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1. Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendation

Author

Hu, Guoqing, Yang, Zhengyi, Cai, Zhibo, Zhang, An, and Wang, Xiang

Subjects
Computer Science - Information Retrieval
Abstract

Recent advancements in generative recommendation systems, particularly in the realm of sequential recommendation tasks, have shown promise in enhancing generalization to new items. Among these approaches, diffusion-based generative recommendation has emerged as an effective tool, leveraging its ability to capture data distributions and generate high-quality samples. Despite effectiveness, two primary challenges have been identified: 1) the lack of consistent modeling of data distribution for oracle items; and 2) the difficulty in scaling to more informative control signals beyond historical interactions. These issues stem from the uninformative nature of ID embeddings, which necessitate random initialization and limit the incorporation of additional control signals. To address these limitations, we propose iDreamRec to involve more concrete prior knowledge to establish item embeddings, particularly through detailed item text descriptions and advanced Text Embedding Models (TEM). More importantly, by converting item descriptions into embeddings aligned with TEM, we enable the integration of intention instructions as control signals to guide the generation of oracle items. Experimental results on four datasets demonstrate that iDreamRec not only outperforms existing diffusion-based generative recommenders but also facilitates the incorporation of intention instructions for more precise and effective recommendation generation.

Published
2024

2. Preference Diffusion for Recommendation

Author

Liu, Shuo, Zhang, An, Hu, Guoqing, Qian, Hong, and Chua, Tat-seng

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Recommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data. Recently, diffusion models (DMs) have gained attention in recommendation for their ability to model complex distributions, yet current DM-based recommenders often rely on traditional objectives like mean squared error (MSE) or recommendation objectives, which are not optimized for personalized ranking tasks or fail to fully leverage DM's generative potential. To address this, we propose PreferDiff, a tailored optimization objective for DM-based recommenders. PreferDiff transforms BPR into a log-likelihood ranking objective and integrates multiple negative samples to better capture user preferences. Specifically, we employ variational inference to handle the intractability through minimizing the variational upper bound and replaces MSE with cosine error to improve alignment with recommendation tasks. Finally, we balance learning generation and preference to enhance the training stability of DMs. PreferDiff offers three key benefits: it is the first personalized ranking loss designed specifically for DM-based recommenders and it improves ranking and faster convergence by addressing hard negatives. We also prove that it is theoretically connected to Direct Preference Optimization which indicates that it has the potential to align user preferences in DM-based recommenders via generative modeling. Extensive experiments across three benchmarks validate its superior recommendation performance and commendable general sequential recommendation capabilities. Our codes are available at \url{https://github.com/lswhim/PreferDiff}.

Published
2024

5. Behaviour of motorcyclists and bicyclists before and after a road safety campaign, China/Comportement des motocyclistes et cyclistes avant et apres une campagne de securite routiere en Chine/Comportamiento de motociclistas y ciclistas antes y despues de una campana de seguridad vial en China

Author

Ning, Peishan, Peng, Ruisha, Zong, Huiying, Schwebel, David C., Xie, Cifu, He, Jieyi, Cheng, Peixia, Li, Li, Rao, Zhenzhen, and Hu, Guoqing

Subjects
International Business Machines Corp., Traffic safety, Motorcyclists, Electric bicycles
Abstract

Introduction Road traffic injuries among motorcyclists and cyclists are a public health concern. In 2019, road traffic crashes killed 237 533 motorcyclists and 66 102 cyclists worldwide. (1) In the [...], Objective To examine changes in red-light running and distracted riding among motorcyclists and cyclists before and after the 2020 implementation of the One Helmet, One Seatbelt campaign in China. Methods We obtained 192 hours of film before (2019) and after (2021) implementation of the campaign in eight road intersections in Changsha. We calculated percentages and ratios of red-light running and distracted riding. To assess the associations between these traffic behaviours and the campaign, we used multivariable logistic regression models to calculate adjusted odds ratios (aOR). Findings We filmed 5256 motorcyclists and cyclists in 2019 and 6269 in 2021. Red-light running decreased from 45.1% to 41.5% during this period (ratio: 0.92; 95% confidence interval, CI: 0.88-0.96), while distracted riding increased from 3.5% to 5.0% (ratio: 1.42; 95% CI: 1.19-1.69). After adjusting for covariates, male riders were more likely to run a red light compared to female riders (aOR: 1.28; 95% CI: 1.06-1.55). Red-light running was also more likely among electric bicycle riders (aOR: 1.46; 95% CI: 1.10-1.95) and motorcyclists (aOR: 1.47; 95% CI: 1.13-1.90) compared to traditional cyclists. All types of riders were less prone to run a red light during peak hours than off-peak hours (aOR: 0.85; 95% CI: 0.73-0.99). Distracted riding was more common on weekends compared to weekdays (aOR: 3.01; 95% CI: 2.02-4.49). Conclusion China's national road safety campaign, which focuses on helmet and seatbelt use, was associated with reduced red-light running. Strict enforcement and targeted modifications could improve the campaign's effectiveness. Objectif Etudier les changements en matiere de franchissement des feux rouges et de distraction chez les motocyclistes et cyclistes avant et apres la mise en reuvre, en 2020, de la campagne <> (Un casque, une ceinture) en Chine. Methodes Nous nous sommes procure 192 heures de video filmees a huit carrefours routiers a Changsha avant (2019) et apres (2021) la mise en reuvre de la campagne. Nous avons ensuite calcule les pourcentages et ratios de franchissement des feux rouges et de cyclistes ou motocyclistes distraits. Pour determiner le lien entre ces comportements de conduite et la campagne, nous avons employe des modeles de regression logistique multivariee nous permettant de calculer les odds ratios ajustes (ORA). Resultats En 2019, 5256 motocyclistes et cyclistes ont ete filmes, contre 6269 en 2021. Le franchissement des feux rouges a diminue, passant de 45,1% a 41,5% durant cette periode (ratio: 0,92; intervalle de confiance de 95%, IC: 0,88-0,96), tandis que la distraction a augmente, passant de 3,5% a 5,0% (ratio: 1,42; IC de 95%: 1,19-1,69). Apres avoir pris en compte les covariables, les conducteurs etaient davantage susceptibles de franchir un feu rouge que les conductrices (ORA: 1,28; IC de 95%: 1,06-1,55). Ce franchissement etait egalement plus frequent chez les velos electriques (ORA: 1,46; IC de 95%: 1,10-1,95) et les motocyclistes (ORA: 1,47; IC de 95%: 1,13-1,90) que chez les cyclistes traditionnels. Tous les types de deux-roues etaient moins enclins a franchir un feu rouge pendant les heures de pointe que pendant les heures creuses (ORA: 0,85; IC de 95%: 0,73-0,99). Enfin, les cyclistes ou motocyclistes distraits etaient plus nombreux le week-end que la semaine (ORA: 3,01; IC de 95%: 2,02-4,49). Conclusion La campagne nationale de securite routiere en Chine, axee sur le port du casque et de la ceinture de securite, a entraine une baisse du nombre de franchissements des feux rouges. Une application stricte des regles ainsi que des modifications ciblees pourraient ameliorer l'efficacite de cette campagne. Objetivo Examinar los cambios en la conduccion distraida y el cruce de semaforos en rojo entre motociclistas y ciclistas antes y despues de la implementacion en 2020 de la campana <> en China. Metodos Se obtuvieron 192 horas de grabacion antes (2019) y despues (2021) de la implementacion de la campana en ocho intersecciones viales de Changsha. Se calcularon los porcentajes y las razones de saltarse semaforos en rojo y de conduccion distraida. Para evaluar las asociaciones entre estos comportamientos de trafico y la campana, se utilizaron modelos de regresion logistica multivariable para calcular las razones de probabilidades ajustadas (aOR). Resultados Se filmaron 5256 motociclistas y ciclistas en 2019 y 6269 en 2021. Saltarse los semaforos en rojo disminuyo del 45,1% al 41,5% durante este periodo (razon: 0,92; intervalo de confianza del 95%, IC: 0,88-0,96), mientras que la conduccion distraida aumento del 3,5% al 5,0% (razon: 1,42; IC del 95%: 1,19-1,69). Tras ajustar por covariables, los conductores varones tenian mas probabilidades de saltarse un semaforo en rojo que las conductoras (aOR: 1,28; IC del 95%: 1,06-1,55). Ademas, los conductores de bicicletas electricas (aOR: 1,46; IC del 95%: 1,10-1,95) y motociclistas (aOR: 1,47; IC del 95%: 1,13-1,90) eran mas propensos a saltarse semaforos en rojo que los ciclistas tradicionales. Todos los tipos de conductores eran menos propensos a saltarse un semaforo en rojo durante las horas de maxima afluencia que fuera de ellas (aOR: 0,85; IC del 95%: 0,73-0,99). La conduccion distraida fue mas frecuente los fines de semana que entre semana (aOR: 3,01; IC del 95%: 2,02-4,49). Conclusion La campana nacional de seguridad vial de China, centrada en el uso del casco y el cinturon de seguridad, se asocio a una reduccion de los conductores que se saltan los semaforos en rojo. Una aplicacion estricta y modificaciones especificas podrian mejorar la eficiencia de la campana.

