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International computer science and engineering Conference (ICSEC)

作者： Saksiri Lertnilkarn Suphakant Phimoltares Department of Mathematics and Computer Science Faculty of Science Advanced Virtual and Intelligent Computing (AVIC) Research Center Chulalongkorn University Pathumwan Bangkok Thailand

ISBN: (纸本)9781665462730

Air pollution is one of the most serious problems in many regions of the world. Thailand also has had to face this trouble unavoidably, especially in the northern region of Thailand, the area that has been highly contaminated by air pollution for so many years. In this paper, an ensemble method was introduced to forecast the level of air quality index (AQI) in the northern part of Thailand. The ensemble method, in this study, is a technique gaining the results from majority vote of outputs of three classification models—k-nearest neighbors, random forest, and support vector machine. The proposed model compared the voted accuracy with the accuracies of existing classification models. It made use of the 2018 - 2021 data from seven stations in four provinces of Northern Thailand. In the end, the proposed model yielded 99.68% - 99.84% accuracy rate on average higher than most of the performance of the other comparative models.

关键词： Support vector machines Training Atmospheric modeling Computational modeling Predictive models Air pollution Prediction algorithms

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Nitrogen-doped carbonaceous electrode modified by biological metal-organic framework for vanadium redox flow batteries

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Surface and Coatings Technology 2024年 480卷

作者： Ou, Yun-Ting Kabtamu, Daniel Manaye Lakshmanan, Keseven Chen, Guan-Cheng Huang, Zih-Jhong Hsu, Ning-Yih Ku, Hung-Hsien Wang, Yao-Ming Wang, Chen-Hao Department of Materials Science and Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei106335 Taiwan Chemistry Division National Atomic Research Institute Taoyuan325207 Taiwan Maritime Innovation & Industry Promotion Department Metal Industries Research & Development Centre Kaohsiung811160 Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei106335 Taiwan

The biological metal-organic framework (Bio-MOF) is utilized to modify the carbonaceous electrode for the vanadium redox flow batteries. The microwave solvothermal method prepared the cobalt-based Bio-MOF on a graphite felt (GF) surface. Then, the nitrogen-doping graphite and porous structure formation were completed simultaneously during the carbonization process. Finally, electrochemical activation was conducted to remove cobalt metal from the GF electrode and raise the concentration of oxygen-containing functional group active sites. By in-situ Raman spectroscopy, the increase of disordered graphitic lattices could be observed by raising the potential from 0.6 V to 1.6 V. The microwave solvothermal method and electrochemical activation can effectively shorten the treatment time compared with the traditional solvothermal method and acidic etching processes. Among the carbonization parameters for Bio-MOF-covered GFs, maintaining the temperature at 900 °C for 2 h under an argon atmosphere is the most suitable. The annealed sample after electrochemical activation (BMDGF-900EA) displays 73.7 % energy efficiency at 120 mA cm−2 and possesses a 90 % retention rate of efficiency after 250 cycles at 180 mA cm−2. The superior performance results from more catalytic active sites, hydrophilic surface features, and larger specific surface area prove that this modification method derived from Bio-MOF indeed provides a novel and potential way to improve the VRFB performance. © 2024

关键词： Carbonization

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Speaker-Independent Depression Detection based on Adversarial Training Method

Speaker-Independent Depression Detection based on Adversaria...

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European Signal Processing Conference (EUSIPCO)

作者： Minggang WANG Shohei KATO Wen GU Jun YAN Fenghui REN Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan School of Computing and Information Technology University of Wollongong Wollongong Australia Center for Innovative Distance Education and Research Japan Advanced Institute of Science and Technology Nomi Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Depression, a worldwide mental health problem, significantly affects people's lives and may even lead to suicide in severe cases. As speech contains a large number of features that can reflect the speaker's mental condition, research has been conducted on depression detection based on speech analysis recently. However, personalized features from various speakers in speech can be mingled with depression-related features, causing a personalization bias in the features and reducing feature generalization. In this paper, we propose an adversarial training method based on the gradient reversal technique to remove speaker characteristics from speech signals, eliminating the personalization bias caused by speaker characteristics on depression features. Experiments carried out on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WoZ) dataset, demonstrate that our method improved the detection performance through more generalized depression feature extraction.

关键词： Training Analytical models Speech analysis Europe Mental health Signal processing Depression Feature extraction Interviews

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Heterogeneous Network Propagation with Optimal Similarity Measure for Drug-Target Associations 25

Heterogeneous Network Propagation with Optimal Similarity Me...

