Wenman, M. Wetstein, A. White, L. H. Whitehead, D. Whittington, M. J. Wilking, A. Wilkinson, C. Wilkinson, Z. Williams, F. Wilson, R. J. Wilson, W. Wisniewski, J. Wolcott, T. Wongjirad, A. Wood, K. Wood, E. Worcester, M. Worcester, K. Wresilo, C. Wret, W. Wu, Y. Xiao, F. Xie, B. Yaeggy, E. Yandel, G. Yang, K. Yang, T. Yang, A. Yankelevich, N. Yershov, K. Yonehara, Y. S. Yoon, T. Young, B. Yu, H. Yu, J. Yu, Y. Yu, W. Yuan, R. Zaki, J. Zalesak, L. Zambelli, B. Zamorano, A. Zani, L. Zazueta, G. P. Zeller, J. Zennamo, K. Zeug, C. Zhang, S. Zhang, Y. Zhang, M. Zhao, E. Zhivun, G. Zhu, E. D. Zimmerman, S. Zucchelli, J. Zuklin, V. Zutshi, R. Zwaska, and DUNE Collaboration

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6 $$\times $$ × 6 $$\times $$ × 6 m $$^3$$ 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.

Published
2022
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13. Plasma Branched-Chain Amino Acid Concentrations and Glucose Homeostasis in Kidney Transplant Recipients and Candidates

Author

G. V. Ramesh Prasad, M. M. Nash, W. Yuan, D. Beriault, M. Yazdanpanah, and P. W. Connelly

Subjects
Diseases of the genitourinary system. Urology, RC870-923
Abstract

Background: Post-transplant diabetes mellitus (PTDM) encompasses new-onset and previously unrecognized type 2 diabetes. Kidney failure masks type 2 diabetes. Branched-chain amino acids (BCAA) are closely associated with glucose metabolism. Therefore, understanding BCAA metabolism both in kidney failure and after kidney transplantation may inform PTDM mechanisms. Objective: To understand the impact of present or absent kidney function on plasma BCAA concentrations. Design: Cross-sectional study of kidney transplant recipients and kidney transplant candidates. Setting: Large kidney transplant center in Toronto, Canada. Measurements: We measured plasma BCAA and aromatic amino acid (AAA) concentrations in 45 pre-kidney transplant candidates (15 with type 2 diabetes, 30 without type 2 diabetes) and 45 post-kidney transplant recipients (15 PTDM, 30 non-PTDM), along with insulin resistance and sensitivity by 75 g oral glucose loading for those in each group without type 2 diabetes. Methods: Plasma AA concentrations were analyzed using MassChrom AA Analysis and compared between groups. The insulin sensitivity for oral glucose tolerance tests or Matsuda index (a measure of whole-body insulin resistance), Homeostatic Model Assessment for Insulin Resistance (a measure of hepatic insulin resistance), and Insulin Secretion-Sensitivity Index-2 (ISSI-2, a measure of pancreatic β-cell response) was calculated from fasting insulin and glucose concentrations, and compared with BCAA concentrations. Results: Each BCAA concentration was higher in post-transplant subjects than pre-transplant subjects ( P < .001 for leucine, isoleucine, valine). In post-transplant subjects, each BCAA concentration was higher in PTDM versus non-PTDM (odds ratio for PTDM 3-4 per 1 SD increase in BCAA concentration, P < .001 for each). Tyrosine concentrations were also higher in post-transplant subjects than pre-transplant subjects, but tyrosine did not differ by PTDM status. By contrast, neither BCAA nor AAA concentrations were different in pre-transplant subjects with or without type 2 diabetes. Whole-body insulin resistance, hepatic insulin resistance, and pancreatic β-cell response did not differ between nondiabetic post-transplant and pre-transplant subjects. Branched-chain amino acid concentrations correlated with the Matsuda index and Homeostatic Model Assessment for Insulin Resistance ( P < .05 for each) only in nondiabetic post-transplant subjects—not in nondiabetic pre-transplant subjects. Branched-chain amino acid concentrations did not correlate with ISSI-2 in either pre-transplant or post-transplant subjects. Limitations: The sample size was small, and subjects were not studied prospectively for the development of type 2 diabetes. Conclusions: Plasma BCAA concentrations are higher post-transplant in type 2 diabetic states, but do not differ by diabetes status in the presence of kidney failure. The association of BCAA with measures of hepatic insulin resistance among nondiabetic post-transplant patients is consistent with impaired BCAA metabolism as a characteristic of kidney transplantation.

Published
2023
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14. A 30 m annual maize phenology dataset from 1985 to 2020 in China

Author

Q. Niu, X. Li, J. Huang, H. Huang, X. Huang, W. Su, and W. Yuan

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Crop phenology indicators provide essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform. First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference in dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets are consistent with in situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding the spatial patterns and temporal dynamics. Furthermore, we observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the changes in temperature and precipitation, which further altered the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the future agroecosystem response to global warming. The data are available at https://doi.org/10.6084/m9.figshare.16437054 (Niu et al., 2021).

Published
2022
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15. LEARNING SOCIAL COMPLIANT MULTI-MODAL DISTRIBUTIONS OF HUMAN PATH IN CROWDS

Author

X. Shi, H. Zhang, W. Yuan, D. Huang, Z. Guo, and R. Shibasaki

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Long-term human path forecasting in crowds is critical for autonomous moving platforms (like autonomous driving cars and social robots) to avoid collision and make high-quality planning. It is not easy for prediction systems to successfully take into account social interactions and predict a distribution of future possible path in a highly interactive and dynamic circumstance. In this paper, we develop a data-driven model for long-term trajectory prediction, which naturally takes into account social interactions through a spatio-temporal graph representation and predicts multi-modes of future trajectories. Different from generative adversarial network (GAN) based models which generate samples and then provide distributions of samples, we use mixture density functions to describe human motion and intuitively map the distribution of future path with explicit densities. To prevent the model from collapsing into a single mode and truly capture the intrinsic multi-modality, we further use a Winner-Takes-All (WTA) loss instead of computing loss over all modes. Extensive experiments over several trajectory prediction benchmarks demonstrate that our method is able to capture the multi-modality of human motion and forecast the distributions of plausible futures in complex scenarios.

Published
2022
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16. High-resolution map of sugarcane cultivation in Brazil using a phenology-based method

Author

Y. Zheng, A. C. dos Santos Luciano, J. Dong, and W. Yuan

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Sugarcane is the most important source of sugar, and its cultivation area has undergone rapid expansion, replacing other crops, pastures, and forests. Brazil is the world's largest sugarcane producer and contributed to approximately 38.6 % of the world's total production in 2019. Sugarcane in Brazil can be harvested from April to December in the south-central area and from September to April in the northeast area. The flexible phenology and harvest conditions of sugarcane in Brazil make it difficult to identify the harvest area at state to country scales. In this study, we developed a phenology-based method to identify the harvest area of sugarcane in Brazil by incorporating the multiple phenology conditions into a time-weighted dynamic time warping method (TWDTW). Then, we produced annual 30 m spatial resolution sugarcane harvest maps (2016–2019) for 14 states in Brazil (over 98 % of the harvest area) based on the proposed method using Landsat-7, Landsat-8, and Sentinel-2 optical data. The proposed method performed well in identifying sugarcane harvest area with limited training sample data. Validations for the 2018 harvest year displayed high accuracy, with the user's, producer's, and overall accuracies of 94.35 %, 87.04 %, and 91.47 % in Brazil, respectively. In addition, the identified harvest area of sugarcane exhibited good correlations with the agricultural statistical data provided by the Brazilian Institute of Geography and Statistics (IBGE) at the municipality, microregion, and mesoregion levels. The 30 m Brazil sugarcane harvest maps can be obtained at https://doi.org/10.6084/m9.figshare.14213909 (Zheng et al., 2021).

Published
2022
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17. Global Carbon Budget 2021

Author

P. Friedlingstein, M. W. Jones, M. O'Sullivan, R. M. Andrew, D. C. E. Bakker, J. Hauck, C. Le Quéré, G. P. Peters, W. Peters, J. Pongratz, S. Sitch, J. G. Canadell, P. Ciais, R. B. Jackson, S. R. Alin, P. Anthoni, N. R. Bates, M. Becker, N. Bellouin, L. Bopp, T. T. T. Chau, F. Chevallier, L. P. Chini, M. Cronin, K. I. Currie, B. Decharme, L. M. Djeutchouang, X. Dou, W. Evans, R. A. Feely, L. Feng, T. Gasser, D. Gilfillan, T. Gkritzalis, G. Grassi, L. Gregor, N. Gruber, Ö. Gürses, I. Harris, R. A. Houghton, G. C. Hurtt, Y. Iida, T. Ilyina, I. T. Luijkx, A. Jain, S. D. Jones, E. Kato, D. Kennedy, K. Klein Goldewijk, J. Knauer, J. I. Korsbakken, A. Körtzinger, P. Landschützer, S. K. Lauvset, N. Lefèvre, S. Lienert, J. Liu, G. Marland, P. C. McGuire, J. R. Melton, D. R. Munro, J. E. M. S. Nabel, S.-I. Nakaoka, Y. Niwa, T. Ono, D. Pierrot, B. Poulter, G. Rehder, L. Resplandy, E. Robertson, C. Rödenbeck, T. M. Rosan, J. Schwinger, C. Schwingshackl, R. Séférian, A. J. Sutton, C. Sweeney, T. Tanhua, P. P. Tans, H. Tian, B. Tilbrook, F. Tubiello, G. R. van der Werf, N. Vuichard, C. Wada, R. Wanninkhof, A. J. Watson, D. Willis, A. J. Wiltshire, W. Yuan, C. Yue, X. Yue, S. Zaehle, and J. Zeng

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Published
2022
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18. A non-coding RNA balancing act: miR-346-induced DNA damage is limited by the long non-coding RNA NORAD in prostate cancer

Author

C. E. Fletcher, L. Deng, F. Orafidiya, W. Yuan, M. P. G. S. Lorentzen, O. W. Cyran, A. Varela-Carver, T. A. Constantin, D. A. Leach, F. M. Dobbs, I. Figueiredo, B. Gurel, E. Parkes, D. Bogdan, R. R. Pereira, S. G. Zhao, A. Neeb, F. Issa, J. Hester, H. Kudo, Y. Liu, Y. Philippou, R. Bristow, K. Knudsen, R. J. Bryant, F. Y. Feng, S. H. Reed, I. G. Mills, J. de Bono, and C. L. Bevan

Subjects
Non-coding RNA, Prostate cancer, DNA damage, microRNA, Long non-coding RNA, Replication stress, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background miR-346 was identified as an activator of Androgen Receptor (AR) signalling that associates with DNA damage response (DDR)-linked transcripts in prostate cancer (PC). We sought to delineate the impact of miR-346 on DNA damage, and its potential as a therapeutic agent. Methods RNA-IP, RNA-seq, RNA-ISH, DNA fibre assays, in vivo xenograft studies and bioinformatics approaches were used alongside a novel method for amplification-free, single nucleotide-resolution genome-wide mapping of DNA breaks (INDUCE-seq). Results miR-346 induces rapid and extensive DNA damage in PC cells - the first report of microRNA-induced DNA damage. Mechanistically, this is achieved through transcriptional hyperactivation, R-loop formation and replication stress, leading to checkpoint activation and cell cycle arrest. miR-346 also interacts with genome-protective lncRNA NORAD to disrupt its interaction with PUM2, leading to PUM2 stabilisation and its increased turnover of DNA damage response (DDR) transcripts. Confirming clinical relevance, NORAD expression and activity strongly correlate with poor PC clinical outcomes and increased DDR in biopsy RNA-seq studies. In contrast, miR-346 is associated with improved PC survival. INDUCE-seq reveals that miR-346-induced DSBs occur preferentially at binding sites of the most highly-transcriptionally active transcription factors in PC cells, including c-Myc, FOXA1, HOXB13, NKX3.1, and importantly, AR, resulting in target transcript downregulation. Further, RNA-seq reveals widespread miR-346 and shNORAD dysregulation of DNA damage, replication and cell cycle processes. NORAD drives target-directed miR decay (TDMD) of miR-346 as a novel genome protection mechanism: NORAD silencing increases mature miR-346 levels by several thousand-fold, and WT but not TDMD-mutant NORAD rescues miR-346-induced DNA damage. Importantly, miR-346 sensitises PC cells to DNA-damaging drugs including PARP inhibitor and chemotherapy, and induces tumour regression as a monotherapy in vivo, indicating that targeting miR-346:NORAD balance is a valid therapeutic strategy. Conclusions A balancing act between miR-346 and NORAD regulates DNA damage and repair in PC. miR-346 may be particularly effective as a therapeutic in the context of decreased NORAD observed in advanced PC, and in transcriptionally-hyperactive cancer cells.

Published
2022
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19. Measurement of the e+e−→Σ0Σ¯0 cross sections at center-of-mass energies from 2.3864 to 3.0200GeV