Published
2024
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9. Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer

Author

Gao, Xiangneng, Ruan, Shulan, Shi, Jun, Hu, Guoqing, and Wei, Wei

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

In the treatment of ovarian cancer, precise residual disease prediction is significant for clinical and surgical decision-making. However, traditional methods are either invasive (e.g., laparoscopy) or time-consuming (e.g., manual analysis). Recently, deep learning methods make many efforts in automatic analysis of medical images. Despite the remarkable progress, most of them underestimated the importance of 3D image information of disease, which might brings a limited performance for residual disease prediction, especially in small-scale datasets. To this end, in this paper, we propose a novel Multi-View Attention Learning (MuVAL) method for residual disease prediction, which focuses on the comprehensive learning of 3D Computed Tomography (CT) images in a multi-view manner. Specifically, we first obtain multi-view of 3D CT images from transverse, coronal and sagittal views. To better represent the image features in a multi-view manner, we further leverage attention mechanism to help find the more relevant slices in each view. Extensive experiments on a dataset of 111 patients show that our method outperforms existing deep-learning methods.

Published
2023

13. Global, regional, and national burden of injuries, and burden attributable to injuries risk factors, 1990 to 2019: results from the Global Burden of Disease study 2019

Author

Malekpour, Mohammad-Reza, Rezaei, Negar, Azadnajafabad, Sina, Khanali, Javad, Azangou-Khyavy, Mohammadreza, Moghaddam, Sahar Saeedi, Heidari-Foroozan, Mahsa, Rezazadeh-Khadem, Sahba, Ghamari, Seyyed-Hadi, Abbasi-Kangevari, Mohsen, Abady, Gdiom Gebreheat, Abdulkader, Rizwan Suliankatchi, Abebe, Ayele Mamo, Abu-Gharbieh, Eman, Acharya, Dilaram, Addo, Isaac Yeboah, Adeagbo, Oluwafemi Atanda, Adegboye, Oyelola A., Adeyinka, Daniel Adedayo, Sakilah Adnani, Qorinah Estiningtyas, Afolabi, Aanuoluwapo Adeyimika, Afzal, Saira, Afzal, Muhammad Sohail, Ahmad, Sajjad, Ahmad, Aqeel, Ahmadi, Ali, Ahmadieh, Hamid, Ahmed, Haroon, Ahmed, Mehrunnisha Sharif, Ajami, Marjan, Akbarialiabad, Hossein, Akunna, Chisom Joyqueenet, Alahdab, Fares, Alanezi, Fahad Mashhour, Alanzi, Turki M., Alhassan, Robert Kaba, Ali, Liaqat, Samakkhah, Shohreh Alian, Alimohamadi, Yousef, Aljunid, Syed Mohamed, Almustanyir, Sami, Al-Sabah, Salman Khalifah, Altirkawi, Khalid A., Amare, Hiwot, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amiri, Sohrab, Andrei, Tudorel, Andrei, Catalina Liliana, Anvari, Davood, Anwar, Sumadi Lukman, Aqeel, Muhammad, Arab-Zozani, Morteza, Arumugam, Ashokan, Aryal, Umesh Raj, Asaad, Malke, Asgary, Saeed, Ashraf, Tahira, Astell-Burt, Thomas, Athari, Seyyed Shamsadin, Atreya, Alok, Aujayeb, Avinash, Awedew, Atalel Fentahun F., Quintanilla, Beatriz Paulina Ayala, Aychiluhm, Setognal Birara, Ayele, Alemu Degu, Azizi, Hosein, Azzam, Ahmed Y., Bakkannavar, Shankar M., Bardhan, Mainak, Barker-Collo, Suzanne Lyn, Barqawi, Hiba Jawdat, Barrow, Amadou, Bashiri, Azadeh, Baskaran, Pritish, Basu, Saurav, Bedi, Neeraj, Bekele, Alehegn, Belo, Luis, Bennett, Derrick A., Bensenor, Isabela M., Berhie, Alemshet Yirga, Bhagavathula, Akshaya Srikanth, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A., Bitaraf, Saeid, Boloor, Archith, Borges, Guilherme, Borschmann, Rohan, Boufous, Soufiane, Brauer, Michael, Briggs, Andrew M., Brown, Julie, Bryazka, Dana, Cámera, Luis Alberto, Cárdenas, Rosario, Carvalho, Márcia, Catalá-López, Ferrán, Cerin, Ester, Charan, Jaykaran, Chattu, Vijay Kumar, Chien, Wai Tong, Chitheer, Abdulaal, Cho, Daniel Youngwhan, McPhee Christensen, Steffan Wittrup, Christopher, Devasahayam J., Chu, Dinh-Toi, Chukwu, Isaac Sunday, Cislaghi, Beniamino, Clark, Scott Richard, Cruz-Martins, Natalia, Cullen, Patricia, Dadras, Omid, Dai, Xiaochen, Damiani, Giovanni, Dandona, Rakhi, Darmstadt, Gary L., Soltani, Reza Darvishi Cheshmeh, Darwesh, Aso Mohammad, Dávila-Cervantes, Claudio Alberto, De Leo, Diego, de Luca, Katie, Demetriades, Andreas K., Demisse, Biniyam, Demisse, Fitsum Wolde, Demissie, Solomon, Desye, Belay, Dharmaratne, Samath Dhamminda, Diress, Mengistie, Djalalinia, Shirin, Dodangeh, Milad, Dongarwar, Deepa, Edinur, Hisham Atan, Eini, Ebrahim, Ekholuenetale, Michael, Elgar, Frank J., Elgendy, Islam Y., Elhabashy, Hala Rashad, Elhadi, Muhammed, El-Huneidi, Waseem, Emamian, Mohammad Hassan, Bain, Luchuo Engelbert, Enyew, Daniel Berhanie, Eshetu, Habitu Birhan, Eskandarieh, Sharareh, Etaee, Farshid, Fagbamigbe, Adeniyi Francis, Faro, Andre, Fasanmi, Abidemi Omolara, Fatehizadeh, Ali, Feng, Xiaoqi, Fereshtehnejad, Seyed-Mohammad, Ferrara, Pietro, Fetensa, Getahun, Fischer, Florian, Franklin, Richard Charles, Fukumoto, Takeshi, Galali, Yaseen, Galehdar, Nasrin, Gankpe, Fortune Gbetoho, Gebrehiwot, Mesfin, Gebremeskel, Teferi Gebru, Geleta, Leta Adugna, Getachew, Motuma Erena, Ghafourifard, Mansour, Nour, Mohammad Ghasemi, Ghashghaee, Ahmad, Gholamrezanezhad, Ali, Gill, Tiffany K., Ginindza, Themba G., Glasbey, James C., Göbölös, Laszlo, Gohari, Kimiya, Golechha, Mahaveer, Goleij, Pouya, Grivna, Michal, Gunawardane, Damitha Asanga, Gupta, Bhawna, Hall, Brian J., Hamadeh, Randah R., Hamal, Pawan Kumar, Hameed, Sajid, Hamidi, Samer, Hamiduzzaman, Mohammad, Hanif, Asif, Haque, Syed Emdadul, Hargono, Arief, Harlianto, Netanja I., Hartono, Risky Kusuma, Hasaballah, Ahmed I., Hasani, Hamidreza, Hassanian-Moghaddam, Hossein, Hassanipour, Soheil, Hassankhani, Hadi, Hayat, Khezar, Heidari, Mohammad, Hendrie, Delia, Heyi, Demisu