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2022 Ieee International Conference on Intelligent education and Intelligent Research, IeIR 2022

作者： Jia, Yuhao Wang, Pingheng Zhang, Zhen Cheng, Chi Li, Zhifei Yu, Xinguo Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China Wuhan Sports University School of Sports Engineering and Information Technology Wuhan China Wuhan University School of Information Management Wuhan China Hubei University School of Computer Science and Information Engineering Wuhan China Central China Normal University National Engineering Research Center for E-Learning Wuhan China

ISBN: (纸本)9798350345629

Solving the math application problem is hot research in intelligence education. An increasing number of research scholars are using pre-trained models to tackle machine solution problems. Noteworthily, the semantic relationships required in the machine solution task are for describing math problems, while those of the BeRT model with pre-training weights are of general significance, which will cause a mismatched word vector representation. To solve this problem, we proposed a self-supervised pre-training method based on loss priority. We use the input data from the downstream task datasets to fine-tune the existing BeRT model so that the dynamic word vector it obtained can better match the downstream tasks. And the size of the loss value of each data batch in each round of training will be recorded to decide which data should be trained in the next round, so that the model has a faster convergence speed. Furthermore, considering that in large-scale mathematics application problems, some problems have almost the same forms of solution. We proposed a machine solution model training algorithm based on the analogy of the same problem type. extensive experiments on two well-known datasets show the superiority of our proposed algorithms compared to other state-of-the-art algorithms. © 2022 Ieee.

关键词： Semantics

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An Investigation of College Students’ learning engagement and Classroom Preferences Under the Smart Classroom environment

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SN Computer science 2022年第3期3卷 205页

作者： Lu, Kaili Shi, Yinghui Li, Jie Yang, Harrison Hao Xu, Min College of Education Science and Technology Nanjing University of Posts and Telecommunications Nanjing Jiangsu 210023 China National Engineering Research Centre for E-Learning Central China Normal University Wuhan Hubei 430079 China National Engineering Laboratory for Educational Big Data Central China Normal University Wuhan Hubei 430079 China School of Education State University of New York at Oswego Oswego 13126 NY United States School of Chinese Language and Literature Central China Normal University Wuhan Hubei 430079 China

A structural equation modeling analysis was conducted to investigate the relationship between the classroom preferences of college students and learning engagement (Le) in the smart classroom learning environment. Furthermore, multiple regression analysis was conducted to investigate the impact of smart classroom preferences on the sub-dimensions of Le. A total of 275 college students, who had studied in the smart classroom environment at least for a semester, responded to the survey. The results showed that three of the eight dimensions of smart classroom preferences, i.e., inquiry learning, reflective thinking, and multiple sources, have a significant positive influence on Le. Noteworthy, inquiry learning and multiple sources could significantly predict behavioral engagement;while inquiry learning, reflective thinking, and multiple sources could predict both emotional engagement and cognitive engagement. These findings of this research have practical implications for instructors and instructional designers;in that they should focus on the key factors that predict students’ different dimensions of Le. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Behavioral engagement Cognitive engagement emotional engagement learning engagement learning environment preferences Smart classroom

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Imaging brain white matter function using resting-state functional MRI

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science Bulletin 2025年第9期70卷 1384-1388页

作者： Ji, Gong-Jun Cui, Zaixu D'Arcy, Ryan C.N. Liao, Wei Biswal, Bharat B. Zhang, Qing Luo, Cheng Zang, Yu-Feng Ding, Zhaohua Zuo, Xi-Nian Gore, John C. Wang, Kai Department of Psychology and Sleep Medicine The Second Affiliated Hospital of Anhui Medical University Anhui Medical University Hefei230032 China School of Mental Health and Psychological Sciences Anhui Medical University Hefei230032 China Chinese Institute for Brain Research Beijing102206 China BrainNET Health and Technology District Simon Fraser University SurreyBCV3V 0E8 Canada School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Department of Biomedical Engineering New Jersey Institute of Technology NewarkNJ07102 United States Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou310000 China Vanderbilt University Institute of Imaging Science NashvilleTN37232-2310 United States State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing100875 China Department of Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN37212 United States Department of Neurology The First Affiliated Hospital of Anhui Medical University Anhui Medical University Hefei230032 China Anhui Institute of Translational Medicine Hefei230032 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230032 China Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders Hefei230032 China Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health Hefei230032 China

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A Dynamic Graph CNN with Cross-Representation Distillation for event-Based Recognition

arXiv

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arXiv 2023年

作者： Deng, Yongjian Chen, Hao Xie, Bochen Liu, Hai Li, Youfu The College of Computer Science Beijing University of Technology Beijing100124 China Ministry of Education Nanjing211189 China Department of Mechanical Engineering City University of Hong Kong Kowloon Hong Kong The National Engineering Research Center for E-Learning Central China Normal University Wuhan430079 China

