149 results on '"Bhattacharya B"'
Search Results
2. Mangrove Carbon Pool Patterns in Maharashtra, India
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Pardeshi, Satish N., Chavan, Manoj, Kale, Manish, Khare, Manoj, Lele, Nikhil, and Bhattacharya, B. K.
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- 2024
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3. Impact of Climate Change on Incidences of Pests in Crops
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Bhagat, S., Kumar, Amrender, Bhattacharya, B. K., Birah, A., Chattopadhyay, C., Chattopadhyay, Nabansu, editor, Stefanski, Robert, editor, Attri, S. D., editor, and Rathore, Laxman Singh, editor
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- 2024
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4. Agrometeorological Approach for Sugarcane Yield Estimation at Regional Scale Using Satellite Remote Sensing
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Tripathy, Rojalin, Nigam, Rahul, and Bhattacharya, B. K.
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- 2023
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5. Bacteriophage and Virus Engineering
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Kumar, Subrat, Pattnaik, R., Sahu, P. S., Bhattacharya, B., Suar, Mrutyunjay, editor, Misra, Namrata, editor, and Dash, Chandravanu, editor
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- 2022
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6. Hydroinformatics education at IHE Delft: past and future
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Solomatine, D. P., primary, Price, R. K., additional, Jonoski, A., additional, Popescu, I., additional, Bhattacharya, B., additional, Alfonso Segura, L., additional, Corzo Perez, G., additional, van Andel, S. J., additional, and Bertini, C., additional
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- 2022
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7. Low cycle fatigue behaviour study of a nano precipitate strengthened ferrite-bainite 780 steel
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Chinara, M., Jayabalan, B., Bhattacharya, B., Durga Prasad, A., Chatterjee, S., and Mukherjee, S.
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- 2023
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8. Estimating fodder crops area using multi-date high resolution satellite data- A case study in Madhya Pradesh, India
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Karwariya, Sateesh, Dutta, Sujay, Singh, Magan, Kumar, Himanshu, Kumar, Sanjeev, Meena, V.K., and Bhattacharya, B. K
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- 2022
9. Energy Absorption of Hourglass Shaped Lattice Metastructures
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Gupta, V., Bhattacharya, B., and Adhikari, S.
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- 2022
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10. Applications of Advanced Geospatial Technology for Expansion of Area under Horticultural Crops in North Eastern Region of India
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Handique, B. K., Goswami, C., Jena, P., Dutta, F., Samiam, R., Nongrum, I., Jha, D., Raju, P. L. N., Deka, C. R., Sarma, R., Sarmah, K., Dutta, H. K., Deb, S., Yari, B., Maibam, S., Thanpuii, H., Sailo, R., Muansangi, V., Katiry, D., Medo, T., Sharma, N. P., Lepcha, B., Roy, A., Bhattacharya, B. K., Patel, J. G., Singh, C. P., Manjunath, K. R., Kimothi, M. M., Mamatha, S., Kumar, P., Tahlani, P., and Ray, S. S.
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- 2022
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11. Field-scale Assessment of Sugarcane for Mill-level Production Forecasting using Indian Satellite Data
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Kumar, Mukesh, Das, Ayan, Chaudhari, K. N., Dutta, Sujay, Dakhore, K. K., and Bhattacharya, B. K.
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- 2022
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12. Towards Fine-Scale Yield Prediction of Three Major Crops of India Using Data from Multiple Satellite
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Tripathy, Rojalin, Chaudhari, K. N., Bairagi, G. D., Pal, Om, Das, Rajesh, and Bhattacharya, B. K.
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- 2022
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13. Conjugation of AMUL and ISRO: Development of Feed and Fodder for Dairy Industries
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Dutta, Sujay, Dwivedi, Shashank, Bhattacharya, B. K., and Sodhi, R. S.
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- 2022
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14. Modelling the Spatial Variation of Methane and Nitrous Oxide Emission from Rice Fields Using DNDC Model
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Tripathi, Rahul, Majhi, M., Sahu, S. G., Mohanty, S., Moharana, K. C., Shahid, Md, Swain, C. K., Lal, B., Gautam, P., Dash, P. K., Chatterjee, D., Kumar, A., Tripathy, R., Bhattacharya, B., Shukla, A. K., and Nayak, A. K.
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- 2021
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15. Validation and bias correction of incoming solar radiation from two reanalysis products over India
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Jain, Anushriya, Eswar, R, and Bhattacharya, B K
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- 2022
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16. Evaluation of Near Real-Time Global Precipitation Measurement (GPM) Precipitation Products for Hydrological Modelling and Flood Inundation Mapping of Sparsely Gauged Large Transboundary Basins—A Case Study of the Brahmaputra Basin
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Jawad, M. Bhattacharya, B. Young, A. van Andel, S. J. and Jawad, M. Bhattacharya, B. Young, A. van Andel, S. J.
- Abstract
Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R2 values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m3 s−1, and the average discharge was 20611 m3 s−1. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping.
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- 2024
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17. Hydrogeomorphological approach for flood analyses at high- detailed scale: Narrow rivers with broad complex alluvial plains
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Lombana, L. Bhattacharya, B. Alfonso, L. Martínez-Graña, A. and Lombana, L. Bhattacharya, B. Alfonso, L. Martínez-Graña, A.
- Abstract
Fluvial flood risk management requires precise knowledge of flooding processes; thus, there is a need to carry out a detailed hydrological, hydraulic, and geomorphologic analysis to understand floods and the factors that influence them. Although the integration of geomorphological methods and hydrological-hydraulic modelling is known to be crucial for flood risk analysis, it remains a challenge to apply these approaches to the understanding of floods in small and medium-sized catchments, focusing on complex fluvial systems that have been highly modified. In this work, a hydrogeomorphological methodology was developed to analyse fluvial floods in narrow rivers with broad complex alluvial plains. The procedure comprises different phases that combine hydrologic-hydraulic modelling and a geomorphological break-down. Critically, this allows for a detailed analysis of existing relationships among erosional and depositional fluvial landforms and hydraulic parameters, comprising geometric sections, discharges, depths, flood extents and, therefore, flood hazards. The approach involves the analysis of specific flood events beforehand and the study of fluvial floods associated with different probabilities of occurrence afterwards. The information employed in the present study incorporates high- detailed inputs comprising hourly time series of hydrometeorological data, LiDAR data to extract high-precision terrain information, fluvial geomorphic maps and flood event extents based on Sentinel-2 imagery. We demonstrate this methodology in the Carrión River, located in the province of Palencia, Spain, whose characteristics include a wide complex alluvial plain, reaches highly modified by anthropic interventions and recurrent flood episodes. This hydrogeomorphological approach allowed the integration of geomorphological knowledge and hydrologic-hydraulic modelling in flood analysis. As a result, it led to a deeper comprehension of the processes of inundation, which would be challeng
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- 2024
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18. Changing Urban Temperature and Rainfall Patterns in Jakarta: A Comprehensive Historical Analysis
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Maheng, D. Bhattacharya, B. Zevenbergen, C. Pathirana, A. and Maheng, D. Bhattacharya, B. Zevenbergen, C. Pathirana, A.
- Abstract
The increasing global population and in-country migration have a significant impact on global land use land cover (LULC) change, which reduces green spaces and increases built-up areas altering the near-surface radiation and energy budgets, as well as the hydrological cycle over an urban area. The LULC change can lead to a combination of hazards such as increasing urban temperatures and intensified rainfall, ultimately resulting in increased flooding. This present study aims to discuss the changing pattern in urban temperature, daily rainfall, and flooding in Jakarta. The daily urban temperature and daily rainfall were based on a 30-year dataset from three meteorological stations of Jakarta in the period between 1987 and 2013. The changing trend was analyzed by using the Mann–Kendall and the Pettitt’s tests. The relation between daily rainfall and flooding was analyzed using a 30-year flooding dataset collected from several sources including the international disaster database, research, and newspaper. The results show that there was an increasing trend in the daily temperature and the daily rainfall in Jakarta. The annual maximum daily temperature showed that an increasing trend started in 2001 at the KMY station, and in 1996 at the SHIA station. In general, the highest annual maximum daily temperature was about 37 °C, while the lowest was about 33 °C. Moreover, the maximum daily rainfall started increasing from 2001. An increase in the maximum daily rainfall was observed mainly in January and February, which coincided with the flood events recorded in these months in Jakarta. This indicates that Jakarta is not only vulnerable to high urban temperature but also to flooding. While these two hazards occur in distinct timeframes, there is potential for their convergence in the same geographical area. This study provides new and essential insights to enhance urban resilience and climate adaptation, advocating a holistic approach required to tackle these combined hazard