Zenbaba, Hiraike, Yuta, Horita, Nobuyuki, Hossain, Mohammad Bellal, Hosseinzadeh, Mehdi, Hoveidaei, Amir Human, Hu, Guoqing, Ilesanmi, Olayinka Stephen, Immurana, Mustapha, Inbaraj, Leeberk Raja, Shariful Islam, Sheikh Mohammed, Islam, Rakibul M., Ismail, Nahlah Elkudssiah, Jagnoor, Jagnoor, Jahrami, Haitham, Jakovljevic, Mihajlo, Jamshidi, Elham, Janodia, Manthan Dilipkumar, Javaheri, Tahereh, Jayapal, Sathish Kumar, Jeganathan, Jayakumar, Jonas, Jost B., Joseph, Nitin, Joukar, Farahnaz, Jürisson, Mikk, Kabir, Ali, Kadashetti, Vidya, Kamath, Rajesh, Kamath, Ashwin, Kamble, Bhushan Dattatray, Kandel, Himal, Kantar, Rami S., Karaye, Ibraheem M., Karkhah, Samad, Kashoo, Faizan Zaffar, Kassa, Bekalu Getnet, Kauppila, Joonas H., Keikavoosi-Arani, Leila, Kemp Bohan, Phillip M., Keykhaei, Mohammad, Khalid, Nauman, Khammarnia, Mohammad, Khan, Moien A.B., Khan, M. Nuruzzaman, Khan, Ejaz Ahmad, Khan, Maseer, Khatatbeh, Moawiah Mohammad, Khubchandani, Jagdish, Kim, Yun Jin, Kim, Gyu Ri, Kisa, Adnan, Kisa, Sezer, Kompani, Farzad, Shivakumar, K.M. Marulasiddaiah Kondlahalli, Koul, Parvaiz A., Koyanagi, Ai, Krishan, Kewal, Krishnamoorthy, Vijay, Kruger, Estie, Bicer, Burcu Kucuk, Kumar, Nithin, Kumar, Narinder, La Vecchia, Carlo, Lam, Hilton, Lami, Faris Hasan, Landires, Iván, Lansingh, Van Charles, Lauriola, Paolo, Dao Le, Long Khanh, Leasher, Janet L., Ledda, Caterina, Lee, Doo Woong, Han Lee, Yo, Lee, Wei-Chen, Makhiringa Likaka, Andrew Tiyamike, Lim, Stephen S., Linn, Shai, Lucchetti, Giancarlo, Lunevicius, Raimundas, Lyons, Ronan A., Abd El Razek, Mohammed Magdy, Prasad, Mahadeshwara, Mahmoudi, Razzagh, Majeed, Azeem, Malagón-Rojas, Jeadran N., Malakan Rad, Elaheh, Malta, Deborah Carvalho, Manla, Yosef, Mansouri, Borhan, Mansournia, Mohammad Ali, Maravilla, Joemer C., Mathews, Elezebeth, Maulik, Pallab K., Mechili, Enkeleint A., Nasab, Entezar Mehrabi, Mendoza, Walter, Mengistu, Dechasa Adare, Mentis, Alexios-Fotios A., Mesregah, Mohamed Kamal, Mestrovic, Tomislav, Miazgowski, Tomasz, Mir, Shabir Ahmad, Mirica, Andreea, Mirrakhimov, Erkin M., Mirza, Moonis, Mohammadi, Soheil, Mohammed, Shafiu, Moitra, Modhurima, Mokdad, Ali H., Molokhia, Mariam, Monasta, Lorenzo, Moradi, Maryam, Morrison, Shane Douglas, Mubarik, Sumaira, Murillo-Zamora, Efren, Mustafa, Ghulam, Fawzy Nabhan, Ashraf, Nangia, Vinay, Ramos Nascimento, Bruno, Natto, Zuhair S., Negoi, Ionut, Nejadghaderi, Seyed Aria, Nena, Evangelia, Nepal, Samata, Nggada, Haruna Asura, Ngunjiri, Josephine W., Nnaji, Chukwudi A., Nzoputam, Ogochukwu Janet, Nzoputam, Chimezie Igwegbe, Oancea, Bogdan, Obamiro, Kehinde O., Odukoya, Oluwakemi Ololade, Oghenetega, Onome Bright, In-Hwan, Oh, Okati-Aliabad, Hassan, Okonji, Osaretin Christabel, Oladunjoye, Adeolu Olufunso, Olagunju, Andrew T., Olana, Diriba Dereje, Bali, Ahmed Omar, Otoiu, Adrian, Owolabi, Mayowa O., Padukudru P A, Mahesh, Padron-Monedero, Alicia, Panda-Jonas, Songhomitra, Pandi-Perumal, Seithikurippu R., Pardhan, Shahina, Eun-Kee, Park, Patel, Jay, Paudel, Uttam, Pawar, Shrikant, Toroudi, Hamidreza Pazoki, Peden, Amy E., Pedersini, Paolo, Pereira, Marcos, Pesudovs, Konrad, Petcu, Ionela-Roxana, Pham, Tom, Phillips, Michael R., Piracha, Zahra Zahid, Polinder, Suzanne, Qattea, Ibrahim, Rafiee, Ata, Raghav, Pankaja, Aziz Rahman, Muhammad, Rahman, Mosiur, Rahmani, Amir Masoud, Rahmanian, Vahid, Ramazanu, Sheena, Rani, Usha, Raru, Temam Beshir, Rashidi, Mohammad-Mahdi, Rastogi, Prateek, Rasul, Azad, Ratan, Zubair Ahmed, Rawassizadeh, Reza, Rezaei, Maryam, Rezaei, Nazila, Rezaeian, Mohsen, Riaz, Muhammad, Rickard, Jennifer, Roberts, Nicholas L.S., Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Ronfani, Luca, Roy, Bedanta, Manjula, S., Chandan, S.N., Sabour, Siamak, Reza Saeb, Mohammad, Saeed, Umar, Safi, Sare, Sahebkar, Amirhossein, Sahiledengle, Biniyam, Ali Sahraian, Mohammad, Salamati, Payman, Sanabria, Juan, Nadeem Saqib, Muhammad Arif, Sarikhani, Yaser, Sarveazad, Arash, Sattin, Davide, Saya, Ganesh Kumar, Schwebel, David C., Seboka, Binyam Tariku, Seidu, Abdul-Aziz, Seylani, Allen, Shah, Pritik A., Shahbandi, Ataollah, Shaheen, Amira A., Shaikh, Masood Ali, Shanawaz, Mohd, Sharew, Nigussie Tadesse, Sharifi, Azam, Sharma, Neeraj, Sharma, Vishal, Shashamo, Bereket Beyene, Sheidaei, Ali, Sheikhi, Rahim Ali, Shen, Jiabin, Shetty, Adithi, Shetty, B. Suresh Kumar, Shiri, Rahman, Shorofi, Seyed Afshin, Shrestha, Roman, Sidamo, Negussie Boti, Silva, Luís Manuel Lopes Rodrigues, Simegn, Wudneh, Singh, Jasvinder A., Singh, Surjit, Singh, Ambrish, Skryabin, Valentin Yurievich, Skryabina, Anna Aleksandrovna, Sleet, David A., Socea, Bogdan, Solomon, Yonatan, Song, Yi, Sotoudeh, Houman, Sousa, Raul A.R. C., Stanaway, Jeffrey D., Stein, Dan J., Steiropoulos, Paschalis, Stokes, Mark A., Subedi, Narayan, Sun, Jing, Tabarés-Seisdedos, Rafael, Soodejani, Moslem Taheri, Tampa, Mircea, Tan, Ker-Kan, Tariqujjaman, M.d., Tarkang, Elvis Enowbeyang, Tat, Nathan Y., Tefera, Yibekal Manaye, Thapar, Rekha, Ticoalu, Jansje Henny Vera, Tripathy, Jaya Prasad, Car, Lorainne Tudor, Tufa, Derara Girma, Ullah, Sana, Ullah, Irfan, Umapathi, Krishna Kishore, Upadhyay, Era, Tahbaz, Sahel Valadan, Valdez, Pascual R., Varthya, Shoban Babu, Veroux, Massimiliano, Vervoort, Dominique, Violante, Francesco S., Vlassov, Vasily, Vo, Bay, Waheed, Yasir, Wang, Yanzhong, Wassie, Gizachew Tadesse, Wiangkham, Taweewat, Wilkerson, Caroline, Wolde, Asrat Arja, Xiao, Hong, Yano, Yuichiro, Yaya, Sanni, Ye, Pengpeng, Yip, Paul, Yonemoto, Naohiro, Younis, Mustafa Z., Yu, Chuanhua, Zaki, Leila, Zastrozhin, Michael, Zhang, Yunquan, Zhang, Zhi-Jiang, Zodpey, Sanjay, Naghavi, Mohsen, Larijani, Bagher, and Farzadfar, Farshad

