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301. An Analytical Theory for the Growth from Planetesimals to Planets by Polydisperse Pebble Accretion

Author

Lyra, Wladimir, Johansen, Anders, Cañas, Manuel H., and Yang, Chao-Chin

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

Pebble accretion is recognized as a significant accelerator of planet formation. Yet, only formulae for single-sized (monodisperse) distribution have been derived in the literature. These can lead to significant underestimates for Bondi accretion, for which the best accreted pebble size may not be the one that dominates the mass distribution. We derive in this paper the polydisperse theory of pebble accretion. We consider a power-law distribution in pebble radius, and we find the resulting surface and volume number density distribution functions. We derive also the exact monodisperse analytical pebble accretion rate for which 3D and 2D accretion are limits. In addition, we find analytical solutions to the polydisperse 2D Hill and 3D Bondi limits. We integrate the polydisperse pebble accretion numerically for the MRN distribution, finding a slight decrease (by an exact factor 3/7) in the Hill regime compared to the monodisperse case. In contrast, in the Bondi regime, we find 1-2 orders of magnitude higher accretion rates compared to monodisperse, also extending the onset of pebble accretion to 1-2 order of magnitude lower in mass. We find Myr-timescales, within the disk lifetime, for Bondi accretion on top of planetary seeds of masses $10^{-6}-10^{-4} M_\oplus$, over a significant range of the parameter space. This mass range overlaps with the high mass end of the planetesimal initial mass function, and thus pebble accretion is possible directly following formation by streaming instability. This alleviates the need for mutual planetesimal collisions as a major contribution to planetary growth., Comment: 17 pages, 12 figures. Accepted to ApJ

Published
2023
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302. High-stakes test anxiety among Taiwanese adolescents: a longitudinal study

Author

Tzu-Yang Chao, Yao-Ting Sung, and Fen-Lan Tseng

Subjects
Academic achievement, adolescents, cram school attendance frequency, the basic competence test, the comprehensive assessment program, test anxiety, Education (General), L7-991
Abstract

AbstractThis study examined changes of test anxiety under the reform of the examination system in Taiwan. We sampled 46,361 Grade 9 students in Taiwan for 9 consecutive years starting from 2011 to collect data on their test anxiety, cram school attendance frequency, and academic achievement. Students’ test anxiety level was compared between three periods: Basic Competence Test (2011–2013), Comprehensive Assessment Program (CAP 1; 2014–2016), and CAP 2(2017–2019). The results indicated that first, during CAP 1, students’ test anxiety increased, but decreased in CAP 2. Second, the test anxiety level of students those with an upper-intermediate level of academic achievement, increased. Finally, the cram school attendance frequency didn’t differ among the three periods. We provide suggestions for countries with a similar social climate. For potentially controversial social issues, such as test anxiety, policy makers should develop corresponding measures in advance on the basis of empirical evidence.

Published
2024
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303. Early mNGS testing for diagnose and prognostic prediction of early onset pneumonia among in-hospital cardiac arrest patients undergoing extracorporeal cardiopulmonary resuscitation

Author

Rui-ming Guo, Xing-xing Li, Yi-heng Zhou, Yi-juan Liu, Jun Li, Guo-wei Fu, Hui Zhao, Xin Zhang, and Yang-chao Zhao

Subjects
in-hospital cardiac arrest, extracorporeal cardiopulmonary resuscitation, metagenomic next-generation sequencing, early onset pneumonia, prognosis, Microbiology, QR1-502
Abstract