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- 2024
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19. Metal-like ductility and malleability in organic plastic crystals
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Mondal, A., primary, Bhattacharya, B., additional, Das, S., additional, Bhunia, S., additional, Chowdhury, R., additional, Dey, S., additional, and Reddy, C. M., additional
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- 2023
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20. Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)
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Spiers, H, Kouli, O, Ahmed, W, Varley, R, Ahari, D, Argus, L, Mclean, K, Kamarajah, S, Coe, P, Griffiths, E, Chan, A, Macutkiewicz, C, Jamdar, S, Wilson, M, Fullwood, C, Toogood, G, Gilchrist, A, Goldsworthy, M, Rashid, M, Pockney, P, Varela, J, Brindl, N, Ramirez, J, Marafante, C, Iwao, Y, Ghzawi, A, Elhadi, M, Gacaferi, H, Varghese, C, Adeyeye, A, Alser, O, Teh, C, Prieto, M, Hasan, A, Al-Naggar, H, Salgado, R, Veracierto, F, Lancelotti, T, Solinas, D, Oddi, R, Garcia, F, Mazza Diez, E, Andrade Ramirez, M, Bracco, R, Fernandez, D, Maraschio, M, Obeide, L, Giordano, E, Alcaraz, A, Marani, M, Aguirre, N, Luna, F, Francesconi, M, Chiham, F, Ramos Cossio, R, Alvarez, F, Pantoja Pachajoa, D, Mandojana, F, Merlo, I, Gonzalez, M, Cervelo, G, Puma, R, Vardaro, G, Davis, A, Jurat, D, Guenoff, C, Raubenheimer, K, Goddard, K, Brown, K, Wegrecki, K, Cheung, H, Yang, M, Siddiqui, J, Ahn, J, Huynh, R, Lam, Y, Afzal, M, Ong, B, Chua, M, Ly, K, Thomson, J, Watson, D, Dawson, A, Drane, A, Van Ruyven, S, Lun, E, Ferguson, M, Jeong, J, De Silva, C, Wills, V, Gundara, J, Mccourt, E, Bong, C, Tabone, R, Wong, W, Gray, A, Koh, D, Pollock, M, Singhal, S, Smith, R, Dudi-Venkata, N, Kanhere, H, Stranz, C, Seow, W, Mansour, L, Wormald, J, Loveday, B, Thomson, B, O'Donnell, T, Milenkovski, N, Herath, M, Trochsler, M, Farfus, A, Maddern, G, Bunjo, Z, Kuan, L, Atanasov, G, Samadov, E, Namazov, I, Asgarov, M, Ibrahimli, A, Srinivasan, M, Saeed, M, Aljawder, H, Juma, I, Coimbra, F, Marques, N, Casteleins, W, Petruzziello, A, Jabur, G, Rodriguez, J, Buso, P, Mackenzie, S, Hsiao, M, Sljivic, I, Tecson, A, Karanicolas, P, Roke, R, Moon, J, Butler, E, Riquelme, F, Yanez, M, Catan, F, Uribe, M, Carriel, F, Oppliger, F, Paredes, A, Daroch, D, Aguayo, J, Perez Rivera, C, Acosta Buitrago, L, Kadamani Abiyomaa, A, Mosquera Paz, M, Cabrera, P, Corso, J, Ozcay, N, Ozant, A, Arslan, K, Besim, H, Almezghwi, H, Azzam, A, Bessa, S, El-Sayes, I, Badawy, A, Wael, M, El-Gendi, A, Azab, M, Fayed, M, El Kassas, M, Gamal, M, Tawheed, A, Al Shafie, A, Emile, S, Elfallal, A, Elfeki, H, Shalaby, M, Sakr, A, Elbahnasawy, M, Shama, M, Abdel-Elsalam, W, Abd-Elsalam, S, Escobar Dominguez, J, Medrano, F, Gaitan, S, Escalon Gonzalez, O, Alfaro Varela, J, Cea, M, Interiano, M, Cabrera, B, Lakkis, Z, Georges, P, Antonot, C, Magnin, J, Kamphues, C, Lauscher, J, Schineis, C, Loch, F, Lee, L, Beyer, K, Bouchagier, K, Galanis, I, Bartziotas, D, Lostoridis, E, Tourountzi, P, Nagorni, E, Charalabopoulos, A, Baili, E, Kyros, E, Vagios, I, Skotsimara, A, Liakakos, T, Alexandrou, A, Papalampros, A, Papadopolous, V, Tooulias, A, Kentarchos, I, Christou, C, Tsoulfas, G, Tale-Rosales, L, Lopez Muralles, I, Melendez, H, Bran, G, Monroy Mahecha, F, Contreras, J, Porras, D, Paiz, E, Soto, E, Ixcayau Hernandez, J, Gupta, A, Rajput, D, Kumar, N, Mani, R, Kant, R, Sonkar, A, Anand, A, Agrawal, M, Gaurav, K, Tripathi, M, Sikora, S, Bharathy, K, Kumar Rangapa, M, Khuller, D, Bhojwwani, R, Ayyar, S, Jain, N, Mehraj, A, Hussain, F, Nazir, I, Shah, M, Chowdri, N, Hilmi, A, Argenio, G, Atelli, P, Palladino, E, Armellino, M, Tamini, N, Nespoli, L, Degrate, L, Angrisani, M, Carissimi, F, Bordoni, P, Fleres, F, Clarizia, G, Spolini, A, Franzini, M, Cucinotta, E, Badessi, G, Mazzeo, C, Viscosi, F, Pintabona, G, Campagnaro, T, Poletto, E, Turri, G, Ruzzenente, A, Conci, S, Guglielmi, A, Feo, C, Fabbri, N, Fazzin, M, Giaccari, S, Massani, M, Pelizzo, P, Colella, M, Tutino, R, Cappellacci, F, Medas, F, Canu, G, Erdas, E, Calo, P, Porcu, A, Perra, T, Scanu, A, Fancellu, A, Germani, P, Giunta, C, Biloslavo, A, Abdallah, H, Aizza, G, Barberis, A, Belli, F, Santoliquido, M, Filauro, M, Canonico, G, Nelli, T, Di Martino, C, Capezzuoli, L, Anastasi, A, Bressan, L, Cortinovis, S, Nagliati, C, Colombo, F, Ferrario, L, Bondurri, A, Guerci, C, Maffioli, A, Catena, F, Perrone, G, Giuffrida, M, Morini, A, Annicchiarico, A, Gallo, G, Carpino, A, Ferrari, F, De Paola, G, Sammarco, G, Callari, C, Licari, L, Sorce, V, Di Miceli, D, Lovisetto, F, Zonta, S, Chessa, A, Fiorini, A, De Manzoni Garberini, A, Angelini, E, Moggia, E, Murgese, A, Mungo, S, Birolo, S, Garino, M, Pipitone Federico, N, Muratore, A, Lunghi, E, Calabro, M, Cianci, P, Enrico, R, Capuzzolo, S, Cafagna, L, Minafra, M, Sasia, D, Gattolin, A, Migliore, M, Rimonda, R, Travaglio, E, De Marco, G, Elter, C, Bargellini, T, D'Amico, S, Zambonin, D, Caponi, A, Calini, G, Puggioni, A, Bresadola, V, Zalla, T, Cantafio, S, Feroci, F, Romoli, L, Giudicissi, R, Picciariello, A, Papagni, V, Dibra, R, Altomare, D, Pinotti, E, Montuori, M, Baronio, G, Tonini, V, Sartarelli, L, Gori, A, Cervellera, M, Lapolla, P, Sapienza, P, Brachini, G, Cirillo, B, Zambon, M, Mingoli, A, Pascariello, A, Boccia, L, Benedetti, S, Mantovani, G, De Angelis, M, Ferrara, F, Testa, V, Borghi, F, Maione, F, Pruiti Ciarello, V, Giraudo, G, Agresta, F, Cestaro, G, Prando, D, Cavallo, F, Zese, M, Cillara, N, Sechi, R, Cardia, R, Cannavera, A, Putzu, G, Frongia, F, Pisanu, A, Delogu, D, Esposito, G, Podda, M, Iossa, A, De Angelis, F, Boru, C, Silecchia, G, Palini, G, Garulli, G, Veneroni, S, Tammaro, P, Maida, P, Leake, P, Wanliss, M, Sato, K, Chiyonobu, N, Imamura, H, Yamazaki, S, Watanabe, M, Qasem, A, Ayasra, F, Al Dahabrh, S, Khaled, A, Alsaafin, S, Al-Thunaibat, A, Olaywah, D, Alqudah, S, Alqawasmi, S, Khamees, A, Guboug, A, Es Salim, M, Althwabteh, T, Bani Khaled, H, El-Hammuri, N, Aljesrawi, A, Alamaadany, F, Eljareh, M, Al Gasi, A, Alsaeiti, S, Alkhafeefi, A, Suliaman, T, Alanasri, A, Haroun, A, Haron, A, Kilani, A, Ahmed, M, Alawami, M, Alawami, A, Albashri, M, Abusannuga, M, Malek, A, Jwaili, N, Aldenfria, A, Elzwawi, F, Almugaddami, A, Egdeer, A, Masoud, M, Alazzabi, B, Alezabi, B, Shuwayyah, A, Alkamkhe, A, Aboulqasim, I, Atiyah, H, Alfagi, R, Abdulmula, A, Bouhuwaish, A, Samer, A, Salim, R, Aboazamazem, H, Almiqlash, B, Biala, M, Alganimi, W, Ghamgh, R, Ben Omar, N, Alsoufi, A, Aldreawi, M, Saleim, N, Sowan, F, Saleem, H, Ahmed, A, Samalavicius, N, Aliosin, O, Dailidenas, S, Dulskas, A, Buckus, B, Kuliesius, Z, Bradunaite, R, Dominguez-Rosado, I, Buerba, G, Posadas-Trujillo, O, Alfaro-Goldaracena, A, Cortes, R, Mercado, M, Beristain-Hernandez, J, Mora-Munoz, V, Mena-Bedolla, J, Palacios Ramirez, A, Astorga Medina, M, Van Aert, G, Ombashi, S, Spillenaar Bilgen, R, Vos, D, Besselink, M, Alberts, V, Busch, O, Bemelman, W, Boermeester, M, Daams, F, Gordinou De Gouberville, M, Van Duijvendijk, P, De Graaff, M, Baaij, J, Gans, S, Bos, K, Goudsmit, B, Den Dekker, B, Braat, A, Kuijpers, A, Breukers, S, Borel Rinkes, I, Andel, D, Hayes, T, Carson, D, Bhat, S, Van Der Have, J, Anderson, C, Bissett, I, Windsor, J, Elliott, B, Scowcroft, H, Mclauchlan, J, Ritchie, D, Jeffery, F, Connor, S, Xu, W, Mashlan, H, Thirayan, V, Ly, J, Mcguinness, M, Ferguson, L, Watt, I, Harmston, C, Akinmade, A, Enoch, E, Kayode-Nissi, V, Ogundele, I, Ayoade, B, Adekoya, A, Nwokoro, C, Opadeyi, A, Yusuf, A, Ojajuni, A, Adepoju, O, Muhammad Dauda, M, Keffi Mubarak, M, Lawal, K, Muhammad, D, Salonga, D, Sael, N, Rey, C, Pestano, M, Tan, D, Bangayan, N, Sy, D, Ang, D, Bernardo, E, Chua, J, Alharthi, M, Bukhari, W, Bakier Mohammed, K, Al Athath, S, Ghunaim, M, Saiedi, H, Sultan, N, Farsi, A, Basendowah, M, Malibary, N, Jaloun, H, Altalhi, D, Organjee, A, Moamena, M, Al Zaidi, T, Alyami, M, Alqannas, M, Al-Urfan, M, Elawad, A, Alawadhi, A, Alalawi, Y, Alqarni, A, Alqahtani, B, Alayed, A, Alsobaie, K, Adi, H, Elhaj, M, Dehlawi, A, Behairy, G, Khaled, I, Kmezic, S, Radenkovic, D, Aleksic, L, Markovic, V, Pejovic, I, Antic, A, Kalkan, M, Vujanovic Gadjanski, O, Dusan, S, Marcetic, B, Thiruchelvam, N, Chiow, A, Mun, D, Tan, E, Koh, Y, Loh, W, Wang, Z, Chan, C, Kloppers, C, Almgla, N, Bernon, M, Kahn, M, Karimbocus, N, Roldan Villavicencio, J, Goitia, V, Gutierrez Rios, R, Garcia Ruiz, S, Lopez Deogracias, M, Turrado-Rodriguez, V, Morales, X, Hessheimer, A, Termes Serra, R, Beltran De Heredia, J, Trujillo-Diaz, J, Herreros-Rodriguez, J, Montes-Manrique, M, De Andres-Asenjo, B, Beltran-Heredia, J, Gimenez Maurel, T, Utrilla Fornals, A, Martin Anoro, L, Cortese, S, Perez Diaz, M, Ballon, M, Morote, M, Cebolla Rojas, L, Oliver Guillen, J, Lopez De Fernandez, A, Del Campo Lavilla, M, Mora-Guzman, I, Escartin, A, Pinillos, A, Vela Polanco, F, Jara Quezada, J, Muriel Alvarez, P, Tur-Martinez, J, Camps, J, Herrero, E, Garcia-Domingo, M, Cugat Andorra, E, Crespi Mir, A, Claramonte Bellmunt, O, Vicens Arbona, J, Fernandez Burgos, I, Sarriugarte Lasarte, A, Marin, H, Tellaeche De La Iglesia, M, Ocerin Alganza, O, Salinas Gomez, J, Ramos-Martin, P, Urbieta, A, Nasimi Sabbagh, R, Castell Gomez, J, Serrablo, A, Paterna -Lopez, S, GutiCrossed D SignCopyrightrrez-Diez, M, Abadia-Forcen, M, Serradilla-Martin, M, Duran Munoz-Cruzado, V, Pareja Ciuro, F, Perea Del Pozo, E, Aparicio Sanchez, D, Dios-Barbeito, S, Marenco De La Cuadra, B, Retamar Gentil, M, Reguera-Rosal, J, Infantes Ormad, M, Lopez-Ruiz, J, Landaluce-Olavarria, A, Zevallos-Quiroz, J, Barrutia Leonardo, J, Emaldi, A, Begona, E, Balciscueta Coltell, I, Sebastian, M, Martinez Ramos, S, Martinez Alcaide, S, Lorenzo Perez, J, Martinez Insfran, L, Lopez-Morales, P, Gimenez Frances, C, Rahy-Martin, A, Pelloni, M, Ortiz-Lopez, D, Benet-Munoz, O, Pinero-Gonzalez, L, Alconchel, F, Nicolas-Lopez, T, Rodrigues, K, Cascales Campos, P, Gomez-Bosch, F, Ramirez Romero, P, Ibrahim, M, Hamid, H, Idres, R, Idris, M, Mohammed, O, Ayran, S, Sinan, A, Ozben, V, Aytac, E, Aliyeva, Z, Mutlu, A, Bilgin, I, Karahasanoglu, T, Hamzaoglu, I, Bozkirli, B, Uprak, T, Kotan, T, Coskun, M, Kara, Y, Somuncu, E, Kocatas, A, Bozkurt, M, Demirli Atici, S, Kaya, T, Sert, I, Emiroglu, M, Jaffar, M, Younis, M, Aziz, T, Ikram, F, Sandal, M, Al Madhloum Al Suwaidi, F, Alshaikh, M, Saber, A, Khammas, A, Nessa, A, Jardine, R, Nicol, L, Clark, C, Mcgee, A, Alkari, B, Feretis, M, Antakia, R, Georgiades, F, Moneim, J, O'Neill, R, Balakrishnan, A, Lunevicius, R, Sud, A, Moutsos, I, Gomez, D, Shahid, S, Majeed, T, 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M., Galea M., Jeyakumar J., Marriott P., Zafar S., Baker A., Yershov D., Galanopoulos G., Jordan R., Peinado Garcia C., Anyaugo N., Bath M. F., Omatseye J., Roberts L., Argyriou E. O., Machesney M., Parmar C., Clark S., Khalil H., Unsworth S., Mlotshwa M., Ayoub N., Aboelkhair A., Iosif E., Mohamed N., Reynolds E., Mackender E., Robinson D., Mufti W., Fischkoff K., Coleman N., Anantha Sathyanarayana S., Deutsch G., Giangola M., Lin D., Weiss M., Chung C., Nguyen A., Mueller J., Dabit M., Gordon J., McGuire E., Rashid O., Georgi E., Gallo M., Kunstman J. W., Peters N. V., O'Connor R., Bhattacharya B., Onkendi E., Santos A. P., Richmond R., Warren M., Zhang K., Broderick R., Clary B., Horgan S., Doucet J., Liepert A., Harmon L., McCall C., Sham J. G., Williams E., Labadie K. P., Clark N. M., Dickerson L. K., Hammill C. W., Williams G., Kushner B., Cos H., Zarate Rodriguez J., Bailey K., Al-Raimi I. M. 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D, Weiss, M, Chung, C, Nguyen, A, Mueller, J, Dabit, M, Gordon, J, Mcguire, E, Rashid, O, Georgi, E, Gallo, M, Kunstman, J, Peters, N, O'Connor, R, Bhattacharya, B, Onkendi, E, Santos, A, Richmond, R, Warren, M, Zhang, K, Broderick, R, Clary, B, Horgan, S, Doucet, J, Liepert, A, Harmon, L, Mccall, C, Sham, J, Williams, E, Labadie, K, Clark, N, Dickerson, L, Hammill, C, Williams, G, Kushner, B, Cos, H, Zarate Rodriguez, J, Bailey, K, Al-Raimi, I, Al-Zazay, K, Ahmed Mohammed Al-Mahdi, S, Mohammed Aldowbli, S, Al-Shehari, M, Shream, S, Al-Ameri, S, Aeed, M, Aldawbali, M, Alsayadi, R, Alsayadi, M, Spiers H. 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M., Pareja Ciuro F., Perea Del Pozo E., Aparicio Sanchez D., Dios-Barbeito S., Marenco De La Cuadra B., Retamar Gentil M., Reguera-Rosal J., Infantes Ormad M., Lopez-Ruiz J. A., Landaluce-Olavarria A., Zevallos-Quiroz J. C., Barrutia Leonardo J., Emaldi A., Begona E., Balciscueta Coltell I., Sebastian M., Martinez Ramos S., Martinez Alcaide S., Lorenzo Perez J., Martinez Insfran L. A., Lopez-Morales P., Gimenez Frances C., Rahy-Martin A., Pelloni M., Ortiz-Lopez D., Benet-Munoz O., Pinero-Gonzalez L., Alconchel F., Nicolas-Lopez T., Rodrigues K., Cascales Campos P. A., Gomez-Bosch F., Ramirez Romero P., Ibrahim M., Hamid H. K. S., Idres R., Idris M., Mohammed O., Ayran S., Sinan A. H., Ozben V., Aytac E., Aliyeva Z., Mutlu A. U., Bilgin I. A., Karahasanoglu T., Hamzaoglu I., Bozkirli B., Uprak T. K., Kotan T., Coskun M., Kara Y., Somuncu E., Kocatas A., Bozkurt M. A., Demirli Atici S., Kaya T., Sert I., Emiroglu M., Jaffar M., Younis M. 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M., Galea M., Jeyakumar J., Marriott P., Zafar S., Baker A., Yershov D., Galanopoulos G., Jordan R., Peinado Garcia C., Anyaugo N., Bath M. F., Omatseye J., Roberts L., Argyriou E. O., Machesney M., Parmar C., Clark S., Khalil H., Unsworth S., Mlotshwa M., Ayoub N., Aboelkhair A., Iosif E., Mohamed N., Reynolds E., Mackender E., Robinson D., Mufti W., Fischkoff K., Coleman N., Anantha Sathyanarayana S., Deutsch G., Giangola M., Lin D., Weiss M., Chung C., Nguyen A., Mueller J., Dabit M., Gordon J., McGuire E., Rashid O., Georgi E., Gallo M., Kunstman J. W., Peters N. V., O'Connor R., Bhattacharya B., Onkendi E., Santos A. P., Richmond R., Warren M., Zhang K., Broderick R., Clary B., Horgan S., Doucet J., Liepert A., Harmon L., McCall C., Sham J. G., Williams E., Labadie K. P., Clark N. M., Dickerson L. K., Hammill C. W., Williams G., Kushner B., Cos H., Zarate Rodriguez J., Bailey K., Al-Raimi I. M. N., Al-Zazay K., Ahmed Mohammed Al-Mahdi S., Mohammed Aldowbli S., Al-Shehari M., Shream S., Al-Ameri S., Aeed M., Aldawbali M., Alsayadi R., and Alsayadi M.
- Abstract
https://www.scopus.com/record/display.uri?eid=2-s2.0-85138595712&origin=inward&txGid=728c82ff11b9726b5d443216a91e7ffa#:~:text=Background: This study,of this pandemic.
- Published
- 2022
21. An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States
- Author
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Guido, B. I. Popescu, I. Samadi, V. Bhattacharya, B. and Guido, B. I. Popescu, I. Samadi, V. Bhattacharya, B.
- Abstract
Floods are among the most destructive natural hazards in the world, posing numerous risks to societies and economies globally. Accurately understanding and modeling floods driven by extreme rainfall events has long been a challenging task in the domains of hydrologic science and engineering. Unusual catchment responses to flooding cause great difficulty in predicting the variability and magnitude of floods, as well as proposing solutions to manage large volumes of overland flow. The usage of nature-based solutions (NBSs) has proved to be effective in the mitigation of flood peak rate and volume in urban or coastal areas, yet it is still not widely implemented due to limited knowledge and testing compared to traditional engineering solutions. This research examined an integrated hydrological and hydraulic modeling system to understand the response of an at-risk watershed system to flooding and evaluate the efficacy of NBS measures. Using the Hydrologic Engineering Center Hydrologic Modeling System and River Analysis System (HEC-HMS and HEC-RAS) software, an integrated hydrologic–hydraulic model was developed for Hurricane Matthew- (2016) and Florence-driven (2018) floods across the Little Pee Dee–Lumber River watershed, North and South Carolina (the Carolinas), US. The focus was on Nichols, a small town that has disproportionately been impacted by flooding during these two hurricane events. The present article proposes a methodology for selecting, modeling, and evaluating the performance of NBS measures within a catchment, which can be extended to other case studies. Different NBS measures, including flood storage ponds, riparian reforestation, and afforestation in croplands, were designed, modeled, and evaluated. Hurricane Matthew's flooding event was used for evaluating the NBS scenarios given its high simulation accuracy in flood inundation compared to the less accurate results obtained for Hurricane Florence. The scenario comparison evidenced that large-scale nat
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- 2023
- Full Text
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22. Improving flash flood forecasting using a frequentist approach to identify rainfall thresholds for flash flood occurrence
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Wu, Z. Bhattacharya, B. Xie, P. Zevenbergen, C. and Wu, Z. Bhattacharya, B. Xie, P. Zevenbergen, C.
- Abstract
Flash Flood Guidance (FFG) is a rainfall threshold which initiates flooding in streams. It merely provides a binary output (yes or no) which has large uncertainties in forecasting. In this paper, we propose a new method by combining FFG with the Frequentist method to present the probability of flash flood occurrence based on historical rainfall events. We first calculated deviation from the log transform rainfall data leading to flash floods. Kernel Density Estimation (KDE) was used to describe the deviation. Normal Distribution Function (NDF) was chosen to fit the KDE output and to calculate probabilities of flooding as per the Frequentist FFG. In order to aid decision making, three probability thresholds (10, 20 and 60%) were used for defining four flood risk classes, namely very low, low, significant and high, and were colour coded respectively as green, yellow, orange and red. The proposed Frequentist FFG method was then applied to the Posina River basin in Italy. Comparison of forecasts from the conventional FFG (with probability 0 or 1) and Frequentist FFG for 94 6-hourly rainfall events, including 23 flood events, shows that the Frequentist FFG presented a probability of flooding varying from 0 to 100% and the corresponding risk class can be used to reduce false alarms while still reducing the disaster risk. The application of the developed approach to the Posina basin shows that decision making regarding flash forecasting is easier with the presented approach compared to the traditional FFG approach.
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- 2023
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23. Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)
