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4. The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory

Author

Cheng, Jiewei, Li, Tingwei, Wang, Yongyi, Ati, Ahmed H., and Sun, Qiang

Subjects
Condensed Matter - Materials Science
Abstract

Motivated by the recent experimental study on hydrogen storage in MXene multilayers [Nature Nanotechnol. 2021, 16, 331], for the first time we propose a workflow to computationally screen 23,857 compounds of MXene to explore the general relation between the activated H2 bond length and adsorption distance. By using density functional theory (DFT), we generate a dataset to investigate the adsorption geometries of hydrogen on MXenes, based on which we train physics-informed atomistic line graph neural networks (ALIGNNs) to predict adsorption parameters. To fit the results, we further derived a formula that quantitatively reproduces the dependence of H2 bond length on the adsorption distance from MXenes within the framework of Pauling's resonating valence bond (RVB) theory, revealing the impact of transition metal's ligancy and valence on activating dihydrogen in H2 storage.

Published
2023
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14. The third Intensive Care Bundle with Blood Pressure Reduction in Acute Cerebral Haemorrhage Trial (INTERACT3): an international, stepped wedge cluster randomised controlled trial

Author

Robinson, Thompson, Miranda, J. Jaime, Anderson, Craig S., You, Chao, Song, Lili, Parry-Jones, Adrian, Sprigg, Nikola, Durrans, Sophie, Harris, Caroline, Bamford, Ann, Smith, Olivia, Herbert, Robert, Chen, Christopher, Whiteley, William, Hu, Rong, Billot, Laurent, Li, Qiang, Mysore, Jayanthi, Hu, Xin, Zhang, Yao, Liu, Feifeng, Sakamoto, Yuki, You, Shoujiang, Han, Qiao, Crutzen, Bernard, Li, Yunke, Cheung, Emily, Jan, Stephen, Liu, Hueiming, Ouyang, Menglu, Sun, Lingli, Chu, Honglin, Anjum, Anila, Gonzalez Mc Cawley, Francisca, Del Rio, Alejandra, Rimoli, Bruna, Cerantola, Rodrigo, Jeevarajah, Thanushanthan, Kannangara, Madhushani, Joseph, Andrene, Nanayakkara, Chamath, Chen, Xiaoying, Malavera, Alejandra, Zhang, Chunmiao, Yang, Zhao, Li, Brook, Meng, Zhuo, Liu, Leibo, Ning, Yi, Dong, Le, Armenis, Manuela, Lim, Joyce, Monaghan, Helen, Ma, Lu, Li, Xi, Luo, Rui, Cheng, Guojuan, Dong, Yilin, Liu, Ziqin, Wang, Shuihong, Zhang, Ying, Cheng, Jipeng, Shi, Hui, Li, Wenjing, Mou, Langming, Yi, Ping, Chen, Chen, Chen, Xue, Weerawardena, Shalomi, Ellawala, Poornima, Ranasinghe, Enalee, Rodrigo, Chrishmi, Wahab, Kolawala, Adeniyi, Sunday, Pandian, Jeyaraj, Khanna, Megha, Muñoz Venturelli, Paula, González, Francisca, Urrutia