ObjectivesMetagenomic next-generation sequencing (mNGS) is emerging as a novel diagnostic technology for various infectious diseases; however, limited studies have investigated its application in etiological diagnosis of early onset pneumonia (EOP) among patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR) following in-hospital cardiac arrest (IHCA), The clinical significance of early mNGS in predicting short-term prognosis of IHCA patients after ECPR remains unclear.MethodsThis retrospective study included 76 patients with IHCA who underwent ECPR at the First Affiliated Hospital of Zhengzhou University from January 2018 to December 2022. Baseline characteristics and etiological data of all patients during their hospitalization were collected and statistically analyzed. The primary outcome of this study was the diagnosis of EOP, while the secondary outcomes included successful extracorporeal membrane oxygenation (ECMO) weaning and survival at discharge. Additionally, the characteristics of bronchoalveolar lavage fluid (BALF) flora in these patients were compared by analyzing both mNGS results and culture results.ResultsMultivariate logistic regression were employed to analyze the predictors of ECMO weaning failure, mortality at discharge, and the incidence of EOP. Ultimately, patients with lower SOFA scores on admission [OR (95%CI): 1.447 (1.107-1.890), p=0.007] and those who underwent early mNGS testing within 48 hours after ECPR [OR (95%CI): 0.273 (0.086-0.865), p=0.027] demonstrated a higher probability of successful weaning from ECMO. Patients with higher SOFA scores on admission [OR (95%CI): 2.404 (1.422-4.064), p=0.001], and elevated lactate levels [OR (95%CI): 1.176 (1.017-1.361), p=0.029] exhibited an increased likelihood of mortality at discharge. Furthermore, early mNGS detection [OR (95%CI): 0.186 (0.035-0.979), p=0.047], and lower CRP levels (48h-7d after ECMO) [OR (95%CI):1.011 (1.003-1.019), p=0.006] were associated with a reduced incidence of EOP. In addition, the pathogens detected by mNGS within 48 hours after ECPR were mainly oral colonizing bacteria and viruses, and viruses were in the majority, while all BALF cultures were negative. In contrast, between 48 hours and 7 days after ECPR, BALF cultures were positive in all EOP patients.ConclusionsEarly mNGS testing to identify microbial flora facilitates timely adjustment of antibiotic regimens, thereby reducing the incidence of EOP and improving short-term prognosis in patients undergoing ECPR following IHCA.

Published
2024
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305. Truck Climbing Dynamics Simulation Based on Trucksim

Author

Bian, Wen, Yang, Jingshuai, Yang, Chao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Wang, Wuhong, editor, Guo, Hongwei, editor, Jiang, Xiaobei, editor, Shi, Jian, editor, and Sun, Dongxian, editor

Published
2024
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306. Research on Jig Shape Design of Deployable Wings Based on CFD CSD Coupling Method

Author

Xu, Yuntao, Tan, Dalin, Yang, Chao, Dai, Yuting, Wang, Qiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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307. Equivalent Beam Modeling Method for Geometric Nonlinear Static Aeroelastic Analysis

Author

Chen, Zhiying, Meng, Yang, Wan, Zhiqiang, Yang, Chao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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308. Influence of Kinematics on Aerodynamic Characteristics of an Albatross-Like Flexible Flapping Wing in Forward Flight

Author

Gao, Nongyue, Xie, Changchuan, Yang, Chao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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311. Effect of Vibrating and Sieving Time on the Reliability of Test Results for Gradation Analysis of Fragile Geomaterials

Author

Yang, Chao, Liu, Shuguang, Bai, Sikai, Zheng, Zheng, Editor-in-Chief, Xi, Zhiyu, Associate Editor, Gong, Siqian, Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Baochang, Series Editor, Zhang, Wei, Series Editor, Zhu, Quanxin, Series Editor, Zheng, Wei, Series Editor, Yuan, Bingxiang, editor, Bilgin, Hüseyin, editor, Luo, Qingzi, editor, and Han, Zejun, editor

Published
2024
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314. Load Balancing Algorithm in a Software-Defined Network Environment with Round Robin and Least Connections

Author

Wijaya, Chandra, Wiryasaputra, Rita, Huang, Chin-Yin, Tanato, Jodi, Yang, Chao-Tung, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Deng, Der-Jiunn, editor, and Chen, Jyh-Cheng, editor

Published
2024
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318. Pavement Distress Detection Using YOLO and Faster RCNN on Edge Devices

Author

Chiu, Chen-Kang, Liu, Jung-Chun, Chan, Yu-Wei, Yang, Chao-Tung, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hung, Jason C., editor, Yen, Neil, editor, and Chang, Jia-Wei, editor

Published
2024
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319. Fire and Smoke Detection Using YOLO Through Kafka

Author

Lien, Kai-Yu, Liu, Jung-Chun, Chan, Yu-Wei, Yang, Chao-Tung, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hung, Jason C., editor, Yen, Neil, editor, and Chang, Jia-Wei, editor

Published
2024
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320. Implementation of a Deep Learning-Based Application for Work-Related Musculoskeletal Disorders’ Classification in Occupational Medicine

Author

Chan, Yu-Wei, Tseng, Yi-Cyuan, Chen, Yu-An, Tsan, Yu-Tse, Liu, Chen-Yen, Lu, Shang-Zhe, Xu, Li-Fan, Yang, Chao-Tung, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hung, Jason C., editor, Yen, Neil, editor, and Chang, Jia-Wei, editor

Published
2024
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321. An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