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Spiers H. V. M., Kouli O., Ahmed W. U., Varley R., Ahari D., Argus L., McLean K. A., Kamarajah S. K., Coe P., Griffiths E. A., Chan A. K. C., Macutkiewicz C., Jamdar S., Wilson M., Fullwood C., Toogood G., McLean K., Ahmed W., Gilchrist A., Goldsworthy M., Rashid M., Pockney P., Varela J., Brindl N., Ramirez J., Marafante C., Iwao Y., Ghzawi A., Elhadi M., Gacaferi H., Varghese C., Adeyeye A., Alser O., Teh C., Prieto M., Hasan A., Al-Naggar H., Salgado R., Veracierto F., Lancelotti T., Solinas D., Oddi R., Garcia F. W., Mazza Diez E., Andrade Ramirez M. R., Bracco R., Fernandez D., Maraschio M. A., Obeide L., Giordano E., Alcaraz A., Marani M. A., Aguirre N., Luna F., Francesconi M., Chiham F., Ramos Cossio R., Alvarez F. A., Pantoja Pachajoa D. A., Mandojana F., Merlo I. G., Gonzalez M. H., Cervelo G., Puma R., Vardaro G. F., Davis A., Jurat D., Guenoff C., Raubenheimer K., Goddard K., Brown K., Wegrecki K. J., Cheung H. Y. C., Yang M., Cheung H., Siddiqui J., Ahn J. H., Huynh R., Lam Y. H., Afzal M., Ong B. S., Chua M. Y. M., Ly K., Thomson J. E., Watson D., Dawson A. C., Drane A., Van Ruyven S., Lun E. W. Y., Ferguson M., Jeong J. Y., De Silva C., Wills V., Gundara J., McCourt E., Bong C., Tabone R., Wong W. J., Gray A., Koh D., Pollock M., Singhal S., Smith R., Dudi-Venkata N. N., Kanhere H., Stranz C., Seow W., Mansour L. T., Wormald J., Loveday B. P. T., Thomson B., O'Donnell T., Milenkovski N., Herath M., Trochsler M., Farfus A., Maddern G., Bunjo Z., Kuan L. L., Atanasov G., Dawson A., Lun E., Samadov E., Namazov I., Asgarov M., Ibrahimli A., Srinivasan M., Saeed M. F., Aljawder H., Juma I., Coimbra F. J., Marques N., Casteleins W. A., Petruzziello A., Jabur G., Rodriguez J. F. P., Buso P. L., Mackenzie S., Hsiao M., Sljivic I., Tecson A., Karanicolas P. J., Roke R., Moon J., Butler E. V., Riquelme F., Yanez M., Catan F., Uribe M., Carriel F., Oppliger F., Paredes A., Daroch D., Aguayo J. C., Perez Rivera C. J., Acosta Buitrago L. M., Kadamani Abiyomaa A., Mosquera Paz M. S., Cabrera P., Corso J., Ozcay N., Ozant A., Arslan K., Besim H., Almezghwi H., Azzam A. Y., Bessa S., El-Sayes I., Badawy A., Wael M., El-Gendi A., Azab M. A., Fayed M., El Kassas M., Gamal M., Tawheed A., Al Shafie A., Emile S., Elfallal A., Elfeki H., Shalaby M., Sakr A., Elbahnasawy M., Shama M., Abdel-Elsalam W., Abd-Elsalam S., Escobar Dominguez J. E., Medrano F., Gaitan S., Escalon Gonzalez O. M., Alfaro Varela J. C., Cea M., Interiano M., Cabrera B., Lakkis Z., Georges P., Antonot C., Magnin J., Kamphues C., Lauscher J. C., Schineis C., Loch F. N., Lee L. D., Beyer K., Bouchagier K., Galanis I., Bartziotas D., Lostoridis E., Tourountzi P., Nagorni E. A., Charalabopoulos A., Baili E., Kyros E., Vagios I., Skotsimara A., Liakakos T., Alexandrou A., Papalampros A., Papadopolous V., Tooulias A., Kentarchos I., Christou C., Tsoulfas G., Tale-Rosales L. F., Lopez Muralles I., Melendez H., Bran G., Monroy Mahecha F. A., Contreras J. R., Porras D. E., Paiz E., Soto E. R., Ixcayau Hernandez J. R., Gupta A., Rajput D., Kumar N., Mani R., Kant R., Sonkar A. A., Anand A., Agrawal M. K., Gaurav K., Tripathi M., Sikora S., Bharathy K., Kumar Rangapa M., Khuller D. S., Bhojwwani R., Ayyar S., Jain N., Mehraj A., Hussain F., Nazir I., Shah M., Chowdri N. A., Hilmi A., Argenio G., Atelli P., Palladino E., Armellino M. F., Tamini N., Nespoli L. C., Degrate L., Angrisani M., Carissimi F., Bordoni P., Fleres F., Clarizia G., Spolini A., Franzini M., Cucinotta E., Badessi G., Mazzeo C., Viscosi F., Pintabona G., Campagnaro T., Poletto E., Turri G., Ruzzenente A., Conci S., Guglielmi A., Feo C., Fabbri N., Fazzin M., Giaccari S., Feo C. V., Massani M., Pelizzo P., Colella M., Tutino R., Cappellacci F., Medas F., Canu G. L., Erdas E., Calo P. G., Porcu A., Perra T., Scanu A. M., Feo C. F., Fancellu A., Germani P., Giunta C., Biloslavo A., Abdallah H., Aizza G., Barberis A., Belli F., Santoliquido M., Filauro M., Canonico G., Nelli T., Di Martino C., Capezzuoli L., Anastasi A., Bressan L., Cortinovis S., Nagliati C., Colombo F., Ferrario L., Bondurri A., Guerci C., Maffioli A., Catena F., Perrone G., Giuffrida M., Morini A., Annicchiarico A., Gallo G., Carpino A., Ferrari F., De Paola G., Sammarco G., Callari C., Licari L., Sorce V., Di Miceli D., Lovisetto F., Zonta S., Chessa A., Fiorini A., De Manzoni Garberini A., Angelini E., Moggia E., Murgese A., Mungo S., Birolo S. L., Garino M., Pipitone Federico N. S., Muratore A., Lunghi E. G., Calabro M., Cianci P., Enrico R., Capuzzolo S., Cafagna L., Minafra M., Sasia D., Gattolin A., Migliore M., Rimonda R., Travaglio E., De Marco G., Elter C., Bargellini T., D'Amico S., Zambonin D., Caponi A., Calini G., Puggioni A., Bresadola V., Zalla T., Cantafio S., Feroci F., Romoli L., Giudicissi R., Picciariello A., Papagni V., Dibra R., Altomare D. F., Pinotti E., Montuori M., Baronio G., Tonini V., Sartarelli L., Gori A., Cervellera M., Lapolla P., Sapienza P., Brachini G., Cirillo B., Zambon M., Mingoli A., Pascariello A., Boccia L., Benedetti S., Mantovani G., De Angelis M., Ferrara F., Testa V., Borghi F., Maione F., Pruiti Ciarello V., Giraudo G., Agresta F., Cestaro G., Prando D., Cavallo F., Zese M., Cillara N., Sechi R., Cardia R., Cannavera A., Putzu G., Frongia F., Pisanu A., Delogu D., Esposito G., Podda M., Iossa A., De Angelis F., Boru C., Silecchia G., Palini G. M., Garulli G., Veneroni S., Tammaro P., Maida P., Leake P. A., Wanliss M. G., Sato K., Chiyonobu N., Imamura H., Yamazaki S., Watanabe M., Qasem A., Ayasra F., Al Dahabrh S., Khaled A., Alsaafin S., Al-Thunaibat A., Olaywah D., Alqudah S., Alqawasmi S., Khamees A., Guboug A., Es Salim M., Althwabteh T., Bani Khaled H., El-Hammuri N., Aljesrawi A., Alamaadany F., Eljareh M., Al Gasi A. E. J., Alsaeiti S., Alkhafeefi A. S., Suliaman T., Alanasri A. H. A., Haroun A. B. A., Haron A., Kilani A. I., Ahmed M., Alawami M., Alawami A., Albashri M., Abusannuga M., Malek A., Jwaili N., Aldenfria A., Elzwawi F., Almugaddami A., Egdeer A. S. A., Masoud M., Alazzabi B., Alezabi B., Shuwayyah A., Alkamkhe A. A. S., Aboulqasim I., Atiyah H., Alfagi R. A. A., Abdulmula A., Bouhuwaish A., Samer A., Salim R., Aboazamazem H., Almiqlash B., Biala M., Alganimi W., Ghamgh R., Ben Omar N., Alsoufi A., Aldreawi M., Saleim N., Sowan F., Saleem H., Ahmed A., Samalavicius N. E., Aliosin O., Dailidenas S., Dulskas A., Buckus B., Kuliesius Z., Bradunaite R., Dominguez-Rosado I., Buerba G. A., Posadas-Trujillo O. E., Alfaro-Goldaracena A., Cortes R., Mercado M. A., Beristain-Hernandez J. L., Mora-Munoz V. S., Mena-Bedolla J. M., Palacios Ramirez A. R., Astorga Medina M. M., Van Aert G., Ombashi S., Spillenaar Bilgen R., Vos D., Besselink M., Alberts V., Busch O., Bemelman W., Boermeester M., Daams F., Gordinou De Gouberville M., Van Duijvendijk P., De Graaff M., Baaij J., Gans S., Bos K., Goudsmit B., Den Dekker B., Braat A., Kuijpers A., Breukers S., Borel Rinkes I., Andel D., Hayes T., Carson D., Bhat S., Van Der Have J., Anderson C., Bissett I., Windsor J., Elliott B. M., Scowcroft H., McLauchlan J., Ritchie D., Jeffery F., Connor S., Xu W., Mashlan H., Thirayan V., Ly J., McGuinness M. J., Ferguson L., Watt I., Harmston C., Akinmade A., Enoch E., Kayode-Nissi V., Ogundele I., Ayoade B. A., Adekoya A., Nwokoro C., Opadeyi A., Yusuf A., Ojajuni A., Adepoju O., Muhammad Dauda M., Keffi Mubarak M., Lawal K., Muhammad D., Salonga D., Sael N. A., Rey C. M., Pestano M., Tan D., Bangayan N. R., Sy D. K., Ang D., Bernardo E., Chua J. P., Alharthi M., Bukhari W., Bakier Mohammed K., Al Athath S., Ghunaim M., Saiedi H., Sultan N., Farsi A., Basendowah M., Malibary N., Jaloun H., Altalhi D., Organjee A., Moamena M., Al Zaidi T. M., Alyami M., Alqannas M., Al-Urfan M., Elawad A., Alawadhi A., Alalawi Y., Alqarni A., Alqahtani B., Alayed A., Alsobaie K., Adi H., Elhaj M., Dehlawi A., Behairy G., Khaled I., Kmezic S., Radenkovic D., Aleksic L., Markovic V., Pejovic I., Antic A., Kalkan M., Vujanovic Gadjanski O., Dusan S., Marcetic B., Thiruchelvam N., Chiow A. K. H., Lee L. S., Mun D. Y. C., Tan E. K., Koh Y. X., Loh W. L., Wang Z., Chan C. Y., Kloppers C., Almgla N., Bernon M., Kahn M., Karimbocus N., Roldan Villavicencio J. I., Goitia V., Gutierrez Rios R. D., Garcia Ruiz S., Lopez Deogracias M., Turrado-Rodriguez V., Morales X., Hessheimer A., Termes Serra R., Beltran De Heredia J., Trujillo-Diaz J., Herreros-Rodriguez J., Montes-Manrique M., De Andres-Asenjo B., Beltran-Heredia J., Gimenez Maurel T., Utrilla Fornals A., Martin Anoro L. F., Cortese S., Perez Diaz M. D., Ballon M., Morote M., Cebolla Rojas L., Oliver Guillen J. R., Lopez De Fernandez A., Del Campo Lavilla M., Mora-Guzman I., Escartin A., Pinillos A., Vela Polanco F. F., Jara Quezada J. H., Muriel Alvarez P., Tur-Martinez J., Camps J., Herrero E., Garcia-Domingo M. I., Cugat Andorra E., Crespi Mir A., Claramonte Bellmunt O., Vicens Arbona J. C., Fernandez Burgos I. R., Sarriugarte Lasarte A., Marin H., Tellaeche De La Iglesia M., Ocerin Alganza O., Salinas Gomez J., Ramos-Martin P., Urbieta A., Nasimi Sabbagh R., Castell Gomez J. T., Serrablo A., Paterna -Lopez S., GutiCrossed D SignCopyrightrrez-Diez M., Abadia-Forcen M. T., Serradilla-Martin M., Duran Munoz-Cruzado V. M., Pareja Ciuro F., Perea Del Pozo E., Aparicio Sanchez D., Dios-Barbeito S., Marenco De La Cuadra B., Retamar Gentil M., Reguera-Rosal J., Infantes Ormad M., Lopez-Ruiz J. A., Landaluce-Olavarria A., Zevallos-Quiroz J. C., Barrutia Leonardo J., Emaldi A., Begona E., Balciscueta Coltell I., Sebastian M., Martinez Ramos S., Martinez Alcaide S., Lorenzo Perez J., Martinez Insfran L. A., Lopez-Morales P., Gimenez Frances C., Rahy-Martin A., Pelloni M., Ortiz-Lopez D., Benet-Munoz O., Pinero-Gonzalez L., Alconchel F., Nicolas-Lopez T., Rodrigues K., Cascales Campos P. A., Gomez-Bosch F., Ramirez Romero P., Ibrahim M., Hamid H. K. S., Idres R., Idris M., Mohammed O., Ayran S., Sinan A. H., Ozben V., Aytac E., Aliyeva Z., Mutlu A. U., Bilgin I. A., Karahasanoglu T., Hamzaoglu I., Bozkirli B., Uprak T. K., Kotan T., Coskun M., Kara Y., Somuncu E., Kocatas A., Bozkurt M. A., Demirli Atici S., Kaya T., Sert I., Emiroglu M., Jaffar M., Younis M. U., Aziz T., Ikram F., Sandal M., Al Madhloum Al Suwaidi F., Alshaikh M. O., Saber A., Khammas A., Nessa A., Jardine R., Nicol L., Clark C., McGee A., Alkari B., Feretis M., Antakia R., Georgiades F., Moneim J., O'Neill R., Balakrishnan A., Lunevicius R., Sud A., Moutsos I., Gomez D., Shahid S., Majeed T., Ibrahim W. K. G., Kadum K., Melia R., Magee C., Chicken D. W., Kumar