Goldsack, Francisca, Wasay, Mohammad, Begum, Dilshad, Pontes-Neto, Octavio, Camilo, Millene, Dias, Francisco, Vincenzi, Octavio, Moro, Carla, Santos, Renata, Texeira, Nara, Longo, Alexandre, Liberato, Rafaela, Martins, Sheila, Pille, Arthur, Chwal, Bruna, Silva, Isabel, Titton, Natacha, Weiss, Gustavo, Mora, Daissy, Ouriques, Magda, Carbonera, Leonardo, Bazan, Rodrigo, Modolo, Gabriel, Winckler, Fernanda, Miranda, Luana, Souza, Juli, Rojo, Alexis, Uslar, Wilhelm, Medel, Lorena, Lopez, Javiera, Herrero, Diego, Lavados, Pablo, Vargas Latorre, Barbara, Conejan, Nathalie, Esparza, Tomas, Sotomayor, Patricio, Wenger, Denisse, Gigoux, Juan Pablo, Letelier, Aldo, Acevedo, Lilian, Moya, Vivianne, Figueroa, Cristian, Vallejos, Nicol, Guerrero, Rodrigo, Velasquez, Mauricio, Vallejos, Jose, Pallauta, Kimerly, Santibanez, Tamara, Queirolo, Angelo, Lobos, Andrea, Jiang, Yongming, Li, Weimin, Huang, Wei, Luo, Ke, Liu, Gangying, Tang, Guanghai, Yang, Guang, Jiang, Hongtao, Zhang, Xu, Jing, Hongyan, Zhu, Sheng, Pu, Bo, Lv, Dong, Kang, Hui, Hu, Qiuping, She, Xiaochun, Jiang, Xiaoming, Chen, Yanli, Yang, Shenghua, He, Jianjun, Li, Zongping, Cheng, Gang, Huang, Hailin, Wang, Xiaoyi, Lin, Jianqiong, Chen, Minhui, Yang, Chenghao, Ding, Hao, Deng, Yunliang, Luo, Fei, Zhang, Rongjun, Wang, Xiaofeng, Zhang, Hongbing, Yang, Xiaoliang, Zhang, Yang, Yang, Chengyi, He, Yu, Liu, Feng, Wang, Rongjie, Zhang, Yuhui, Xin, Xiaodong, Feng, Bin, Hao, Wanru, Song, Chang, Guo, Yun, Jiang, Dehua, Chen, Jie, Tang, Changtong, Zhu, Hongliang, Li, Xin, Cui, Jin, Xu, Haidong, Li, Boyang, Tang, Fusheng, Li, Yuanbin, Gao, Min, Yang, Bo, Xu, Xuejun, Deng, Bing, Zheng, Yi, Ge, Yuanhong, Chen, Keyu, Liu, Yang, Li, Xinshen, Zhong, Tingting, Xu, Jianfeng, Zhang, Hai, Wang, Jiyue, Zhu, Jianxin, Sun, Hanyu, Yu, Fuhua, Zhang, Xueguang, Xu, Jianguo, Zhang, Mingsen, Wang, Bin, Ma, Yiming, Jiang, Donglin, Zhou, Jun, Liu, Cong, Nie, Wenhong, Li, Mingguo, Tian, Tao, Li, Yong, He, Mingfang, Tu, Xiaolong, Wu, Zhengjun, Liu, Hong, Zhong, Dongsheng, Jiang, Rongcai, Sun, Jian, Tian, Ye, Wei, Yingsheng, An, Shuo, Wei, Pingbo, Luo, Le, Lin, Bin, Liu, Gang, Wen, Yan, Cai, Qiang, Chen, Qianxue, Lei, Pan, Li, Zhiyang, Zhang, Meifang, He, Jiaquan, Chen, Yan, Liu, Jun, Liu, Xinghai, Li, Junyan, Chen, Min, Wang, Jing, Zhou, Bingzhi, Ye, Baichun, Zhang, Jiancheng, Zhang, Manyuan, Pan, Xuming, Yu, Xiaoxiang, Xu, Jian, Xiao, Qingbao, Wang, Yuefei, Tao, Liang, Shi, Lin, Zheng, Niandong, You, Guoliang, Lei, Bo, Chen, Shu, Wu, Honggang, Hu, Jin, Zhao, Jianlan, Yu, Jian, Yuan, Qiang, Du, Zhuoying, Tang, Xielin, Li, Qianke, Liu, Shenghua, Yang, Feilong, Xiao, Kui, Luo, Chao, Wang, Guang, Che, Xudong, Teng, Zhipeng, Wan, Wenwu, Li, Jun, Liu, Yu, Fan, Mingbo, Zhang, Tao, Cai, Lun, Ma, Yuan, Ma, Zhifeng, Li, Bin, He, Linlin, Li, Jinghui, Zhang, Weibing, Zhang, Shuxin, Zhang, Hongzhen, Dai, Yingguang, Lei, Jun, Mao, Lei, Huang, Yiyang, Zhou, Zhi, Chen, Ping, Chen, Fang, Wei, Pan, Li, Tiangui, Chen, Honglin, Zeng, Mengfei, Mou, Kejie, Xue, Jun, Jiang, Yong, Tang, Xiaoping, Chen, Tao, Zhang, Yalan, Xu, Yanbing, Gu, Yuchen, Chen, Lei, Zhao, Yujun, Yang, Bin, Kuai, Peng, Wang, Xi, Yang, Yuwang, Hu, Xueling, Zhang, Huitian, Yang, Yintao, Wang, Weifeng, Zhang, Junyi, Cheng, Wei, Zhang, Xiaoxue, Ma, Xiaowen, He, Qin, Zhang, Li, Gao, Rong, Liu, Huixiang, Ye, Jingwei, Xu, Ping, Wu, Xin, Yuan, Yuan, Zou, Peng, Zhang, Zhen, Cheng, Jiyong, Zhou, Zhangming, Zeng, Yijun, Liang, Zhang, Du, Deming, Yu, Shui, Cao, Yongjun, Xu, Jiaping, Huang, Zhichao, Chen, Dongqin, Xiao, Wenfeng, Zhu, Li, Yuan, Miao, Wang, Yuhai, Shi, Dongliang, Hu, Xu, Xiang, Dingchao, Shi, Like, Wang, Hongqin, Yang, Liu, Miao, Wang, Hu, Yiyi, Zhao, Yuchun, Hu, Xi, Zhou, Weiduo, Sun, Chao, Tang, Dong, Yao, Kun, You, Jin, Chen, Shishi, Yao, Jianmin, Li, Huanmei, Liu, Jinmei, Bai, Ailin, Yi, Yong, Deng, Qingshan, Luo, Peng, Wang, Han, Jiang, Jingcheng, Yang, Qingwei, He, Shunpo, Wang, Jun, Chen, Yu, He, Hua, Deng, Yuyang, Cao, Zhikai, Yi, Xuxia, Luo, Jinbiao, Luo, Shuang, Gong, Min, Liu, Li, Gao, Xuejun, Liu, Jia, Wu, Li'e, Zhang, Jia, Sun, Hongying, Li, Xinhui, Jia, Lu, Wu, Jianbing, Zhang, Jie, Zhang, Huajun, Du, Chunfu, Li, Shun, Yang, Xiaobin, He, Jie, Liao, Lei, Zhou, Gezhi, Dong, Wentao, Chen, Yunxiang, Lin, Xiaofeng, Shui, Xujian, Zhang, Peng, Zhao, Yuan, Yang, Hongli, Zhao, Wenbin, Zhang, Xiaoyi, Chen, Jincao, Wu, Qian, Dai, Xuan, Tang, Baogui, Wang, Yinjuan, Liu, Tao, Zhang, Haixia, Duan, Faliang, Luo, Ming, Jiao, Qingfang, Lei, Guoliang, Wang, Dong, Song, Chunwang, Tan, Haopeng, Ye, Feng, Qin, Xinghu, Liang, Xiaolong, Liu, Junling, Yang, Lang, Yang, Jie, Lin, Yapeng, Yang, Qian, Ma, Xuntai, Qi, Yinkuang, Pan, Baogen, Jiang, Caixia, Ye, Zhanying, Dong, Ce, Yue, Xiongfei, Yang, Xiaopeng, Maimaitiyiming, Tuoheti, Dong, Jun, Wu, Yonggang, Gao, Feng, Zhao, Deqiang, Zhang, Xinghai, Wang, PengJun, Jiang, Hongbo, Li, Jianping, Zhang, Wei, Chen, Jing, Tong, Haibo, Wang, Yonghong, Qiao, Kaipeng, Guo, Fuyou, Zhang, Mingchu, Hu, Yan, Feng, Mengzhao, Song, Dengpan, Zuo, Yi, Chen, Shangjun, Qian, Chao, Li, Baoming, Ma, Jingku, Zhang, Sunfu, Kong, Bin, Dong, Xingyu, Fang, Sheng, Lu, Bin, Li, Yang, Yang, Yongling, Yu, Hong, Sun, Huaiyu, Wang, Yue, Wang, Weimin, Li, Tong, Li, Shengli, Xu, Zhiming, Wang, Yongyi, Dong, Qiang, Tang, Yuping, Chu, Heling, Lu, Ying, Wang, Zhong, Sun, Xiaoou, Zhao, Jianhua, Yang, Shuaifeng, Qian, Xiying, Saroja, Aralikatte Onkarappa, Naik