Author

Wang, Lu-Yan, Liu, Jung-Chun, Huang, Cheng-Kai, Wei, Shih-Jie, Yang, Chao-Tung, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hung, Jason C., editor, Yen, Neil, editor, and Chang, Jia-Wei, editor

Published
2024
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322. CMGN: Cross-Modal Grounding Network for Temporal Sentence Retrieval in Video

Author

Zhang, Qun, Jiang, Bin, Zhang, Bolin, Yang, Chao, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Wang, Tong, editor, Fan, Hongfei, editor, Liu, Dongning, editor, and Du, Bowen, editor

Published
2024
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323. Investigation and Wind Tunnel Test on Gust Load Alleviation for a Flying Wing

Author

Xu, Yuntao, Liu, Yi, Wu, Zhigang, Yang, Chao, Chinese Society of Aeronautics and Astronautics, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, and Xu, Jinyang, Editorial Board Member

Published
2024
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324. MFHBT: Hybrid Binary Translation System with Multi-stage Feedback Powered by LLVM

Author

Yang, Zhaoxin, Chen, Xuehai, Wang, Liangpu, Guo, Weiming, Zhao, Dongru, Yang, Chao, Zhang, Fuxin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Chao, editor, Li, Zhenhua, editor, Shen, Li, editor, Wu, Fan, editor, and Gong, Xiaoli, editor

Published
2024
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325. On-Demand Triggered Memory Management Unit in Dynamic Binary Translator

Author

Xie, Benyi, Li, Xinyu, Yan, Yue, Yan, Chenghao, Liu, Tianyi, Zhang, Tingting, Yang, Chao, Zhang, Fuxin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Chao, editor, Li, Zhenhua, editor, Shen, Li, editor, Wu, Fan, editor, and Gong, Xiaoli, editor

Published
2024
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326. CCS: A Motif-Based Storage Format for Micro-execution Dependence Graph

Author

Zheng, Yawen, Han, Chenji, Zhang, Tingting, Yang, Chao, Wang, Jian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Chao, editor, Li, Zhenhua, editor, Shen, Li, editor, Wu, Fan, editor, and Gong, Xiaoli, editor

Published
2024
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327. Molecular Evolution and Increasing Macrolide Resistance of Bordetella pertussis, Shanghai, China, 2016-2022

Author

Fu, Pan, Zhou, Jinlan, Yang, Chao, Nijiati, Yaxier, Zhou, Lijun, Yan, Gangfen, Lu, Guoping, Zhai, Xiaowen, and Wang, Chuanqing

Subjects
Nucleotide sequencing -- Analysis -- Genetic aspects, Genomics -- Analysis -- Genetic aspects, Genetic research -- Analysis -- Genetic aspects, Genes -- Genetic aspects -- Analysis, Drug resistance in microorganisms -- Genetic aspects, DNA sequencing -- Analysis -- Genetic aspects, Genomes -- Genetic aspects -- Analysis, Infants -- Analysis, Bordetella pertussis -- Genetic aspects -- Analysis, Public health -- Analysis, RNA -- Analysis -- Genetic aspects, Evolutionary biology -- Genetic aspects -- Analysis, Health
Abstract

Whooping cough (pertussis) is a contagious respiratory illness caused by Bordetella pertussis. The introduction of the whole-cell vaccine (WCV) successfully decreased the incidence of pertussis. Although vaccination has been successful, [...]

Published
2024
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328. Delivery service for a service-oriented manufacturing supply chain with procurement and delivery time decisions.

Author

Yang, Zhuolin, Zheng, Yuting, Li, Jianbin, Zhu, Stuart X., and Yang, Chao

Subjects
SUPPLY chain management, NASH equilibrium, SUPPLY chains, CONSUMERS, MANUFACTURING industries
Abstract

This paper proposes a service-oriented manufacturing strategy in a two-echelon supply chain with random demand. When there is no inventory for the retailer to meet demand, customers can choose to order products through the retailer and products will be delivered to them by the manufacturer directly in the promised time, and we call this delivery service in service-oriented manufacturing. We employ a Stackelberg game-theoretic model to analyse the procurement quantity and delivery time decisions by the manufacturer as the leader and the retailer as the follower. Using the performance of the pure-manufacturing strategy as a benchmark, our main results follow. First, the equilibrium strategies of the two players are obtained. Clear decision rules are also given. Second, delivery service in service-oriented manufacturing can be an effective way to reduce the holding cost of the retailer. Third, we describe the customer loss caused by waiting and find how the manufacturer influences procurement quantity of the retailer through the decision of delivery time. Finally, delivery service in service-oriented manufacturing cannot always improve the profit of the manufacturer and the retailer. We give guidance for managers to provide it. [ABSTRACT FROM AUTHOR]

Published
2024
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329. Electrotaxis of alveolar epithelial cells in direct-current electric fields.