Published
2024
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15. Neural network-assisted model of interfacial fluids with explicit coarse-grained molecular structures.

Author

Ma, Shuhao, Li, Dechang, Li, Xuejin, and Hu, Guoqing

Subjects
PROPERTIES of fluids, BIOLOGICAL membranes, MOLECULAR structure, MULTISCALE modeling, MACHINE learning
Abstract

Interfacial fluids are ubiquitous in systems ranging from biological membranes to chemical droplets and exhibit a complex behavior due to their nonlinear, multiphase, and multicomponent nature. The development of accurate coarse-grained (CG) models for such systems poses significant challenges, as these models must effectively capture the intricate many-body interactions, both inter- and intramolecular, arising from atomic-level phenomena, and account for the diverse density distributions and fluctuations at the interface. In this study, we use advanced machine learning techniques incorporating force matching and diffusion probabilistic models to construct a robust CG model of interfacial fluids. We evaluate our model through simulations in various settings, including the water–air interface, bulk decane, and dipalmitoylphosphatidylcholine monolayer membranes. Our results show that our CG model accurately reproduces the essential many-body and interfacial properties of interfacial fluids and proves effective across different CG mapping strategies. This work not only validates the utility of our model for multiscale simulations, but also lays the groundwork for future improvements in the simulation of complex interfacial systems. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Design of magnetic flux concentrator composed of nanospheres for high-sensitivity magnetometers.

Author

Li, Huiyu, Zhao, Lin, Hu, Guoqing, Zhou, Zhehai, and Chen, Guangwei

Subjects
MAGNETIC fields, SOFT magnetic materials, MAGNETIC flux density, MAGNETIC structure, MAGNETIC flux
Abstract

Magnetometers have received considerable attention in recent years. Magnetic components offer an alternative methodology to improve the sensitivity. Due to their exceedingly small structural dimensions, metasurfaces exhibit significant competitiveness in field modulation. A magnetic field concentration phenomenon of spheres at the nanoscale is presented in this paper. The sensitivity of a magnetometer is, therefore, improved through the enhanced static or quasistatic magnetic field by the nanosphere concentrator. Magnetic field redistribution due to the assistance of nanospheres is discussed in this paper using the finite element method. The numerical method is verified with classical analytical equations with a single sphere. The simulation results show that the magnetic field concentrates in the near field behind the nanosphere along the direction of the magnetic flux density. The radius, material or permeability exactly, and distribution are critical parameters to the concentration strength. The magnetic gain of a single nanosphere with typical positive permeability of the typical soft magnetic material reaches 3, and thus, the field along the magnetic flux direction concentrates. Furthermore, the amplification factor is more prominent with the nanosphere arrays compared to a single sphere with the same scale of size, and amplification improves with the sphere numbers and distributions in the array arrangement, which provides a novel approach for the designing of the magnetic flux concentrator being monolithically integrated with the magnetometer probe. Our simulation results provide a new degree of freedom by using nanoscale structures to manipulate magnetic fields. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Road traffic death coding quality in the WHO Mortality Database/Qualite des codes attribues aux accidents mortels de la circulation dans la base de donnees de l'OMS sur la mortalite/Calidad de la codificacion de los fallecimientos por accidentes de trafico en la Base de datos sobre mortalidad de la OMS

Author

Hua, Junjie, Li, Li, Ning, Peishan, Schwebel, David C., He, Jieyi, Rao, Zhenzhen, Cheng, Peixia, Li, Ruotong, Fu, Yanhong, Li, Jie, Wang, Wanhui, Zhang, Na, and Hu, Guoqing

Subjects
World Health Organization -- Investigations, Accidents, Comparative analysis, Health aspects, Forecasts and trends, Investigations, Company legal issue, Information accessibility, Market trend/market analysis, Mortality -- China, Traffic safety -- Comparative analysis -- Health aspects -- Investigations -- Forecasts and trends, Traffic accidents -- Health aspects -- Comparative analysis -- Investigations -- Forecasts and trends -- Accidents, Information management -- Forecasts and trends -- Health aspects -- Investigations -- Accidents -- Comparative analysis, Public health -- Health aspects -- Forecasts and trends -- Investigations -- Comparative analysis, Government securities -- Forecasts and trends -- Health aspects -- Comparative analysis -- Investigations
Abstract

