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3. Simulation Analysis of Large Diameter Shield Cutting through Diaphragm Wall Impact on Metro Station Deformation

Author

YUAN Xiao, LI Zhanglin, HE Guojun, ZHANG Mengxi, and ZHAO Huiling

Subjects
shield tunneling, metro station, underground diaphragm wall, cutting through, Transportation engineering, TA1001-1280
Abstract

［Objective］ Cutting through the existing underground structure in the process of shield tunneling will cause adverse effects on the structure. Therefore, it is necessary to study the consequent deformation of the structure and the stratum caused by shield tunneling. ［Method］ Based on the actual project of the super-large diameter shield tunnel of Nanjing Jianning West Road river-crossing passage that cuts through the diaphragm wall of the existing metro station, a three-dimensional numerical model of tunnel-station-stratum is established, to study the ground settlement and the deformation of the station structure when the tunnel cuts through the diaphragm wall and pile foundation of the station. ［Result & Conclusion］ The simulation research results show that the station pile foundation has a significant effect on settlement control. After the shield tunneling cuts off the lower part of the pile, a large settlement occurs on the ground surface above the incision. After the diaphragm wall being cut through, a settlement trough forms centered on the cut place of the wall, resulting in uneven settlement. The shield cutting through the underground diaphragm wall of the station causes differential settlement of two rails on the operating metro line, but still within the range of safety control index. The predictive results of this simulation analysis can provide a reference for the following construction and monitoring of this project, as well as for the construction optimization of similar shield tunnels cutting through existing underground engineering.

Published
2025
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8. Incidence and clinical features of HHV-7 detection in lower respiratory tract in patients with severe pneumonia: a multicenter, retrospective study

Author

Xu, Jun, Zhong, Lin, Shao, Huanzhang, Wang, Qianqian, Dai, Muhua, Shen, Peng, Xiong, Yonghui, Zhang, Weijun, Deng, Xutao, Wang, Mingqiang, Zhu, Yue, Reng, Xindie, Jiang, Yongpo, Chen, Mengyuan, Zhu, Chengcong, Fang, Xueling, He, Guojun, Han, Yijiao, Huang, Xiaohan, He, Xuwei, Xu, Yinghe, Cai, Hongliu, and Huang, Lingtong

Published
2023
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13. The Level of Fibrinogen-to-Albumin Ratio Was Associated with the Severity of Cerebral Small Vessel Disease in Patients with Transient Ischemic Attack.

Author

Liu, Changxia, Chen, Li, Sun, Dingming, Guo, Yan, Zhu, Honghong, Li, Lei, Sun, Shifu, He, Guojun, and Cheng, Yongqing

Abstract

Purpose: Inflammation plays a role in cerebral small vessel disease (CSVD) pathophysiology. This study aimed to explore the association of the fibrinogen-to-albumin ratio (FAR), a novel inflammatory marker, with CSVD burden in patients with transient ischemic attack (TIA). Patients and Methods: From October 1, 2022, to November 30, 2023, continuous patients with TIA were recruited in the study. The total CSVD burden score and modified total CSVD burden score were used to assess the severity of CSVD. Multivariable regression analysis was used to explore the correlation between the FAR and CSVD in TIA patients. Results: A total of 455 participants were recruited, of whom 225 (48.35%), according to the total CSVD burden score, and 181 (40.67%), according to the modified CSVD burden score were finally identified as moderate-severe CSVD. Spearman correlation analysis showed that levels of FAR correlated with the total CSVD (r=0.392, P< 0.001) and the modified total CSVD burden scores (r=0.379, P< 0.001). Multivariable logistic regression analysis showed that FAR was independently associated with moderate-severe CSVD, both as a continuous variable and as a tertile variable (P< 0.001). Conclusion: The level of FAR on admission was independently associated with the severity of CSVD in patients with TIA. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Cd‐Free High‐Bandgap Cu2ZnSnS4 Solar Cell with 10.7% Certified Efficiency Enabled by Engineering Sn‐Related Defects.