Recent advances in event-based research prioritize sparsity and temporal precision. Approaches using dense frame-based representations processed via well-pretrained CNNs are being replaced by the use of sparse point-based representations learned through graph CNNs (GCN). Yet, the efficacy of these graph methods is far behind their frame-based counterparts with two limitations. (i) Biased graph construction without carefully integrating variant attributes (i.e., semantics, spatial and temporal cues) for each vertex, leading to imprecise graph representation. (ii) Deficient learning because of the lack of well-pretrained models available. Here we solve the first problem by proposing a new event-based GCN (eDGCN), with a dynamic aggregation module to integrate all attributes of vertices adaptively. To address the second problem, we introduce a novel learning framework called cross-representation distillation (CRD), which leverages the dense representation of events as a cross-representation auxiliary to provide additional supervision and prior knowledge for the event graph. This frame-to-graph distillation allows us to benefit from the large-scale priors provided by CNNs while still retaining the advantages of graph-based models. extensive experiments show our model and learning framework are effective and generalize well across multiple vision tasks.1 Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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The Role of Dihydroresveratrol in enhancing the Synergistic effect of Ligilactobacillus salivarius Liol and Resveratrol in Ameliorating Colitis in Mice

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Research 2022年第4期2022卷 223-238页

作者： Yiqiu Fei Shuobo Zhang Shengyi Han Bo Qiu Yanmeng Lu Weixing Huang Fang Li Deying Chen Bjorn Berglund Hang Xiao Lanjuan Li Mingfei Yao State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious DiseasesCollaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesThe First Afiliated HospitalZhejiang University School of MedicineHangzhou 310003China jinan Microecological Biomedicine Shandong Laboratory Jinan 250021China Zhejiang Tongchuang Yuecheng Health Science and Technology Co. Ltd.ShaoxingChina Cardiometabolic Genomics Program Division of CardiologyDepartment of MedicineColumbia University Irving Medical Center630W168thStPeS10-401NewYorkNY10032USA Department of Food Science University of MassachusettsAmherstMassachusetts 01003USA Department of Biomedical and Clinical Sciences Linkoping UniversityLinkopingSweden Zhejiang University Department of Food Science and NutritionZhejiang Key Laboratory for Agro-Food ProcessingFuli Institute of Food ScienceZhejiang R e D Center for Food Technology and EquipmentHangzhou 310058China

Currently approved therapeutical strategies for inflammatory bowel diseases(IBD)suffer from variable efficacy and association with risk of serious side ***,efforts have been made in searching for alternative therapeutics strategies utilizing gut microbiota *** this study,we show that the probiotic strain Ligilactobacillus salivarius Li01(Li01)and the phytochemical prebiotic resveratrol(RSV)have synergistic effect in ameliorating colitis in *** coadministration of Li01(109 CFU/d)and RSV(1.5 g/kg/d)promoted restoration of various inflammatory injuries and gut microbiota composition,exhibiting a favorable anti-inflammatory effect in DSS-induced colitis *** combination treatment was associated with reductions in the levels of proinflammatory cytokines IL-1βand IL-6 and increases in the levels of the anti-inflammatory cytokine IL-17A in mouse ***,the combination treatment was found to alter the composition and metabolism of the gut microbiota,especially influencing the production of short chain fatty acids and anti-inflammatory related *** mechanism underlying the improved anti-inflammatory effect from the RSV and Li01 combination treatment was found to be associated with the environmental sensor mammalian aryl hydrocarbon receptor(AHR)and tryptophan metabolism *** of RSV in combination with Li01 in different mouse model led to enhanced conversion of RSV into metabolites,including dihydroresveratrol(DHR),resveratrol-sulfate,and *** was found to be the dominant metabolite of RSV in conventional and colitis *** increased DHR/RSV ratio was confirmed to activate AHR and contribute to an enhanced anti-inflammatory *** is considered as a potential AHR *** DHR/RSV ratio also affected the serotonin pathway by controlling the expression of Tph1,SeRT,and 5-HT7R leading to amelioration of colitis in *** data suggest that treatment with a combination of Li01 an

关键词： metabolism injuries resveratrol

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evaluating groundwater ecosystem dynamics in response to post in-situ remediation of mixed chlorinated volatile organic compounds (CVOCs): An insight into microbial community resilience, adaptability, and metabolic functionality for sustainable remediation and ecosystem restoration

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science of the Total environment 2024年 920卷 170874页