Author

M. Ablikim, M.N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, A. Amoroso, Q. An, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, J.V. Bennett, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Biernat, J. Bloms, A. Bortone, I. Boyko, R.A. Briere, H. Cai, X. Cai, A. Calcaterra, G.F. Cao, N. Cao, S.A. Cetin, J.F. Chang, W.L. Chang, G. Chelkov, D.Y. Chen, G. Chen, H.S. Chen, M.L. Chen, S.J. Chen, X.R. Chen, Y.B. Chen, W. Cheng, G. Cibinetto, F. Cossio, X.F. Cui, H.L. Dai, J.P. Dai, X.C. Dai, A. Dbeyssi, R.B. de Boer, D. Dedovich, Z.Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L.Y. Dong, M.Y. Dong, S.X. Du, J. Fang, S.S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C.Q. Feng, M. Fritsch, C.D. Fu, Y. Fu, X.L. Gao, Y. Gao, Y.G. Gao, I. Garzia, E.M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W.X. Gong, W. Gradl, M. Greco, L.M. Gu, M.H. Gu, S. Gu, Y.T. Gu, C.Y. Guan, A.Q. Guo, L.B. Guo, R.P. Guo, Y.P. Guo, A. Guskov, S. Han, T.T. Han, T.Z. Han, X.Q. Hao, F.A. Harris, K.L. He, F.H. Heinsius, T. Held, Y.K. Heng, M. Himmelreich, T. Holtmann, Y.R. Hou, Z.L. Hou, H.M. Hu, J.F. Hu, T. Hu, Y. Hu, G.S. Huang, L.Q. Huang, X.T. Huang, Z. Huang, N. Huesken, T. Hussain, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q.P. Ji, X.B. Ji, X.L. Ji, H.B. Jiang, X.S. Jiang, X.Y. Jiang, J.B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X.S. Kang, R. Kappert, M. Kavatsyuk, B.C. Ke, I.K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O.B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M.G. Kurth, W. Kühn, J.J. Lane, J.S. Lange, P. Larin, L. Lavezzi, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C.H. Li, Cheng Li, D.M. Li, F. Li, G. Li, H.B. Li, H.J. Li, J.L. Li, J.Q. Li, Ke Li, L.K. Li, Lei Li, P.L. Li, P.R. Li, S.Y. Li, W.D. Li, W.G. Li, X.H. Li, X.L. Li, Z.B. Li, Z.Y. Li, H. Liang, Y.F. Liang, Y.T. Liang, L.Z. Liao, J. Libby, C.X. Lin, B. Liu, B.J. Liu, C.X. Liu, D. Liu, D.Y. Liu, F.H. Liu, Fang Liu, Feng Liu, H.B. Liu, H.M. Liu, Huanhuan Liu, Huihui Liu, J.B. Liu, J.Y. Liu, K. Liu, K.Y. Liu, Ke Liu, L. Liu, Q. Liu, S.B. Liu, Shuai Liu, T. Liu, X. Liu, Y.B. Liu, Z.A. Liu, Z.Q. Liu, Y.F. Long, X.C. Lou, F.X. Lu, H.J. Lu, J.D. Lu, J.G. Lu, X.L. Lu, Y. Lu, Y.P. Lu, C.L. Luo, M.X. Luo, P.W. Luo, T. Luo, X.L. Luo, S. Lusso, X.R. Lyu, F.C. Ma, H.L. Ma, L.L. Ma, M.M. Ma, Q.M. Ma, R.Q. Ma, R.T. Ma, X.N. Ma, X.X. Ma, X.Y. Ma, Y.M. Ma, F.E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q.A. Malik, A. Mangoni, Y.J. Mao, Z.P. Mao, S. Marcello, Z.X. Meng, J.G. Messchendorp, G. Mezzadri, T.J. Min, R.E. Mitchell, X.H. Mo, Y.J. Mo, N.Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I.B. Nikolaev, Z. Ning, S. Nisar, S.L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H.P. Peng, K. Peters, J. Pettersson, J.L. Ping, R.G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H.R. Qi, M. Qi, T.Y. Qi, S. Qian, W.-B. Qian, Z. Qian, C.F. Qiao, L.Q. Qin, X.P. Qin, X.S. Qin, Z.H. Qin, J.F. Qiu, S.Q. Qu, K.H. Rashid, K. Ravindran, C.F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, A. Sarantsev, M. Savrié, Y. Schelhaas, C. Schnier, K. Schoenning, D.C. Shan, W. Shan, X.Y. Shan, M. Shao, C.P. Shen, P.X. Shen, X.Y. Shen, H.C. Shi, R.S. Shi, X. Shi, X.D. Shi, J.J. Song, Q.Q. Song, W.M. Song, Y.X. Song, S. Sosio, S. Spataro, F.F. Sui, G.X. Sun, J.F. Sun, L. Sun, S.S. Sun, T. Sun, W.Y. Sun, Y.J. Sun, Y.K. Sun, Y.Z. Sun, Z.T. Sun, Y.H. Tan, Y.X. Tan, C.J. Tang, G.Y. Tang, J. Tang, V. Thoren, B. Tsednee, I. Uman, B. Wang, B.L. Wang, C.W. Wang, D.Y. Wang, H.P. Wang, K. Wang, L.L. Wang, M. Wang, M.Z. Wang, Meng Wang, W.H. Wang, W.P. Wang, X. Wang, X.F. Wang, X.L. Wang, Y. Wang, Y.D. Wang, Y.F. Wang, Y.Q. Wang, Z. Wang, Z.Y. Wang, Ziyi Wang, Zongyuan Wang, T. Weber, D.H. Wei, P. Weidenkaff, F. Weidner, S.P. Wen, D.J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J.F. Wu, L.H. Wu, L.J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S.Y. Xiao, Y.J. Xiao, Z.J. Xiao, X.H. Xie, Y.G. Xie, Y.H. Xie, T.Y. Xing, X.A. Xiong, G.F. Xu, J.J. Xu, Q.J. Xu, W. Xu, X.P. Xu, L. Yan, W.B. Yan, W.C. Yan, Xu Yan, H.J. Yang, H.X. Yang, L. Yang, R.X. Yang, S.L. Yang, Y.H. Yang, Y.X. Yang, Yifan Yang, Zhi Yang, M. Ye, M.H. Ye, J.H. Yin, Z.Y. You, B.X. Yu, C.X. Yu, G. Yu, J.S. Yu, T. Yu, C.Z. Yuan, W. Yuan, X.Q. Yuan, Y. Yuan, Z.Y. Yuan, C.X. Yue, A. Yuncu, A.A. Zafar, Y. Zeng, B.X. Zhang, Guangyi Zhang, H.H. Zhang, H.Y. Zhang, J.L. Zhang, J.Q. Zhang, J.W. Zhang, J.Y. Zhang, J.Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. Zhang, Lei Zhang, S. Zhang, S.F. Zhang, T.J. Zhang, X.Y. Zhang, Y. Zhang, Y.H. Zhang, Y.T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z.H. Zhang, Z.Y. Zhang, G. Zhao, J. Zhao, J.Y. Zhao, J.Z. Zhao, Lei Zhao, Ling Zhao, M.G. Zhao, Q. Zhao, S.J. Zhao, Y.B. Zhao, Y.X. Zhao, Z.G. Zhao, A. Zhemchugov, B. Zheng, J.P. Zheng, Y. Zheng, Y.H. Zheng, B. Zhong, C. Zhong, L.P. Zhou, Q. Zhou, X. Zhou, X.K. Zhou, X.R. Zhou, A.N. Zhu, J. Zhu, K. Zhu, K.J. Zhu, S.H. Zhu, W.J. Zhu, X.L. Zhu, Y.C. Zhu, Z.A. Zhu, B.S. Zou, and J.H. Zou

Subjects
BESIII, Σ0 hyperon, Born cross section, Form factor, Physics, QC1-999
Abstract

The Born cross sections of e+e−→Σ0Σ¯0 are measured at center-of-mass energies from 2.3864 to 3.0200 GeV using data samples with an integrated luminosity of 328.5 pb−1 collected with the BESIII detector operating at the BEPCII collider. The analysis makes use of a novel reconstruction method for energies near production threshold, while a single-tag method is employed at other center-of-mass energies. The measured cross sections are consistent with earlier results from BaBar, with a substantially improved precision. The cross-section lineshape can be well described by a perturbative QCD-driven energy function. In addition, the effective form factors of the Σ0 baryon are determined. The results provide precise experimental input for testing various theoretical predictions.

Published
2022
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20. GRAPH NEURAL NETWORK BASED MULTI-FEATURE FUSION FOR BUILDING CHANGE DETECTION

Author

W. Yuan, X. Yuan, Z. Fan, Z. Guo, X. Shi, J. Gong, and R. Shibasaki

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Building Change Detection (BCD) via multi-temporal remote sensing images is essential for various applications such as urban monitoring, urban planning, and disaster assessment. However, most building change detection approaches only extract features from different kinds of remote sensing images for change index determination, which can not determine the insignificant changes of small buildings. Given co-registered multi-temporal remote sensing images, the illumination variations and misregistration errors always lead to inaccurate change detection results. This study investigates the applicability of multi-feature fusion from both directly extract 2D features from remote sensing images and 3D features extracted by the dense image matching (DIM) generated 3D point cloud for accurate building change index generation. This paper introduces a graph neural network (GNN) based end-to-end learning framework for building change detection. The proposed framework includes feature extraction, feature fusion, and change index prediction. It starts with a pre-trained VGG-16 network as a backend and uses U-net architecture with five layers for feature map extraction. The extracted 2D features and 3D features are utilized as input into GNN based feature fusion parts. In the GNN parts, we introduce a flexible context aggregation mechanism based on attention to address the illumination variations and misregistration errors, enabling the framework to reason about the image-based texture information and depth information introduced by DIM generated 3D point cloud jointly. After that, the GNN generated affinity matrix is utilized for change index determination through a Hungarian algorithm. The experiment conducted on a dataset that covered Setagaya-Ku, Tokyo area, shows that the proposed method generated change map achieved the precision of 0.762 and the F1-score of 0.68 at pixel-level. Compared to traditional image-based change detection methods, our approach learns prior over geometrical structure information from the real 3D world, which robust to the misregistration errors. Compared to CNN based methods, the proposed method learns to fuse 2D and 3D features together to represent more comprehensive information for building change index determination. The experimental comparison results demonstrated that the proposed approach outperforms the traditional methods and CNN based methods.

Published
2021
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21. Measurement report: PM2.5-bound nitrated aromatic compounds in Xi'an, Northwest China – seasonal variations and contributions to optical properties of brown carbon

Author

W. Yuan, R.-J. Huang, L. Yang, T. Wang, J. Duan, J. Guo, H. Ni, Y. Chen, Q. Chen, Y. Li, U. Dusek, C. O'Dowd, and T. Hoffmann

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Nitrated aromatic compounds (NACs) are a group of key chromophores for brown carbon (light-absorbing organic carbon, i.e., BrC) aerosol, which affects radiative forcing. The chemical composition and sources of NACs and their contributions to BrC absorption, however, are still not well understood. In this study, PM2.5-bound NACs in Xi'an, Northwest China, were investigated for 112 daily PM2.5 filter samples from 2015 to 2016. Both the total concentrations and contributions from individual species of NACs show distinct seasonal variations. The seasonally averaged concentrations of NACs are 2.1 (spring), 1.1 (summer), 12.9 (fall), and 56 ng m−3 (winter). Thereinto, 4-nitrophenol is the major NAC component in spring (58 %). The concentrations of 5-nitrosalicylic acid and 4-nitrophenol dominate in summer (70 %), and the concentrations of 4-nitrocatechol and 4-nitrophenol dominate in fall (58 %) and winter (55 %). The NAC species show different seasonal patterns in concentrations, indicating differences in emissions and formation pathways. Source apportionment results using positive matrix factorization (PMF) further show large seasonal differences in the sources of NACs. Specifically, in summer, NACs were highly influenced by secondary formation and vehicle emissions (∼ 80 %), while in winter, biomass burning and coal combustion contributed the most (∼ 75 %). Furthermore, the light absorption contributions of NACs to BrC are wavelength-dependent and vary greatly by season, with maximum contributions at ∼ 330 nm in winter and fall and ∼ 320 nm in summer and spring. The differences in the contribution to light absorption are associated with the higher mass fractions of 4-nitrocatechol (λmax⁡= 345 nm) and 4-nitrophenol (λmax⁡= 310 nm) in fall and winter, 4-nitrophenol in spring, and 5-nitrosalicylic acid (λmax⁡= 315 nm) and 4-nitrophenol in summer. The mean contributions of NACs to BrC light absorption at a wavelength of 365 nm in different seasons are 0.14 % (spring), 0.09 % (summer), 0.36 % (fall), and 0.91 % (winter), which are about 6–9 times higher than their mass fractional contributions of carbon in total organic carbon. Our results indicate that the composition and sources of NACs have profound impacts on the BrC light absorption.

Published
2021
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22. Global Carbon Budget 2020

Author

P. Friedlingstein, M. O'Sullivan, M. W. Jones, R. M. Andrew, J. Hauck, A. Olsen, G. P. Peters, W. Peters, J. Pongratz, S. Sitch, C. Le Quéré, J. G. Canadell, P. Ciais, R. B. Jackson, S. Alin, L. E. O. C. Aragão, A. Arneth, V. Arora, N. R. Bates, M. Becker, A. Benoit-Cattin, H. C. Bittig, L. Bopp, S. Bultan, N. Chandra, F. Chevallier, L. P. Chini, W. Evans, L. Florentie, P. M. Forster, T. Gasser, M. Gehlen, D. Gilfillan, T. Gkritzalis, L. Gregor, N. Gruber, I. Harris, K. Hartung, V. Haverd, R. A. Houghton, T. Ilyina, A. K. Jain, E. Joetzjer, K. Kadono, E. Kato, V. Kitidis, J. I. Korsbakken, P. Landschützer, N. Lefèvre, A. Lenton, S. Lienert, Z. Liu, D. Lombardozzi, G. Marland, N. Metzl, D. R. Munro, J. E. M. S. Nabel, S.-I. Nakaoka, Y. Niwa, K. O'Brien, T. Ono, P. I. Palmer, D. Pierrot, B. Poulter, L. Resplandy, E. Robertson, C. Rödenbeck, J. Schwinger, R. Séférian, I. Skjelvan, A. J. P. Smith, A. J. Sutton, T. Tanhua, P. P. Tans, H. Tian, B. Tilbrook, G. van der Werf, N. Vuichard, A. P. Walker, R. Wanninkhof, A. J. Watson, D. Willis, A. J. Wiltshire, W. Yuan, X. Yue, and S. Zaehle

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ±  0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

Published
2020
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23. Early-season mapping of winter wheat in China based on Landsat and Sentinel images

Author

J. Dong, Y. Fu, J. Wang, H. Tian, S. Fu, Z. Niu, W. Han, Y. Zheng, J. Huang, and W. Yuan

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Early-season crop identification is of great importance for monitoring crop growth and predicting yield for decision makers and private sectors. As one of the largest producers of winter wheat worldwide, China outputs more than 18 % of the global production of winter wheat. However, there are no distribution maps of winter wheat over a large spatial extent with high spatial resolution. In this study, we applied a phenology-based approach to distinguish winter wheat from other crops by comparing the similarity of the seasonal changes of satellite-based vegetation index over all croplands with a standard seasonal change derived from known winter wheat fields. Especially, this study examined the potential of early-season large-area mapping of winter wheat and developed accurate winter wheat maps with 30 m spatial resolution for 3 years (2016–2018) over 11 provinces, which produce more than 98 % of the winter wheat in China. A comprehensive assessment based on survey samples revealed producer's and user's accuracies higher than 89.30 % and 90.59 %, respectively. The estimated winter wheat area exhibited good correlations with the agricultural statistical area data at the municipal and county levels. In addition, the earliest identifiable time of the geographical location of winter wheat was achieved by the end of March, giving a lead time of approximately 3 months before harvest, and the optimal identifiable time of winter wheat was at the end of April with an overall accuracy of 89.88 %. These results are expected to aid in the timely monitoring of crop growth. The 30 m winter wheat maps in China are available via an open-data repository (DOI: https://doi.org/10.6084/m9.figshare.12003990, Dong et al., 2020a).

Published
2020
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24. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Author

Y. Zheng, R. Shen, Y. Wang, X. Li, S. Liu, S. Liang, J. M. Chen, W. Ju, L. Zhang, and W. Yuan

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at the site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05∘ latitude by 0.05∘ longitude and 8 d interval by revising a light use efficiency model (i.e., EC-LUE model). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 95 towers from the FLUXNET2015 dataset, covering nine major ecosystem types around the globe, were used to calibrate and validate the model. In general, the revised EC-LUE model could effectively reproduce the spatial, seasonal, and annual variations in the tower-estimated GPP at most sites. The revised EC-LUE model could explain 71 % of the spatial variations in annual GPP over 95 sites. At more than 95 % of the sites, the correlation coefficients (R2) of seasonal changes between tower-estimated and model-simulated GPP are larger than 0.5. Particularly, the revised EC-LUE model improved the model performance in reproducing the interannual variations in GPP, and the averaged R2 between annual mean tower-estimated and model-simulated GPP is 0.44 over all 55 sites with observations longer than 5 years, which is significantly higher than those of the original EC-LUE model (R2=0.36) and other LUE models (R2 ranged from 0.06 to 0.30 with an average value of 0.16). At the global scale, GPP derived from light use efficiency models, machine learning models, and process-based biophysical models shows substantial differences in magnitude and interannual variations. The revised EC-LUE model quantified the mean global GPP from 1982 to 2017 as 106.2±2.9 Pg C yr−1 with the trend 0.15 Pg C yr−1. Sensitivity analysis indicated that GPP simulated by the revised EC-LUE model was sensitive to atmospheric CO2 concentration, VPD, and radiation. Over the period of 1982–2017, the CO2 fertilization effect on the global GPP (0.22±0.07 Pg C yr−1) could be partly offset by increased VPD (-0.17±0.06 Pg C yr−1). The long-term changes in the environmental variables could be well reflected in global GPP. Overall, the revised EC-LUE model is able to provide a reliable long-term estimate of global GPP. The GPP dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v3 (Zheng et al., 2019).