Author

Wang, Ao, Huang, Jialiang, Yan, Chang, He, Guojun, Cui, Xin, Yuan, Xiaojie, Zhou, Shujie, He, Mingrui, Qiu, Tianyun, Zhao, Chenghan, Green, Martin A, Sun, Kaiwen, and Hao, Xiaojing

Subjects
SOLAR cells, OXIDATION states, LEAD, KESTERITE, TIN
Abstract

Cd‐free high‐bandgap Cu2ZnSnS4 (CZTS) solar cells are expected to offer green and low‐cost solutions to single‐junction and tandem photovoltaic markets. Despite attracting considerable attention in past years, reported certified efficiency has yet to exceed the 10% threshold. Sn‐related defects, especially those originating from Sn2+ oxidation states, lead to severe recombination loss and limit device performance. Here, the formation of these detrimental defects is suppressed by creating a more benign chemical environment during sulfurization annealing. With dominant Sn4+ states in the source material and the precursor, the oxidation state of Sn remains primarily in its native Sn4+ state during annealing and in the final film. Engineering the Sn‐related defects leads to shallower tail states in bulk CZTS and fewer interfacial defects. As a result, both radiative and non‐radiative recombination losses are alleviated, contributing to a certified 10.7% efficiency of the Cd‐free high‐bandgap CZTS solar cell. This strategy may advance various kesterite materials and other technologies with multivalent constituents. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Global COVID-19 lockdown highlights humans as both threats and custodians of the environment