k, Ravishankar, Chindhi, Sandip, Pampaniya, Nakul, Amaresh, Kurubara, Iype, Thomas, R, Dileep, Rajan, Reeja, Panicker, Praveen, Das, Rupjyoti, Choudhury, Nupur, Gohain, Pankaja, Webster, Jemin, Pakma, Biyol, Sangi, Lalbiak, Sebastian, Ivy, Aggrawal, Gaurav, Raj, Komal, Rajoura, Deepankshi, Singh, Sulena, Aggrawal, Varun, Narang, Amit, Arauz, Antonio, Cano-Nigenda, Vanesa, López-Mena, Diego, Valdez-Ruvalcaba, Héctor, Toledo-Treviño, Roberto, Obiako, Reginald, Abubakar, Sani, Emeka, Oguike, Olayemi, Balogun, Lois, Melika, Philip, Ibinaiye, Comfort O, Olurishe, Okubadejo, Njideka, Agabi, Osigwe, Ojo, Oluwadamilola, Wahab, Kolawole, Bello, Abiodun, Ibukun, Oyinloye, Sanayaolu, Olufemi, Jimoh, Abdulraheem, Waheed, Shahid, Kamal, Dr.Ayeesha, Shoaib, Raja Farhat, Orooj, Fizza, Majid, Sadaf, Zehra, Taskeen, Khan, Abdus Salam, Shanker, Ravi, Syed, Nadir Ali, Ahmad, Nashwa, Abanto, Carlos, Valencia, Ana, Barrientos, Danny, Ramirez, Jorge, Calle, Pilar, Palliyeguruge, Dilum, Muthucumarana, Sumudu, Ratnayaka, Shiroma, Ganihiarachchi, Dilhara, Bandaranayake, Arundathi, Somaratne, S.D.B, Narayana, Saumya, Gallage, Sithara, Senanayake, Bimsara, Samarasiri, Udari, Luke, Dunya, Sivapathasundaram, Mythily, Sahadevan, Vithoosan, Rasmi, Amani, Deshaka, Yuran, Fernando, Nilukshi, Munasinghe, Aruna, Rathnapriya, Kapilanga, Nissanka, A.S, Karunathilake, Kanchana, Gayan, Isuru, Wijenayake, Kaminda, Gunasekara, Hasitha, Vidyarathne, Jagath, Keshavaraj, Ajantha, Janarthanan, Kanagasabapathy, Gerald Jeevathasan, Arhivalaky, Sivamainthan, Sivaram, John Priyanth, Mathyamuthan, John Priyanth, Abirami, Rajendiran, Thambippillai, Alwis, Sanjeewa, Gunasekare, Nushara, Liyanarachchi, Vasundara, Dissanayake, Athula, Uluwattage, Wimalasiri Mewa, Ratnayake, Gimhani, Rajinee, Charika, Jayawardana, Sakura, Peiris, Janaka, Wicramasinghe, Ranjith, Fernando, Chamila, Abbas, Jessie, Withanage, Nethmini, Bandara, Makaranda, Mai, Duy Ton, Nguyen, Van Chi, Dao, Viet Phuong, Vuong, Xuan Trung, Nguyen, Tien Dung, Dinh, Trung Hieu, Phan, Ha Quan, Bui, Quoc Viet, Phung, Dinh Tho, Pham, Quang Tho, Pham, Dinh Dai, Do, Duc Thuan, Dang, Phuc Duc, Dang, Minh Duc, Nguyen, Dang Hai, Nguyen, Thi Phuong Nga, Nguyen, Quoc Huy, Pham, Quoc Dai, Chau, Quoc Vinh, Tai, Vinh Thy Van, Le, Tran Vinh, Le, Cong Tri, Tran, Ha Mai Khuong, Nguyen, Huu Khanh, Ngyen, Hoang Minh Thao, Vo, Duc Chien, Nguyen, Thai My Phuong, Tran, Trung Thanh, Vo, Thi Hanh Vi, Cao, Hao Nhien, Nguyen, Ba Thang, Le, Thi Ngoc Suong, La, Thien Duc, Pham, Chi Duc, Thai, Huy, Muñoz-Venturelli, Paula, de Silva, Asita, Thang, Nguyen Huy, Wahab, Kolawole W, Pandian, Jeyaraj D, Pontes-Neto, Octavio M, Shi, Haiping, Wang, Xia, Zhao, Yang, Li, Hao, Liu, Yi, Jiang, Yan, Wu, Bo, Liu, Ming, and Anderson, Craig S