Published
2020
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25. Measurement of the absolute branching fraction of the inclusive decay $$\Lambda _c^+ \rightarrow K_S^0X$$ Λ c + → K S 0 X

Author

M. Ablikim, M. N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, A. Amoroso, Q. An, Anita, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, J. V. Bennett, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J Biernat, J. Bloms, A. Bortone, I. Boyko, R. A. Briere, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, W. L. Chang, G. Chelkov, D. Y. Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, X. R. Chen, Y. B. Chen, W. Cheng, G. Cibinetto, F. Cossio, X. F. Cui, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. B. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, S. X. Du, J. Fang, S. S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, M. Fritsch, C. D. Fu, Y. Fu, X. L. Gao, Y. Gao, Y. G. Gao, I. Garzia, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, L. M. Gu, M. H. Gu, S. Gu, Y. T. Gu, C. Y Guan, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, A. Guskov, S. Han, T. T. Han, T. Z. Han, X. Q. Hao, F. A. Harris, K. L. He, F. H. Heinsius, T. Held, Y. K. Heng, M. Himmelreich, T. Holtmann, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, L. Q. Huang, X. T. Huang, Z. Huang, N. Huesken, T. Hussain, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, H. B. Jiang, X. S. Jiang, X. Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, I. K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O. B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M. G. Kurth, W. Kühn, J. J. Lane, J. S. Lange, P. Larin, L. Lavezzi, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, J. L. Li, J. Q. Li, Ke Li, L. K. Li, Lei Li, P. L. Li, P. R. Li, S. Y. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Z. B. Li, Z. Y. Li, H. Liang, Y. F. Liang, Y. T. Liang, L. Z. Liao, J. Libby, C. X. Lin, B. Liu, B. J. Liu, C. X. Liu, D. Liu, D. Y. Liu, F. H. Liu, Fang Liu, Feng Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, Q. Liu, S. B. Liu, Shuai Liu, T. Liu, X. Liu, Y. B. Liu, Z. A. Liu, Z. Q. Liu, Y. F. Long, X. C. Lou, H. J. Lu, J. D. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, C. L. Luo, M. X. Luo, P. W. Luo, T. Luo, X. L. Luo, S. Lusso, X. R. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. N. Ma, X. X. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, N. Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H. P. Peng, K. Peters, J. Pettersson, J. L. Ping, R. G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W.-B. Qian, Z. Qian, C. F. Qiao, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, K. H. Rashid, K. Ravindran, C. F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, A. Sarantsev, M. Savrié, Y. Schelhaas, C. Schnier, K. Schoenning, D. C. Shan, W. Shan, X. Y. Shan, M. Shao, C. P. Shen, P. X. Shen, X. Y. Shen, H. C. Shi, R. S. Shi, X. Shi, X. D Shi, J. J. Song, Q. Q. Song, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, F. F. Sui, G. X. Sun, J. F. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. J. Sun, Y. K Sun, Y. Z. Sun, Z. T. Sun, Y. H. Tan, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, V. Thoren, B. Tsednee, I. Uman, B. Wang, B. L. Wang, C. W. Wang, D. Y. Wang, H. P. Wang, K. Wang, L. L. Wang, M. Wang, M. Z. Wang, Meng Wang, W. H. Wang, W. P. Wang, X. Wang, X. F. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. Q. Wang, Z. Wang, Z. Y. Wang, Ziyi Wang, Zongyuan Wang, T. Weber, D. H. Wei, P. Weidenkaff, F. Weidner, S. P. Wen, D. J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S. Y. Xiao, Y. J. Xiao, Z. J. Xiao, X. H. Xie, Y. G. Xie, Y. H. Xie, T. Y. Xing, X. A. Xiong, G. F. Xu, J. J. Xu, Q. J. Xu, W. Xu, X. P. Xu, L. Yan, W. B. Yan, W. C. Yan, Xu Yan, H. J. Yang, H. X. Yang, L. Yang, R. X. Yang, S. L. Yang, Y. H. Yang, Y. X. Yang, Yifan Yang, Zhi Yang, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, C. Z. Yuan, W. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. Yuncu, A. A. Zafar, Y. Zeng, B. X. Zhang, Guangyi Zhang, H. H. Zhang, H. Y. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. Zhang, Lei Zhang, S. Zhang, S. F. Zhang, T. J. Zhang, X. Y. Zhang, Y. Zhang, Y. H. Zhang, Y. T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z. H. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Q. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, Y. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, L. P. Zhou, Q. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, S. H. Zhu, W. J. Zhu, X. L. Zhu, Y. C. Zhu, Z. A. Zhu, B. S. Zou, and J. H. Zou

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract We report the first measurement of the absolute branching fraction of the inclusive decay $$\Lambda _c^+ \rightarrow K_S^0X$$ Λ c + → K S 0 X . The analysis is performed using an $$e^+e^-$$ e + e - collision data sample corresponding to an integrated luminosity of 567 $$\hbox {pb}^{-1}$$ pb - 1 taken at $$\sqrt{s}$$ s = 4.6 GeV with the BESIII detector. Using eleven Cabibbo-favored $${\bar{\Lambda }}_c^-$$ Λ ¯ c - decay modes and the double-tag technique, this absolute branching fraction is measured to be $${\mathcal {B}}(\Lambda _c^+ \rightarrow K_S^0X)=(9.9\pm 0.6\pm 0.4)\%$$ B ( Λ c + → K S 0 X ) = ( 9.9 ± 0.6 ± 0.4 ) % , where the first uncertainty is statistical and the second systematic. The relative deviation between the branching fractions for the inclusive decay and the observed exclusive decays is $$(18.7\pm 8.3)\%$$ ( 18.7 ± 8.3 ) % , which indicates that there may be some unobserved decay modes with a neutron or excited baryons in the final state.

Published
2020
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26. END-TO-END BUILDING CHANGE DETECTION MODEL IN AERIAL IMAGERY AND DIGITAL SURFACE MODEL BASED ON NEURAL NETWORKS

Author

X. Lian, W. Yuan, Z. Guo, Z. Cai, X. Song, and R. Shibasaki

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Multi-temporal building change detection is one of the most essential major issues of photogrammetry and remote sensing at current stage, which is of great significance for wide applications as offering real estate indicators as well as monitoring urban environment. Although current photogrammetry methodologies could be applicated to 2-D remote sensing imagery for rectification with sensor parameters, multi-temporal aerial or satellite imagery is not adequate to offer spectral and textual features for building change detection. Alongside recent development of Dense Image Matching (DIM) technology, the acquisition of 3-D point cloud and Digital Surface Model (DSM) has been generally realized, which could be combined with imagery, making building change detection more effective with greater spatial structure and texture information. Over the past years, scholars have put forward vast change detection techniques including traditional and model-based solutions. Nevertheless, existing appropriate methodology combined with Neural Networks (NN) for accurate building change detection with multi-temporal imagery and DSM remains to be of great research focus currently due to the inevitable limitations and omissions of existing NN-based methods, which is of great research prospect. This study proposed a novel end-to-end model framework based on deep learning for pixel-level building change detection from high-spatial resolution aerial ortho imagery and corresponding DSM sharing same resolution, which is from the dataset of Tokyo whole area.

Published
2020
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27. UNSUPERVISED MULTI-CONSTRAINT DEEP NEURAL NETWORK FOR DENSE IMAGE MATCHING

Author

W. Yuan, Z. Fan, X. Yuan, J. Gong, and R. Shibasaki

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Dense image matching is essential to photogrammetry applications, including Digital Surface Model (DSM) generation, three dimensional (3D) reconstruction, and object detection and recognition. The development of an efficient and robust method for dense image matching has been one of the technical challenges due to high variations in illumination and ground features of aerial images of large areas. Nowadays, due to the development of deep learning technology, deep neural network-based algorithms outperform traditional methods on a variety of tasks such as object detection, semantic segmentation and stereo matching. The proposed network includes cost-volume computation, cost-volume aggregation, and disparity prediction. It starts with a pre-trained VGG-16 network as a backend and using the U-net architecture with nine layers for feature map extraction and a correlation layer for cost volume calculation, after that a guided filter based cost aggregation is adopted for cost volume filtering and finally the soft Argmax function is utilized for disparity prediction. The experimental conducted on a UAV dataset demonstrated that the proposed method achieved the RMSE (root mean square error) of the reprojection error better than 1 pixel in image coordinate and in-ground positioning accuracy within 2.5 ground sample distance. The comparison experiments on KITTI 2015 dataset shows the proposed unsupervised method even comparably with other supervised methods.

Published
2020
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28. Precise measurements of branching fractions for D s + $$ {\mathrm{D}}_{\mathrm{s}}^{+} $$ meson decays to two pseudoscalar mesons

Author

The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, A. Amoroso, Q. An, Anita, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, J. V. Bennett, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Biernat, J. Bloms, A. Bortone, I. Boyko, R. A. Briere, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, W. L. Chang, G. Chelkov, D. Y. Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, X. R. Chen, Y. B. Chen, W. Cheng, G. Cibinetto, F. Cossio, X. F. Cui, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. B. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, S. X. Du, J. Fang, S. S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, M. Fritsch, C. D. Fu, Y. Fu, X. L. Gao, Y. Gao, Y. G. Gao, I. Garzia, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, L. M. Gu, M. H. Gu, S. Gu, Y. T. Gu, C. Y Guan, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, A. Guskov, S. Han, T. T. Han, T. Z. Han, X. Q. Hao, F. A. Harris, K. L. He, F. H. Heinsius, T. Held, Y. K. Heng, M. Himmelreich, T. Holtmann, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, L. Q. Huang, X. T. Huang, Z. Huang, N. Huesken, T. Hussain, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, H. B. Jiang, X. S. Jiang, X. Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, I. K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O. B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M. G. Kurth, W. Kühn, J. J. Lane, J. S. Lange, P. Larin, L. Lavezzi, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, J. L. Li, J. Q. Li, Ke Li, L. K. Li, Lei Li, P. L. Li, P. R. Li, S. Y. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Z. B. Li, Z. Y. Li, H. Liang, Y. F. Liang, Y. T. Liang, L. Z. Liao, J. Libby, C. X. Lin, B. Liu, B. J. Liu, C. X. Liu, D. Liu, D. Y. Liu, F. H. Liu, Fang Liu, Feng Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, Q. Liu, S. B. Liu, Shuai Liu, T. Liu, X. Liu, Y. B. Liu, Z. A. Liu, Z. Q. Liu, Y. F. Long, X. C. Lou, F. X. Lu, H. J. Lu, J. D. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, C. L. Luo, M. X. Luo, P. W. Luo, T. Luo, X. L. Luo, S. Lusso, X. R. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. N. Ma, X. X. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, N. Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H. P. Peng, K. Peters, J. Pettersson, J. L. Ping, R. G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W.-B. Qian, Z. Qian, C. F. Qiao, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, K. H. Rashid, K. Ravindran, C. F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, A. Sarantsev, M. Savrié, Y. Schelhaas, C. Schnier, K. Schoenning, D. C. Shan, W. Shan, X. Y. Shan, M. Shao, C. P. Shen, P. X. Shen, X. Y. Shen, H. C. Shi, R. S. Shi, X. Shi, X. D Shi, J. J. Song, Q. Q. Song, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, F. F. Sui, G. X. Sun, J. F. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. J. Sun, Y. K Sun, Y. Z. Sun, Z. T. Sun, Y. H. Tan, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, V. Thoren, B. Tsednee, I. Uman, B. Wang, B. L. Wang, C. W. Wang, D. Y. Wang, H. P. Wang, K. Wang, L. L. Wang, M. Wang, M. Z. Wang, Meng Wang, W. H. Wang, W. P. Wang, X. Wang, X. F. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. Q. Wang, Z. Wang, Z. Y. Wang, Ziyi Wang, Zongyuan Wang, D. H. Wei, P. Weidenkaff, F. Weidner, S. P. Wen, D. J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S. Y. Xiao, Y. J. Xiao, Z. J. Xiao, X. H. Xie, Y. G. Xie, Y. H. Xie, T. Y. Xing, X. A. Xiong, G. F. Xu, J. J. Xu, Q. J. Xu, W. Xu, X. P. Xu, L. Yan, W. B. Yan, W. C. Yan, Xu Yan, H. J. Yang, H. X. Yang, L. Yang, R. X. Yang, S. L. Yang, Y. H. Yang, Y. X. Yang, Yifan Yang, Zhi Yang, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, C. Z. Yuan, W. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. Yuncu, A. A. Zafar, Y. Zeng, B. X. Zhang, Guangyi Zhang, H. H. Zhang, H. Y. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. Zhang, Lei Zhang, S. Zhang, S. F. Zhang, T. J. Zhang, X. Y. Zhang, Y. Zhang, Y. H. Zhang, Y. T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z. H. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Q. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, Y. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, L. P. Zhou, Q. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, S. H. Zhu, W. J. Zhu, X. L. Zhu, Y. C. Zhu, Z. A. Zhu, B. S. Zou, and J. H. Zou

Subjects
Branching fraction, Charm physics, e +-e − Experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract We measure the branching fractions for seven D s + $$ {D}_s^{+} $$ two-body decays to pseudo-scalar mesons, by analyzing data collected at s $$ \sqrt{s} $$ = 4.178 ∼ 4.226 GeV with the BESIII detector at the BEPCII collider. The branching fractions are determined to be B D s + → K + η ' = 2.68 ± 0.17 ± 0.17 ± 0.08 × 10 − 3 , B D s + → η ' π + = 37.8 ± 0.4 ± 2.1 ± 1.2 × 10 − 3 , B D s + → K + η = 1.62 ± 0.10 ± 0.03 ± 0.05 × 10 − 3 , B D s + → η π + = 17.41 ± 0.18 ± 0.27 ± 0.54 × 10 − 3 , B D s + → K + K S 0 = 15.02 ± 0.10 ± 0.27 ± 0.47 × 10 − 3 , B D s + → K S 0 π + = 1.109 ± 0.034 ± 0.023 ± 0.035 × 10 − 3 , B D s + → K + π 0 = 0.748 ± 0.049 ± 0.018 ± 0.035 × 10 − 3 , $$ {\displaystyle \begin{array}{c}\mathcal{B}\left({D}_s^{+}\to {K}^{+}\eta \hbox{'}\right)=\left(2.68\pm 0.17\pm 0.17\pm 0.08\right)\times {10}^{-3},\\ {}\mathcal{B}\left({D}_s^{+}\to \eta \hbox{'}{\pi}^{+}\right)=\left(37.8\pm 0.4\pm 2.1\pm 1.2\right)\times {10}^{-3},\\ {}\mathcal{B}\left({D}_s^{+}\to {K}^{+}\eta \right)=\left(1.62\pm 0.10\pm 0.03\pm 0.05\right)\times {10}^{-3},\\ {}\mathcal{B}\left({D}_s^{+}\to \eta {\pi}^{+}\right)=\left(17.41\pm 0.18\pm 0.27\pm 0.54\right)\times {10}^{-3},\\ {}\mathcal{B}\left({D}_s^{+}\to {K}^{+}{K}_S^0\right)=\left(15.02\pm 0.10\pm 0.27\pm 0.47\right)\times {10}^{-3},\\ {}\mathcal{B}\left({D}_s^{+}\to {K}_S^0{\pi}^{+}\right)=\left(1.109\pm 0.034\pm 0.023\pm 0.035\right)\times {10}^{-3},\\ {}\mathcal{B}\left({D}_s^{+}\to {K}^{+}{\pi}^0\right)=\left(0.748\pm 0.049\pm 0.018\pm 0.035\right)\times {10}^{-3},\end{array}} $$ where the first uncertainties are statistical, the second are systematic, and the third are from external input branching fraction of the normalization mode D s + $$ {D}_s^{+} $$ → K + K − π +. Precision of our measurements is significantly improved compared with that of the current world average values.

Published
2020
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29. Characterization of the light-absorbing properties, chromophore composition and sources of brown carbon aerosol in Xi'an, northwestern China

Author

W. Yuan, R.-J. Huang, L. Yang, J. Guo, Z. Chen, J. Duan, T. Wang, H. Ni, Y. Han, Y. Li, Q. Chen, Y. Chen, T. Hoffmann, and C. O'Dowd

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

The impact of brown carbon aerosol (BrC) on the Earth's radiative forcing balance has been widely recognized but remains uncertain, mainly because the relationships among BrC sources, chromophores and optical properties of aerosol are poorly understood. In this work, the light absorption properties and chromophore composition of BrC were investigated for samples collected in Xi'an, northwestern China, from 2015 to 2016. Both absorption Ångström exponent (AAE) and mass absorption efficiency (MAE) show distinct seasonal differences, which could be attributed to the differences in sources and chromophore composition of BrC. Three groups of light-absorbing organics were found to be important BrC chromophores, including compounds that have multiple absorption peaks at wavelengths > 350 nm (12 polycyclic aromatic hydrocarbons and their derivatives) and compounds that have a single absorption peak at wavelengths < 350 nm (10 nitrophenols and nitrosalicylic acids and 3 methoxyphenols). These measured BrC chromophores show distinct seasonal differences and contribute on average about 1.1 % and 3.3 % of light absorption of methanol-soluble BrC at 365 nm in summer and winter, respectively, about 7 and 5 times higher than the corresponding carbon mass fractions in total organic carbon. The sources of BrC were resolved by positive matrix factorization (PMF) using these chromophores instead of commonly used non-light-absorbing organic markers as model inputs. Our results show that vehicular emissions and secondary formation are major sources of BrC (∼ 70 %) in spring, coal combustion and vehicular emissions are major sources (∼ 70 %) in fall, biomass burning and coal combustion become major sources (∼ 80 %) in winter, and secondary BrC dominates (∼ 60 %) in summer.

Published
2020
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30. Equatorial plasma bubbles developing around sunrise observed by an all-sky imager and global navigation satellite system network during storm time

Author

K. Wu, J. Xu, X. Yue, C. Xiong, W. Wang, W. Yuan, C. Wang, Y. Zhu, and J. Luo

Subjects
Science, Physics, QC1-999, Geophysics. Cosmic physics, QC801-809
Abstract

A large number of studies have shown that equatorial plasma bubbles (EPBs) occur mainly after sunset, and they usually drift eastward. However, in this paper, an unusual EPB event was simultaneously observed by an all-sky imager and the global navigation satellite system (GNSS) network in southern China, during the recovery phase of a geomagnetic storm that happened on 6–8 November 2015. Observations from both techniques show that the EPBs appeared near dawn. Interestingly, the observational results show that the EPBs continued to develop after sunrise, and they disappeared about 1 h after sunrise. The development stage of EPBs lasted for at least about 3 h. To our knowledge, this is the first time that the evolution of EPBs developing around sunrise was observed by an all-sky imager and the GNSS network. Our observation showed that the EPBs drifted westward, which was different from the usual eastward drifts of post-sunset EPBs. The simulation from the Thermosphere–Ionosphere–Electrodynamics General Circulation Model (TIE-GCM) suggest that the westward drift of EPBs should be related to the enhanced westward winds at storm time. Besides this, bifurcation and merging processes of EPBs were observed by the all-sky imager in the event. Associated with the development of EPBs, an increase in the peak height of the ionospheric F region was also observed near sunrise, and we suggest the enhanced upward vertical plasma drift during the geomagnetic storm plays a major role in triggering the EPBs near sunrise.