Author

Bates, Amanda E., Primack, Richard B., Biggar, Brandy S., Bird, Tomas J., Clinton, Mary E., Command, Rylan J., Richards, Cerren, Shellard, Marc, Geraldi, Nathan R., Vergara, Valeria, Acevedo-Charry, Orlando, Colón-Piñeiro, Zuania, Ocampo, David, Ocampo-Peñuela, Natalia, Sánchez-Clavijo, Lina M., Adamescu, Cristian M., Cheval, Sorin, Racoviceanu, Tudor, Adams, Matthew D., Kalisa, Egide, Kuuire, Vincent Z., Aditya, Vikram, Anderwald, Pia, Wiesmann, Samuel, Wipf, Sonja, Badihi, Gal, Henderson, Matthew G., Loetscher, Hanspeter, Baerenfaller, Katja, Benedetti-Cecchi, Lisandro, Bulleri, Fabio, Bertocci, Iacopo, Maggi, Elena, Rindi, Luca, Ravaglioli, Chiara, Boerder, Kristina, Bonnel, Julien, Mathias, Delphine, Archambault, Philippe, Chauvaud, Laurent, Braun, Camrin D., Thorrold, Simon R., Brownscombe, Jacob W., Midwood, Jonathan D., Boston, Christine M., Brooks, Jill L., Cooke, Steven J., China, Victor, Roll, Uri, Belmaker, Jonathan, Zvuloni, Assaf, Coll, Marta, Ortega, Miquel, Connors, Brendan, Lacko, Lisa, Jayathilake, Dinusha R.M., Costello, Mark J., Crimmins, Theresa M., Barnett, LoriAnne, Denny, Ellen G., Gerst, Katharine L., Marsh, R.L., Posthumus, Erin E., Rodriguez, Reilly, Rosemartin, Alyssa, Schaffer, Sara N., Switzer, Jeff R., Wong, Kevin, Cunningham, Susan J., Sumasgutner, Petra, Amar, Arjun, Thomson, Robert L., Stofberg, Miqkayla, Hofmeyr, Sally, Suri, Jessleena, Stuart-Smith, Rick D., Day, Paul B., Edgar, Graham J., Cooper, Antonia T., De Leo, Fabio Cabrera, Garner, Grant, Des Brisay, Paulson G., Schrimpf, Michael B., Koper, Nicola, Diamond, Michael S., Dwyer, Ross G., Baker, Cameron J., Franklin, Craig E., Efrat, Ron, Berger-Tal, Oded, Hatzofe, Ohad, Eguíluz, Víctor M., Rodríguez, Jorge P., Fernández-Gracia, Juan, Elustondo, David, Calatayud, Vicent, English, Philina A., Archer, Stephanie K., Dudas, Sarah E., Haggarty, Dana R., Gallagher, Austin J., Shea, Brendan D., Shipley, Oliver N., Gilby, Ben L., Ballantyne, Jasmine, Olds, Andrew D., Henderson, Christopher J., Schlacher, Thomas A., Halliday, William D., Brown, Nicholas A.W., Woods, Mackenzie B., Balshine, Sigal, Juanes, Francis, Rider, Mitchell J., Albano, Patricia S., Hammerschlag, Neil, Hays, Graeme C., Esteban, Nicole, Pan, Yuhang, He, Guojun, Tanaka, Takanao, Hensel, Marc J.S., Orth, Robert J., Patrick, Christopher J., Hentati-Sundberg, Jonas, Olsson, Olof, Hessing-Lewis, Margot L., Higgs, Nicholas D., Hindell, Mark A., McMahon, Clive R., Harcourt, Rob, Guinet, Christophe, Hirsch, Sarah E., Perrault, Justin R., Hoover, Shelby R., Reilly, Jennifer D., Hobaiter, Catherine, Gruber, Thibaud, Huveneers, Charlie, Udyawer, Vinay, Clarke, Thomas M., Kroesen, Laura P., Hik, David S., Cherry, Seth G., Del Bel Belluz, Justin A., Jackson, Jennifer M., Lai, Shengjie, Lamb, Clayton T., LeClair, Gregory D., Parmelee, Jeffrey R., Chatfield, Matthew W.H., Frederick, Cheryl A., Lee, Sangdon, Park, Hyomin, Choi, Jaein, LeTourneux, Frédéric, Grandmont, Thierry, de-Broin, Frédéric Dulude, Bêty, Joël, Gauthier, Gilles, Legagneux, Pierre, Lewis, Jesse S., Haight, Jeffrey, Liu, Zhu, Lyon, Jarod P., Hale, Robin, D'Silva, Dallas, MacGregor-Fors, Ian, Arbeláez-Cortés, Enrique, Estela, Felipe A., Sánchez-Sarria, Camilo E., García-Arroyo, Michelle, Aguirre-Samboní, Giann K., Franco Morales, Juan C., Malamud, Shahar, Gavriel, Tal, Buba, Yehezkel, Salingré, Shira, Lazarus, Mai, Yahel, Ruthy, Ari, Yigael Ben, Miller, Eyal, Sade, Rotem, Lavian, Guy, Birman, Ziv, Gury, Manor, Baz, Harel, Baskin, Ilia, Penn, Alon, Dolev, Amit, Licht, Ogen, Karkom, Tabi, Davidzon, Sharon, Berkovitch, Avi, Yaakov, Ofer, Manenti, Raoul, Mori, Emiliano, Ficetola, Gentile Francesco, Lunghi, Enrico, March, David, Godley, Brendan J., Martin, Cecilia, Mihaly, Steven F., Barclay, David R., Thomson, Dugald J.M., Dewey, Richard, Bedard, Jeannette, Miller, Aroha, Dearden, Amber, Chapman, Jennifer, Dares, Lauren, Borden, Laura, Gibbs, Donna, Schultz, Jessica, Sergeenko, Nikita, Francis, Fiona, Weltman, Amanda, Moity, Nicolas, Ramírez-González, Jorge, Mucientes, Gonzalo, Alonso-Fernández, Alexandre, Namir, Itai, Bar-Massada, Avi, Chen, Ron, Yedvab, Shmulik, Okey, Thomas A., Oppel, Steffen, Arkumarev, Volen, Bakari, Samuel, Dobrev, Vladimir, Saravia-Mullin, Victoria, Bounas, Anastasios, Dobrev, Dobromir, Kret, Elzbieta, Mengistu, Solomon, Pourchier, Cloé, Ruffo, Alazar, Tesfaye, Million, Wondafrash, Mengistu, Nikolov, Stoyan C., Palmer, Charles, Sileci, Lorenzo, Rex, Patrick T., Lowe, Christopher G., Peters, Francesc, Pine, Matthew K., Radford, Craig A., Wilson, Louise, McWhinnie, Lauren, Scuderi, Alessia, Jeffs, Andrew G., Prudic, Kathleen L., Larrivée, Maxim, McFarland, Kent P., Solis, Rodrigo, Hutchinson, Rebecca A., Queiroz, Nuno, Furtado, Miguel A., Sims, David W., Southall, Emily, Quesada-Rodriguez, Claudio A., Diaz-Orozco, Jessica P., Rodgers, Ku'ulei S., Severino, Sarah J.L., Graham, Andrew T., Stefanak, Matthew P., Madin, Elizabeth M.P., Ryan, Peter G., Maclean, Kyle, Weideman, Eleanor A., Şekercioğlu, Çağan H., Kittelberger, Kyle D., Kusak, Josip, Seminoff, Jeffrey A., Hanna, Megan E., Shimada, Takahiro, Meekan, Mark G., Smith, Martin K.S., Mokhatla, Mohlamatsane M., Soh, Malcolm C.K., Pang, Roanna Y.T., Ng, Breyl X.K., Lee, Benjamin P.Y.-H., Loo, Adrian H.B., Er, Kenneth B.H., Souza, Gabriel B.G., Stallings, Christopher D., Curtis, Joseph S., Faletti, Meaghan E., Peake, Jonathan A., Schram, Michael J., Wall, Kara R., Terry, Carina, Rothendler, Matt, Zipf, Lucy, Ulloa, Juan Sebastián, Hernández-Palma, Angélica, Gómez-Valencia, Bibiana, Cruz-Rodríguez, Cristian, Herrera-Varón, Yenifer, Roa, Margarita, Rodríguez-Buriticá, Susana, Ochoa-Quintero, Jose Manuel, Vardi, Reut, Vázquez, Víctor, Requena-Mesa, Christian, Warrington, Miyako H., Taylor, Michelle E., Woodall, Lucy C., Stefanoudis, Paris V., Zhang, Xiangliang, Yang, Qiang, Zukerman, Yuval, Sigal, Zehava, Ayali, Amir, Clua, Eric E.G., Carzon, Pamela, Seguine, Clementine, Corradini, Andrea, Pedrotti, Luca, Foley, Catherine M., Gagnon, Catherine Alexandra, Panipakoochoo, Elijah, Milanes, Celene B., Botero, Camilo M., Velázquez, Yunior R., Milchakova, Nataliya A., Morley, Simon A., Martin, Stephanie M., Nanni, Veronica, Otero, Tanya, Wakeling, Julia, Abarro, Sarah, Piou, Cyril, Sobral, Ana F.L., Soto, Eulogio H., Weigel, Emily G., Bernal-Ibáñez, Alejandro, Gestoso, Ignacio, Cacabelos, Eva, Cagnacci, Francesca, Devassy, Reny P., Loretto, Matthias-Claudio, Moraga, Paula, Rutz, Christian, and Duarte, Carlos M.