作者： Huang, Shih-Wei Hussain, Bashir Chen, Jung-Sheng Asif, Aslia Hsu, Bing-Mu Center for environmental Toxin and Emerging Contaminant Cheng Shiu University Kaohsiung Taiwan Institute of Environmental Toxin and Emerging Contaminant Cheng Shiu University Kaohsiung Taiwan Department of Earth and Environmental Sciences National Chung Cheng University Chiayi County Taiwan Department of Biomedical Sciences National Chung Cheng University Chiayi Taiwan Department of Medical Research E-Da Hospital I-Shou University Kaohsiung Taiwan Doctoral Program in Science Technology Environment and Mathematics National Chung Cheng University Chiayi Taiwan

The in-situ remediation of groundwater contaminated with mixed chlorinated volatile organic compounds (CVOCs) has become a significant global research interest. However, limited attention has been given in understanding the effects of these remediation efforts on the groundwater microbial communities, which are vital for maintaining ecosystem health through their involvement in biogeochemical cycles. Hence, this study aimed to provide valuable insights into the impacts of in-situ remediation methods on groundwater microbial communities and ecosystem functionality, employing high-throughput sequencing coupled with functional and physiological assays. The results showed that both bioremediation and chemical remediation methods adversely affected microbial diversity and abundance compared to non-polluted sites. Certain taxa such as Pseudomonas, Acinetobacter, and Vogesella were sensitive to these remediation methods, while Aquabacterium exhibited greater adaptability. Functional annotation unveiled the beneficial impact of bioremediation on the sulfur cycle and specific taxa such as Cellvibrio, Massilia, Algoriphagus, and Flavobacterium which showed a significant positive relationship with dark oxidation of sulfur compounds. In contrast, chemical remediation showed adverse impacts on the nitrogen cycle with a reduced abundance of nitrogen and nitrate respiration along with a reduced utilization of amines (nitrogen rich substrate). The findings of this study offer valuable insights into the potential impacts of in-situ remediation methods on groundwater microbial communities and ecosystem functionality, emphasizing the need for meticulous consideration to ensure the implementation of effective and sustainable remediation strategies that safeguard ecosystem health and function. © 2024

关键词： Groundwater

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A green recyclable Li3VO4-pectin electrode exhibiting pseudocapacitive effect as an advanced anode for lithium-ion battery

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Journal of energy Storage 2023年 72卷

作者： Su, Yu-Hsuan Chung, Chin-Yi Chen, Yan-Ruei Wu, Feng-Yu Lin, Ya-Huei Chi, Po-Wei Wu, Phillip M. Paul, Tanmoy Lin, Hwai-en Chang-Liao, Kuei-Shu Wang, Sea-Fue Wu, Maw-Kuen Institute of Physics Academia Sinica 128 Section 2 Academia Road Taipei11529 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Tsing Hua University 128 Section 2 Academia Road Taipei11529 Taiwan Department of Engineering and System Science National Tsing Hua University 101 Section 2 Kuang-Fu Road Hsinchu300044 Taiwan 1 Section 3 Zhongxiao E. Rd. Taipei106 Taiwan 1 Section 3 Zhongxiao E. Road Taipei106 Taiwan Research Institute for Sustainable Energy TCG Centres for Research and Education in Science and Technology BIPL Building Salt Lake Kolkata700091 India

Battery system design and development is prioritizing environmentally friendly recycling processes to ensure a sustainable future for our planet. The reuse of active materials in Li-ion batteries is critical for this goal. Herein, we investigated the electrochemical performance of Li3VO4 (LVO) anode with pectin as a binder. Our Li-ion half-cells showed stable capacity of 230 mAh g−1 at a current density of 0.02 A g−1 after 100 cycles. In addition, we observed a novel enhanced specific capacity of 400 mAh g−1 during delithiation at a high current density of 1 A g−1, which did not occur during lithiation. This higher capacity was can be attributed to the pseudocapacitive lithium storage behavior in the LVO-pectin electrode, as identified by CV and eIS analyses. The heterostructure of LVO-pectin resulted in an interfacial surface charge that contributed to the capacity, as demonstrated by impedance spectroscopy in conjunction with the distribution of relaxation times. The utilization of pectin as an eco-friendly binder demonstrated nearly 90 % material recovery and 100 % capability for subsequent re-lithiation, indicating recyclability. Furthermore, the pseudocapacitive effect becomes increasingly dominant in regenerated LVO-pectin cells. These results exemplify a promising approach towards the development of environmentally friendly Li-ion batteries with high energy and power density. © 2023 elsevier Ltd

关键词： Lithium-ion batteries

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Uncovering the microbial community structure and physiological profiles of terrestrial mud volcanoes: A comprehensive metagenomic insight towards their trichloroethylene biodegradation potentiality

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environmental Research 2024年 258卷 119457页