Published
2020
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32. Measurement of the inclusive branching fraction for ψ(3686)→KS0+anything

Author

M. Ablikim, M.N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, R. Aliberti, A. Amoroso, Q. An, X.H. Bai, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, J.V. Bennett, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Biernat, J. Bloms, A. Bortone, I. Boyko, R.A. Briere, H. Cai, X. Cai, A. Calcaterra, G.F. Cao, N. Cao, S.A. Cetin, J.F. Chang, W.L. Chang, G. Chelkov, D.Y. Chen, G. Chen, H.S. Chen, M.L. Chen, S.J. Chen, X.R. Chen, Y.B. Chen, Z.J. Chen, W.S. Cheng, G. Cibinetto, F. Cossio, X.F. Cui, H.L. Dai, X.C. Dai, A. Dbeyssi, R.E. de Boer, D. Dedovich, Z.Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L.Y. Dong, M.Y. Dong, S.X. Du, J. Fang, S.S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C.Q. Feng, M. Fritsch, C.D. Fu, Y. Fu, X.L. Gao, Y. Gao, Y.G. Gao, I. Garzia, E.M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W.X. Gong, W. Gradl, M. Greco, L.M. Gu, M.H. Gu, S. Gu, Y.T. Gu, C.Y. Guan, A.Q. Guo, L.B. Guo, R.P. Guo, Y.P. Guo, A. Guskov, S. Han, T.T. Han, T.Z. Han, X.Q. Hao, F.A. Harris, K.L. He, F.H. Heinsius, C.H. Heinz, T. Held, Y.K. Heng, M. Himmelreich, T. Holtmann, Y.R. Hou, Z.L. Hou, H.M. Hu, J.F. Hu, T. Hu, Y. Hu, G.S. Huang, L.Q. Huang, X.T. Huang, Y.P. Huang, Z. Huang, T. Hussain, N. Hüsken, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q.P. Ji, X.B. Ji, X.L. Ji, H.B. Jiang, X.S. Jiang, J.B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X.S. Kang, R. Kappert, M. Kavatsyuk, B.C. Ke, I.K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O.B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M.G. Kurth, W. Kühn, J.J. Lane, J.S. Lange, P. Larin, A. Lavania, L. Lavezzi, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C.H. Li, Cheng Li, D.M. Li, F. Li, G. Li, H. Li, H.B. Li, H.J. Li, J.L. Li, J.Q. Li, Ke Li, L.K. Li, Lei Li, P.L. Li, P.R. Li, S.Y. Li, W.D. Li, W.G. Li, X.H. Li, X.L. Li, Z.Y. Li, H. Liang, Y.F. Liang, Y.T. Liang, G.R. Liao, L.Z. Liao, J. Libby, C.X. Lin, B. Liu, B.J. Liu, C.X. Liu, D. Liu, D.Y. Liu, F.H. Liu, Fang Liu, Feng Liu, H.B. Liu, H.M. Liu, Huanhuan Liu, Huihui Liu, J.B. Liu, J.Y. Liu, K. Liu, K.Y. Liu, L. Liu, Q. Liu, S.B. Liu, Shuai Liu, T. Liu, W.M. Liu, X. Liu, Y.B. Liu, Z.A. Liu, Z.Q. Liu, X.C. Lou, F.X. Lu, H.J. Lu, J.D. Lu, J.G. Lu, X.L. Lu, Y. Lu, Y.P. Lu, C.L. Luo, M.X. Luo, P.W. Luo, T. Luo, X.L. Luo, S. Lusso, X.R. Lyu, F.C. Ma, H.L. Ma, L.L. Ma, M.M. Ma, Q.M. Ma, R.Q. Ma, R.T. Ma, X.N. Ma, X.X. Ma, X.Y. Ma, Y.M. Ma, F.E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q.A. Malik, A. Mangoni, Y.J. Mao, Z.P. Mao, S. Marcello, Z.X. Meng, J.G. Messchendorp, G. Mezzadri, T.J. Min, R.E. Mitchell, X.H. Mo, N.Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I.B. Nikolaev, Z. Ning, S. Nisar, S.L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H.P. Peng, K. Peters, J. Pettersson, J.L. Ping, R.G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H.R. Qi, M. Qi, T.Y. Qi, S. Qian, W.B. Qian, Z. Qian, C.F. Qiao, L.Q. Qin, X.S. Qin, Z.H. Qin, J.F. Qiu, S.Q. Qu, K.H. Rashid, K. Ravindran, C.F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, A. Sarantsev, Y. Schelhaas, C. Schnier, K. Schoenning, M. Scodeggio, D.C. Shan, W. Shan, X.Y. Shan, M. Shao, C.P. Shen, P.X. Shen, X.Y. Shen, H.C. Shi, R.S. Shi, X. Shi, X.D. Shi, J.J. Song, Q.Q. Song, W.M. Song, Y.X. Song, S. Sosio, S. Spataro, F.F. Sui, G.X. Sun, J.F. Sun, L. Sun, S.S. Sun, T. Sun, W.Y. Sun, X. Sun, Y.J. Sun, Y.K. Sun, Y.Z. Sun, Z.T. Sun, Y.H. Tan, Y.X. Tan, C.J. Tang, G.Y. Tang, J. Tang, J.X. Teng, V. Thoren, I. Uman, B. Wang, B.L. Wang, C.W. Wang, D.Y. Wang, H.P. Wang, K. Wang, L.L. Wang, M. Wang, M.Z. Wang, Meng Wang, W.H. Wang, W.P. Wang, X. Wang, X.F. Wang, X.L. Wang, Y. Wang, Y.D. Wang, Y.F. Wang, Y.Q. Wang, Z. Wang, Z.Y. Wang, Ziyi Wang, Zongyuan Wang, D.H. Wei, P. Weidenkaff, F. Weidner, S.P. Wen, D.J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J.F. Wu, L.H. Wu, L.J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S.Y. Xiao, Y.J. Xiao, Z.J. Xiao, X.H. Xie, Y.G. Xie, Y.H. Xie, T.Y. Xing, X.A. Xiong, G.F. Xu, J.J. Xu, Q.J. Xu, W. Xu, X.P. Xu, Y.C. Xu, F. Yan, L. Yan, W.B. Yan, W.C. Yan, Xu Yan, H.J. Yang, H.X. Yang, L. Yang, R.X. Yang, S.L. Yang, Y.H. Yang, Y.X. Yang, Yifan Yang, Zhi Yang, M. Ye, M.H. Ye, J.H. Yin, Z.Y. You, B.X. Yu, C.X. Yu, G. Yu, J.S. Yu, T. Yu, C.Z. Yuan, W. Yuan, X.Q. Yuan, Y. Yuan, Z.Y. Yuan, C.X. Yue, A.A. Zafar, Y. Zeng, B.X. Zhang, Guangyi Zhang, H. Zhang, H.H. Zhang, H.Y. Zhang, J.L. Zhang, J.Q. Zhang, J.W. Zhang, J.Y. Zhang, J.Z. Zhang, Jianyu Zhang, Jiawei Zhang, Lei Zhang, S. Zhang, S.F. Zhang, T.J. Zhang, X.Y. Zhang, Y. Zhang, Y.H. Zhang, Y.T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z.Y. Zhang, G. Zhao, J. Zhao, J.Y. Zhao, J.Z. Zhao, Lei Zhao, Ling Zhao, M.G. Zhao, Q. Zhao, S.J. Zhao, Y.B. Zhao, Y.X. Zhao, Z.G. Zhao, A. Zhemchugov, B. Zheng, J.P. Zheng, Y.H. Zheng, B. Zhong, C. Zhong, L.P. Zhou, Q. Zhou, X. Zhou, X.K. Zhou, X.R. Zhou, A.N. Zhu, J. Zhu, K. Zhu, K.J. Zhu, S.H. Zhu, W.J. Zhu, Y.C. Zhu, Z.A. Zhu, B.S. Zou, and J.H. Zou

Subjects
ψ(3686), Inclusive branching fraction, KS0, BESIII, Physics, QC1-999
Abstract

Using 5.9 pb−1 of e+e− annihilation data collected at center-of-mass energies from 3.640 to 3.701 GeV with the BESIII detector at the BEPCII Collider, we measure the observed cross sections of e+e−→KS0X (where X=anything). From a fit to these observed cross sections with the sum of continuum and ψ(3686) and J/ψ Breit-Wigner functions and considering initial state radiation and the BEPCII beam energy spread, we obtain for the first time the product of ψ(3686) leptonic width and inclusive decay branching fraction Γψ(3686)eeB(ψ(3686)→KS0X)=(373.8±6.7±20.0) eV, and assuming Γψ(3686)ee is (2.33±0.04) keV from PDG value, we measure B(ψ(3686)→KS0X)=(16.04±0.29±0.90)%, where the first uncertainty is statistical and the second is systematic.

Published
2021
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33. Measurement of the absolute branching fraction of Λc+→pKS0η decays

Author

M. Ablikim, M.N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, R. Aliberti, A. Amoroso, Q. An, Anita, X.H. Bai, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, J.V. Bennett, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Biernat, J. Bloms, A. Bortone, I. Boyko, R.A. Briere, H. Cai, X. Cai, A. Calcaterra, G.F. Cao, N. Cao, S.A. Cetin, J.F. Chang, W.L. Chang, G. Chelkov, D.Y. Chen, G. Chen, H.S. Chen, M.L. Chen, S.J. Chen, X.R. Chen, Y.B. Chen, Z.J. Chen, W.S. Cheng, G. Cibinetto, F. Cossio, X.F. Cui, H.L. Dai, J.P. Dai, X.C. Dai, A. Dbeyssi, R.B. de Boer, D. Dedovich, Z.Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L.Y. Dong, M.Y. Dong, S.X. Du, J. Fang, S.S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C.Q. Feng, M. Fritsch, C.D. Fu, Y. Fu, X.L. Gao, Y. Gao, Y.G. Gao, I. Garzia, E.M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W.X. Gong, W. Gradl, M. Greco, L.M. Gu, M.H. Gu, S. Gu, Y.T. Gu, C.Y. Guan, A.Q. Guo, L.B. Guo, R.P. Guo, Y.P. Guo, A. Guskov, S. Han, T.T. Han, T.Z. Han, X.Q. Hao, F.A. Harris, K.L. He, F.H. Heinsius, C.H. Heinz, T. Held, Y.K. Heng, M. Himmelreich, T. Holtmann, Y.R. Hou, Z.L. Hou, H.M. Hu, J.F. Hu, T. Hu, Y. Hu, G.S. Huang, L.Q. Huang, X.T. Huang, Y.P. Huang, Z. Huang, N. Huesken, T. Hussain, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q.P. Ji, X.B. Ji, X.L. Ji, H.B. Jiang, X.S. Jiang, X.Y. Jiang, J.B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X.S. Kang, R. Kappert, M. Kavatsyuk, B.C. Ke, I.K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O.B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M.G. Kurth, W. Kühn, J.J. Lane, J.S. Lange, P. Larin, L. Lavezzi, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C.H. Li, Cheng Li, D.M. Li, F. Li, G. Li, H. Li, H.B. Li, H.J. Li, J.L. Li, J.Q. Li, Ke Li, L.K. Li, Lei Li, P.L. Li, P.R. Li, S.Y. Li, W.D. Li, W.G. Li, X.H. Li, X.L. Li, Z.B. Li, Z.Y. Li, H. Liang, Y.F. Liang, Y.T. Liang, L.Z. Liao, J. Libby, C.X. Lin, B. Liu, B.J. Liu, C.X. Liu, D. Liu, D.Y. Liu, F.H. Liu, Fang Liu, Feng Liu, H.B. Liu, H.M. Liu, Huanhuan Liu, Huihui Liu, J.B. Liu, J.Y. Liu, K. Liu, K.Y. Liu, Ke Liu, L. Liu, Q. Liu, S.B. Liu, Shuai Liu, T. Liu, X. Liu, Y.B. Liu, Z.A. Liu, Z.Q. Liu, Y.F. Long, X.C. Lou, F.X. Lu, H.J. Lu, J.D. Lu, J.G. Lu, X.L. Lu, Y. Lu, Y.P. Lu, C.L. Luo, M.X. Luo, P.W. Luo, T. Luo, X.L. Luo, S. Lusso, X.R. Lyu, F.C. Ma, H.L. Ma, L.L. Ma, M.M. Ma, Q.M. Ma, R.Q. Ma, R.T. Ma, X.N. Ma, X.X. Ma, X.Y. Ma, Y.M. Ma, F.E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q.A. Malik, A. Mangoni, Y.J. Mao, Z.P. Mao, S. Marcello, Z.X. Meng, J.G. Messchendorp, G. Mezzadri, T.J. Min, R.E. Mitchell, X.H. Mo, Y.J. Mo, N.Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I.B. Nikolaev, Z. Ning, S. Nisar, S.L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H.P. Peng, K. Peters, J. Pettersson, J.L. Ping, R.G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H.R. Qi, M. Qi, T.Y. Qi, S. Qian, W.-B. Qian, Z. Qian, C.F. Qiao, L.Q. Qin, X.S. Qin, Z.H. Qin, J.F. Qiu, S.Q. Qu, K.H. Rashid, K. Ravindran, C.F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, A. Sarantsev, Y. Schelhaas, C. Schnier, K. Schoenning, M. Scodeggio, D.C. Shan, W. Shan, X.Y. Shan, M. Shao, C.P. Shen, P.X. Shen, X.Y. Shen, H.C. Shi, R.S. Shi, X. Shi, X.D. Shi, J.J. Song, Q.Q. Song, W.M. Song, Y.X. Song, S. Sosio, S. Spataro, F.F. Sui, G.X. Sun, J.F. Sun, L. Sun, S.S. Sun, T. Sun, W.Y. Sun, X. Sun, Y.J. Sun, Y.K. Sun, Y.Z. Sun, Z.T. Sun, Y.H. Tan, Y.X. Tan, C.J. Tang, G.Y. Tang, J. Tang, V. Thoren, I. Uman, B. Wang, B.L. Wang, C.W. Wang, D.Y. Wang, H.P. Wang, K. Wang, L.L. Wang, M. Wang, M.Z. Wang, Meng Wang, W.H. Wang, W.P. Wang, X. Wang, X.F. Wang, X.L. Wang, Y. Wang, Y.D. Wang, Y.F. Wang, Y.Q. Wang, Z. Wang, Z.Y. Wang, Ziyi Wang, Zongyuan Wang, D.H. Wei, P. Weidenkaff, F. Weidner, S.P. Wen, D.J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J.F. Wu, L.H. Wu, L.J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S.Y. Xiao, Y.J. Xiao, Z.J. Xiao, X.H. Xie, Y.G. Xie, Y.H. Xie, T.Y. Xing, X.A. Xiong, G.F. Xu, J.J. Xu, Q.J. Xu, W. Xu, X.P. Xu, F. Yan, L. Yan, W.B. Yan, W.C. Yan, Xu Yan, H.J. Yang, H.X. Yang, L. Yang, R.X. Yang, S.L. Yang, Y.H. Yang, Y.X. Yang, Yifan Yang, Zhi Yang, M. Ye, M.H. Ye, J.H. Yin, Z.Y. You, B.X. Yu, C.X. Yu, G. Yu, J.S. Yu, T. Yu, C.Z. Yuan, W. Yuan, X.Q. Yuan, Y. Yuan, Z.Y. Yuan, C.X. Yue, A. Yuncu, A.A. Zafar, Y. Zeng, B.X. Zhang, Guangyi Zhang, H.H. Zhang, H.Y. Zhang, J.L. Zhang, J.Q. Zhang, J.W. Zhang, J.Y. Zhang, J.Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. Zhang, Lei Zhang, S. Zhang, S.F. Zhang, T.J. Zhang, X.Y. Zhang, Y. Zhang, Y.H. Zhang, Y.T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z.H. Zhang, Z.Y. Zhang, G. Zhao, J. Zhao, J.Y. Zhao, J.Z. Zhao, Lei Zhao, Ling Zhao, M.G. Zhao, Q. Zhao, S.J. Zhao, Y.B. Zhao, Y.X. Zhao, Z.G. Zhao, A. Zhemchugov, B. Zheng, J.P. Zheng, Y. Zheng, Y.H. Zheng, B. Zhong, C. Zhong, L.P. Zhou, Q. Zhou, X. Zhou, X.K. Zhou, X.R. Zhou, A.N. Zhu, J. Zhu, K. Zhu, K.J. Zhu, S.H. Zhu, W.J. Zhu, X.L. Zhu, Y.C. Zhu, Z.A. Zhu, B.S. Zou, and J.H. Zou

Subjects
Charmed baryon, Λc+ decays, Absolute branching fraction, BESIII, Physics, QC1-999
Abstract

Based on 586 pb−1 of e+e− annihilation data collected at a center-of-mass energy of s=4.6GeV with the BESIII detector at the BEPCII collider, the absolute branching fraction of Λc+→pKS0η decays is measured for the first time to be B(Λc+→pKS0η)=(0.414±0.084±0.028)%, where the first uncertainty is statistical and the second is systematic. The result is compatible with a previous CLEO result on the relative branching fraction B(Λc+→pKS0η)B(Λc+→pK−π+), and consistent with theoretical predictions of SU(3) flavor symmetry.