Author

Yang, Chao-Yue, Sun, Jian-Hui, Du, Juan, Zhang, Ying, Lu, Cong-Hua, Liu, Wen-Yi, Zhang, Ke-Jun, Zhang, An-Qiang, Zeng, Ling, Jiang, Jian-Xin, Li, Li, and Zhu, Kan

Subjects
Acute lung injury, Alveolar epithelial cells, Electric fields, Electrotaxis, Migration, Humans, Rats, Animals, Alveolar Epithelial Cells, Lung, Lung Injury, Cell Movement
Abstract

PURPOSE: This study aims to elucidate the electrotaxis response of alveolar epithelial cells (AECs) in direct-current electric fields (EFs), explore the impact of EFs on the cell fate of AECs, and lay the foundation for future exploitation of EFs for the treatment of acute lung injury. METHODS: AECs were extracted from rat lung tissues using magnetic-activated cell sorting. To elucidate the electrotaxis responses of AECs, different voltages of EFs (0, 50, 100, and 200 mV/mm) were applied to two types of AECs, respectively. Cell migrations were recorded and trajectories were pooled to better demonstrate cellular activities through graphs. Cell directionality was calculated as the cosine value of the angle formed by the EF vector and cell migration. To further demonstrate the impact of EFs on the pulmonary tissue, the human bronchial epithelial cells transformed with Ad12-SV40 2B (BEAS-2B cells) were obtained and experimented under the same conditions as AECs. To determine the influence on cell fate, cells underwent electric stimulation were collected to perform Western blot analysis. RESULTS: The successful separation and culturing of AECs were confirmed through immunofluorescence staining. Compared with the control, AECs in EFs demonstrated a significant directionality in a voltage-dependent way. In general, type Ⅰ alveolar epithelial cells migrated faster than type Ⅱ alveolar epithelial cells, and under EFs, these two types of cells exhibited different response threshold. For type Ⅱ alveolar epithelial cells, only EFs at 200 mV/mm resulted a significant difference to the velocity, whereas for, EFs at both 100 mV/mm and 200 mV/mm gave rise to a significant difference. Western blotting suggested that EFs led to an increased expression of a AKT and myeloid leukemia 1 and a decreased expression of Bcl-2-associated X protein and Bcl-2-like protein 11. CONCLUSION: EFs could guide and accelerate the directional migration of AECs and exert antiapoptotic effects, which indicated that EFs are important biophysical signals in the re-epithelialization of alveolar epithelium in lung injury.

Published
2023

330. R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor

Author

Zhu, Bai, Yang, Chao, Dai, Jinkun, Fan, Jianwei, and Ye, Yuanxin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatically identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method (named R2FD2) that is robust to radiation and rotation differences. Our R2FD2 is conducted in two critical contributions, consisting of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor (MALG) is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points (IPs) with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor (RMLG), which consists of two components: fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map (RMIM) is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to depict a more discriminative feature representation, which improves RMLG's resistance to radiation and rotation variances.Experimental results show that the proposed R2FD2 outperforms five state-of-the-art feature matching methods, and has superior advantages in adaptability and universality. Moreover, our R2FD2 achieves the accuracy of matching within two pixels and has a great advantage in matching efficiency over other state-of-the-art methods., Comment: 33 pages, 15 figures

Published
2022

331. Endomorphism property of vertex operator algebras over arbitrary fields

Author

Yang, Chao and Liu, Jianqi

Subjects
Mathematics - Quantum Algebra, Mathematics - Rings and Algebras
Abstract

In this paper, we study the endomorphism properties of vertex operator algebras over an arbitrary field $\mathbb{F}$, with $\text{Char}(\mathbb{F}) \neq 2$. Let $V$ be a strongly finitely generated vertex operator algebra over $\mathbb{F}$, and $M$ be an irreducible admissible $V$-module. We prove that every element in $\text{End}_V(M)$ is algebraic over $\mathbb{F}$ and that $\text{End}_V(M)$ is also finite-dimensional. As an application, we prove Schur's lemma for strongly finitely generated vertex operator algebras over arbitrary algebraically closed fields, and we give a test for absolute irreducibility of $V$-modules.