Introduction Globally, road traffic crashes cause approximately 1.3 million preventable deaths and 50 million injuries each year. (1) In 2021, the United Nations (UN) General Assembly resolution 74/299 committed to [...], Objective To evaluate the precision and dependability of road traffic mortality data recorded in the World Health Organization Mortality Database and investigate how uncorrected data influence vital mortality statistics used in traffic safety programmes worldwide. Methods We assessed country and territory-specific data quality from 2015 to 2020 by calculating the proportions of five types of nonspecific cause of death codes related to road traffic mortality. We compared age-adjusted road traffic mortality and changes in the average annual mortality rate before and after correcting the deaths with nonspecific codes. We generated road traffic mortality projections with both corrected and uncorrected codes, and redistributed the data using the proportionate method. Findings We analysed data from 124 countries and territories with at least one year of mortality data from 2015 to 2020. The number of countries and territories reporting more than 20% of deaths with ill-defined or unknown cause was 2; countries reporting injury deaths with undetermined intent was 3; countries reporting unspecified unintentional injury deaths was 21; countries reporting unspecified transport crash deaths was 3; and countries reporting unspecified unintentional road traffic deaths was 30. After redistributing deaths with nonspecific codes, road traffic mortality changed by greater than 50% in 7% (5/73) to 18% (9/51) of countries and territories. Conclusion Nonspecific codes led to inaccurate mortality estimates in many countries. We recommend that injury researchers and policymakers acknowledge the potential pitfalls of relying on raw or uncorrected road traffic mortality data and instead use corrected data to ensure more accurate estimates when improving road traffic safety programmes. Objectif Determiner le niveau de precision et de fiabilite des informations concernant le nombre de deces lies aux accidents de la route enregistrees dans la base de donnees de l'Organisation mondiale de la Sante sur la mortalite, mais aussi etudier l'influence des donnees non corrigees sur les statistiques de mortalite, qui jouent un role essentiel dans les programmes de securite routiere a travers le monde. Methodes Nous avons evalue la qualite des donnees propres a chaque pays et territoire entre 2015 et 2020 en calculant la proportion que representent cinq types de codes non specifiques relatifs aux causes de deces dans des accidents de la route. Nous avons compare la mortalite routiere selon l'age et l'evolution du taux de mortalite annuel moyen avant et apres avoir corrige les deces portant des codes non specifiques. Pour terminer, nous avons genere des projections en matiere de mortalite routiere a l'aide des codes corriges et non corriges, puis redistribue les donnees par integration proportionnelle. Resultats Nous avons analyse les donnees issues de 124 pays et territoires, avec au moins un an de donnees de mortalite entre 2015 et 2020. Deux pays et territoires mentionnaient plus de 20% de deces imputables a des causes inconnues ou mal definies ; trois pays signalaient des deces dus a des blessures non intentionnelles ; 21 faisaient etat de blessures mortelles non intentionnelles et non specifiees ; trois ne precisaient pas le type de vehicule implique dans les collisions mortelles ; et enfin, 30 pays rapportaient des accidents de la route mortels, indetermines et involontaires. Apres redistribution des deces munis de codes non specifiques, le taux de mortalite routiere a change de plus de 50% dans 7% (5/73) a 18% (9/51) des pays et territoires. Conclusion L'emploi de codes non specifiques a entraine une mauvaise estimation de la mortalite dans de nombreux pays. Nous recommandons aux responsables politiques et chercheurs actifs dans le domaine des traumatismes de tenir compte des eventuels pieges que recele l'usage de donnees brutes ou non corrigees sur la mortalite routiere, et leur conseillons de se fonder sur des donnees corrigees afin d'obtenir des estimations plus precises pour l'amelioration des programmes de securite routiere. Objetivo Evaluar la precision y la fiabilidad de los datos sobre mortalidad por accidentes de trafico registrados en la Base de datos sobre mortalidad de la Organizacion Mundial de la Salud. Asimismo, investigar como los datos incorrectos influyen en las estadisticas vitales sobre mortalidad utilizadas en programas de seguridad vial a nivel mundial. Metodos Evaluamos la calidad de los datos en funcion del pais y del territorio desde 2015 a 2020 calculando los porcentajes de cinco tipos de codigos de fallecimientos sin causa especifica relacionados con la mortalidad por accidentes de trafico. Comparamos la mortalidad por accidentes de trafico en funcion de la edad, asi como los cambios en el indice medio anual de mortalidad antes y despues de corregir los fallecimientos con codigos no especificos. Generamos las proyecciones de mortalidad por accidentes de trafico con codigos corregidos y sin corregir, y distribuimos de nuevo los datos utilizando el metodo proporcional. Resultados Analizamos los datos correspondientes a 124 paises y territorios que contaban con datos sobre mortalidad de al menos un ano, del periodo comprendido entre 2015 y 2020. El numero de paises y territorios que notificaron mas de un 20% de fallecimientos por causas imprecisas o desconocidas fue de 2; los paises que notificaron fallecimientos por lesiones con intencionalidad indeterminada fueron 3; los paises que notificaron fallecimientos por lesiones no intencionadas y no especificadas fueron 21; los paises que notificaron fallecimientos no especificados en accidentes de transporte fueron 3; y los paises que notificaron fallecimientos no especificados y no intencionados en accidentes de trafico fueron 30. Tras redistribuir los fallecimientos con codigos no especificos, la mortalidad por accidentes de trafico cambio en mas de un 50% en un intervalo del 7% (5/73) al 18% (9/51) de los paises y territorios. Conclusion Los codigos no especificos dieron lugar a estimaciones imprecisas sobre mortalidad en numerosos paises. Instamos a que los investigadores y los responsables de politicas de lesiones reconozcan las potenciales deficiencias que supone el hecho de basarse en datos no tratados ni corregidos, y a que, en lugar de eso, utilicen datos corregidos con el fin de garantizar estimaciones mas precisas a la hora de mejorar los programas sobre seguridad vial. [phrase omitted]

Published
2023
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20. An Adaptive Color Image Visible Watermark Algorithm Supporting for Interested Area and its Application System Based on Internet

Author

Hu Guoqing and Chen Xinlong

Subjects
visible watermark, color image, Internet, Engineering (General). Civil engineering (General), TA1-2040
Abstract

This paper presents an interested area visible watermark algorithm for digital color image based on internet. By converting color image from RGB to HSI and using the original image information to obtain the watermark coefficient, the algorithm embeds watermark on the spatial domain and selects the interested area by clicking on the picture. By visiting the site (http://dgdz.ccee.cqu.edu.cn/watermark/watermarkb.aspx) and uploading the original image and watermark image, the color image with the visible watermark can be gained on web. Experimental results show that the algorithm has only a little influence on the original image and the image of watermark is clearly visible. It is very suitable for obvious identification of the copyright from digital image through Internet.

Published
2015
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21. A mesoscale model for quantitative phospholipid monolayer simulations at the air-water interface

Author

Zhu, Yongzheng, Bai, Xuan, and Hu, Guoqing

Subjects
Physics - Biological Physics, Physics - Chemical Physics
Abstract

A mesoscale model with molecular resolutions is presented for the dipalmitoyl-phosphatidylcholine (DPPC) and 1-palmitoyl-2-oleyl-sn-glycero-3-phosphocholine (POPC) monolayer simulations at the air-water interface using many-body dissipative particle dynamics (MDPD). The parameterization scheme is rigorously based on reproducing the physical properties of water and alkane and the interfacial property of the phospholipid monolayer by comparing with our experimental results. The MDPD model yields a similar surface pressure-area isotherm as well as the similar pressure-related morphologies compared with the all-atomistic simulations and experiments. Moreover, the compressibility modulus, order parameter of lipid tails, and thickness of the MDPD phospholipid monolayer are quantitatively in line with the all-atomistic simulations and experiments. This model can also capture the sensitive changes in the pressure-area isotherms of the mixed DPPC/POPC monolayers with altered mixed ratios by comparing with the experiments, indicating that our model scheme is promising in applications for complex natural phospholipid monolayers. These results demonstrate a significant improvement on quantitative phospholipid monolayer simulations over the previous coarse-grained models.