Published
2023
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20. The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory.

Author

Cheng, Jiewei, Li, Tingwei, Wang, Yongyi, Ati, Ahmed H., and Sun, Qiang

Subjects
CHEMICAL bond lengths, ADSORPTION (Chemistry), VALENCE bonds, DENSITY functional theory, HYDROGEN storage, TRANSITION metals
Abstract

Motivated by the recent experimental study on hydrogen storage in MXene multilayers [Liu et al., Nat. Nanotechnol. 16, 331 (2021)], for the first time we propose a workflow to computationally screen 23 857 compounds of MXene to explore the general relation between the activated H2 bond length and adsorption distance. By using density functional theory we generate a dataset to investigate the adsorption geometries of hydrogen on MXenes, based on which we train physics-informed atomistic line graph neural networks (ALIGNNs) to predict adsorption parameters. To fit the results, we further derived a formula that quantitatively reproduces the dependence of H2 bond length on the adsorption distance from MXenes within the framework of Pauling's resonating valence bond theory, revealing the impact of transition metal's ligancy and valence on activating dihydrogen in H2 storage. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
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29. Construction of risk prognosis model for malignant pleural mesothelioma based on ferroptosis gene and study on tumor immune microenvironment.

Author

CHEN Zilin, YANG Sihai, MA Honghui, WANG Yongyi, YE Xiaoying, HUANG Haoyu, and WANG Wenqian

Subjects
TUMOR microenvironment, PLEURA cancer, PROGNOSIS, B cells, MESOTHELIOMA, LIGANDS (Biochemistry)
Abstract