Published
2021
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34. A Novel Method to Design Stub-loaded Microstrip Filters with Arbitrary Passband Based on the Reflection Theory and Monte Carlo Method

Author

Y.H. Ma, Y. Yuan, W. Yuan, W. Wu, and N. Yuan

Subjects
Electromagnetic reflection, microstrip filters, Monte Carlo methods, reflection coefficient, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

In this paper, a novel method for microstrip filters with arbitrary passband design is proposed. A kind of stub-loaded microstrip filter with asymmetric structure is proposed. The mathematic function of total reflection coefficient at the input port of the filter is derived based on the reflection theory, an ultra-wide-band (UWB) filter which can work from 11GHz to 39GHz is fabricated and measured to verify that our function can calculate the reflection coefficient precisely. The procedure to design stub-loaded microstrip filters with arbitrary passbands is given in this paper, this design procedure is based on the Monte Carlo method. A bandpass filter (BPF) which can work on 7--9.5GHz is designed and measured as an example. The comparison between calculated dimensions and practical dimensions of this filter shows a satisfactory fitting degree. A low-pass filter (LPF) which can work below 4GHz is designed and compared with some prior arts in order to show that our proposed filter is simple in structure and small in size. By using our proposed method, the stub-loaded microstrip filter can be designed quickly and easily. Based on the results mentioned above, we can count that the method proposed in this paper has a good application in microstrip filter design.

Published
2019

35. Diel variation in mercury stable isotope ratios records photoreduction of PM2.5-bound mercury

Author

Q. Huang, J. Chen, W. Huang, J. R. Reinfelder, P. Fu, S. Yuan, Z. Wang, W. Yuan, H. Cai, H. Ren, Y. Sun, and L. He

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Mercury (Hg) bound to fine aerosols (PM2.5-Hg) may undergo photochemical reaction that causes isotopic fractionation and obscures the initial isotopic signatures. In this study, we quantified Hg isotopic compositions for 56 PM2.5 samples collected between 15 September and 16 October 2015 from Beijing, China, among which 26 were collected during daytime (between 08:00 and 18:30 LT) and 30 during night (between 19:00 and 07:30 LT). The results show that diel variation was statistically significant (p < 0.05) for Hg content, Δ199Hg and Δ200Hg, with Hg content during daytime (0.32±0.14 µg g−1) lower than at night (0.48±0.24 µg g−1) and Δ199Hg and Δ200Hg values during daytime (mean of 0.26 ‰±0.40 ‰ and 0.09 ‰±0.06 ‰, respectively) higher than during nighttime (0.04 ‰±0.22 ‰ and 0.06 ‰±0.05 ‰, respectively), whereas PM2.5 concentrations and δ202Hg values showed insignificant (p > 0.05) diel variation. Geochemical characteristics of the samples and the air mass backward trajectories (PM2.5 source related) suggest that diel variation in Δ199Hg values resulted primarily from the photochemical reduction of divalent PM2.5-Hg, rather than variations in emission sources. The importance of photoreduction is supported by the strong correlations between Δ199Hg and (i) Δ201Hg (positive, slope = 1.1), (ii) δ202Hg (positive, slope = 1.15), (iii) content of Hg in PM2.5 (negative), (iv) sunshine durations (positive) and (v) ozone concentration (positive) observed for consecutive day–night paired samples. Our results provide isotopic evidence that local, daily photochemical reduction of divalent Hg is of critical importance to the fate of PM2.5-Hg in urban atmospheres and that, in addition to variation in sources, photochemical reduction appears to be an important process that affects both the particle mass-specific abundance and isotopic composition of PM2.5-Hg.

Published
2019
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36. Measurements of Σ+ and Σ− time-like electromagnetic form factors for center-of-mass energies from 2.3864 to 3.0200 GeV

Author

M. Ablikim, M.N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, A. Amoroso, Q. An, Anita, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, J.V. Bennett, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Biernat, J. Bloms, A. Bortone, I. Boyko, R.A. Briere, H. Cai, X. Cai, A. Calcaterra, G.F. Cao, N. Cao, S.A. Cetin, J.F. Chang, W.L. Chang, G. Chelkov, D.Y. Chen, G. Chen, H.S. Chen, M.L. Chen, S.J. Chen, X.R. Chen, Y.B. Chen, W. Cheng, G. Cibinetto, F. Cossio, X.F. Cui, H.L. Dai, J.P. Dai, X.C. Dai, A. Dbeyssi, R.B. de Boer, D. Dedovich, Z.Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L.Y. Dong, M.Y. Dong, S.X. Du, J. Fang, S.S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C.Q. Feng, M. Fritsch, C.D. Fu, Y. Fu, X.L. Gao, Y. Gao, Y.G. Gao, I. Garzia, E.M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W.X. Gong, W. Gradl, M. Greco, L.M. Gu, M.H. Gu, S. Gu, Y.T. Gu, C.Y. Guan, A.Q. Guo, L.B. Guo, R.P. Guo, Y.P. Guo, A. Guskov, S. Han, T.T. Han, T.Z. Han, X.Q. Hao, F.A. Harris, K.L. He, F.H. Heinsius, T. Held, Y.K. Heng, M. Himmelreich, T. Holtmann, Y.R. Hou, Z.L. Hou, H.M. Hu, J.F. Hu, T. Hu, Y. Hu, G.S. Huang, L.Q. Huang, X.T. Huang, Z. Huang, N. Huesken, T. Hussain, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q.P. Ji, X.B. Ji, X.L. Ji, H.B. Jiang, X.S. Jiang, X.Y. Jiang, J.B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X.S. Kang, R. Kappert, M. Kavatsyuk, B.C. Ke, I.K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O.B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M.G. Kurth, W. Kühn, J.J. Lane, J.S. Lange, P. Larin, L. Lavezzi, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C.H. Li, Cheng Li, D.M. Li, F. Li, G. Li, H.B. Li, H.J. Li, J.L. Li, J.Q. Li, Ke Li, L.K. Li, Lei Li, P.L. Li, P.R. Li, S.Y. Li, W.D. Li, W.G. Li, X.H. Li, X.L. Li, Z.B. Li, Z.Y. Li, H. Liang, Y.F. Liang, Y.T. Liang, L.Z. Liao, J. Libby, C.X. Lin, B. Liu, B.J. Liu, C.X. Liu, D. Liu, D.Y. Liu, F.H. Liu, Fang Liu, Feng Liu, H.B. Liu, H.M. Liu, Huanhuan Liu, Huihui Liu, J.B. Liu, J.Y. Liu, K. Liu, K.Y. Liu, Ke Liu, L. Liu, Q. Liu, S.B. Liu, Shuai Liu, T. Liu, X. Liu, Y.B. Liu, Z.A. Liu, Z.Q. Liu, Y.F. Long, X.C. Lou, F.X. Lu, H.J. Lu, J.D. Lu, J.G. Lu, X.L. Lu, Y. Lu, Y.P. Lu, C.L. Luo, M.X. Luo, P.W. Luo, T. Luo, X.L. Luo, S. Lusso, X.R. Lyu, F.C. Ma, H.L. Ma, L.L. Ma, M.M. Ma, Q.M. Ma, R.Q. Ma, R.T. Ma, X.N. Ma, X.X. Ma, X.Y. Ma, Y.M. Ma, F.E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q.A. Malik, A. Mangoni, Y.J. Mao, Z.P. Mao, S. Marcello, Z.X. Meng, J.G. Messchendorp, G. Mezzadri, T.J. Min, R.E. Mitchell, X.H. Mo, Y.J. Mo, N.Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I.B. Nikolaev, Z. Ning, S. Nisar, S.L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H.P. Peng, K. Peters, J. Pettersson, J.L. Ping, R.G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H.R. Qi, M. Qi, T.Y. Qi, S. Qian, W.-B. Qian, Z. Qian, C.F. Qiao, L.Q. Qin, X.P. Qin, X.S. Qin, Z.H. Qin, J.F. Qiu, S.Q. Qu, K.H. Rashid, K. Ravindran, C.F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, A. Sarantsev, M. Savrié, Y. Schelhaas, C. Schnier, K. Schoenning, D.C. Shan, W. Shan, X.Y. Shan, M. Shao, C.P. Shen, P.X. Shen, X.Y. Shen, H.C. Shi, R.S. Shi, X. Shi, X.D. Shi, J.J. Song, Q.Q. Song, W.M. Song, Y.X. Song, S. Sosio, S. Spataro, F.F. Sui, G.X. Sun, J.F. Sun, L. Sun, S.S. Sun, T. Sun, W.Y. Sun, Y.J. Sun, Y.K. Sun, Y.Z. Sun, Z.T. Sun, Y.H. Tan, Y.X. Tan, C.J. Tang, G.Y. Tang, J. Tang, V. Thoren, B. Tsednee, I. Uman, B. Wang, B.L. Wang, C.W. Wang, D.Y. Wang, H.P. Wang, K. Wang, L.L. Wang, M. Wang, M.Z. Wang, Meng Wang, W.H. Wang, W.P. Wang, X. Wang, X.F. Wang, X.L. Wang, Y. Wang, Y.D. Wang, Y.F. Wang, Y.Q. Wang, Z. Wang, Z.Y. Wang, Ziyi Wang, Zongyuan Wang, T. Weber, D.H. Wei, P. Weidenkaff, F. Weidner, S.P. Wen, D.J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J.F. Wu, L.H. Wu, L.J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S.Y. Xiao, Y.J. Xiao, Z.J. Xiao, X.H. Xie, Y.G. Xie, Y.H. Xie, T.Y. Xing, X.A. Xiong, G.F. Xu, J.J. Xu, Q.J. Xu, W. Xu, X.P. Xu, L. Yan, W.B. Yan, W.C. Yan, Xu Yan, H.J. Yang, H.X. Yang, L. Yang, R.X. Yang, S.L. Yang, Y.H. Yang, Y.X. Yang, Yifan Yang, Zhi Yang, M. Ye, M.H. Ye, J.H. Yin, Z.Y. You, B.X. Yu, C.X. Yu, G. Yu, J.S. Yu, T. Yu, C.Z. Yuan, W. Yuan, X.Q. Yuan, Y. Yuan, Z.Y. Yuan, C.X. Yue, A. Yuncu, A.A. Zafar, Y. Zeng, B.X. Zhang, Guangyi Zhang, H.H. Zhang, H.Y. Zhang, J.L. Zhang, J.Q. Zhang, J.W. Zhang, J.Y. Zhang, J.Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. Zhang, Lei Zhang, S. Zhang, S.F. Zhang, T.J. Zhang, X.Y. Zhang, Y. Zhang, Y.H. Zhang, Y.T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z.H. Zhang, Z.Y. Zhang, G. Zhao, J. Zhao, J.Y. Zhao, J.Z. Zhao, Lei Zhao, Ling Zhao, M.G. Zhao, Q. Zhao, S.J. Zhao, Y.B. Zhao, Y.X. Zhao Zhao, Z.G. Zhao, A. Zhemchugov, B. Zheng, J.P. Zheng, Y. Zheng, Y.H. Zheng, B. Zhong, C. Zhong, L.P. Zhou, Q. Zhou, X. Zhou, X.K. Zhou, X.R. Zhou, A.N. Zhu, J. Zhu, K. Zhu, K.J. Zhu, S.H. Zhu, W.J. Zhu, X.L. Zhu, Y.C. Zhu, Z.A. Zhu, B.S. Zou, and J.H. Zou

Subjects
BESIII, Σ hyperon, Cross section, Electromagnetic form factor, Physics, QC1-999
Abstract

The Born cross sections of the e+e−→Σ+Σ¯− and e+e−→Σ−Σ¯+ processes are determined for center-of-mass energy from 2.3864 to 3.0200 GeV with the BESIII detector. The cross section lineshapes can be described properly by a pQCD function and the resulting ratio of effective form factors for the Σ+ and Σ− is consistent with 3. In addition, ratios of the Σ+ electric and magnetic form factors, |GE/GM|, are obtained at three center-of-mass energies through an analysis of the angular distributions. These measurements, which are studied for the first time in the off-resonance region, provide precision experimental input for understanding baryonic structure. The observed new features of the Σ± form factors require more theoretical discussions for the hyperons.

Published
2021
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37. Observation of a resonant structure in e+e− → ωη and another in e+e− → ωπ0 at center-of-mass energies between 2.00 and 3.08 GeV