Published
2021

30. The injury mortality burden in Guinea

Author

Mamady Keita, Yao Hongyan, Zhang Xujun, Xiang Huiyun, Tan Hongzhuan, and Hu Guoqing

Subjects
Injury, Mortality, Cause of death, Guinea, Public aspects of medicine, RA1-1270
Abstract

Abstract Background The injury mortality burden of Guinea has been rarely addressed. The paper aimed to report patterns of injury mortality burden in Guinea. Methods We retrieved the mortality data from the Guinean Annual Health Statistics Report 2007. The information about underlying cause of deaths was collected based on Guinean hospital discharge data, Hospital Mortuary and City Council Mortuary data. The causes of death are coded in the 9th International Classification of Diseases (ICD-9). Multivariate Poisson regression was used to test the impacts of sex and age on mortality rates. The statistical analyses were performed using Statatm 10.0. Results In 2007, 7066 persons were reported dying of injuries in Guinea (mortality: 72.8 per 100,000 population). Transportation, fire/burn, falls, homicide and drowning were the five leading causes of fatal injuries for the whole population, accounting for 37%, 22%, 12%, 10% and 6% of total deaths, respectively. In general, age-specific injury causes displayed similar patterns of the whole population except that poisoning replaced falls as a leading cause among children under five years old. Males were at 30-50% more risk of dying from six commonest causes than females and old age groups had higher injury mortality rates than younger age groups. Conclusion Transportation, fire/burn, falls, homicide, and drowning accounted for the majority of total injury mortality burden in Guinea. Males and old adults were high-risk population of fatal injuries and should be targeted by injury prevention. Lots of work is needed to improve weak capacities for injury control in order to reduce the injury mortality burden.

Published
2012
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31. A step forward for understanding the morbidity burden in Guinea: a national descriptive study

Author

Hu Guoqing and Mamady Keita

Subjects
Public aspects of medicine, RA1-1270
Abstract

Abstract Background Little evidence on the burden of disease has been reported about Guinea. This study was conducted to demonstrate the morbidity burden in Guinea and provide basic evidence for setting health priorities. Methods A retrospective descriptive study was designed to present the morbidity burden of Guinea. Morbidity data were extracted from the National Health Statistics Report of Guinea of 2008. The data are collected based on a pyramid of facilities which includes two national hospitals (teaching hospitals), seven regional hospitals, 26 prefectural hospitals, 8 communal medical centers, 390 health centers, and 628 health posts. Morbidity rates were calculated to measure the burden of non-fatal diseases. The contributions of the 10 leading diseases were presented by sex and age group. Results In 2008, a total of 3,936,599 cases occurred. The morbidity rate for males was higher than for females, 461 versus 332 per 1,000 population. Malaria, respiratory infections, diarrheal diseases, helminthiases, and malnutrition ranked in the first 5 places and accounted for 74% of the total burden, respectively having a rate of 148, 64, 33, 32, and 14 per 1,000 population. The elderly aged 65+ had the highest morbidity rate (611 per 1,000 population) followed by working-age population (458 per 1,000 population) and children (396 per 1,000 population) while the working-age population aged 25-64 contributed the largest part (39%) to total cases. The sex- and age-specific spectrum of morbidity burden showed a similar profile except for small variations. Conclusion Guinea has its unique morbidity burden. The ten leading causes of morbidity burden, especially for malaria, respiratory infections, diarrheal diseases, helminthiases, and malnutrition, need to be prioritized in Guinea.

Published
2011
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46. Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