S., Alshibshoubi M., Van Laarhoven S., Dewi F., Williams J., Byrne B., Wilkerson P., Tang C. B., Farhangmehr N., Jonas A., Charavanamuttu V., Almeida K., Efthimiou E., Boardley J., White A., Butt M. A., Menzies D., Gundkalli Z., Hassanzadeh-Baboli D., Jones O., Mistry P., Saha S., Gerrard A., Evans J., Rajeev S., Ali W., Ross E., Gilliam A., Hitchins C., Emslie K., Spellar K., Sked H., Briggs C., Brown L., A Hemadasa N., Apollos J. R., Belgaumkar A., Tawfik A., Brewin L., Oyewole B., Wadhawan H., Massie E., Rutherford D., McGivern K., McElroy L., De'Ath H. D., Tobbal M., Nagendram S., Patel P., Handa S., Houghton G., Sundaralingam S. S., Parker J., Morgan R., Gala T., Ibrahim S., Harby R., Abdelkarim M., Holroyd D., Thomas R., McLennan E., Boardley R., Jamieson N. B., Ebied H., Gossage J., Davies A., Wheatstone S., Jawad Z., Jiao L., Rajagopal P., Sodergren M., Lami M., Wiberg A., Bond-Smith G., Gemmill E., Lenzi E., Sapre D., Herrod P., Boyd-Carson H., Garcea G., Issa E., Jackson A., Fashina T., Pan H., Farquharson B., Shafiq H., Emanuel O., Mahdi S., Jeyarajah S., Finch L., Whiting G., Pigott L., Martin J., Siriwardena A. K., Beatson K., Abawi L., Lam W., Rea W., Andrews B., Al-Sarireh B., Soliman F., Burridge J., Jenvey C., Hammoda M., Hollyman M., Merker L., Richards J., Sukumaran V., Rogers S., Payne C., Bibi S., Raza K., Ul Ain N., Dronamraju S., Patil S., Nachimuthu S., Ravindran S., Patel S., Ivanov B., Patel M., Ejtehadi F., Jebamani J., Akhter Rahman M. M., Woodun H., De Prendergast A., Afzal A., Bota E., Abdul S. R., Karmarkar R., Crockett E., Evans L., Appleton B., Griffiths E., Dada O., Kulkarni R., Albirnawi H., Gravestock P., Vincenti C., Taribagil S., Dent B., Tse C., Clayton B., Burdekin E., Bannister L., Alam I., Gray J., Mactier M., Pollock A., Gough V., Kanchustambam S. R., Ridgway M., Arujunan K., Gopalswamy S., Monteiro De Barros J., Lyons T., Griffith D., Awan A. K., Latif J., Bandlamudi N., Bhatti I., Raptis D. A., Machairas N., Pissanou T., Mestre-Costa J., Hidalgo Salinas C., Pollok J. M., Al-Ardah M., Watson-Jones E., Rontree-Carey T., Boyce T., Hawkin P., Elmaradny A., Ross K., Adu-Peprah E., Pinto K., Dunne D., McCready R., Nita G., Szatmary P., Tay V. L., Rajput K., Rajendran I., Chaudhury M., Zambas G., Swaminathan C., Atif Q. A. A., Barrow T., Williams O., Malik A., Conroy S., Lindley S., Gilmore K., Boden E., Richards S. K., Hraishawi I., Polak P., McLaughlin D., Deeny D., Shuttleworth R., Harris A., Peilober-Richardson A., Morris G. C., Sara X., Almourad H., Ang Y., Smyth R., Ding D., Foster J., Bond A., Kumar Y., Ahmad A., Radoi D., Alkaili-Alyamani A., Balakrishnan S., Satchidanand R. Y., Danwaththa Liyanage A. S., Blake I., Ransome M., Weerasinghe C., Kenington C., Mayo K., Mohammed M., Cockbain A. J., Peckham-Cooper A., McCauley G., Gordon C., Smith A., Hawkins W., Chakravartty S., Baillie C., Kenny R., Kumar A., Koimtzis G., Bellamy E., Menon A., Kanakala A., Nevins E. J., Madhavan A., Thulasiraman S., France K., O'Connor A., Idama D., Raslan C., Sridhar S., Parveen M., Mubashar T., Jarvis S., Cakmak I., Wright C., Andrews S., Abdelsaid K Abdul Aal Y., Jayasankar B., Morilla J., Shehata M., Subba N., Tewari N., El-Sayed C., Somaie D., Beheiry N., Douka E., Arumugam S., Wijetunga I., Leivers E., Ibrahim B., Khan K., Wheat J., Christopher J., Barnett R., Elberm H., Booker J., Ashai S., Berry D., Luhmann A., Sgro A., Rashid M. M., Galea M., Jeyakumar J., Marriott P., Zafar S., Baker A., Yershov D., Galanopoulos G., Jordan R., Peinado Garcia C., Anyaugo N., Bath M. F., Omatseye J., Roberts L., Argyriou E. O., Machesney M., Parmar C., Clark S., Khalil H., Unsworth S., Mlotshwa M., Ayoub N., Aboelkhair A., Iosif E., Mohamed N., Reynolds E., Mackender E., Robinson D., Mufti W., Fischkoff K., Coleman N., Anantha Sathyanarayana S., Deutsch G., Giangola M., Lin D., Weiss M., Chung C., Nguyen A., Mueller J., Dabit M., Gordon J., McGuire E., Rashid O., Georgi E., Gallo M., Kunstman J. W., Peters N. V., O'Connor R., Bhattacharya B., Onkendi E., Santos A. P., Richmond R., Warren M., Zhang K., Broderick R., Clary B., Horgan S., Doucet J., Liepert A., Harmon L., McCall C., Sham J. G., Williams E., Labadie K. P., Clark N. M., Dickerson L. K., Hammill C. W., Williams G., Kushner B., Cos H., Zarate Rodriguez J., Bailey K., Al-Raimi I. M. N., Al-Zazay K., Ahmed Mohammed Al-Mahdi S., Mohammed Aldowbli S., Al-Shehari M., Shream S., Al-Ameri S., Aeed M., Aldawbali M., Alsayadi R., Alsayadi M., Spiers H.V.M., Kouli O., Ahmed W.U., Varley R., Ahari D., Argus L., McLean K.A., Kamarajah S.K., Coe P., Griffiths E.A., Chan A.K.C., Macutkiewicz C., Jamdar S., Wilson M., Fullwood C., Toogood G., McLean K., Ahmed W., Gilchrist A., Goldsworthy M., Rashid M., Pockney P., Varela J., Brindl N., Ramirez J., Marafante C., Iwao Y., Ghzawi A., Elhadi M., Gacaferi H., Varghese C., Adeyeye A., Alser O., Teh C., Prieto M., Hasan A., Al-Naggar H., Salgado R., Veracierto F., Lancelotti T., Solinas D., Oddi R., Garcia F.W., Mazza Diez E., Andrade Ramirez M.R., Bracco R., Fernandez D., Maraschio M.A., Obeide L., Giordano E., Alcaraz A., Marani M.A., Aguirre N., Luna F., Francesconi M., Chiham F., Ramos Cossio R., Alvarez F.A., Pantoja Pachajoa D.A., Mandojana F., Merlo I.G., Gonzalez M.H., Cervelo G., Puma R., Vardaro G.F., Davis A., Jurat D., Guenoff C., Raubenheimer K., Goddard K., Brown K., Wegrecki K.J., Cheung H.Y.C., Yang M., Cheung H., Siddiqui J., Ahn J.H., Huynh R., Lam Y.H., Afzal M., Ong B.S., Chua M.Y.M., Ly K., Thomson J.E., Watson D., Dawson A.C., Drane A., Van Ruyven S., Lun E.W.Y., Ferguson M., Jeong J.Y., De Silva C., Wills V., Gundara J., McCourt E., Bong C., Tabone R., Wong W.J., Gray A., Koh D., Pollock M., Singhal S., Smith R., Dudi-Venkata N.N., Kanhere H., Stranz C., Seow W., Mansour L.T., Wormald J., Loveday B.P.T., Thomson B., O'Donnell T., Milenkovski N., Herath M., Trochsler M., Farfus A., Maddern G., Bunjo Z., Kuan L.L., Atanasov G., Dawson A., Lun E., Samadov E., Namazov I., Asgarov M., Ibrahimli A., Srinivasan M., Saeed M.F., Aljawder H., Juma I., Coimbra F.J., Marques N., Casteleins W.A., Petruzziello A., Jabur G., Rodriguez J.F.P., Buso P.L., Mackenzie S., Hsiao M., Sljivic I., Tecson A., Karanicolas P.J., Roke R., Moon J., Butler E.V., Riquelme F., Yanez M., Catan F., Uribe M., Carriel F., Oppliger F., Paredes A., Daroch D., Aguayo J.C., Perez Rivera C.J., Acosta Buitrago L.M., Kadamani Abiyomaa A., Mosquera Paz M.S., Cabrera P., Corso J., Ozcay N., Ozant A., Arslan K., Besim H., Almezghwi H., Azzam A.Y., Bessa S., El-Sayes I., Badawy A., Wael M., El-Gendi A., Azab M.A., Fayed M., El Kassas M., Gamal M., Tawheed A., Al Shafie A., Emile S., Elfallal A., Elfeki H., Shalaby M., Sakr A., Elbahnasawy M., Shama M., Abdel-Elsalam W., Abd-Elsalam S., Escobar Dominguez J.E., Medrano F., Gaitan S., Escalon Gonzalez O.M., Alfaro Varela J.C., Cea M., Interiano M., Cabrera B., Lakkis Z., Georges P., Antonot C., Magnin J., Kamphues C., Lauscher J.C., Schineis C., Loch F.N., Lee L.D., Beyer K., Bouchagier K., Galanis I., Bartziotas D., Lostoridis E., Tourountzi P., Nagorni E.A., Charalabopoulos A., Baili E., Kyros E., Vagios I., Skotsimara A., Liakakos T., Alexandrou A., Papalampros A., Papadopolous V., Tooulias A., Kentarchos I., Christou C., Tsoulfas G., Tale-Rosales L.F., Lopez Muralles I., Melendez H., Bran G., Monroy Mahecha F.A., Contreras J.R., Porras D.E., Paiz E., Soto E.R., Ixcayau Hernandez J.R., Gupta A., Rajput D., Kumar N., Mani R., Kant R., Sonkar A.A., Anand A., Agrawal M.K., Gaurav K., Tripathi M., Sikora S., Bharathy K., Kumar Rangapa M., Khuller D.S., Bhojwwani R., Ayyar S., Jain N., Mehraj A., Hussain F., Nazir I., Shah M., Chowdri N.A., Hilmi A., Argenio G., Atelli P., Palladino E., Armellino M.F., Tamini N., Nespoli L.C., Degrate L., Angrisani M., Carissimi F., Bordoni P., Fleres F., Clarizia G., Spolini A., Franzini M., Cucinotta E., Badessi G., Mazzeo C., Viscosi F., Pintabona G., Campagnaro T., Poletto E., Turri G., Ruzzenente A., Conci S., Guglielmi A., Feo C., Fabbri N., Fazzin M., Giaccari S., Feo C.V., Massani M., Pelizzo P., Colella M., Tutino R., Cappellacci F., Medas F., Canu G.L., Erdas E., Calo P.G., Porcu A., Perra T., Scanu A.M., Feo C.F., Fancellu A., Germani P., Giunta C., Biloslavo A., Abdallah H., Aizza G., Barberis A., Belli F., Santoliquido M., Filauro M., Canonico G., Nelli T., Di Martino C., Capezzuoli L., Anastasi A., Bressan L., Cortinovis S., Nagliati C., Colombo F., Ferrario L., Bondurri A., Guerci C., Maffioli A., Catena F., Perrone G., Giuffrida M., Morini A., Annicchiarico A., Gallo G., Carpino A., Ferrari F., De Paola G., Sammarco G., Callari C., Licari L., Sorce V., Di Miceli D., Lovisetto F., Zonta S., Chessa A., Fiorini A., De Manzoni Garberini A., Angelini E., Moggia E., Murgese A., Mungo S., Birolo S.L., Garino M., Pipitone Federico N.S., Muratore A., Lunghi E.G., Calabro M., Cianci P., Enrico R., Capuzzolo S., Cafagna L., Minafra M., Sasia D., Gattolin A., Migliore M., Rimonda R., Travaglio E., De Marco G., Elter C., Bargellini T., D'Amico S., Zambonin D., Caponi A., Calini G., Puggioni A., Bresadola V., Zalla T., Cantafio S., Feroci F., Romoli L., Giudicissi R., Picciariello A., Papagni V., Dibra R., Altomare D.F., Pinotti E., Montuori M., Baronio G., Tonini V., Sartarelli L., Gori 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Alezabi, B., Shuwayyah, A., Alkamkhe, Aas, Aboulqasim, I., Atiyah, H., Alfagi, Raa, Abdulmula, A., Bouhuwaish, A., Samer, A., Salim, R., Aboazamazem, H., Almiqlash, B., Biala, M., Alganimi, W., Ghamgh, R., Ben Omar, N., Alsoufi, A., Aldreawi, M., Saleim, N., Sowan, F., Saleem, H., Ahmed, Aqueelah, Samalavičius, Narimantas Evaldas, Aliosin, O., Dailidėnas, S., Dulskas, Audrius, Buckus, Bronius, Kuliešius, Z., Bradūnaitė, R., Dominguez-Rosado, I., Buerba, G A, Posadas-Trujillo, O E, Alfaro-Goldaracena, A., Cortes, R., Mercado, M A, Beristain-Hernandez, J L, Mora-Munoz, V S, Mena-Bedolla, J M, Palacios Ramirez, A R, Astorga Medina, M M, Van Aert, G., Ombashi, S., Spillenaar Bilgen, R., Vos, D., Besselink, M., Alberts, V., Busch, O., Bemelman, W., Boermeester, M., Daams, F., Gordinou