Author

M. Ablikim, M.N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, A. Amoroso, Q. An, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, J.V. Bennett, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Biernat, J. Bloms, A. Bortone, I. Boyko, R.A. Briere, H. Cai, X. Cai, A. Calcaterra, G.F. Cao, N. Cao, S.A. Cetin, J.F. Chang, W.L. Chang, G. Chelkov, D.Y. Chen, G. Chen, H.S. Chen, M.L. Chen, S.J. Chen, X.R. Chen, Y.B. Chen, W. Cheng, G. Cibinetto, F. Cossio, X.F. Cui, H.L. Dai, J.P. Dai, X.C. Dai, A. Dbeyssi, R.B. de Boer, D. Dedovich, Z.Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L.Y. Dong, M.Y. Dong, S.X. Du, J. Fang, S.S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C.Q. Feng, M. Fritsch, C.D. Fu, Y. Fu, X.L. Gao, Y. Gao, Y.G. Gao, I. Garzia, E.M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W.X. Gong, W. Gradl, M. Greco, L.M. Gu, M.H. Gu, S. Gu, Y.T. Gu, C.Y. Guan, A.Q. Guo, L.B. Guo, R.P. Guo, Y.P. Guo, A. Guskov, S. Han, T.T. Han, T.Z. Han, X.Q. Hao, F.A. Harris, K.L. He, F.H. Heinsius, T. Held, Y.K. Heng, M. Himmelreich, T. Holtmann, Y.R. Hou, Z.L. Hou, H.M. Hu, J.F. Hu, T. Hu, Y. Hu, G.S. Huang, L.Q. Huang, X.T. Huang, Z. Huang, N. Huesken, T. Hussain, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q.P. Ji, X.B. Ji, X.L. Ji, H.B. Jiang, X.S. Jiang, X.Y. Jiang, J.B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X.S. Kang, R. Kappert, M. Kavatsyuk, B.C. Ke, I.K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O.B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M.G. Kurth, W. Kühn, J.J. Lane, J.S. Lange, P. Larin, A. Lavania, L. Lavezzi, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C.H. Li, Cheng Li, D.M. Li, F. Li, G. Li, H.B. Li, H.J. Li, J.L. Li, J.Q. Li, Ke Li, L.K. Li, Lei Li, P.L. Li, P.R. Li, S.Y. Li, W.D. Li, W.G. Li, X.H. Li, X.L. Li, Z.B. Li, Z.Y. Li, H. Liang, Y.F. Liang, Y.T. Liang, L.Z. Liao, J. Libby, C.X. Lin, B. Liu, B.J. Liu, C.X. Liu, D. Liu, D.Y. Liu, F.H. Liu, Fang Liu, Feng Liu, H.B. Liu, H.M. Liu, Huanhuan Liu, Huihui Liu, J.B. Liu, J.Y. Liu, K. Liu, K.Y. Liu, Ke Liu, L. Liu, Q. Liu, S.B. Liu, Shuai Liu, T. Liu, X. Liu, Y.B. Liu, Z.A. Liu, Z.Q. Liu, Y.F. Long, X.C. Lou, F.X. Lu, H.J. Lu, J.D. Lu, J.G. Lu, X.L. Lu, Y. Lu, Y.P. Lu, C.L. Luo, M.X. Luo, P.W. Luo, T. Luo, X.L. Luo, S. Lusso, X.R. Lyu, F.C. Ma, H.L. Ma, L.L. Ma, M.M. Ma, Q.M. Ma, R.Q. Ma, R.T. Ma, X.N. Ma, X.X. Ma, X.Y. Ma, Y.M. Ma, F.E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q.A. Malik, A. Mangoni, Y.J. Mao, Z.P. Mao, S. Marcello, Z.X. Meng, J.G. Messchendorp, G. Mezzadri, T.J. Min, R.E. Mitchell, X.H. Mo, Y.J. Mo, N.Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I.B. Nikolaev, Z. Ning, S. Nisar, S.L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H.P. Peng, K. Peters, J. Pettersson, J.L. Ping, R.G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H.R. Qi, M. Qi, T.Y. Qi, S. Qian, W.-B. Qian, Z. Qian, C.F. Qiao, L.Q. Qin, X.P. Qin, X.S. Qin, Z.H. Qin, J.F. Qiu, S.Q. Qu, K.H. Rashid, K. Ravindran, C.F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, A. Sarantsev, M. Savrié, Y. Schelhaas, C. Schnier, K. Schoenning, D.C. Shan, W. Shan, X.Y. Shan, M. Shao, C.P. Shen, P.X. Shen, X.Y. Shen, H.C. Shi, R.S. Shi, X. Shi, X.D. Shi, J.J. Song, Q.Q. Song, W.M. Song, Y.X. Song, S. Sosio, S. Spataro, F.F. Sui, G.X. Sun, J.F. Sun, L. Sun, S.S. Sun, T. Sun, W.Y. Sun, Y.J. Sun, Y.K. Sun, Y.Z. Sun, Z.T. Sun, Y.H. Tan, Y.X. Tan, C.J. Tang, G.Y. Tang, J. Tang, V. Thoren, B. Tsednee, I. Uman, B. Wang, B.L. Wang, C.W. Wang, D.Y. Wang, H.P. Wang, K. Wang, L.L. Wang, M. Wang, M.Z. Wang, Meng Wang, W.H. Wang, W.P. Wang, X. Wang, X.F. Wang, X.L. Wang, Y. Wang, Y.D. Wang, Y.F. Wang, Y.Q. Wang, Z. Wang, Z.Y. Wang, Ziyi Wang, Zongyuan Wang, T. Weber, D.H. Wei, P. Weidenkaff, F. Weidner, S.P. Wen, D.J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J.F. Wu, L.H. Wu, L.J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S.Y. Xiao, Y.J. Xiao, Z.J. Xiao, X.H. Xie, Y.G. Xie, Y.H. Xie, T.Y. Xing, X.A. Xiong, G.F. Xu, J.J. Xu, Q.J. Xu, W. Xu, X.P. Xu, L. Yan, W.B. Yan, W.C. Yan, Xu Yan, H.J. Yang, H.X. Yang, L. Yang, R.X. Yang, S.L. Yang, Y.H. Yang, Y.X. Yang, Yifan Yang, Zhi Yang, M. Ye, M.H. Ye, J.H. Yin, Z.Y. You, B.X. Yu, C.X. Yu, G. Yu, J.S. Yu, T. Yu, C.Z. Yuan, W. Yuan, X.Q. Yuan, Y. Yuan, Z.Y. Yuan, C.X. Yue, A. Yuncu, A.A. Zafar, Y. Zeng, B.X. Zhang, Guangyi Zhang, H.H. Zhang, H.Y. Zhang, J.L. Zhang, J.Q. Zhang, J.W. Zhang, J.Y. Zhang, J.Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. Zhang, Lei Zhang, S. Zhang, S.F. Zhang, T.J. Zhang, X.Y. Zhang, Y. Zhang, Y.H. Zhang, Y.T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z.H. Zhang, Z.Y. Zhang, G. Zhao, J. Zhao, J.Y. Zhao, J.Z. Zhao, Lei Zhao, Ling Zhao, M.G. Zhao, Q. Zhao, S.J. Zhao, Y.B. Zhao, Y.X. Zhao, Z.G. Zhao, A. Zhemchugov, B. Zheng, J.P. Zheng, Y. Zheng, Y.H. Zheng, B. Zhong, C. Zhong, L.P. Zhou, Q. Zhou, X. Zhou, X.K. Zhou, X.R. Zhou, A.N. Zhu, J. Zhu, K. Zhu, K.J. Zhu, S.H. Zhu, W.J. Zhu, X.L. Zhu, Y.C. Zhu, Z.A. Zhu, B.S. Zou, and J.H. Zou

Subjects
BESIII, ϕ(2170), Excited ω states, Excited ρ states, Physics, QC1-999
Abstract

Born cross sections for the processes e+e−→ωη and e+e−→ωπ0 have been determined for center-of-mass energies between 2.00 and 3.08 GeV with the BESIII detector at the BEPCII collider. The results obtained in this work are consistent with previous measurements but with improved precision. Two resonant structures are observed. In the e+e−→ωη cross sections, a resonance with a mass of (2176±24±3)MeV/c2 and a width of (89±50±5)MeV is observed with a significance of 6.2σ. Its properties are consistent with the ϕ(2170). In the e+e−→ωπ0 cross sections, a resonance denoted Y(2040) is observed with a significance of more than 10σ. Its mass and width are determined to be (2034±13±9)MeV/c2 and (234±30±25)MeV, respectively, where the first uncertainties are statistical and the second ones are systematic.

Published
2021
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38. Corrigendum to 'Measurement of the e+e− → π+π− cross section between 600 and 900 MeV using initial state radiation' [Phys. Lett. B 753 (2016) 629–638]

Author

M. Ablikim, M.N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, R. Aliberti, A. Amoroso, Q. An, X.H. Bai, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, K. Begzsuren, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Biernat, J. Bloms, A. Bortone, I. Boyko, R.A. Briere, H. Cai, X. Cai, A. Calcaterra, G.F. Cao, N. Cao, S.A. Cetin, J.F. Chang, W.L. Chang, G. Chelkov, D.Y. Chen, G. Chen, H.S. Chen, M.L. Chen, S.J. Chen, X.R. Chen, Y.B. Chen, Z.J. Chen, W.S. Cheng, G. Cibinetto, F. Cossio, X.F. Cui, H.L. Dai, X.C. Dai, A. Dbeyssi, R.E. de Boer, D. Dedovich, Z.Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, C. Dong, J. Dong, L.Y. Dong, M.Y. Dong, X. Dong, S.X. Du, J. Fang, S.S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C.Q. Feng, M. Fritsch, C.D. Fu, Y. Gao, Y.G. Gao, I. Garzia, E.M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W.X. Gong, W. Gradl, M. Greco, L.M. Gu, M.H. Gu, S. Gu, Y.T. Gu, C.Y. Guan, A.Q. Guo, L.B. Guo, R.P. Guo, Y.P. Guo, A. Guskov, T.T. Han, X.Q. Hao, F.A. Harris, K.L. He, F.H. Heinsius, C.H. Heinz, T. Held, Y.K. Heng, C. Herold, M. Himmelreich, T. Holtmann, Y.R. Hou, Z.L. Hou, H.M. Hu, J.F. Hu, T. Hu, Y. Hu, G.S. Huang, L.Q. Huang, X.T. Huang, Y.P. Huang, Z. Huang, N. Huesken, T. Hussain, W. Ikegami Andersson, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. Ji, Q.P. Ji, X.B. Ji, X.L. Ji, H.B. Jiang, X.S. Jiang, J.B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, X.S. Kang, R. Kappert, M. Kavatsyuk, B.C. Ke, I.K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, O.B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, M.G. Kurth, W. Kühn, J.J. Lane, J.S. Lange, P. Larin, A. Lavania, L. Lavezzi, Z.H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C.H. Li, Cheng Li, D.M. Li, F. Li, G. Li, H. Li, H.B. Li, H.J. Li, J.L. Li, J.Q. Li, Ke Li, L.K. Li, Lei Li, P.L. Li, P.R. Li, S.Y. Li, W.D. Li, W.G. Li, X.H. Li, X.L. Li, Z.Y. Li, H. Liang, Y.F. Liang, Y.T. Liang, G.R. Liao, L.Z. Liao, J. Libby, C.X. Lin, B.J. Liu, C.X. Liu, D. Liu, F.H. Liu, Fang Liu, Feng Liu, H.B. Liu, H.M. Liu, Huanhuan Liu, Huihui Liu, J.B. Liu, J.Y. Liu, K. Liu, K.Y. Liu, Ke Liu, L. Liu, M.H. Liu, Q. Liu, S.B. Liu, Shuai Liu, T. Liu, W.M. Liu, X. Liu, Y.B. Liu, Z.A. Liu, Z.Q. Liu, X.C. Lou, F.X. Lu, H.J. Lu, J.D. Lu, J.G. Lu, X.L. Lu, Y. Lu, Y.P. Lu, C.L. Luo, M.X. Luo, P.W. Luo, T. Luo, X.L. Luo, S. Lusso, X.R. Lyu, F.C. Ma, H.L. Ma, L.L. Ma, M.M. Ma, Q.M. Ma, R.Q. Ma, R.T. Ma, X.X. Ma, X.Y. Ma, F.E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q.A. Malik, A. Mangoni, Y.J. Mao, Z.P. Mao, S. Marcello, Z.X. Meng, J.G. Messchendorp, G. Mezzadri, T.J. Min, R.E. Mitchell, X.H. Mo, Y.J. Mo, N.Yu. Muchnoi, H. Muramatsu, S. Nakhoul, Y. Nefedov, F. Nerling, I.B. Nikolaev, Z. Ning, S. Nisar, S.L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H.P. Peng, K. Peters, J. Pettersson, J.L. Ping, R.G. Ping, A. Pitka, R. Poling, V. Prasad, H. Qi, H.R. Qi, K.H. Qi, M. Qi, T.Y. Qi, S. Qian, W.-B. Qian, Z. Qian, C.F. Qiao, L.Q. Qin, X.S. Qin, Z.H. Qin, J.F. Qiu, S.Q. Qu, K. Ravindran, C.F. Redmer, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, H.S. Sang, A. Sarantsev, Y. Schelhaas, C. Schnier, K. Schoenning, M. Scodeggio, D.C. Shan, W. Shan, X.Y. Shan, M. Shao, C.P. Shen, P.X. Shen, X.Y. Shen, H.C. Shi, R.S. Shi, X. Shi, X.D. Shi, J.J. Song, W.M. Song, Y.X. Song, S. Sosio, S. Spataro, K.X. Su, F.F. Sui, G.X. Sun, H.K. Sun, J.F. Sun, L. Sun, S.S. Sun, T. Sun, W.Y. Sun, X. Sun, Y.J. Sun, Y.K. Sun, Y.Z. Sun, Z.T. Sun, Y.H. Tan, Y.X. Tan, C.J. Tang, G.Y. Tang, J. Tang, J.X. Teng, V. Thoren, I. Uman, B. Wang, C.W. Wang, D.Y. Wang, H.P. Wang, K. Wang, L.L. Wang, M. Wang, M.Z. Wang, Meng Wang, W.H. Wang, W.P. Wang, X. Wang, X.F. Wang, X.L. Wang, Y. Wang, Y.D. Wang, Y.F. Wang, Y.Q. Wang, Z. Wang, Z.Y. Wang, Ziyi Wang, Zongyuan Wang, D.H. Wei, P. Weidenkaff, F. Weidner, S.P. Wen, D.J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J.F. Wu, L.H. Wu, L.J. Wu, X. Wu, Z. Wu, L. Xia, H. Xiao, S.Y. Xiao, Z.J. Xiao, X.H. Xie, Y.G. Xie, Y.H. Xie, T.Y. Xing, G.F. Xu, J.J. Xu, Q.J. Xu, W. Xu, X.P. Xu, Y.C. Xu, F. Yan, L. Yan, W.B. Yan, W.C. Yan, Xu Yan, H.J. Yang, H.X. Yang, L. Yang, S.L. Yang, Y.H. Yang, Y.X. Yang, Yifan Yang, Zhi Yang, M. Ye, M.H. Ye, J.H. Yin, Z.Y. You, B.X. Yu, C.X. Yu, G. Yu, J.S. Yu, T. Yu, C.Z. Yuan, L. Yuan, W. Yuan, X.Q. Yuan, Y. Yuan, Z.Y. Yuan, C.X. Yue, A. Yuncu, A.A. Zafar, Y. Zeng, B.X. Zhang, Guangyi Zhang, H. Zhang, H.H. Zhang, H.Y. Zhang, J.J. Zhang, J.L. Zhang, J.Q. Zhang, J.W. Zhang, J.Y. Zhang, J.Z. Zhang, Jianyu Zhang, Jiawei Zhang, Lei Zhang, S. Zhang, S.F. Zhang, Shulei Zhang, X.D. Zhang, X.Y. Zhang, Y. Zhang, Y.H. Zhang, Y.T. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, Z.H. Zhang, Z.Y. Zhang, G. Zhao, J. Zhao, J.Y. Zhao, J.Z. Zhao, Lei Zhao, Ling Zhao, M.G. Zhao, Q. Zhao, S.J. Zhao, Y.B. Zhao, Y.X. Zhao, Z.G. Zhao, A. Zhemchugov, B. Zheng, J.P. Zheng, Y. Zheng, Y.H. Zheng, B. Zhong, C. Zhong, L.P. Zhou, Q. Zhou, X. Zhou, X.K. Zhou, X.R. Zhou, A.N. Zhu, J. Zhu, K. Zhu, K.J. Zhu, S.H. Zhu, T.J. Zhu, W.J. Zhu, Y.C. Zhu, Z.A. Zhu, B.S. Zou, and J.H. Zou

Subjects
Hadronic cross section, Muon anomaly, Initial state radiation, Pion form factor, Covariance matrix, BESIII, Physics, QC1-999
Abstract

In Ref. [1] the BESIII collaboration published a cross section measurement of the process e+e−→π+π− in the energy range between 600 and 900 MeV. In this corrigendum, we report a corrected evaluation of the statistical errors in terms of a fully propagated covariance matrix. The correction also yields a reduced statistical uncertainty for the hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon, which now reads as aμππ,LO(600−900MeV)=(368.2±1.5stat±3.3syst)×10−10. The central values of the cross section measurement and of aμππ,LO, as well as the systematic uncertainties remain unchanged.

Published
2021
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39. A Method Based on the Theory of Small Reflections to Design Arbitrary Passband Microstrip Filters

Author

Y. Yuan, W. Wu, W. Yuan, S. Wu, and N. Yuan

Subjects
Theory of Small Reflections, reflection coefficient, broadband filter, dual-band filter, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

A method to design a kind of microstrip filters consisting of only opened stubs and shorted stubs is proposed in this paper. This method is based on the Theory of Small Reflections, and the equations to calculate dimensions of this type of filters is exhibited. Two filters developed by our method are simulated and fabricated. The first filter has a passband on 5~8GHz. The second one is a dual-band filter and can work on both 2.4GHz and 5.8GHz. By using our method, the designed filters only contain opened stubs and shorted stubs, therefore, it is easy to design and fabricate this type of filters. The comparison between calculated and measured reflection coefficient curves shows a satisfactory fitting degree, this manifestation verifies that the method in our paper has a good application in filter design. The position of transmission zeros about the second filter are investigated. All the results show that our method can be a good guidance in microstrip filter design.