Published
2022

332. Dynamic Mode Decomposition for Extrapolating Non-equilibrium Green's Functions Dynamics

Author

Reeves, Cian C., Yin, Jia, Zhu, Yuanran, Ibrahim, Khaled Z., Yang, Chao, and Vlcek, Vojtech

Subjects
Physics - Computational Physics
Abstract

The HF-GKBA offers an approximate numerical procedure for propagating the two-time non-equilibrium Green's function(NEGF). Here we compare the HF-GKBA to exact results for a variety of systems with long and short-range interactions, different two-body interaction strengths and various non-equilibrium preparations. We find excellent agreement between the HF-GKBA and exact time evolution in models when more realistic long-range exponentially decaying interactions are considered. This agreement persists for long times and for intermediate to strong interaction strengths. In large systems, HF-GKBA becomes prohibitively expensive for long-time evolutions. For this reason, look at the use of dynamical mode decomposition(DMD) to reconstruct long-time NEGF trajectories from a sample of the initial trajectory. Using no more than 16\% of the total time evolution we reconstruct the total trajectory with high fidelity. Our results show the potential for DMD to be used in conjunction with HF-GKBA to calculate long time trajectories in large-scale systems.

Published
2022
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333. Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming

Author

Hung, Yun-Ning, Yang, Chao-Han Huck, Chen, Pin-Yu, and Lerch, Alexander

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. In addition to the known, input-independent, reprogramming method, we propose an advanced reprogramming paradigm: Input-dependent NMR, to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a small genre classification dataset., Comment: Accepted to IEEE ICASSP 2023. The implementation is available at https://github.com/biboamy/music-repro

Published
2022

334. A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition

Author

Yang, Chao-Han Huck, Li, Bo, Zhang, Yu, Chen, Nanxin, Sainath, Tara N., Siniscalchi, Sabato Marco, and Lee, Chin-Hui

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, Quantum Physics
Abstract

We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on classical-to-quantum feature encoding. Different from existing quantum convolution techniques, we utilize QKL with features in the quantum space to design kernel-based classifiers. Experimental results on challenging spoken command recognition tasks for a few low-resource languages, such as Arabic, Georgian, Chuvash, and Lithuanian, show that the proposed QKL-based hybrid approach attains good improvements over existing classical and quantum solutions., Comment: Submitted to ICASSP 2023

Published
2022
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335. Inference and Denoise: Causal Inference-based Neural Speech Enhancement

Author

Hsieh, Tsun-An, Yang, Chao-Han Huck, Chen, Pin-Yu, Siniscalchi, Sabato Marco, and Tsao, Yu

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Sound
Abstract

This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. Based on the potential outcome framework, the proposed causal inference-based speech enhancement (CISE) separates clean and noisy frames in an intervened noisy speech using a noise detector and assigns both sets of frames to two mask-based enhancement modules (EMs) to perform noise-conditional SE. Specifically, we use the presence of noise as guidance for EM selection during training, and the noise detector selects the enhancement module according to the prediction of the presence of noise for each frame. Moreover, we derived a SE-specific average treatment effect to quantify the causal effect adequately. Experimental evidence demonstrates that CISE outperforms a non-causal mask-based SE approach in the studied settings and has better performance and efficiency than more complex SE models., Comment: Submitted to ICASSP 2023

Published
2022

336. Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

Author

Huang, Jhih-Cing, Tsai, Yu-Lin, Yang, Chao-Han Huck, Su, Cheng-Fang, Yu, Chia-Mu, Chen, Pin-Yu, and Kuo, Sy-Yen

Subjects
Quantum Physics, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Signal Processing
Abstract

Recently, quantum classifiers have been found to be vulnerable to adversarial attacks, in which quantum classifiers are deceived by imperceptible noises, leading to misclassification. In this paper, we propose the first theoretical study demonstrating that adding quantum random rotation noise can improve robustness in quantum classifiers against adversarial attacks. We link the definition of differential privacy and show that the quantum classifier trained with the natural presence of additive noise is differentially private. Finally, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples, supported by experimental results simulated with noises from IBM's 7-qubits device., Comment: Accepted to IEEE ICASSP 2023