Author

Haakenstad, Annie, Yearwood, Jamal Akeem, Fullman, Nancy, Bintz, Corinne, Bienhoff, Kelly, Weaver, Marcia R, Nandakumar, Vishnu, LeGrand, Kate E, Knight, Megan, Abbafati, Cristiana, Abbasi-Kangevari, Mohsen, Abdoli, Amir, Abeldaño Zuñiga, Roberto Ariel, Adedeji, Isaac Akinkunmi, Adekanmbi, Victor, Adetokunboh, Olatunji O, Afzal, Muhammad Sohail, Afzal, Saira, Agudelo-Botero, Marcela, Ahinkorah, Bright Opoku, Ahmad, Sajjad, Ahmadi, Ali, Ahmadi, Sepideh, Ahmed, Ali, Ahmed Rashid, Tarik, Aji, Budi, Akande-Sholabi, Wuraola, Alam, Khurshid, Al Hamad, Hanadi, Alhassan, Robert Kaba, Ali, Liaqat, Alipour, Vahid, Aljunid, Syed Mohamed, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amu, Hubert, Amugsi, Dickson A, Ancuceanu, Robert, Andrade, Pedro Prata, Anjum, Afifa, Arabloo, Jalal, Arab-Zozani, Morteza, Ariffin, Hany, Arulappan, Judie, Aryan, Zahra, Ashraf, Tahira, Atnafu, Desta Debalkie, Atreya, Alok, Ausloos, Marcel, Avila-Burgos, Leticia, Ayano, Getinet, Ayanore, Martin Amogre, Azari, Samad, Badiye, Ashish D, Baig, Atif Amin, Bairwa, Mohan, Bakkannavar, Shankar M, Baliga, Shrikala, Banik, Palash Chandra, Bärnighausen, Till Winfried, Barra, Fabio, Barrow, Amadou, Basu, Sanjay, Bayati, Mohsen, Belete, Rebuma, Bell, Arielle Wilder, Bhagat, Devidas S., Bhagavathula, Akshaya Srikanth, Bhardwaj, Pankaj, Bhardwaj, Nikha, Bhaskar, Sonu, Bhattacharyya, Krittika, Bhurtyal, Ashok, Bhutta, Zulfiqar A, Bibi, Sadia, Bijani, Ali, Bikbov, Boris, Biondi, Antonio, Bolarinwa, Obasanjo Afolabi, Bonny, Aime, Brenner, Hermann, Buonsenso, Danilo, Burkart, Katrin, Busse, Reinhard, Butt, Zahid A, Butt, Nadeem Shafique, Caetano dos Santos, Florentino Luciano, Cahuana-Hurtado, Lucero, Cámera, Luis Alberto, Cárdenas, Rosario, Carneiro, Vera L A, Catalá-López, Ferrán, Chandan, Joht Singh, Charan, Jaykaran, Chavan, Prachi P, Chen, Simiao, Chen, Shu, Choudhari, Sonali Gajanan, Chowdhury, Enayet Karim, Chowdhury, Mohiuddin Ahsanul Kabir, Cirillo, Massimo, Corso, Barbara, Dadras, Omid, Dahlawi, Saad M A, Dai, Xiaochen, Dandona, Lalit, Dandona, Rakhi, Dangel, William James, Dávila-Cervantes, Claudio Alberto, Davletov, Kairat, Deuba, Keshab, Dhimal, Meghnath, Dhimal, Mandira Lamichhane, Djalalinia, Shirin, Do, Huyen Phuc, Doshmangir, Leila, Duncan, Bruce B, Effiong, Andem, Ehsani-Chimeh, Elham, Elgendy, Islam Y, Elhadi, Muhammed, El Sayed, Iman, El Tantawi, Maha, Erku, Daniel Asfaw, Eskandarieh, Sharareh, Fares, Jawad, Farzadfar, Farshad, Ferrero, Simone, Ferro Desideri, Lorenzo, Fischer, Florian, Foigt, Nataliya A., Foroutan, Masoud, Fukumoto, Takeshi, Gaal, Peter Andras, Gaihre, Santosh, Gardner, William M., Garg, Tushar, Getachew Obsa, Abera, Ghafourifard, Mansour, Ghashghaee, Ahmad, Ghith, Nermin, Gilani, Syed Amir, Gill, Paramjit Singh, Goharinezhad, Salime, Golechha, Mahaveer, Guadamuz, Jenny S., Guo, Yuming, Gupta, Rajat Das, Gupta, Rajeev, Gupta, Vivek Kumar, Gupta, Veer Bala, Hamiduzzaman, Mohammad, Hanif, Asif, Haro, Josep Maria, Hasaballah, Ahmed I., Hasan, Md. Mehedi, Hasan, M Tasdik, Hashi, Abdiwahab, Hay, Simon I, Hayat, Khezar, Heidari, Mohammad, Heidari, Golnaz, Henry, Nathaniel J, Herteliu, Claudiu, Holla, Ramesh, Hossain, Sahadat, Hossain, Sheikh Jamal, Hossain, Mohammad Bellal Hossain, Hosseinzadeh, Mehdi, Hostiuc, Sorin, Hoveidamanesh, Soodabeh, Hsieh, Vivian Chia-rong, Hu, Guoqing, Huang, Junjie, Huda, M Mamun, Ifeagwu, Susan C., Ikuta, Kevin S, Ilesanmi, Olayinka Stephen, Irvani, Seyed Sina Naghibi, Islam, Rakibul M, Islam, Sheikh Mohammed Shariful, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Isola, Gaetano, Itumalla, Ramaiah, Iwagami, Masao, Jahani, Mohammad Ali, Jahanmehr, Nader, Jain, Rajesh, Jakovljevic, Mihajlo, Janodia, Manthan Dilipkumar, Jayapal, Sathish Kumar, Jayaram, Shubha, Jha, Ravi Prakash, Jonas, Jost B., Joo, Tamas, Joseph, Nitin, Jürisson, Mikk, Kabir, Ali, Kalankesh, Leila R, Kalhor, Rohollah, Kamath, Aruna M., Kamenov, Kaloyan, Kandel, Himal, Kantar, Rami S., Kapoor, Neeti, Karanikolos, Marina, Katikireddi, Srinivasa Vittal, Kavetskyy, Taras, Kawakami, Norito, Kayode, Gbenga A., Keikavoosi-Arani, Leila, Keykhaei, Mohammad, Khader, Yousef Saleh, Khajuria, Himanshu, Khalilov, Rovshan, Khammarnia, Mohammad, Khan, Md Nuruzzaman, Khan, Moien AB, Khan, Maseer, Khezeli, Mehdi, Kim, Min Seo, Kim, Yun Jin, Kisa, Sezer, Kisa, Adnan, Klymchuk, Vitalii, Koly, Kamrun Nahar, Korzh, Oleksii, Kosen, Soewarta, Koul, Parvaiz A, Kuate Defo, Barthelemy, Kumar, G Anil, Kusuma, Dian, Kyu, Hmwe Hmwe, Larsson, Anders O, Lasrado, Savita, Lee, Wei-Chen, Lee, Yo Han, Lee, Chiachi Bonnie, Li, Shanshan, Lucchetti, Giancarlo, Mahajan, Preetam Bhalchandra, Majeed, Azeem, Makki, Alaa, Malekzadeh, Reza, Malik, Ahmad Azam, Malta, Deborah Carvalho, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Martinez-Valle, Adolfo, Martins-Melo, Francisco Rogerlândio, Masoumi, Seyedeh Zahra, Mathur, Manu Raj, Maude, Richard James, Maulik, Pallab K, McKee, Martin, Mendoza, Walter, Menezes, Ritesh G, Mensah, George A, Meretoja, Atte, Meretoja, Tuomo J, Mestrovic, Tomislav, Michalek, Irmina Maria, Mirrakhimov, Erkin M, Misganaw, Awoke, Misra, Sanjeev, Moazen, Babak, Mohammadi, Mokhtar, Mohammed, Shafiu, Moitra, Modhurima, Mokdad, Ali H, Molokhia, Mariam, Monasta, Lorenzo, Moni, Mohammad Ali, Moradi, Ghobad, Moreira, Rafael Silveira, Mosser, Jonathan F., Mostafavi, Ebrahim, Mouodi, Simin, Nagarajan, Ahamarshan Jayaraman, Nagata, Chie, Naghavi, Mohsen, Nangia, Vinay, Narasimha Swamy, Sreenivas, Narayana, Aparna Ichalangod, Nascimento, Bruno Ramos, Nassereldine, Hasan, Nayak, Biswa Prakash, Nazari, Javad, Negoi, Ionut, Nepal, Samata, Neupane Kandel, Sandhya, Ngunjiri, Josephine W., Nguyen, Huong Lan Thi, Nguyen, Cuong Tat, Ningrum, Dina Nur Anggraini, Noubiap, Jean Jacques, Oancea, Bogdan, Oghenetega, Onome Bright, Oh, In-Hwan, Olagunju, Andrew T, Olakunde, Babayemi Oluwaseun, Omar Bali, Ahmed, Omer, Emad, Onwujekwe, Obinna E, Otoiu, Adrian, Padubidri, Jagadish Rao, Palladino, Raffaele, Pana, Adrian, Panda-Jonas, Songhomitra, Pandi-Perumal, Seithikurippu R, Pardhan, Shahina, Pasupula, Deepak Kumar, Pathak, Praveen Kumar, Patton, George C, Pawar, Shrikant, Pereira, Jeevan, Pilania, Manju, Piroozi, Bakhtiar, Podder, Vivek, Pokhrel, Khem Narayan, Postma, Maarten J, Prada, Sergio I, Quazi Syed, Zahiruddin, Rabiee, Navid, Radhakrishnan, Raghu Anekal, Rahman, Md. Mosfequr, Rahman, Mosiur, Rahman, Mahfuzar, Rahman, Mohammad Hifz Ur, Rahmani, Amir Masoud, Ranabhat, Chhabi Lal, Rao, Chythra R, Rao, Sowmya J, Rasella, Davide, Rawaf, Salman, Rawaf, David Laith, Rawal, Lal, Renzaho, Andre M.N., Reshmi, Bhageerathy, Resnikoff, Serge, Rezapour, Aziz, Riahi, Seyed Mohammad, Ripon, Rezaul Karim, Sacco, Simona, Sadeghi, Masoumeh, Saeed, Umar, Sahebkar, Amirhossein, Sahiledengle, Biniyam, Sahoo, Harihar, Sahu, Maitreyi, Salama, Joseph S, Salamati, Payman, Samy, Abdallah M, Sanabria, Juan, Santric-Milicevic, Milena M., Sathian, Brijesh, Sawhney, Monika, Schmidt, Maria Inês, Seidu, Abdul-Aziz, Sepanlou, Sadaf G, Seylani, Allen, Shaikh, Masood Ali, Sheikh, Aziz, Shetty, Adithi, Shigematsu, Mika, Shiri, Rahman, Shivakumar, K M, Shokri, Azad, Singh, Jasvinder A., Sinha, Dhirendra Narain, Skryabin, Valentin Yurievich, Skryabina, Anna Aleksandrovna, Sofi-Mahmudi, Ahmad, Sousa, Raúl A R C, Stephens, Jacqueline H, Sun, Jing, Szócska, Miklós, Tabarés-Seisdedos, Rafael, Tadbiri, Hooman, Tamiru, Animut Tagele, Thankappan, Kavumpurathu Raman, Topor-Madry, Roman, Tovani-Palone, Marcos Roberto, Tran, Mai Thi Ngoc, Tran, Bach Xuan, Tripathi, Niharika, Tripathy, Jaya Prasad, Troeger, Christopher E, Uezono, Deinzel Robles, Ullah, Saif, Ullah, Anayat, Unnikrishnan, Bhaskaran, Vacante, Marco, Valadan Tahbaz, Sahel, Valdez, Pascual R, Vasic, Milena, Veroux, Massimiliano, Vervoort, Dominique, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vo, Bay, Waheed, Yasir, Wamai, Richard G, Wang, Yuan-Pang, Wang, Yanzhong, Ward, Paul, Wiangkham, Taweewat, Yadav, Lalit, Yahyazadeh Jabbari, Seyed Hossein, Yamagishi, Kazumasa, Yaya, Sanni, Yazdi-Feyzabadi, Vahid, Yi, Siyan, Yiğit, Vahit, Yonemoto, Naohiro, Younis, Mustafa Z, Yu, Chuanhua, Yunusa, Ismaeel, Zaman, Sojib Bin, Zastrozhin, Mikhail Sergeevich, Zhang, Zhi-Jiang, Zhong, Chenwen, Zuniga, Yves Miel H, Lim, Stephen S, Murray, Christopher J L, and Lozano, Rafael