Published
2021
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19. MASS cohort: Multicenter, longitudinal, and prospective study of the role of microbiome in severe pneumonia and host susceptibility.

Author

Wei, Xin, Guo, Li, Cai, Hongliu, Gu, Silan, Tang, Lingling, Leng, Yuxin, Cheng, Minghui, He, Guojun, Han, Yijiao, Ren, Xindie, Lin, Baoyue, Lv, Longxian, Shao, Huanzhang, Wang, Mingqiang, Wang, Hongyu, Dang, Dan, Wang, Shengfeng, Wang, Nan, Shen, Peng, and Wang, Qianqian

Subjects
POSITIVE end-expiratory pressure, ADULT respiratory distress syndrome, MEDICAL personnel, VENTILATOR-associated pneumonia, EMERGING infectious diseases, ENTEROTYPES, PNEUMONIA-related mortality
Abstract

The MASS cohort is a study conducted in Chinese ICU settings that aims to investigate the role of the microbiome in severe pneumonia and host susceptibility. The cohort consists of 286 patients with severe pneumonia, and various biological samples are collected from them. The study highlights the need for further research to establish causality and explore the impact of environmental factors on severe pneumonia. The authors conclude that the cohort provides valuable insights into emerging pathogens, risk factors, and determinants of survival among ICU patients. [Extracted from the article]

Published
2024
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28. Does the Squeaky Wheel Get More Grease? The Direct and Indirect Effects of Citizen Participation on Environmental Governance in China.