De Gouberville, M., Van Duijvendijk, P., De Graaff, M., Baaij, J., Gans, S., Bos, K., Goudsmit, B., Den Dekker, B., Braat, A., Kuijpers, A., Breukers, S., Borel Rinkes, I., Andel, D., Hayes, T., Carson, D., Bhat, S., van der Have, J., Anderson, C., Bissett, I., Windsor, J., Elliott, B M, Scowcroft, H., Mclauchlan, J., Ritchie, D., Jeffery, F., Connor, S., Xu, W., Mashlan, H., Thirayan, V., Ly, J., Mcguinness, M J, Ferguson, L., Watt, I., Harmston, C., Akinmade, A., Enoch, E., Kayode-Nissi, V., Ogundele, I., Ayoade, B A, Adekoya, A., Nwokoro, C., Opadeyi, A., Yusuf, A., Ojajuni, A., Adepoju, O., Muhammad Dauda, Maigatari, Keffi Mubarak, Musa, Lawal, Khalid, Muhammad, Daniyan, Salonga, D., Sael, N A, Rey, C M, Pestano, M., Tan, D., Bangayan, N R, Sy, D K, Ang, D., Bernardo, E., Chua, J P, Alharthi, M., Bukhari, W., Bakier Mohammed, K., Al Athath, S., Ghunaim, M., Saiedi, H., Sultan, N., Farsi, A., Basendowah, M., Malibary, N., Jaloun, H., Altalhi, Db, Organjee, A., Moamena, M., Al Zaidi, T M, Alyami, M., Alqannas, M., Al-Urfan, M., Elawad, A., Alawadhi, A., Alalawi, Y., Alqarni, A., Alqahtani, B., Alayed, A., Alsobaie, K., Adi, H., Elhaj, M., Dehlawi, A., Behairy, G., Khaled, I., Kmezic, S., Radenkovic, D., Aleksic, L., Markovic, V., Pejovic, I., Antic, A., Kalkan, M., Vujanovic Gadjanski, O., Dusan, S., Marčetić, B., Thiruchelvam, N., Chiow, Akh, Lee, L S, Mun, Dyc, Tan, E K, Koh, Y X, Loh, W L, Wang, Z., Chan, C Y, Ibrahim, M., Hamid, Hks, Idres, R., Idris, M., Mohammed, O., Ayran, S., Sinan, A H, Ozben, V., Aytac, E., Aliyeva, Z., Mutlu, A U, Bilgin, I A, Karahasanoglu, T., Hamzaoglu, I., Bozkirli, B., Uprak, T K, Kotan, T., Coskun, M., Kara, Y., Somuncu, E., Kocatas, A., Bozkurt, M A, Demirli Atici, S., Kaya, T., Sert, I., Emiroglu, M., Jaffar, M., Younis, M U, Aziz, T., Ikram, F., Sandal, M., Al Madhloum Al Suwaidi, F., Alshaikh, M O, Saber, A., Khammas, A., Nessa, A., Jardine, R., Nicol, L., Clark, C., Mcgee, A., Alkari, B., Feretis, M., Antakia, R., Georgiades, F., Moneim, J., O'Neill, R., Balakrishnan, A., Lunevicius, R., Sud, A., Moutsos, I., Gomez, D., Shahid, S., Majeed, T., Ibrahim, Wkg, Kadum, K., Melia, R., Magee, C., Chicken, D W, Kumar, S., Alshibshoubi, M., van Laarhoven, S., Dewi, F., Williams, J., Byrne, B., Wilkerson, P., Tang, C B, Farhangmehr, N., Jonas, A., Charavanamuttu, V., Almeida, K., Efthimiou, E., Boardley, J., White, A., Butt, M A, Menzies, D., Gundkalli, Z., Hassanzadeh-Baboli, D., Jones, O., Mistry, P., Saha, S., Gerrard, A., Evans, J., Rajeev, S., Ali, W., Ross, E., Gilliam, A., Hitchins, C., Emslie, K., Spellar, K., Sked, H., Briggs, C., Brown, L., A Hemadasa, N., Apollos, J R, Belgaumkar, A., Tawfik, A., Brewin, L., Oyewole, B., Wadhawan, H., Massie, E., Rutherford, D., Mcgivern, K., Mcelroy, L., De'Ath, H D, Tobbal, M., Nagendram, S., Patel, P., Handa, S., Houghton, G., Sundaralingam, S S, Parker, J., Morgan, R., Gala, T., Ibrahim, S., Mithany, R., Abdelkarim, M., Holroyd, D., Thomas, R., Mclennan, E., Boardley, R., Jamieson, N B, Ebied, H., Gossage, J., Davies, A., Wheatstone, S., Jawad, Z., Jiao, L., Rajagopal, P., Sodergren, M., Lami, M., Wiberg, A., Bond-Smith, G., Gemmill, E., Lenzi, E., Sapre, D., Herrod, P., Boyd-Carson, H., Garcea, G., Issa, E., Jackson, A., Fashina, T., Pan, H., Farquharson, B., Shafiq, H., Emanuel, O., Mahdi, S., Jeyarajah, S., Finch, L., Whiting, G., Pigott, L., Martin, J., Siriwardena, A., Beatson, K., Abawi, L., Lam, W., Rea, W., Andrews, B., Al-Sarireh, B., Soliman, F., Burridge, J., Jenvey, C., Hammoda, M., Hollyman, M., Merker, L., Richards, J., Sukumaran, V., Rogers, S., Payne, C., Bibi, S., Raza, K., Ul Ain, N., Dronamraju, S., Patil, S., Nachimuthu, S., Ravindran, S., Patel, S., Ivanov, B., Patel, M., Ejtehadi, F., Jebamani, J., Akhter Rahman, M M, Woodun, H., De Prendergast, A., Afzal, A., Bota, E., Abdul, S R, Karmarkar, R., Crockett, E., Evans, L., Appleton, B., Griffiths, E., Dada, O., Kulkarni, R., Albirnawi, H., Gravestock, P., Vincenti, C., Taribagil, S., Dent, B., Tse, C., Clayton, B., Burdekin, E., Bannister, L., Alam, I., Gray, J., Mactier, M., Pollock, A., Gough, V., Kanchustambam, S R, Ridgway, M., Arujunan, K., Gopalswamy, S., Monteiro De Barros, J., Lyons, T., Griffith, D., Awan, A K, Latif, J., Bandlamudi, N., Bhatti, I., Raptis, D A, Machairas, N., Pissanou, T., Mestre-Costa, J., Hidalgo Salinas, C., Pollok, J M, Al-Ardah, M., Watson-Jones, E., Rontree-Carey, T., Boyce, T., Hawkin, P., Elmaradny, A., Ross, K., Adu-Peprah, E., Pinto, K., Dunne, D., Mccready, R., Nita, G., Szatmary, P., Tay, V L, Rajput, K., Rajendran, I., Chaudhury, M., Zambas, G., Swaminathan, C., Atif, Qaa, Barrow, T., Williams, O., Malik, A., Conroy, S., Lindley, S., Gilmore, K., Boden, E., Richards, S K, Hraishawi, I., Polak, P., Mclaughlin, D., Deeny, D., Shuttleworth, R., Harris, A., Peilober-Richardson, A., Morris, G C, Sara, X., Almourad, H., Ang, Y., Smyth, R., Ding, D., Foster, J., Bond, A., Kumar, Y., Ahmad, A., Radoi, D., Alkaili-Alyamani, A., Balakrishnan, S., Satchidanand, R Y, Danwaththa Liyanage, A S, Blake, I., Ransome, M., Weerasinghe, C., Kenington, C., Mayo, K., Mohammed, M., Cockbain, A J, Peckham-Cooper, A., Mccauley, G., Gordon, C., Smith, A., Hawkins, W., Chakravartty, S., Baillie, C., Kenny, R., Kumar, A., Koimtzis, G., Bellamy, E., Menon, A., Kanakala, A., Nevins, E J, Madhavan, A., Thulasiraman, S., France, K., O'Connor, A., Idama, D., Raslan, C., Sridhar, S., Parveen, M., Mubashar, T., Jarvis, S., Cakmak, I., Wright, C., Andrews, S., Abdelsaid K Abdul Aal, Y., Jayasankar, B., Morilla, J., Shehata, M., Subba, N., Tewari, N., El-Sayed, C., Somaie, D., Beheiry, N., Douka, E., Arumugam, S., Wijetunga, I., Leivers, E., Ibrahim, B., Khan, K., Wheat, J., Christopher, J., Barnett, R., Elberm, H., Booker, J., Ashai, S., Berry, D., Luhmann, A., Sgro, A., Rashid, M M, Galea, M., Jeyakumar, J., Marriott, P., Zafar, S., Baker, A., Yershov, D., Galanopoulos, G., Jordan, R., Peinado Garcia, C., Anyaugo, N., Bath, M F, Omatseye, J., Roberts, L., Argyriou, E O, Machesney, M., Parmar, C., Clark, S., Khalil, H., Unsworth, S., Mlotshwa, M., Ayoub, N., Aboelkhair, A., Iosif, E., Mohamed, N., Reynolds, E., Mackender, E., Robinson, D., Mufti, W., Fischkoff, K., Coleman, N., Anantha Sathyanarayana, S., Deutsch, G., Giangola, M., Lin, D., Weiss, M., Chung, C., Nguyen, A., Mueller, J., Dabit, M., Gordon, J., McGuire, E., Rashid, O., Georgi, E., Gallo, M., Kunstman, J W, Peters, N V, O'Connor, R., Bhattacharya, B., Onkendi, E., Santos, A P, Richmond, R., Warren, M., Zhang, K., Broderick, R., Clary, B., Horgan, S., Doucet, J., Liepert, A., Harmon, L., McCall, C., Sham, J G, Williams, E., Labadie, K P, Clark, N M, Dickerson, L K, Hammill, C W, Williams, G., Kushner, B., Cos, H., Zarate Rodriguez, J., Bailey, K., Al-Raimi, Imn, Al-Zazay, K., Ahmed Mohammed Al-Mahdi, S., Mohammed Aldowbli, S., Al-Shehari, M., Shream, S., Al-Ameri, S., Aeed, M., Aldawbali, M., Alsayadi, R., and Alsayadi, M.
- Subjects
Pandemic ,SARS-CoV-2 ,acute cholecystitis ,COVID-19 ,management ,Cholecystitis, Acute ,acute ,General Medicine ,pandemics ,cholecystectomy ,humans ,cholecystitis ,Cholecystectomy ,Humans ,Pandemics ,Settore MED/18 - Chirurgia Generale ,Human - Abstract
Background This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic.
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- 2022
24. A Parametric Study Analysing the Effect of Bolster Suspension Parameters on the Carbody Dynamic Response in a Freight Wagon Fitted with Three-Piece Bogie
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Pandey, M. and Bhattacharya, B.
- Abstract
The carbody of a rail vehicle has different natural frequencies in empty and loaded conditions in different fundamental vibration modes. Moreover, the vertical and lateral track excitation in vertical and lateral directions have several dominant frequency modes. In the case of a freight wagon fitted with three-piece freight bogie, the problem of suspension design is more challenging. The lateral stiffness of the bolster springs is dependent on load, as well as to the spring group height. The damping force is load dependent and is dependent on stiffness/deflection of the bolster springs, the stiffness of the friction control spring (snubber springs) and the coefficient of friction between the surface of the wedge block and the side frame/bolster. There is large variation between the tare weight and the gross weight. This paper attempts to analyse the effect of different bolster suspension parameters of a three-piece bogie on the freight carbody dynamic responses in various modes. Multibody models in New and Untried Cars Analytic Regime Simulation (NUCARS) consisting of eleven bodies and seventy connections have been used for the study. The models for empty and loaded wagon contain forty-nine degrees and fifty-two degrees of freedom, respectively. Attempts have been made to validate the simulation models as per the methodology provided by the EN 14363:2016 standard. Effect of 36 bolster suspension combinations has been analysed. Snubbing force plays a very important role in the dynamic response analysis. The simulation study suggests that the choice of wedge friction coefficient is challenging as it is affecting the dynamic responses in various modes differently. High coefficient of friction is adversely affecting the vertical and lateral dynamic responses in empty condition and lateral dynamic response in loaded condition. But it is favourably affecting the vertical response in loaded condition. Increase in lateral stiffness is found to be affecting the lateral dynamic response adversely. Selection of the bogie vertical stiffness is very much involved. The optimisation of carbody dynamic response for a freight wagon fitted with three-piece bogie can be formulated as a many-objective optimisation problem wherein four of the representative dynamic response acceleration modes, i.e. vertical acceleration on straight track in empty and loaded condition, lateral acceleration on 2° curve in empty and loaded condition, may be selected as objective functions.