Published
2018

40. Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report

Author

A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, G. Adamov, D. Adams, M. Adinolfi, A. Aduszkiewicz, Z. Ahmad, J. Ahmed, T. Alion, S. Alonso Monsalve, M. Alrashed, C. Alt, A. Alton, P. Amedo, J. Anderson, C. Andreopoulos, M. P. Andrews, F. Andrianala, S. Andringa, N. Anfimov, A. Ankowski, M. Antonova, S. Antusch, A. Aranda-Fernandez, A. Ariga, L. O. Arnold, M. A. Arroyave, J. Asaadi, A. Aurisano, V. Aushev, D. Autiero, M. Ayala-Torres, F. Azfar, A. Back, H. Back, J. J. Back, C. Backhouse, P. Baesso, I. Bagaturia, L. Bagby, S. Balasubramanian, P. Baldi, B. Baller, B. Bambah, F. Barao, G. Barenboim, G. J. Barker, W. Barkhouse, C. Barnes, G. Barr, J. Barranco Monarca, N. Barros, J. L. Barrow, A. Basharina-Freshville, A. Bashyal, V. Basque, E. Belchior, J. B. R. Battat, F. Battisti, F. Bay, J. L. Bazo Alba, J. F. Beacom, E. Bechetoille, B. Behera, L. Bellantoni, G. Bellettini, V. Bellini, O. Beltramello, D. Belver, N. Benekos, F. Bento Neves, S. Berkman, P. Bernardini, R. M. Berner, H. Berns, S. Bertolucci, M. Betancourt, A. Betancur Rodríguez, M. Bhattacharjee, S. Bhuller, B. Bhuyan, S. Biagi, J. Bian, M. Biassoni, K. Biery, B. Bilki, M. Bishai, A. Bitadze, A. Blake, F. D. M. Blaszczyk, G. C. Blazey, E. Blucher, J. Boissevain, S. Bolognesi, T. Bolton, L. Bomben, M. Bonesini, M. Bongrand, F. Bonini, A. Booth, C. Booth, S. Bordoni, A. Borkum, T. Boschi, N. Bostan, P. Bour, C. Bourgeois, S. B. Boyd, D. Boyden, J. Bracinik, D. Braga, D. Brailsford, A. Brandt, J. Bremer, C. Brew, E. Brianne, S. J. Brice, C. Brizzolari, C. Bromberg, G. Brooijmans, J. Brooke, A. Bross, G. Brunetti, M. Brunetti, N. Buchanan, H. Budd, I. Cagnoli, D. Caiulo, P. Calafiura, J. Calcutt, M. Calin, S. Calvez, E. Calvo, A. Caminata, M. Campanelli, K. Cankocak, D. Caratelli, G. Carini, B. Carlus, P. Carniti, I. Caro Terrazas, H. Carranza, T. Carroll, J. F. Castaño Forero, A. Castillo, C. Castromonte, E. Catano-Mur, C. Cattadori, F. Cavalier, F. Cavanna, S. Centro, G. Cerati, A. Cervelli, A. Cervera Villanueva, M. Chalifour, A. Chappell, E. Chardonnet, N. Charitonidis, A. Chatterjee, S. Chattopadhyay, H. Chen, M. Chen, Y. Chen, Z. Chen, D. Cherdack, C. Chi, S. Childress, A. Chiriacescu, G. Chisnall, K. Cho, S. Choate, D. Chokheli, S. Choubey, A. Christensen, D. Christian, G. Christodoulou, A. Chukanov, E. Church, V. Cicero, P. Clarke, T. E. Coan, A. G. Cocco, J. A. B. Coelho, E. Conley, R. Conley, J. M. Conrad, M. Convery, S. Copello, L. Corwin, L. Cremaldi, L. Cremonesi, J. I. Crespo-Anadón, E. Cristaldo, R. Cross, A. Cudd, C. Cuesta, Y. Cui, D. Cussans, M. Dabrowski, O. Dalager, H. da Motta, L. Da Silva Peres, C. David, Q. David, G. S. Davies, S. Davini, J. Dawson, K. De, R. M. De Almeida, P. Debbins, I. De Bonis, M. P. Decowski, A. de Gouvêa, P. C. De Holanda, I. L. De Icaza Astiz, A. Deisting, P. De Jong, A. Delbart, D. Delepine, M. Delgado, A. Dell’Acqua, P. De Lurgio, J. R. T. de Mello Neto, D. M. DeMuth, S. Dennis, C. Densham, G. W. Deptuch, A. De Roeck, V. De Romeri, G. De Souza, R. Dharmapalan, F. Diaz, J. S. Díaz, S. Di Domizio, L. Di Giulio, P. Ding, L. Di Noto, C. Distefano, R. Diurba, M. Diwan, Z. Djurcic, N. Dokania, S. Dolan, M. J. Dolinski, L. Domine, D. Douglas, D. Douillet, G. Drake, F. Drielsma, D. Duchesneau, K. Duffy, P. Dunne, T. Durkin, H. Duyang, O. Dvornikov, D. A. Dwyer, A. S. Dyshkant, M. Eads, A. Earle, D. Edmunds, J. Eisch, L. Emberger, S. Emery, A. Ereditato, C. O. Escobar, G. Eurin, J. J. Evans, E. Ewart, A. C. Ezeribe, K. Fahey, A. Falcone, C. Farnese, Y. Farzan, J. Felix, M. Fernandes Carneiro da Silva, E. Fernandez-Martinez, P. Fernandez Menendez, F. Ferraro, L. Fields, F. Filthaut, A. Fiorentini, R. S. Fitzpatrick, W. Flanagan, B. Fleming, R. Flight, D. V. Forero, J. Fowler, W. Fox, J. Franc, K. Francis, D. Franco, J. Freeman, J. Freestone, J. Fried, A. Friedland, S. Fuess, I. Furic, A. P. Furmanski, A. Gabrielli, A. Gago, H. Gallagher, A. Gallas, A. Gallego-Ros, N. Gallice, V. Galymov, E. Gamberini, T. Gamble, R. Gandhi, R. Gandrajula, F. Gao, S. Gao, D. Garcia-Gamez, M. Á. García-Peris, S. Gardiner, D. Gastler, G. Ge, B. Gelli, A. Gendotti, S. Gent, Z. Ghorbani-Moghaddam, D. Gibin, I. Gil-Botella, S. Gilligan, C. Girerd, A. K. Giri, D. Gnani, O. Gogota, M. Gold, S. Gollapinni, K. Gollwitzer, R. A. Gomes, L. V. Gomez Bermeo, L. S. Gomez Fajardo, F. Gonnella, J. A. Gonzalez-Cuevas, D. Gonzalez-Diaz, M. Gonzalez-Lopez, M. C. Goodman, O. Goodwin, S. Goswami, C. Gotti, E. Goudzovski, C. Grace, M. Graham, R. Gran, E. Granados, P. Granger, A. Grant, C. Grant, D. Gratieri, P. Green, L. Greenler, J. Greer, W. C. Griffith, M. Groh, J. Grudzinski, K. Grzelak, W. Gu, V. Guarino, R. Guenette, E. Guerard, M. Guerzoni, A. Guglielmi, B. Guo, K. K. Guthikonda, R. Gutierrez, P. Guzowski, M. M. Guzzo, S. Gwon, A. Habig, H. Hadavand, R. Haenni, A. Hahn, J. Haiston, P. Hamacher-Baumann, T. Hamernik, P. Hamilton, J. Han, D. A. Harris, J. Hartnell, J. Harton, T. Hasegawa, C. Hasnip, R. Hatcher, K. W. Hatfield, A. Hatzikoutelis, C. Hayes, E. Hazen, A. Heavey, K. M. Heeger, J. Heise, K. Hennessy, S. Henry, M. A. Hernandez Morquecho, K. Herner, L. Hertel, J. Hewes, A. Higuera, T. Hill, S. J. Hillier, A. Himmel, J. Hoff, C. Hohl, A. Holin, E. Hoppe, G. A. Horton-Smith, M. Hostert, A. Hourlier, B. Howard, R. Howell, J. Huang, J. Hugon, G. Iles, N. Ilic, A. M. Iliescu, R. Illingworth, G. Ingratta, A. Ioannisian, L. Isenhower, R. Itay, A. Izmaylov, S. Jackson, V. Jain, E. James, B. Jargowsky, F. Jediny, D. Jena, Y. S. Jeong, C. Jesús-Valls, X. Ji, L. Jiang, S. Jiménez, A. Jipa, R. Johnson, N. Johnston, B. Jones, S. B. Jones, M. Judah, C. K. Jung, T. Junk, Y. Jwa, M. Kabirnezhad, A. Kaboth, I. Kadenko, I. Kakorin, F. Kamiya, N. Kaneshige, G. Karagiorgi, G. Karaman, A. Karcher, M. Karolak, Y. Karyotakis, S. Kasai, S. P. Kasetti, L. Kashur, N. Kazaryan, E. Kearns, P. Keener, K. J. Kelly, E. Kemp, O. Kemularia, W. Ketchum, S. H. Kettell, M. Khabibullin, A. Khotjantsev, A. Khvedelidze, D. Kim, B. King, B. Kirby, M. Kirby, J. Klein, K. Koehler, L. W. Koerner, S. Kohn, P. P. Koller, L. Kolupaeva, M. Kordosky, T. Kosc, U. Kose, V. A. Kostelecký, K. Kothekar, F. Krennrich, I. Kreslo, Y. Kudenko, V. A. Kudryavtsev, S. Kulagin, J. Kumar, P. Kumar, R. Kumar, P. Kunze, N. Kurita, C. Kuruppu, V. Kus, T. Kutter, A. Lambert, B. Land, K. Lande, C. E. Lane, K. Lang, T. Langford, J. Larkin, P. Lasorak, D. Last, C. Lastoria, A. Laundrie, G. Laurenti, A. Lawrence, I. Lazanu, R. LaZur, T. Le, S. Leardini, J. Learned, P. LeBrun, T. LeCompte, G. Lehmann Miotto, R. Lehnert, M. A. Leigui de Oliveira, M. Leitner, L. Li, S. W. Li, T. Li, Y. Li, H. Liao, C. S. Lin, Q. Lin, S. Lin, A. Lister, B. R. Littlejohn, J. Liu, S. Lockwitz, T. Loew, M. Lokajicek, I. Lomidze, K. Long, K. Loo, D. Lorca, T. Lord, J. M. LoSecco, W. C. Louis, X. G. Lu, K. B. Luk, X. Luo, N. Lurkin, T. Lux, V. P. Luzio, D. MacFarlane, A. A. Machado, P. Machado, C. T. Macias, J. R. Macier, A. Maddalena, A. Madera, P. Madigan, S. Magill, K. Mahn, A. Maio, A. Major, J. A. Maloney, G. Mandrioli, R. C. Mandujano, J. Maneira, L. Manenti, S. Manly, A. Mann, K. Manolopoulos, M. Manrique Plata, V. N. Manyam, L. Manzanillas, M. Marchan, A. Marchionni, W. Marciano, D. Marfatia, C. Mariani, J. Maricic, R. Marie, F. Marinho, A. D. Marino, D. Marsden, M. Marshak, C. M. Marshall, J. Marshall, J. Marteau, J. Martin-Albo, N. Martinez, D. A. Martinez Caicedo, S. Martynenko, K. Mason, A. Mastbaum, M. Masud, S. Matsuno, J. Matthews, C. Mauger, N. Mauri, K. Mavrokoridis, I. Mawby, R. Mazza, A. Mazzacane, E. Mazzucato, T. McAskill, E. McCluskey, N. McConkey, K. S. McFarland, C. McGrew, A. McNab, A. Mefodiev, P. Mehta, P. Melas, O. Mena, S. Menary, H. Mendez, D. P. Méndez, A. Menegolli, G. Meng, M. D. Messier, W. Metcalf, T. Mettler, M. Mewes, H. Meyer, T. Miao, G. Michna, T. Miedema, J. Migenda, V. Mikola, R. Milincic, W. Miller, J. Mills, C. Milne, O. Mineev, O. G. Miranda, S. Miryala, C. S. Mishra, S. R. Mishra, A. Mislivec, D. Mladenov, I. Mocioiu, K. Moffat, N. Moggi, R. Mohanta, T. A. Mohayai, N. Mokhov, J. Molina, L. Molina Bueno, A. Montanari, C. Montanari, D. Montanari, E. Montagna, L. M. Montano Zetina, J. Moon, M. Mooney, A. F. Moor, D. Moreno, C. Morris, C. Mossey, E. Motuk, C. A. Moura, J. Mousseau, W. Mu, L. Mualem, J. Mueller, M. Muether, S. Mufson, F. Muheim, A. Muir, M. Mulhearn, D. Munford, H. Muramatsu, S. Murphy, J. Musser, J. Nachtman, S. Nagu, M. Nalbandyan, R. Nandakumar, D. Naples, S. Narita, D. Navas-Nicolás, A. Navrer-Agasson, N. Nayak, M. Nebot-Guinot, K. Negishi, J. K. Nelson, J. Nesbit, M. Nessi, D. Newbold, M. Newcomer, D. Newhart, H. Newton, M. Niccolo, R. Nichol, F. Nicolas-Arnaldos, M. Nicoletta, E. Niner, K. Nishimura, A. Norman, A. Norrick, R. Northrop, P. Novella, J. A. Nowak, M. Oberling, J. P. Ochoa-Ricoux, A. Olivares Del Campo, A. Olivier, A. Olshevskiy, Y. Onel, Y. Onishchuk, J. Ott, L. Pagani, S. Pakvasa, G. Palacio, O. Palamara, S. Palestini, J. M. Paley, M. Pallavicini, C. Palomares, J. L. Palomino-Gallo, E. Pantic, V. Paolone, V. Papadimitriou, R. Papaleo, A. Papanestis, S. Paramesvaran, S. Parke, Z. Parsa, M. Parvu, S. Pascoli, L. Pasqualini, J. Pasternak, J. Pater, C. Patrick, L. Patrizii, R. B. Patterson, S. J. Patton, T. Patzak, A. Paudel, B. Paulos, L. Paulucci, Z. Pavlovic, G. Pawloski, D. Payne, V. Pec, S. J. M. Peeters, E. Pennacchio, A. Penzo, O. L. G. Peres, J. Perry, D. Pershey, G. Pessina, G. Petrillo, C. Petta, R. Petti, F. Piastra, L. Pickering, F. Pietropaolo, R. Plunkett, R. Poling, X. Pons, N. Poonthottathil, F. Poppi, S. Pordes, J. Porter, M. Potekhin, R. Potenza, B. V. K. S. Potukuchi, J. Pozimski, M. Pozzato, S. Prakash, T. Prakash, S. Prince, D. Pugnere, X. Qian, M. C. Queiroga Bazetto, J. L. Raaf, V. Radeka, J. Rademacker, B. Radics, A. Rafique, E. Raguzin, M. Rai, M. Rajaoalisoa, I. Rakhno, A. Rakotonandrasana, L. Rakotondravohitra, Y. A. Ramachers, R. Rameika, M. A. Ramirez Delgado, B. Ramson, A. Rappoldi, G. Raselli, P. Ratoff, S. Raut, R. F. Razakamiandra, J. S. Real, B. Rebel, M. Reggiani-Guzzo, T. Rehak, J. Reichenbacher, S. D. Reitzner, H. Rejeb Sfar, A. Renshaw, S. Rescia, F. Resnati, A. Reynolds, C. Riccio, G. Riccobene, L. C. J. Rice, J. Ricol, A. Rigamonti, Y. Rigaut, D. Rivera, L. Rochester, M. Roda, P. Rodrigues, M. J. Rodriguez Alonso, E. Rodriguez Bonilla, J. Rodriguez Rondon, S. Rosauro-Alcaraz, M. Rosenberg, P. Rosier, B. Roskovec, M. Rossella, J. Rout, P. Roy, S. Roy, A. Rubbia, C. Rubbia, F. C. Rubio, B. Russell, D. Ruterbories, R. Saakyan, S. Sacerdoti, T. Safford, R. Sahay, N. Sahu, P. Sala, N. Samios, O. Samoylov, M. C. Sanchez, D. A. Sanders, D. Sankey, S. Santana, M. Santos-Maldonado, N. Saoulidou, P. Sapienza, C. Sarasty, I. Sarcevic, G. Savage, V. Savinov, A. Scaramelli, A. Scarff, A. Scarpelli, T. Schaffer, H. Schellman, P. Schlabach, D. Schmitz, K. Scholberg, A. Schukraft, E. Segreto, J. Sensenig, I. Seong, A. Sergi, D. Sgalaberna, M. H. Shaevitz, S. Shafaq, M. Shamma, R. Sharankova, H. R. Sharma, R. Sharma, T. Shaw, C. Shepherd-Themistocleous, S. Shin, D. Shooltz, R. Shrock, L. Simard, F. Simon, N. Simos, J. Sinclair, G. Sinev, J. Singh, V. Singh, R. Sipos, F. W. Sippach, G. Sirri, A. Sitraka, K. Siyeon, K. Skarpaas VIII, A. Smith, E. Smith, P. Smith, J. Smolik, M. Smy, E. L. Snider, P. Snopok, M. Soares Nunes, H. Sobel, M. Soderberg, C. J. Solano Salinas, S. Söldner-Rembold, S. Soleti, N. Solomey, V. Solovov, W. E. Sondheim, M. Sorel, J. Soto-Oton, A. Sousa, K. Soustruznik, F. Spagliardi, M. Spanu, J. Spitz, N. J. C. Spooner, K. Spurgeon, R. Staley, M. Stancari, L. Stanco, R. Stanley, R. Stein, H. M. Steiner, J. Stewart, B. Stillwell, J. Stock, F. Stocker, T. Stokes, M. Strait, T. Strauss, S. Striganov, A. Stuart, J. G. Suarez, H. Sullivan, D. Summers, A. Surdo, V. Susic, L. Suter, C. M. Sutera, R. Svoboda, B. Szczerbinska, A. M. Szelc, R. Talaga, H. A. Tanaka, B. Tapia Oregui, A. Tapper, S. Tariq, E. Tatar, R. Tayloe, A. M. Teklu, M. Tenti, K. Terao, C. A. Ternes, F. Terranova, G. Testera, A. Thea, J. L. Thompson, C. Thorn, S. C. Timm, J. Todd, A. Tonazzo, D. Torbunov, M. Torti, M. Tortola, F. Tortorici, D. Totani, M. Toups, C. Touramanis, N. Tosi, R. Travaglini, J. Trevor, S. Trilov, W. H. Trzaska, Y. T. Tsai, Z. Tsamalaidze, K. V. Tsang, N. Tsverava, S. Tufanli, C. Tull, E. Tyley, M. Tzanov, M. A. Uchida, J. Urheim, T. Usher, S. Uzunyan, M. R. Vagins, P. Vahle, G. A. Valdiviesso, E. Valencia, P. Valerio, Z. Vallari, J. W. F. Valle, S. Vallecorsa, R. Van Berg, R. G. Van de Water, F. Varanini, D. Vargas, G. Varner, J. Vasel, S. Vasina, G. Vasseur, N. Vaughan, K. Vaziri, S. Ventura, A. Verdugo, S. Vergani, M. A. Vermeulen, M. Verzocchi, M. Vicenzi, H. Vieira de Souza, C. Vignoli, C. Vilela, B. Viren, T. Vrba, T. Wachala, A. V. Waldron, M. Wallbank, H. Wang, J. Wang, L. Wang, M. H. L. S. Wang, Y. Wang, K. Warburton, D. Warner, M. Wascko, D. Waters, A. Watson, P. Weatherly, A. Weber, M. Weber, H. Wei, A. Weinstein, D. Wenman, M. Wetstein, A. White, L. H. Whitehead, D. Whittington, M. J. Wilking, C. Wilkinson, Z. Williams, F. Wilson, R. J. Wilson, J. Wolcott, T. Wongjirad, A. Wood, K. Wood, E. Worcester, M. Worcester, C. Wret, W. Wu, Y. Xiao, E. Yandel, G. Yang, K. Yang, S. Yang, T. Yang, A. Yankelevich, N. Yershov, K. Yonehara, T. Young, B. Yu, H. Yu, J. Yu, W. Yuan, R. Zaki, J. Zalesak, L. Zambelli, B. Zamorano, A. Zani, L. Zazueta, G. Zeit, G. P. Zeller, J. Zennamo, K. Zeug, C. Zhang, M. Zhao, E. Zhivun, G. Zhu, P. Zilberman, E. D. Zimmerman, M. Zito, S. Zucchelli, J. Zuklin, V. Zutshi, R. Zwaska, and On behalf of the DUNE Collaboration

Subjects
neutrino, near detector, neutrino oscillations, Deep Underground Neutrino Experiment, DUNE, Physics, QC1-999, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents.