Objective: To screen ferroptosis genes related to prognosis of malignant pleural mesothelioma (MPM), explore the relationship between ferroptosis and tumor immune microenvironment and provide a new perspective for targeting and immunotherapy of MPM patients. Methods: The differentially expressed genes (DEGs) in MPM tumor group and normal group were analyzed in GEO database; intersection of DEGs and ferroptosis genes to obtain differentially expressed ferroptosis-related genes (DE-FRGs). GO, KEGG function enrichment and protein protein interaction (PPI) were used to identify the signal pathways mainly involved by DEFRGs. The prognosis related ferroptosis genes were identified by univariate COX analysis. LASSO regression analysis was used to screen the best DE-FRGs for establishing the risk prediction model, and a risk prognosis model based on the best DE-FRGs was established by multivariate cox analysis to verify the prediction effect of the model. Finally, CIBERSORT and other algorithms were used to analyze tumor immune cell infiltration and evaluate immune microenvironment. Results: Twenty-four prognosis related DE-FRGs were screened, which were mainly concentrated in ferroptosis, transcriptional regulation and response to inorganic substances. A MPM risk prediction model based on five ferroptosis-related genes (ALDH3A2, CAV1, HRAS, CDCA3 and RRM2) was established and validated. In the model, the proportion of CD8+T cells and macrophages in high-risk group were higher, while the proportion of B lymphocytes was lower. In addition, PD-1, CTLA-4 and their ligands at immune checkpoint had higher expression status in high-risk group. Conclusion: The MPM risk prediction model based on five ferroptosis-related genes is established, and the immune status in the model is clarified. It provides a certain research basis for targeting and immunotherapy of MPM. The predictive ability of this model in MPM needs to be further verified in clinical practice to better predict disease stratification and treatment management. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Rapid and simultaneous identification of Lonicera Japonicae Flos and Lonicerae Flos using MLPA‐HRM and rhPCR‐HRM.

Author

Mo, Jing, Wang, Wenbin, Wang, Yongyi, Cheng, Huachun, Li, Xiaofang, and Wang, Bo

Subjects
JAPANESE honeysuckle, CHINESE medicine, TEMPERATURE, SINGLE nucleotide polymorphisms, GENOTYPES
Abstract

Background: Lonicera Japonicae Flos (LJF) and Lonicerae Flos (LF), derived from different species of Lonicera, are two different types of traditional Chinese medicine (TCM) according to their differences in composition and effectiveness. Because of the high price, good effectiveness of the medicinal, and functional values, LJF is often adulterated by LF in the market. Therefore, two single nucleotide polymorphism‐based genotyping methods, multiplex ligation‐dependent probe amplification high‐resolution melting (MLPA‐HRM) and RNase H2‐dependant PCR (rhPCR)‐HRM, were established for the identification of LF and LJF. Results: Highly specific MLPA probes and rhPCR‐primers were designed based on the trnL‐trnF regions. The melting temperature (Tm) of amplicons was derived from the peak of the HRM curve, and the differences in Tm values were used to identify LJF and LF. The two methods have both shown good specificity for identifying LJF and LF without cross‐reaction, and have high sensitivity with the detection limits of 0.1 ng DNA template. For mixed samples, MLPA‐HRM could detect LJF mixed with 10% LF while rhPCR‐HRM could detect 5% LF in mixed samples. The MLPA‐HRM and rhPCR‐HRM were performed on DNA extracted from 54 samples of LJF and LF randomly selected from the medicinal material market, all samples were successfully identified and further verified with DNA barcoding. Conclusion: MLPA‐HRM and rhPCR‐HRM methods developed in our study not only provided fast, simple, sensitive, and practical identification of LJF and LF, but also provided technical references for the adulteration identification of other herb‐medicines, they are of great significance to the quality control and safe use of plant medicinal materials. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Personal, Health, Academic, and Environmental Predictors of Stress for Residence Hall Students

Author

Dusselier, Lauri, Dunn, Brian, and Wang, Yongyi

Abstract

The authors studied contributors to stress among undergraduate residence hall students at a midwestern, land grant university using a 76-item survey consisting of personal, health, academic, and environmental questions and 1 qualitative question asking what thing stressed them the most. Of 964 students selected at random, 462 (48%) responded to the survey. The authors weighted data to reflect the overall university-wide undergraduate population (55% men, 12% minority or international, and 25% freshmen). Women and US citizens experienced greater stress than did men and non-US citizens, respectively. Frequency of experiencing chronic illness, depression, anxiety disorder, seasonal affective disorder, mononucleosis, and sleep difficulties were significant stress predictors. Although alcohol use was a positive predictor, drug use was a negative predictor of stress. Both a conflict and a satisfactory relationship with a roommate, as well as a conflict with a faculty or staff member, were also significant predictors of stress. (Contains 1 note.)