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- 2024
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25. Modelling of evapotranspiration using land surface energy balance and thermal infrared remote sensing
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Singh, R. P., Paramanik, S., Bhattacharya, B. K., and Behera, Mukunda D.
- Abstract
Accurate estimation of crop evapotranspiration (ET) is a key factor in crop water scheduling. The objective of this study was to estimate ET from the high-resolution satellite remote sensing data with integration ofin situobservation. The surface energy balance model, Mapping Evapotranspiration with Internalized Calibration (METRIC) was utilised in this study for its simplicity, advantages, and effectiveness. It is a one-source model, which calculates the net radiation, soil heat flux, and sensible heat flux at every pixel level, and estimates the latent heat flux as the residual term in that energy budget equation. Intermediate steps like calculation of NDVI, surface temperature, and albedo served as important input parameters for ET estimate. Landat-8 satellite images were used to compute the ET in paddy field near CRRI, Cuttack, Odisha state in eastern India. Results indicated that the METRIC algorithm provided reasonably good ET over the study area with marginal overestimation in comparison to field observation by eddy covariance data. The satellite-based ET estimates represented in spatial scale has potential in improving irrigation scheduling and precise water resource management at local scales.
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- 2024
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26. Size Determination in Chemically Grown PbS Nano Particles Using Different Theoretical Approach
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Kumar, Vinay, primary, Kumar, Pranav, additional, Bhattacharya, B., additional, Alheety, Mustafa A., additional, Zakariya'u, Ibrahim, additional, and Kumar, Pawan, additional
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- 2023
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27. Algorithms for Sample Preparation with Microfluidic Lab-on-Chip
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Bhattacharjee, S., Bhattacharya, B. B., and Krishnendu Chakrabarty
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- 2022
28. Extreme on-demand contactless modulation of elastic properties in magnetostrictive lattices
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Singh, A, primary, Mukhopadhyay, T, additional, Adhikari, S, additional, and Bhattacharya, B, additional
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- 2022
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29. Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India
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Yeditha, P. K. Rathinasamy, M. Neelamsetty, S. S. Bhattacharya, B. Agarwal, A. and Yeditha, P. K. Rathinasamy, M. Neelamsetty, S. S. Bhattacharya, B. Agarwal, A.
- Abstract
Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in Nash-Sutcliffe Efficiency Coefficient (NSC) values of 0.68 and 0.67, while ELM models for Ma
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- 2022
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30. Ultrasonic-Assisted Extraction of Phytochemicals from Eggplant Peel: Physicochemical and Antibacterial Evaluation
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Sharma, S., primary, Ojha, A. K., additional, Chakkaravarthi, S., additional, and Bhattacharya, B., additional
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- 2022
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31. Change in charge carrier dynamics by incorporating ionic liquid into poly ethylene oxide–based sodium acetate polymer electrolytes
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Gupta, Sandhya, primary, Singh, Pramod K, additional, and Bhattacharya, B, additional
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- 2022
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32. Investigation of satellite rainfall-driven rainfall–runoff model using deep learning approaches in two different catchments in India
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Yeditha, P., Rathinasamy, M., Neelamsetty, S., Bhattacharya, B., and Agarwal, A.
- Abstract
Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.
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- 2022
33. On strongly almost convergence of doubles equences via complex uncertain variable
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Nath, J., primary, Tripathy, B. C., additional, and Bhattacharya, B., additional
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- 2022
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34. Active multi-physical modulation of Poisson’s ratios in composite piezoelectric lattices: On-demand sign reversal
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Singh, A., primary, Mukhopadhyay, T., additional, Adhikari, S., additional, and Bhattacharya, B., additional
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- 2022
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35. TRISHNA: An Indo-French Space Mission to Study the Thermography of the Earth at Fine Spatio-Temporal Resolution
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Roujean, J.-L., primary, Bhattacharya, B., additional, Gamet, P., additional, Pandya, M. R., additional, Boulet, G., additional, Olioso, A., additional, Singh, S. K., additional, Shukla, M. V., additional, Mishra, M., additional, Babu, S., additional, Raju, P. V., additional, Murthy, C. S., additional, Briottet, X., additional, Rodler, A., additional, Autret, E., additional, Dadou, I., additional, Adlakha, D., additional, Sarkar, M., additional, Picard, G., additional, Kouraev, A., additional, Ferrari, C., additional, Irvine, M., additional, Delogu, E., additional, Vidal, T., additional, Hagolle, O., additional, Maisongrande, P., additional, Sekhar, M., additional, and Mallick, K., additional
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- 2021
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36. Field-scale Assessment of Sugarcane for Mill-level Production Forecasting using Indian Satellite Data
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Kumar, Mukesh, primary, Das, Ayan, additional, Chaudhari, K. N., additional, Dutta, Sujay, additional, Dakhore, K. K., additional, and Bhattacharya, B. K., additional
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- 2021
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37. Geometric systems of unbiased representatives
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Banik, A., Bhattacharya, B. B., Bhore, S., and Leonardo Martínez-Sandoval
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Computational Geometry (cs.CG) ,FOS: Computer and information sciences ,010102 general mathematics ,0102 computer and information sciences ,16. Peace & justice ,01 natural sciences ,Computer Science Applications ,Theoretical Computer Science ,010201 computation theory & mathematics ,Signal Processing ,FOS: Mathematics ,Computer Science - Computational Geometry ,Mathematics - Combinatorics ,Combinatorics (math.CO) ,0101 mathematics ,Information Systems - Abstract
Let $P$ be a set of points in $\mathbb{R}^d$, $B$ a bicoloring of $P$ and $\Oo$ a family of geometric objects (that is, intervals, boxes, balls, etc). An object from $\Oo$ is called balanced with respect to $B$ if it contains the same number of points from each color of $B$. For a collection $\B$ of bicolorings of $P$, a geometric system of unbiased representatives (G-SUR) is a subset $\Oo'\subseteq\Oo$ such that for any bicoloring $B$ of $\B$ there is an object in $\Oo'$ that is balanced with respect to $B$. We study the problem of finding G-SURs. We obtain general bounds on the size of G-SURs consisting of intervals, size-restricted intervals, axis-parallel boxes and Euclidean balls. We show that the G-SUR problem is NP-hard even in the simple case of points on a line and interval ranges. Furthermore, we study a related problem on determining the size of the largest and smallest balanced intervals for points on the real line with a random distribution and coloring. Our results are a natural extension to a geometric context of the work initiated by Balachandran et al. on arbitrary systems of unbiased representatives., Comment: Appears in the Proceedings of the 31st Canadian Conference on Computational Geometry (CCCG 2019)
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- 2022
38. Monitoring cotton crop condition through synergy of optical and radar remote sensing.
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Haldar, Dipanwita, Tripathy, Rojalin, Dave, Viral, Dave, Rucha, Bhattacharya, B. K., and Misra, Arundhati
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REMOTE sensing by radar ,OPTICAL remote sensing ,COTTON ,CROPS - Abstract
Synergistic use of Optical and Microwave remotely sensed data for cotton condition based on biophysical traits, indices from ground measurements and satellite derived reflectance were assessed. Regression models derived for estimating LAI, biomass and VWC using ground based indices were applied to satellite products. LAI, biomass and VWC of cotton with optical VIs as input (R
2 = 0.42, 0.51 and 0.52) respectively were estimated. Correlation between RVI and plant height, crop age and VWC were found between 0.4 and 0.6. Fresh biomass from RVI ranged 100 –4000 gm−2 , dry biomass from NDVI ranged 50–950 gm−2 and VWC 65–85%. Correlation between VIs and RVI was found to be non- significant. A multiple linear regression model using NDVI vs. LSWI and RVI were developed to assess LAI, biomass and VWC. The model predicted LAI with R2 of 0.5 but failed to estimate biomass and VWC. [ABSTRACT FROM AUTHOR]- Published
- 2022
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39. Guest Editor's Note.
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Bhattacharya, B. K.
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ETHICAL decision making ,ARTIFICIAL intelligence ,BUSINESS ethics ,BUSINESS schools ,INDUSTRIAL robots - Abstract
The article focuses on the pervasive influence of Artificial Intelligence (AI) across various sectors, highlighting management, research activities, and business ethics. Topics include the affordability, effectiveness, and potential bias of AI in management, the role of AI in overcoming barriers in research activities, and the transformative effects of AI and Machine Learning (ML) on healthcare services.
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- 2023
40. Revisiting Agrometeorological Research in Context of Climate Change.
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HUDA, A. K. S., MUKHERJEE, A., LELLYETT, S. C., BHATTACHARYA, B. K., and SARKAR, N.
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CLIMATE research ,SATELLITE meteorology ,CLIMATE change ,ENVIRONMENTAL security ,NATURAL resources - Abstract
The article reports that The Snowmelt-Runoff Model (SRM) was used to evaluate the impact of climate change on hydrological aspects of Lidder River Catchment of the Himalayan Region. It was observed that the river has an average discharge of 1082.49 cusecs. The coefficient of determination (R2 ) was varies in the range 0.90-0.95 during model validation period (2013-2018).
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- 2022
41. Suppression of Metastasis and Angiogenesis by Taxifolin Ruthenium- p -cymene Loaded PLGA Nanoparticles in Lung Carcinoma.
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Das A, Bhattacharya B, Gayen S, and Roy S
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- Humans, Animals, Cell Line, Tumor, A549 Cells, Mice, Polylactic Acid-Polyglycolic Acid Copolymer chemistry, Ruthenium chemistry, Ruthenium pharmacology, Vascular Endothelial Growth Factor A metabolism, Cell Proliferation drug effects, beta Catenin metabolism, Tumor Suppressor Protein p53 metabolism, Monoterpenes pharmacology, Monoterpenes chemistry, Angiogenesis, Cymenes pharmacology, Cymenes chemistry, Lung Neoplasms drug therapy, Lung Neoplasms pathology, Nanoparticles chemistry, Quercetin analogs & derivatives, Quercetin pharmacology, Quercetin chemistry, Apoptosis drug effects, Neovascularization, Pathologic drug therapy
- Abstract
Flavonoid-based organometallic complexes were revealed to be novel bioactive compounds. The taxifolin ruthenium- p -cymene nanoparticle (TaxRu-NPs) was produced in this study, and the toxicological assessment was done prior to in vivo chemotherapeutic research. Furthermore, the in vitro chemotherapeutic investigation used the A549 and NCI-H460 lung cancer cell lines. The in vitro study found that TaxRu-NPs induced apoptosis in lung cancer cells and hindered their ability to form colonies and migrate. The in vivo study showed that treatment with TaxRu-NPs restored the histological structure of a normal lung with less hyperplasia and lymphocytic infiltration. Furthermore, the treatment downregulated the angiogenic marker VEGF and the cell survival protein β-catenin and upregulated apoptotic markers like p53 and caspase-3. TaxRu-NPs treatment additionally raised the apoptotic index and decreased cancer cell growth. Finally, TaxRu-NPs effectively alleviate lung cancer by activating p53-mediated apoptosis and preventing angiogenesis and metastasis by decreasing the VEGF/β-catenin pathway.
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- 2024
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42. Comments on "Protective effect of quercetin on rat testes against cadmium toxicity by alleviating oxidative stress and autophagy".
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Bhattacharya B
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- 2024
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43. Unraveling the chemotherapeutic potential of taxifolin ruthenium-p-cymene complex in breast carcinoma: Insights into AhR signaling pathway in vitro and in vivo.