Author

Buntaine, Mark T., Greenstone, Michael, He, Guojun, Liu, Mengdi, Wang, Shaoda, and Zhang, Bing

Subjects
POLITICAL participation, EMISSIONS (Air pollution), FIELD research, ECONOMIC expansion, SOCIAL media, KUZNETS curve
Abstract

We conducted a nationwide field experiment in China to evaluate the direct and indirect impacts of assigning firms to public or private citizen appeals when they violate pollution standards. There are three main findings. First, public appeals to the regulator through social media substantially reduce violations and pollution emissions, while private appeals cause more modest environmental improvements. Second, public appeals appear to tilt regulators' focus away from facilitating economic growth and toward avoiding pollution-induced public unrest. Third, pollution reductions by treated firms are not offset by control firms, based on randomly varying the proportion of treated firms at the prefecture level. (JEL D22, L82, P28, P31, Q52, Q53, Q58) [ABSTRACT FROM AUTHOR]

Published
2024
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40. Subjective Performance Evaluation, Influence Activities, and Bureaucratic Work Behavior: Evidence from China.

Author

de Janvry, Alain, He, Guojun, Sadoulet, Elisabeth, Wang, Shaoda, and Zhang, Qiong

Subjects
JOB performance, CIVIL service, BUREAUCRACY
Abstract

Subjective performance evaluation could induce influence activities: employees might devote too much effort to pleasing their evaluator, relative to working toward the goals of the organization itself. We conduct a randomized field experiment among Chinese local civil servants to study the existence and implications of influence activities. We find that civil servants do engage in evaluator-specific influence to affect evaluation outcomes, partly in the form of reallocating work efforts toward job tasks that are more important and observable to the evaluator. Importantly, we show that introducing uncertainty about the evaluator's identity discourages evaluator-specific influence activities and improves bureaucratic work performance. (JEL D73, H83, J45, M54, O17, O18, P25) [ABSTRACT FROM AUTHOR]

Published
2023
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41. iEarth: an interdisciplinary framework in the era of big data and AI for sustainable development.

Author

Gong, Peng, Guo, Huadong, Chen, Bin, Chen, Fang, He, Guojun, Liang, Dong, Liu, Zhonghui, Sun, Zhongchang, Wu, Jin, Xu, Zhenci, Yan, Dongmei, and Zhang, Hongsheng

Subjects
BIG data, SUSTAINABLE development, SCIENTIFIC knowledge, ARTIFICIAL intelligence, INTERDISCIPLINARY approach to knowledge, REINFORCEMENT learning, NATURAL language processing
Abstract

I iEarth data. i iEarth data encompasses all Earth system data, including natural systems and human societies, forming the foundation for data-driven analysis and scientific discovery [[2]]. 1), including (i) iEarth data, (ii) iEarth science, (iii) iEarth analytics and (iv) iEarth decision. It is built upon the outcomes from iEarth data, iEarth science and iEarth analytics, to form a data-science-analytics-decision support system for SDGs monitoring, evaluation and response, and to advise spatially and temporally explicit pathways on achieving SDGs. [Extracted from the article]

Published
2023
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43. Learning Task-Oriented Channel Allocation for Multi-Agent Communication.

Author

He, Guojun, Cui, Shibo, Dai, Yueyue, and Jiang, Tao

Subjects
*REINFORCEMENT learning, *PARTIALLY observable Markov decision processes, *ARTIFICIAL intelligence
Abstract

Benefiting from the rapid progress of wireless communication and artificial intelligence, multi-agent collaboration opens up new opportunities for various fields. To facilitate multi-agent acting as a group, effective communication plays a crucial role. Recently, many efforts based on multi-agent reinforcement learning have been made to enable effective multi-agent communication under limited bandwidth or noisy channel. However, current methods do not explore wireless resource allocation strategy explicitly. Moreover, due to ignoring task-relevant significance of information, traditional wireless resource allocation schemes may fail to guarantee the transmission efficiency and reliability for multi-agent communication. To this end, in this paper, we propose a task-oriented communication principle for multi-agent communication. We model the task-oriented channel allocation problem as a decentralized partially observable Markov decision process and propose a multi-agent reinforcement learning framework as a solution. Specifically, we design a novel variational information bottleneck to extract task-relevant information from local observation. Furthermore, a task-oriented channel allocation mechanism is developed to choose the allocation pattern with maximum expected gain. Finally, a double attention mechanism is developed to motivate the efficient utilization of task-relevant information. Experimental results show that our method can improve the effectiveness and efficiency of multi-agent communication, enhancing collaboration performance compared to baselines. [ABSTRACT FROM AUTHOR]

Published
2022
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