Published
2022

337. Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network

Author

Zheng, Yu, Dang, Zhangxuan, Peng, Chunlei, Yang, Chao, and Gao, Xinbo

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Network traffic classification is the basis of many network security applications and has attracted enough attention in the field of cyberspace security. Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. In this paper, we propose an MLP-Mixer based multi-view multi-label neural network for network traffic classification. Compared with the existing CNN-based methods, our method adopts the MLP-Mixer structure, which is more in line with the structure of the packet than the conventional convolution operation. In our method, one packet is divided into the packet header and the packet body, together with the flow features of the packet as input from different views. We utilize a multi-label setting to learn different scenarios simultaneously to improve the classification performance by exploiting the correlations between different scenarios. Taking advantage of the above characteristics, we propose an end-to-end network traffic classification method. We conduct experiments on three public datasets, and the experimental results show that our method can achieve superior performance., Comment: 17 pages,6 figures

Published
2022

338. An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition

Author

Yang, Chao-Han Huck, Qi, Jun, Siniscalchi, Sabato Marco, and Lee, Chin-Hui

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Signal Processing
Abstract

We propose an ensemble learning framework with Poisson sub-sampling to effectively train a collection of teacher models to issue some differential privacy (DP) guarantee for training data. Through boosting under DP, a student model derived from the training data suffers little model degradation from the models trained with no privacy protection. Our proposed solution leverages upon two mechanisms, namely: (i) a privacy budget amplification via Poisson sub-sampling to train a target prediction model that requires less noise to achieve a same level of privacy budget, and (ii) a combination of the sub-sampling technique and an ensemble teacher-student learning framework that introduces DP-preserving noise at the output of the teacher models and transfers DP-preserving properties via noisy labels. Privacy-preserving student models are then trained with the noisy labels to learn the knowledge with DP-protection from the teacher model ensemble. Experimental evidences on spoken command recognition and continuous speech recognition of Mandarin speech show that our proposed framework greatly outperforms existing DP-preserving algorithms in both speech processing tasks., Comment: Accepted to ISCA, ISCSLP 2022, Singapore. 5 Pages

Published
2022

339. An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition

Author

Yang, Chao-Han Huck, Chen, I-Fan, Stolcke, Andreas, Siniscalchi, Sabato Marco, and Lee, Chin-Hui

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Differential privacy (DP) is one data protection avenue to safeguard user information used for training deep models by imposing noisy distortion on privacy data. Such a noise perturbation often results in a severe performance degradation in automatic speech recognition (ASR) in order to meet a privacy budget $\varepsilon$. Private aggregation of teacher ensemble (PATE) utilizes ensemble probabilities to improve ASR accuracy when dealing with the noise effects controlled by small values of $\varepsilon$. We extend PATE learning to work with dynamic patterns, namely speech utterances, and perform a first experimental demonstration that it prevents acoustic data leakage in ASR training. We evaluate three end-to-end deep models, including LAS, hybrid CTC/attention, and RNN transducer, on the open-source LibriSpeech and TIMIT corpora. PATE learning-enhanced ASR models outperform the benchmark DP-SGD mechanisms, especially under strict DP budgets, giving relative word error rate reductions between 26.2% and 27.5% for an RNN transducer model evaluated with LibriSpeech. We also introduce a DP-preserving ASR solution for pretraining on public speech corpora., Comment: 5 pages. Accepted to IEEE SLT 2022. A first version draft was finished in Aug 2021

Published
2022

340. Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning

Author

Chan, Yan-Mong, Manger, Natascha, Li, Yin, Yang, Chao-Chin, Zhu, Zhaohuan, Armitage, Philip J., and Ho, Shirley

Subjects
Astrophysics - Earth and Planetary Astrophysics, Computer Science - Machine Learning, Physics - Fluid Dynamics
Abstract

We investigate the utility of deep learning for modeling the clustering of particles that are aerodynamically coupled to turbulent fluids. Using a Lagrangian particle module within the Athena++ hydrodynamics code, we simulate the dynamics of particles in the Epstein drag regime within a periodic domain of isotropic forced hydrodynamic turbulence. This setup is an idealized model relevant to the collisional growth of micron to mm-sized dust particles in early stage planet formation. The simulation data are used to train a U-Net deep learning model to predict gridded three-dimensional representations of the particle density and velocity fields, given as input the corresponding fluid fields. The trained model qualitatively captures the filamentary structure of clustered particles in a highly non-linear regime. We assess model fidelity by calculating metrics of the density field (the radial distribution function) and of the velocity field (the relative velocity and the relative radial velocity between particles). Although trained only on the spatial fields, the model predicts these statistical quantities with errors that are typically <10%. Our results suggest that, given appropriately expanded training data, deep learning could complement direct numerical simulations in predicting particle clustering within turbulent flows., Comment: 20 pages, 13 figures; Accepted to ApJ on 2023 October 30

Published
2022
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