作者： Asif, Aslia Koner, Suprokash Chen, Jung-Sheng Hussain, Ashiq Huang, Shih-Wei Hussain, Bashir Hsu, Bing-Mu Department of Earth and Environmental Sciences National Chung Cheng University Chiayi County Taiwan Doctoral Program in Science Technology Environment and Mathematics National Chung Cheng University Chiayi County Taiwan Department of Medical Research E-Da Hospital I-Shou University Kaohsiung Taiwan Center for Environmental Toxin and Emerging Contaminant Cheng Shiu University Kaohsiung Taiwan Institute of Environmental Toxin and Emerging Contaminant Cheng Shiu University Kaohsiung Taiwan

Mud volcanoes are dynamic geological features releasing methane (CH4), carbon dioxide (CO2), and hydrocarbons, harboring diverse methane and hydrocarbon-degrading microbes. However, the potential application of these microbial communities in chlorinated hydrocarbons bioremediation purposes such as trichloroethylene (TCe) has not yet been explored. Hence, this study investigated the mud volcano's microbial diversity functional potentiality in TCe degradation as well as their eco-physiological profiling using metabolic activity. Geochemical analysis of the mud volcano samples revealed variations in pH, temperature, and oxidation-reduction potential, indicating diverse environmental conditions. The Biolog ecoplate™ carbon substrates utilization pattern showed that the Tween 80 was highly consumed by mud volcanic microbial community. Similarly, MicroResp® analysis results demonstrated that presence of additive C-substrates condition might enhanced the cellular respiration process within mud-volcanic microbial community. Full-length 16 S rRNA sequencing identified Proteobacteria as the dominant phylum, with genera like Pseudomonas and Hydrogenophaga associated with chloroalkane degradation, and methanotrophic bacteria such as Methylomicrobium and Methylophaga linked to methane oxidation. Functional analysis uncovered diverse metabolic functions, including sulfur and methane metabolism and hydrocarbon degradation, with specific genes involved in methane oxidation and sulfur metabolism. These findings provide insights into the microbial diversity and metabolic capabilities of mud volcano ecosystems, which could facilitate their effective application in the bioremediation of chlorinated compounds. © 2024

关键词： Methane

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Math Word Problem Solver Based on Text-to-Text Transformer Model

Math Word Problem Solver Based on Text-to-Text Transformer M...

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Ieee International Conference on Teaching, Assessment and learning for engineering (TALe)

作者： Chuanzhi Yang Runze Huang Xinguo Yu Rao Peng School of Computer Science and Technology Wuhan University of Technology Wuhan China National Engineering Research Center for E-Learning Central China Normal University Wuhan China

ISBN: (纸本)9781665436885

In recent years, automatic problem solving for math-ematical words has attracted increasing attention. Therefore, algorithms developed for solving mathematical word problems like humans are a key technology to facilitate the development of digital education. In this paper, a deep learning model based on a text-to-text conversion model is proposed to solve mathematical word problems. The deep learning model treats each mathematical word problem as a “text-to-text” problem, i.e. taking a text as input and producing a new text as output. However, the output text appears in the form of a mathematical expression. In our experiments, this paper evaluates a deep learning model of Ape210K, which consists of 210K Chinese primary school level maths problems. The experimental results show that our deep learning model presented in this paper can solve 78.61% of the mathematical word problems as a whole. In addition, the problems were classified into six categories based on the knowledge points involved, and this paper explores the model's performance on different types of problems.

关键词： Deep learning education Transformers Mathematical models Problem-solving

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An Algorithm for Correcting Video-Audio Asynchronization Based on Syncnet

An Algorithm for Correcting Video-Audio Asynchronization Bas...

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Ieee International Conference on Teaching, Assessment and learning for engineering (TALe)

作者： Runze Huang Chuanzhi Yang Xinguo Yu Rao Peng School of Computer Science and Technology Wuhan University of Technology Wuhan China National Engineering Research Center for E-Learning Central China Normal University Wuhan China

ISBN: (纸本)9781665436885

The research on video-audio synchronization has attracted much attention in recent years. With the popularity of online education, the asynchrony between video and audio affects the quality of teaching and learning. This paper introduces a correction algorithm for video-audio asynchronization in online education. First, the video data were preprocessed using the S3FD and Librosa package to extract the lip images and MFCC as visual and auditory features; then, the Syncnet, consisting of a two-stream neural network, was retrained on the preprocessed dataset to obtain the semantic similarity of video-audio. Next, it calculated the synchronization error by using a sliding window method based on the similarity. Finally, a correction program adopting the FFmpeg framework fixed the asynchronous problem. In the experiment, 300 hours of Chinese video-audio synchronization dataset and 20 hours of Chinese live classroom record dataset were collected to test the algorithm. The experimental results show that the proposed algorithm can achieve 94% correction accuracy.

关键词： Visualization Lips education Semantics Neural networks Feature extraction Synchronization

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