Published
2021
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41. BDS/GPS INTEGRATED POSITIONING METHOD RESEARCH BASED ON NONLINEAR KALMAN FILTERING

Author

Y. Ma, W. Yuan, and H. Sun

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

In order to realize fast and accurate BDS/GPS integrated positioning, it is necessary to overcome the adverse effects of signal attenuation, multipath effect and echo interference to ensure the result of continuous and accurate navigation and positioning. In this paper, pseudo-range positioning is used as the mathematical model. In the stage of data preprocessing, using precise and smooth carrier phase measurement value to promote the rough pseudo-range measurement value without ambiguity. At last, the Extended Kalman Filter(EKF), the Unscented Kalman Filter(UKF) and the Particle Filter(PF) algorithm are applied in the integrated positioning method for higher positioning accuracy. The experimental results show that the positioning accuracy of PF is the highest, and UKF is better than EKF.

Published
2017
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42. ESTIMATION OF SHIE GLACIER SURFACE MOVEMENT USING OFFSET TRACKING TECHNIQUE WITH COSMO-SKYMED IMAGES

Author

Q. Wang, W. Zhou, J. Fan, W. Yuan, H. Li, J. J. Sousa, and Z. Guo

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Movement is one of the most important characteristics of glaciers which can cause serious natural disasters. For this reason, monitoring this massive blocks is a crucial task. Synthetic Aperture Radar (SAR) can operate all day in any weather conditions and the images acquired by SAR contain intensity and phase information, which are irreplaceable advantages in monitoring the surface movement of glaciers. Moreover, a variety of techniques like DInSAR and offset tracking, based on the information of SAR images, could be applied to measure the movement. Sangwang lake, a glacial lake in the Himalayas, has great potentially danger of outburst. Shie glacier is situated at the upstream of the Sangwang lake. Hence, it is significant to monitor Shie glacier surface movement to assess the risk of outburst. In this paper, 6 high resolution COSMO-SkyMed images spanning from August to December, 2016 are applied with offset tracking technique to estimate the surface movement of Shie glacier. The maximum velocity of Shie glacier surface movement is 51 cm/d, which was observed at the end of glacier tongue, and the velocity is correlated with the change of elevation. Moreover, the glacier surface movement in summer is faster than in winter and the velocity decreases as the local temperature decreases. Based on the above conclusions, the glacier may break off at the end of tongue in the near future. The movement results extracted in this paper also illustrate the advantages of high resolution SAR images in monitoring the surface movement of small glaciers.

Published
2017
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43. MINING LAND SUBSIDENCE MONITORING USING SENTINEL-1 SAR DATA

Author

W. Yuan, Q. Wang, J. Fan, and H. Li

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

In this paper, DInSAR technique was used to monitor land subsidence in mining area. The study area was selected in the coal mine area located in Yuanbaoshan District, Chifeng City, and Sentinel-1 data were used to carry out DInSAR techniqu. We analyzed the interferometric results by Sentinel-1 data from December 2015 to May 2016. Through the comparison of the results of DInSAR technique and the location of the mine on the optical images, it is shown that DInSAR technique can be used to effectively monitor the land subsidence caused by underground mining, and it is an effective tool for law enforcement of over-mining.

Published
2017
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44. Emission-dominated gas exchange of elemental mercury vapor over natural surfaces in China

Author

X. Wang, C.-J. Lin, W. Yuan, J. Sommar, W. Zhu, and X. Feng

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Mercury (Hg) emission from natural surfaces plays an important role in global Hg cycling. The present estimate of global natural emission has large uncertainty and remains unverified against field data, particularly for terrestrial surfaces. In this study, a mechanistic model is developed for estimating the emission of elemental mercury vapor (Hg0) from natural surfaces in China. The development implements recent advancements in the understanding of air–soil and air–foliage exchange of Hg0 and redox chemistry in soil and on surfaces, incorporates the effects of soil characteristics and land use changes by agricultural activities, and is examined through a systematic set of sensitivity simulations. Using the model, the net exchange of Hg0 between the atmosphere and natural surfaces of mainland China is estimated to be 465.1 Mg yr−1, including 565.5 Mg yr−1 from soil surfaces, 9.0 Mg yr−1 from water bodies, and −100.4 Mg yr−1 from vegetation. The air–surface exchange is strongly dependent on the land use and meteorology, with 9 % of net emission from forest ecosystems; 50 % from shrubland, savanna, and grassland; 33 % from cropland; and 8 % from other land uses. Given the large agricultural land area in China, farming activities play an important role on the air–surface exchange over farmland. Particularly, rice field shift from a net sink (3.3 Mg uptake) during April–October (rice planting) to a net source when the farmland is not flooded (November–March). Summing up the emission from each land use, more than half of the total emission occurs in summer (51 %), followed by spring (28 %), autumn (13 %), and winter (8 %). Model verification is accomplished using observational data of air–soil/air–water fluxes and Hg deposition through litterfall for forest ecosystems in China and Monte Carlo simulations. In contrast to the earlier estimate by Shetty et al. (2008) that reported large emission from vegetative surfaces using an evapotranspiration approach, the estimate in this study shows natural emissions are primarily from grassland and dry cropland. Such an emission pattern may alter the current understanding of Hg emission outflow from China as reported by Lin et al. (2010b) because a substantial natural Hg emission occurs in West China.

Published
2016
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45. POOR TEXTURAL IMAGE MATCHING BASED ON GRAPH THEORY

Author

S. Chen, X. Yuan, W. Yuan, and Y. Cai

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Image matching lies at the heart of photogrammetry and computer vision. For poor textural images, the matching result is affected by low contrast, repetitive patterns, discontinuity or occlusion, few or homogeneous textures. Recently, graph matching became popular for its integration of geometric and radiometric information. Focused on poor textural image matching problem, it is proposed an edge-weight strategy to improve graph matching algorithm. A series of experiments have been conducted including 4 typical landscapes: Forest, desert, farmland, and urban areas. And it is experimentally found that our new algorithm achieves better performance. Compared to SIFT, doubled corresponding points were acquired, and the overall recall rate reached up to 68%, which verifies the feasibility and effectiveness of the algorithm.

Published
2016
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46. AN AERIAL-IMAGE DENSE MATCHING APPROACH BASED ON OPTICAL FLOW FIELD

Author

W. Yuan, S. Chen, Y. Zhang, J. Gong, and R. Shibasaki

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Dense matching plays an important role in many fields, such as DEM (digital evaluation model) producing, robot navigation and 3D environment reconstruction. Traditional approaches may meet the demand of accuracy. But the calculation time and out puts density is hardly be accepted. Focus on the matching efficiency and complex terrain surface matching feasibility an aerial image dense matching method based on optical flow field is proposed in this paper. First, some high accurate and uniformed control points are extracted by using the feature based matching method. Then the optical flow is calculated by using these control points, so as to determine the similar region between two images. Second, the optical flow field is interpolated by using the multi-level B-spline interpolation in the similar region and accomplished the pixel by pixel coarse matching. Final, the results related to the coarse matching refinement based on the combined constraint, which recognizes the same points between images. The experimental results have shown that our method can achieve per-pixel dense matching points, the matching accuracy achieves sub-pixel level, and fully meet the three-dimensional reconstruction and automatic generation of DSM-intensive matching’s requirements. The comparison experiments demonstrated that our approach’s matching efficiency is higher than semi-global matching (SGM) and Patch-based multi-view stereo matching (PMVS) which verifies the feasibility and effectiveness of the algorithm.

Published
2016
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47. Simulations of large winds and wind shears induced by gravity wave breaking in the mesosphere and lower thermosphere (MLT) region

Author

X. Liu, J. Xu, H.-L. Liu, J. Yue, and W. Yuan

Subjects
Science, Physics, QC1-999, Geophysics. Cosmic physics, QC801-809
Abstract

Using a fully nonlinear two-dimensional (2-D) numerical model, we simulated gravity waves (GWs) breaking and their contributions to the formation of large winds and wind shears in the mesosphere and lower thermosphere (MLT). An eddy diffusion coefficient is used in the 2-D numerical model to parameterize realistic turbulent mixing. Our study shows that the momentum deposited by breaking GWs accelerates the mean wind. The resultant large background wind increases the GW's apparent horizontal phase velocity and decreases the GW's intrinsic frequency and vertical wavelength. Both the accelerated mean wind and the decreased GW vertical wavelength contribute to the enhancement of wind shears. This, in turn, creates a background condition that favors the occurrence of GW instability, breaking, and momentum deposition, as well as mean wind acceleration, which further enhances the wind shears. We find that GWs with longer vertical wavelengths and faster horizontal phase velocity can induce larger winds, but they may not necessarily induce larger wind shears. In addition, the background temperature can affect the time and height of GW breaking, thus causing accelerated mean winds and wind shears.

Published
2014
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48. FPI observations of nighttime mesospheric and thermospheric winds in China and their comparisons with HWM07

Author

W. Yuan, X. Liu, J. Xu, Q. Zhou, G. Jiang, and R. Ma

Subjects
Science, Physics, QC1-999, Geophysics. Cosmic physics, QC801-809
Abstract

We analyzed the nighttime horizontal neutral winds in the middle atmosphere (~ 87 and ~ 98 km) and thermosphere (~ 250 km) derived from a Fabry–Perot interferometer (FPI), which was installed at Xinglong station (40.2° N, 117.4° E) in central China. The wind data covered the period from April 2010 to July 2012. We studied the annual, semiannual and terannual variations of the midnight winds at ~ 87 km, ~ 98 km and ~ 250 km for the first time and compared them with Horizontal Wind Model 2007 (HWM07). Our results show the following: (1) at ~ 87 km, both the observed and model zonal winds have similar phases in the annual and semiannual variations. However, the HWM07 amplitudes are much larger. (2) At ~ 98 km, the model shows strong eastward wind in the summer solstice, resulting in a large annual variation, while the observed strongest component is semiannual. The observation and model midnight meridional winds agree well. Both are equatorward throughout the year and have small amplitudes in the annual and semiannual variations. (3) There are large discrepancies between the observed and HWM07 winds at ~ 250 km. This discrepancy is largely due to the strong semiannual zonal wind in the model and the phase difference in the annual variation of the meridional wind. The FPI annual variation coincides with the results from Arecibo, which has similar geomagnetic latitude as Xinglong station. In General, the consistency of FPI winds with model winds is better at ~ 87 and ~ 98 km than that at ~ 250 km. We also studied the seasonally and monthly averaged nighttime winds. The most salient features include the following: (1) the seasonally averaged zonal winds at ~ 87 and ~ 98 km typically have small variations throughout the night. (2) The model zonal and meridional nighttime wind variations are typically much larger than those of observations at ~ 87 km and ~ 98 km. (3) At ~ 250 km, model zonal wind compares well with the observation in the winter. For spring and autumn, the model wind is more eastward before ~ 03:00 LT but more westward after. The observed nighttime zonal and meridional winds on average are close to zero in the summer and autumn, which indicates a lack of strong stable tides. The consistency of FPI zonal wind with model wind at ~ 250 km is better than the meridional wind.

Published
2013
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49. MONITORING THE CHANGES OF LAKES IN THE SOURCE REGION OF THREE RIVERS WITH REMOTE SENSING DATA FROM 1976 TO 2009

Author

L. Chengfeng, W. Yuan, J. Haoyan, and L. Zhengjun

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

As the birthplace of Yangtze River, the Yellow River and Lancang Rive, Source Region of Three Rivers (SRTR) is an important resource for fresh water supplement in China. SRTR also has very obvious ecological function which forms ecological security barrier for China's Qinghai-Tibet plateau. The inland lakes here play an important role for the water cycle in the plateau. The monitoring results were extracted with TM data from 1976 to 2009. The results show that from 1976 to 2009 the lakes' area in SRTR dropped first and then expanded with 2000 as sector. The lakes area was 6778 km2 in 2009, about 1.90% of the whole region, and increased than 1976 by 133.15 km2. Most of the large lakes above 80 km2 have the same change trend. The expanded lakes increased in number gradually, while the changes in the amplitude and time characteristics were different. From 1976 to 2000, the number of new lakes increased while died lakes dropped; and from 2000 to 2009 it is just on the contrary. In the study the index of lake change trend (ILCT) was adopted to contrast lake atrophy condition. With ILCT 24.55 there is an expansion trend for the lakes in SRTR during the last 35 years. The lakes with ILCT's absolute value greater than 1 were those merged with or disconnected from surrounding smaller lakes. Here the precipitation and snow melt are main supplies for the lakes. The change of lakes' area has well correlated with precipitation, and weak correlated with temperature from 1976 to 2009. But from 2000 to 2009, there has a strong correlation with precipitation, temperature. All these show from the side that the precipitation and snow melt are important factors to influence the lakes’ change. The lakes have the coordination function for the good ecological environment in the region. The conclusions from the study can provide references in response to climate change research and rational utilization of water resources in SRTR.

Published
2013
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50. Investigation of a mesospheric bore event over northern China

Author

Q. Li, J. Xu, J. Yue, X. Liu, W. Yuan, B. Ning, S. Guan, and J. P. Younger

Subjects
Science, Physics, QC1-999, Geophysics. Cosmic physics, QC801-809
Abstract

A mesospheric bore event was observed using an OH all-sky airglow imager (ASAI) at Xinglong (40.2° N, 117.4° E), in northern China, on the night of 8–9 January 2011. Simultaneous observations by a Doppler meteor radar, a broadband sodium lidar, and TIMED/SABER OH intensity and temperature measurements are used to investigate the characteristics and environment of the bore propagation and the possible relations with the Na density perturbations. The bore propagated from northeast to southwest and divided the sky into bright and dark halves. The calculations show that the bore has an average phase velocity of 68 m s−1. The crests following the bore have a horizontal wavelength of ~ 22 km. These parameters are consistent with the hydraulic jump theory proposed by Dewan and Picard, as well as the previous bore reports. Simultaneous wind measurements from the Doppler meteor radar at Shisanling (40.3° N, 116.2° E) and temperature data from SABER on board the TIMED satellite are used to characterize the propagating environment of the bore. The result shows that a thermal-Doppler duct exists near the OH layer that supports the horizontal propagation of the bore. Simultaneous Na lidar observations at Yanqing (40.4° N, 116.0° E) suggest that there is a downward displacement of Na density during the passage of the mesospheric bore event.

Published
2013
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