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Das A, Bhattacharya B, Gayen S, and Roy S
- Abstract
Background: Mammary carcinoma is the most frequently diagnosed form of carcinoma in women worldwide. The organometallic compounds showed a prospective anticancer activity. This research explored the anticancer efficacy of taxifolin ruthenium-p-cymene counter to breast cancer., Methods: The anticancer efficacy of the novel organometallic compound was investigated via various in vitro and in vivo techniques using breast cancer cell lines and breast cancer model of rat., Results: Target proteins were identified via pharmacophore analysis, which revealed a high binding affinity towards AhR, EGFR, and β-catenin. The compound induced apoptotic events and prevented cancer cell colony formation. Furthermore, decreased expression of AhR, EGFR, and N-cadherin inhibited cancer cell growth, migration, and proliferation. The compound provoked the cell cycle arrest at sub G0/G1 phase, S phase and G2/M phase and inaugurated the caspase-3 dependent apoptotic events. The in-vivo experimentation displayed the fruitful restoration of breast tissue since the complex treatment in DMBA persuaded breast carcinoma in rat. Moreover, the upstream of p53 and caspase-3 expression along with substantially downstream of vimentin, β-catenin, m-TOR and Akt expression., Conclusions: In conclusion, the compound repressed the cancerous cellular viability, migration, and EMT via modulating the AhR/EGFR/ PI3K transduction pathway and the expression of EMT biomarkers such as N-cadherin, E-cadherin, thus eventually revoked the EMT facilitated metastasis of malignant cells., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024. Published by Elsevier Inc.)
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- 2024
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44. Bandgap formation mechanism in tacticity inspired elastic mechanical metastructures.
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Dwivedi A, Munian RK, Bhattacharya B, and Adhikari S
- Abstract
Tacticity is long known as a significant contributor in changing the chemical and mechanical properties of the polymers drastically. This study explores mechanism of bandgap formation in elastic mechanical metastructures designed with a focus on tacticity. We introduce metabeams, comprising a primary slender beam embedded with short secondary beams featuring end masses at their tips. The investigation delves into the numerically simulated vibration characteristics of metabeams using finite element analysis, with a subsequent comparison to experimental results for fabricated metabeams. Employing a unit-cell design approach that manipulates spatial and physical parameters, we explore a wide range of uniform and non-uniform metabeam configurations based on the distance between secondary beams and distribution of local resonators as per tacticity. Hence, drawing inspiration from tacticity, we extend our investigation to isotactic and syndiotactic metabeams, altering physical parameters (mass) within the unit cell for both configurations. The strategic distribution of end masses on attached secondary beams introduces unique characteristics to isotactic and syndiotactic metabeams, allowing for the modulation of bandgaps without altering the natural frequencies of the resonators in symmetric and anti-symmetric metabeam designs. Our research demonstrates, incorporating tacticity in metabeam design offers a novel and unconventional approach to modulate the bandgap formation mechanism., (© 2024. The Author(s).)
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- 2024
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45. Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.
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Bergquist T, Loomba J, Pfaff E, Xia F, Zhao Z, Zhu Y, Mitchell E, Bhattacharya B, Shetty G, Munia T, Delong G, Tariq A, Butzin-Dozier Z, Ji Y, Li H, Coyle J, Shi S, Philips RV, Mertens A, Pirracchio R, van der Laan M, Colford JM Jr, Hubbard A, Gao J, Chen G, Velingker N, Li Z, Wu Y, Stein A, Huang J, Dai Z, Long Q, Naik M, Holmes J, Mowery D, Wong E, Parekh R, Getzen E, Hightower J, and Blase J
- Subjects
- Humans, United States epidemiology, Algorithms, Post-Acute COVID-19 Syndrome, Cohort Studies, Crowdsourcing, COVID-19 epidemiology, Machine Learning, SARS-CoV-2 isolation & purification
- Abstract
Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID., Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.)., Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events., Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions., Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438., Competing Interests: Declaration of interests Danielle Mowery serves as an unpaid member of the Epic Cosmos Governing Council. Romain Pirracchio received funding from the FDA CERSI grant U01FD005978 and the PCORI grant P0562155 and received a consulting honorarium from Phillips. Martin van der Laan received funding from the NIAID grant 5R01AI074345. Johanna Loomba received contract funding from the NIH RECOVER program. Emily Pfaff received funding from the NIH and PCORI. The views expressed in this manuscript are solely those of the authors and do not necessarily represent those of the National Institutes of Health, the U.S. Department of Health and Human Services or the U.S. government. Qi Long was supported by grants from the NIH., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
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- 2024
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46. Ratiometric Molecularly Imprinted Particle Probes for Reliable Fluorescence Signaling of Carboxylate-Containing Molecules.
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Sun Y, Gawlitza K, Valderrey V, Bhattacharya B, and Rurack K
- Abstract
In addition to sensitivity, selectivity, and portability, chemical sensing systems must generate reliable signals and offer modular configurability to address various small molecule targets, particularly in environmental applications. We present a versatile, modular strategy utilizing ratiometric molecularly imprinted particle probes based on BODIPY indicators and dyes for recognition and internal referencing. Our approach employs polystyrene core particles doped with a red fluorescent BODIPY as an internal standard, providing built-in reference for environmental influences. A molecularly imprinted polymer (MIP) recognition shell, incorporating a green-fluorescent BODIPY indicator monomer with a thiourea binding site for carboxylate-containing analytes, is grafted from the core particles in the presence of the analyte as the template. The dual-fluorescent MIP probe detects fexofenadine as the model analyte with a change in green emission signal referenced against a stable red signal, achieving a detection limit of 0.13 μM and a broad dynamic range from 0.16 μM to 1.2 mM, with good discrimination against other antibiotics in acetonitrile. By selecting a versatile dye scaffold and recognition element, this approach can be extended to other carboxylate-containing analytes and/or wavelength combinations, potentially serving as a robust multiplexing platform.
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- 2024
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47. A single-cell atlas of spatial and temporal gene expression in the mouse cranial neural plate.
- Author
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Brooks ER, Moorman AR, Bhattacharya B, Prudhomme I, Land M, Alcorn HL, Sharma R, Pe'er D, and Zallen JA
- Abstract
The formation of the mammalian brain requires regionalization and morphogenesis of the cranial neural plate, which transforms from an epithelial sheet into a closed tube that provides the structural foundation for neural patterning and circuit formation. Sonic hedgehog (SHH) signaling is important for cranial neural plate patterning and closure, but the transcriptional changes that give rise to the spatially regulated cell fates and behaviors that build the cranial neural tube have not been systematically analyzed. Here we used single-cell RNA sequencing to generate an atlas of gene expression at six consecutive stages of cranial neural tube closure in the mouse embryo. Ordering transcriptional profiles relative to the major axes of gene expression predicted spatially regulated expression of 870 genes along the anterior-posterior and mediolateral axes of the cranial neural plate and reproduced known expression patterns with over 85% accuracy. Single-cell RNA sequencing of embryos with activated SHH signaling revealed distinct SHH-regulated transcriptional programs in the developing forebrain, midbrain, and hindbrain, suggesting a complex interplay between anterior-posterior and mediolateral patterning systems. These results define a spatiotemporally resolved map of gene expression during cranial neural tube closure and provide a resource for investigating the transcriptional events that drive early mammalian brain development., Competing Interests: Competing interests DP is on the scientific advisory board of Insitro. The other authors declare that no competing interests exist.
- Published
- 2024
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48. Applications of Zebrafish Embryo Models to Predict Developmental Toxicity for Agrochemical Product Development.
- Author
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Bianchi E, Bhattacharya B, Bowling AJ, Pence HE, Mundy PC, Jones G, Muriana A, Grever WE, Pappas-Garton A, Sriram S, LaRocca J, and Bondesson M
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- Animals, Genetically Modified, Embryo, Nonmammalian, Gene Expression Regulation, Developmental drug effects, Genetic Markers, Larva genetics, RNA genetics, Zebrafish, Animals, Agrochemicals toxicity, Spine drug effects
- Abstract
The development of safe crop protection products is a complex process that traditionally relies on intensive animal use for hazard identification. Methods that capture toxicity in early stages of agrochemical discovery programs enable a more efficient and sustainable product development pipeline. Here, we explored whether the zebrafish model can be leveraged to identify mammalian-relevant toxicity. We used transgenic zebrafish to assess developmental toxicity following exposures to known mammalian teratogens and captured larval morphological malformations, including bone and vascular perturbations. We further applied toxicogenomics to identify common biomarker signatures of teratogen exposure. The results show that the larval malformation assay predicted teratogenicity with 82.35% accuracy, 87.50% specificity, and 77.78% sensitivity. Similar and slightly lower accuracies were obtained with the vascular and bone assays, respectively. A set of 20 biomarkers were identified that efficiently segregated teratogenic chemicals from nonteratogens. In conclusion, zebrafish are valuable, robust, and cost-effective models for toxicity testing in the early stages of product development.
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- 2024
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49. Retinal vascular pathology in a mouse model of Lafora progressive myoclonus epilepsy.
- Author
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Patil RP, Kumar N, Kaur A, Munian RK, Bhattacharya B, Ganesh S, and Parihar R
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- Animals, Mice, Mice, Inbred C57BL, Male, Myoclonic Epilepsies, Progressive genetics, Myoclonic Epilepsies, Progressive physiopathology, Myoclonic Epilepsies, Progressive pathology, Disease Models, Animal, Lafora Disease genetics, Lafora Disease pathology, Lafora Disease physiopathology, Retinal Vessels pathology
- Abstract
Neurodegenerative diseases (ND) affect distinct populations of neurons and manifest various clinical and pathological symptoms. A subset of ND prognoses has been linked to vascular risk factors. Consequently, the current study investigated retinal vascular abnormalities in a murine model of Lafora neurodegenerative disease (LD), a fatal and genetic form of progressive myoclonus epilepsy that affects children. Here, arterial rigidity was evaluated by measuring pulse wave velocity and vasculature deformations in the retina. Our findings in the LD mouse model indicate altered pulse wave velocity, retinal vascular thinning, and convoluted retinal arteries., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
- Published
- 2024
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50. To close or not to close? Wound management in emergent colorectal surgery, an EAST multicenter prospective cohort study.
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Feather CB, Rehrig S, Allen R, Barth N, Kugler EM, Cullinane DC, Falank CR, Bhattacharya B, Maung AA, Seng S, Ratnasekera A, Bass GA, Butler D, Pascual JL, Srikureja D, Winicki N, Lynde J, Nowak B, Azar F, Thompson LA, Nahmias J, Manasa M, Tesoriero R, Kumar SB, Collom M, Kincaid M, Sperwer K, Santos AP, Klune JR, and Turcotte J
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- Humans, Male, Female, Prospective Studies, Middle Aged, Adult, Aged, Rectum surgery, Rectum injuries, Wound Closure Techniques, Colon surgery, Colon injuries, Surgical Wound Infection epidemiology, Length of Stay statistics & numerical data, Hospital Mortality
- Abstract
Background: This study aimed to determine the clinical impact of wound management technique on surgical site infection (SSI), hospital length of stay (LOS), and mortality in emergent colorectal surgery., Methods: A prospective observational study (2021-2023) of urgent or emergent colorectal surgery patients at 15 institutions was conducted. Pediatric patients and traumatic colorectal injuries were excluded. Patients were classified by wound closure technique: skin closed (SC), skin loosely closed (SLC), or skin open (SO). Primary outcomes were SSI, hospital LOS, and in-hospital mortality rates. Multivariable regression was used to assess the effect of wound closure on outcomes after controlling for demographics, patient characteristics, intensive care unit admission, vasopressor use, procedure details, and wound class. A priori power analysis indicated that 138 patients per group were required to detect a 10% difference in mortality rates., Results: In total, 557 patients were included (SC, n = 262; SLC, n = 124; SO, n = 171). Statistically significant differences in body mass index, race/ethnicity, American Society of Anesthesiologist scores, EBL, intensive care unit admission, vasopressor therapy, procedure details, and wound class were observed across groups. Overall, average LOS was 16.9 ± 16.4 days, and rates of in-hospital mortality and SSI were 7.9% and 18.5%, respectively, with the lowest rates observed in the SC group. After risk adjustment, SO was associated with increased risk of mortality (OR, 3.003; p = 0.028) in comparison with the SC group. Skin loosely closed was associated with increased risk of superficial SSI (OR, 3.439; p = 0.014), after risk adjustment., Conclusion: When compared with the SC group, the SO group was associated with mortality but comparable when considering all other outcomes, while the SLC was associated with increased superficial SSI. Complete skin closure may be a viable wound management technique in emergent colorectal surgery., Level of Evidence: Therapeutic/Care Management; Level III., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)
- Published
- 2024
- Full Text
- View/download PDF
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