Published
2022
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48. Mapping development and health effects of cooking with solid fuels in low-income and middle-income countries, 2000–18: a geospatial modelling study

Author

Frostad, Joseph Jon, Nguyen, QuynhAnh P, Baumann, Mathew M, Blacker, Brigette F, Marczak, Laurie B, Deshpande, Aniruddha, Wiens, Kirsten E, LeGrand, Kate E, Johnson, Kimberly B, Abbasi-Kangevari, Mohsen, Abdoli, Amir, Abolhassani, Hassan, Abreu, Lucas Guimarães, Abrigo, Michael R M, Abu-Rmeileh, Niveen ME, Adekanmbi, Victor, Agrawal, Anurag, Ahmed, Muktar Beshir, Al-Aly, Ziyad, Alanezi, Fahad Mashhour, Alcalde-Rabanal, Jacqueline Elizabeth, Alipour, Vahid, Altirkawi, Khalid A, Alvis-Guzman, Nelson, Alvis-Zakzuk, Nelson J, Amegah, Adeladza Kofi, Amini, Saeed, Amiri, Fatemeh, Amugsi, Dickson A, Ancuceanu, Robert, Andrei, Catalina Liliana, Andrei, Tudorel, Antriyandarti, Ernoiz, Anvari, Davood, Arabloo, Jalal, Arab-Zozani, Morteza, Athari, Seyyed Shamsadin, Ausloos, Marcel, Ayano, Getinet, Aynalem, Yared Asmare, Azari, Samad, Badiye, Ashish D, Baig, Atif Amin, Balakrishnan, Kalpana, Banach, Maciej, Basu, Sanjay, Bedi, Neeraj, Bell, Michelle L, Bennett, Derrick A, Bhattacharyya, Krittika, Bhutta, Zulfiqar A, Bibi, Sadia, Bohlouli, Somayeh, Boufous, Soufiane, Bragazzi, Nicola Luigi, Braithwaite, Dejana, Burugina Nagaraja, Sharath, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Car, Josip, Cárdenas, Rosario, Carvalho, Felix, Castaldelli-Maia, Joao Mauricio, Castañeda-Orjuela, Carlos A, Cerin, Ester, Chattu, Soosanna Kumary, Chattu, Vijay Kumar, Chaturvedi, Pankaj, Chaturvedi, Sarika, Chen, Simiao, Chu, Dinh-Toi, Chung, Sheng-Chia, Dahlawi, Saad M A, Damiani, Giovanni, Dandona, Lalit, Dandona, Rakhi, Darwesh, Aso Mohammad, Das, Jai K, Dash, Aditya Prasad, Dávila-Cervantes, Claudio Alberto, De Leo, Diego, De Neve, Jan-Walter, Demissie, Getu Debalkie, Denova-Gutiérrez, Edgar, Dey, Sagnik, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Dhungana, Govinda Prasad, Diaz, Daniel, Dipeolu, Isaac Oluwafemi, Dorostkar, Fariba, Doshmangir, Leila, Duraes, Andre Rodrigues, Edinur, Hisham Atan, Efendi, Ferry, El Tantawi, Maha, Eskandarieh, Sharareh, Fadhil, Ibtihal, Fattahi, Nazir, Fauk, Nelsensius Klau, Fereshtehnejad, Seyed-Mohammad, Folayan, Morenike Oluwatoyin, Foroutan, Masoud, Fukumoto, Takeshi, Gaidhane, Abhay Motiramji, Ghafourifard, Mansour, Ghashghaee, Ahmad, Gilani, Syed Amir, Gill, Tiffany K, Goulart, Alessandra C, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Gubari, Mohammed Ibrahim Mohialdeen, Guido, Davide, Guo, Yuming, Gupta, Rajat Das, Gupta, Rajeev, Gutiérrez, Reyna Alma, Hafezi-Nejad, Nima, Hamadeh, Randah R, Hasaballah, Ahmed I, Hassanipour, Soheil, Hayat, Khezar, Heibati, Behzad, Heidari-Soureshjani, Reza, Henry, Nathaniel J, Herteliu, Claudiu, Hosseinzadeh, Mehdi, Hsairi, Mohamed, Hu, Guoqing, Ibitoye, Segun Emmanuel, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Ilic, Milena D, Irvani, Seyed Sina Naghibi, Islam, Sheikh Mohammed Shariful, Iwu, Chidozie C D, Jaafari, Jalil, Jakovljevic, Mihajlo, Javaheri, Tahereh, Jha, Ravi Prakash, Ji, John S, Jonas, Jost B, Kabir, Ali, Kabir, Zubair, Kalhor, Rohollah, Kamyari, Naser, Kanchan, Tanuj, 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Michael, Hay, Simon I, and Reiner, Robert C, Jr

Published
2022
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50. Dual terahertz comb spectroscopy with a single free-running fibre laser

Author

Hu, Guoqing, Mizuguchi, Tatsuya, Oe, Ryo, Nitta, Kazuki, Zhao, Xin, Minamikawa, Takeo, Li, Ting, Zheng, Zheng, and Yasui, Takeshi

Subjects
Physics - Instrumentation and Detectors, Physics - Optics
Abstract

Dual THz comb spectroscopy has the potential to be used as universal THz spectroscopy with high spectral resolution, high spectral accuracy, and broad spectral coverage; however, the requirement for dual stabilized femtosecond lasers hampers its versatility due to the bulky size, high complexity, and high cost. We here report the first demonstration of dual THz comb spectroscopy using a single free-running fibre laser. By tuning the cavity-loss-dependent gain profile with an intracavity Lyot filter together with precise management of the cavity length and dispersion, dual-wavelength pulsed light beams with slightly detuned repetition frequencies are generated in a single laser cavity. Due to sharing of the same cavity, such pulsed light beams suffer from common-mode fluctuation of the repetition frequency, and hence the corresponding frequency difference between them is passively stable around a few hundred hertz within millihertz fluctuation. This considerably stable frequency difference enables dual THz comb spectroscopy with a single free-running fibre laser. While greatly reducing the size, complexity, and cost of the laser source by use of a single free-running fibre laser, the dual THz comb spectroscopy system maintains a spectral bandwidth and dynamic range of spectral power comparable to a system equipped with dual stabilized fibre lasers, and can be effectively applied to high-precision spectroscopy of acetonitrile gas at atmospheric pressure. The demonstrated results indicate that this system is an attractive solution for practical applications of not only THz spectroscopy but also THz-pulse-based measurements., Comment: 29 pages, 7 figures

Published
2018
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