Published
2005

42. Early Retinal Microvasculopathy in Prediabetic Patients and Correlated Factors.

Author

Xu, Yi, Zhu, Xiaofeng, Wang, Yongyi, Chu, Zhongdi, Wang, Ruikang K., Lu, Lina, and Zou, Haidong

Subjects
LDL cholesterol, OPTICAL coherence tomography, GLOMERULAR filtration rate, KIDNEY physiology, PREDIABETIC state
Abstract

Introduction: We aimed to detect early retinal microcirculation changes in prediabetic patients and investigate their correlation with clinical examinations. Methods: Forty-seven prediabetic individuals, 29 controls, and 81 type 2 diabetic mellitus (T2DM) patients were enrolled in this study. A review of clinical data and spectral-domain optical coherence tomography angiography (SD-OCTA) parameters of macular vessel diameter (VD), foveal avascular zone (FAZ), and macular vessel area density (VAD) was performed. Results: Levels of low-density lipoprotein cholesterol and triglycerides in prediabetes and T2DM groups were significantly higher than those in the control group. The urine microalbumin-to-creatinine ratio (ACR) was mildly and moderately increased in the prediabetes and T2DM groups, respectively. The estimated glomerular filtration rate of the three groups was within the normal range. SD-OCTA showed that VAD in the superficial macular area was decreased in the prediabetes group compared to the control group (p = 0.01). The FAZ size, particularly in the deep layer, was expanded in the prediabetes group. In the deep retinal layer of the macular area, VD and FAZ size in the prediabetes group were larger than those in the control group. In the prediabetes group, the axial length was significantly correlated with macular VD and FAZ size (p < 0.05), and ACR was correlated with FAZ size (p < 0.05). Age had a negative correlation with VAD (p < 0.01). ACR had a positive correlation with FAZ size (p < 0.05). Conclusions: Enlargement and irregularity of the FAZ area, deep capillary dilation, and a decrease in VAD occur in the retina of prediabetic patients with mild kidney function impairment. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Progressive Fracture Behavior and Acoustic Emission Release of CJBs Affected by Joint Distance Ratio.

Author

Wang, Yongyi, Gong, Bin, Zhang, Yongjun, Yang, Xiaoyu, and Tang, Chun'an

Subjects
*ACOUSTIC emission, *DIGITAL image correlation, *PROGRESSIVE collapse, *COLUMNS
Abstract

The progressive collapse behavior and energy release of columnar jointed basalts (CJBs) can be greatly influenced by different joint distance ratios. By adopting the digital image correlation, a series of heterogeneous CJB models are established. The continuous fracture process and acoustic emissions (AEs) are captured numerically under varying lateral pressures. The load curves under different joint distance ratios and model boundaries are analyzed. Meanwhile, the strength, deformation modulus and AE rule are discussed. The data indicate that under plane strain, the troughs of compression strength appear at the column dip angle β = 30°, 150°, 210° or 330°; the equivalent deformation modulus changes in an elliptical way with β increasing; the compression strength and equivalent deformation modulus are higher than the case between plane stress and plane strain under different joint distance ratios. When β = 30°, the accumulation of AE energy corresponding to the stress peak under plane strain are higher than the case between plane stress and plane strain but becomes lower when β increases to 60°, which implies the critical transformation of the AE energy-related failure precursor affected by column dip angle. These achievements will contribute to the design, construction and support of slopes and tunnels encountering CJBs. [ABSTRACT FROM AUTHOR]

Published
2022
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