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25th International computer science and engineering Conference, ICSEC 2021

作者： Tangmanussukum, Piyanut Kawichai, Thitipong Suratanee, Apichat Plaimas, Kitiporn Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulachomklao Royal Military Academy Academic Division Department of Mathematics and Computer Science Nakhon Nayok Thailand Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Sci. and Technol. Res. Inst. King Mongkut's Univ. of Technol. N. Bangkok Department of Mathematics Bangkok Thailand Center Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand

ISBN: (纸本)9781665411974

Identification of drug target proteins is an important process in drug discovery and development. To perform predictions of large-scale drug-Target interactions, many computational methods have been proposed using the information of target-Target and drug-drug similarities into a network. In this work, we propose an enhanced heterogeneous network model with various biological aspects of target-Target and drug-drug similarities to predict drug-Target associations. Network propagation was performed through the heterogeneous network to predict new potential targets of a certain drug. Cross-validation and the Area Under the Receiver Operating Characteristic curve (AUROC) were then used to qualify the best model in the drug-Target association prediction. The result shows that the model using the similarity measures based on drug-disease associations and target protein sequences significantly outperforms the other models and the original model. Overall, our best model yields an accuracy of 93.5%. In conclusion, the heterogeneous model with optimal similarity measures is a well-suited approach to predict a new target for a certain drug. © 2021 IEEE.

关键词： Heterogeneous networks

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Disruption of cyclin D1 degradation leads to the development of mantle cell lymphoma

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Acta Pharmaceutica Sinica B 2024年第7期14卷 2977-2991页

作者： Ke Lu Ming Zhang Hongyu Qin Siyu Shen Haiqing Song Hua Jiang Chunxiang Zhang Guozhi Xiao Liping Tong Qing Jiang Di Chen Research Center for Computer-aided Drug Discovery Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Faculty of Pharmaceutical Sciences Shenzhen Institute of Advanced TechnologyShenzhen 518055China Oncology Department Johns Hopkins University School of MedicineBaltimoreMD 21205USA Division of Spine Surgery the First Affiliated Hospital of Guangxi Medical UniversityNanning 530021China Division of Sports Medicine and Adult Reconstructive Surgery Department of Orthopedic SurgeryNanjing Drum Tower HospitalAffiliated Hospital of Medical SchoolState Key Laboratory of Pharmaceutical BiotechnologyNanjing UniversityNanjing 210008China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325000China Department of Cardiology Basic Medicine Innovation Center for Cardiometabolic Diseases of Ministry of EducationInstitute of Cardiovascular Researchthe Affiliated HospitalSouthwest Medical UniversityLuzhou 646000China School of Medicine Southern University of Science and TechnologyShenzhen 518055China

Cyclin D1 has been recognized as an oncogene due to its abnormal upregulation in different types of ***,we demonstrated that cyclin D1 is SUMOylated,and we identified Itch as a specific E3 ligase recognizing SUMOylated cyclin D1 and mediating SUMO-induced ubiquitination and proteasome degradation of cyclin *** generated cyclin D1 mutant mice with mutations in the SUMOylation site,phosphorylation site,or both sites of cyclin D1,and found that double mutant mice developed a Mantle cell lymphoma(MCL)-like *** showed that arsenic trioxide(ATO)enhances cyclin D1 SUMOylation-mediated degradation through inhibition of cyclin D1 deSUMOylation enzymes,leading to MCL cell *** of severe combined immunodeficiency(SCID)mice grafted with MCL cells with ATO resulted in a significant reduction in tumor *** this study,we provide novel insights into the mechanisms of MCL tumor development and cyclin D1 regulation and discover a new strategy for MCL treatment.

关键词： Cyclin D1 SUMOylation Mantle cell lymphoma Arsenic trioxide SENP2 Proteasome degradation

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Butterfly-Accelerated Solution of Coupled Volume Integral and Hydrodynamic Equations

Butterfly-Accelerated Solution of Coupled Volume Integral an...

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USNC-URSI Radio science Meeting (Joint with AP-S Symposium)

作者： Doolos Aibek Uulu Yang Liu Abdulkadir C. Yucel Hakan Bagci Department of Mathematics and Natural Sciences University of Central Asia (UCA) Naryn Kyrgyzstan Applied Mathematics & Computational Research Division Lawrence Berkeley National Laboratory Berkeley USA School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

In recent years, advancements in plasmonic devices have enabled the localization of electromagnetic fields at subwavelength scales, contributing to breakthroughs in various scientific and engineering fields (J. C. Ndukaife, et al., ACS Nano, 2014,8,9,9035-9043). These structures are often made of metals and their electric properties are typically represented by the classical Drude model (S. A. Maier, 2007, Springer, New York). As the size of the plasmonic structures approaches the nanoscale, the classical Drude model falls short in explaining the collective movement of free electrons in metals. The interaction between moving charges and the electromagnetic fields can be described by a coupled system of the Maxwell and hydrodynamic equations (X. Zheng, et al., IEEE Trans. Antennas Propag., 2018,66,9, 4759-4771).

关键词： Location awareness Conferences Integral equations Metals Plasmons Hydrodynamics Mathematical models

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Numerical treatment for double diffusion phenomenon with generalized heat flux effects in 3d nanomaterial flow over bi-directional stretched wall

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Numerical Heat Transfer;Part A: Applications 2024年第14期86卷

作者： Awan, Saeed Ehsan Awais, Muhammad Raja, Muhammad Asif Zahoor Bukhari, Syed Hashim Raza Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Pakistan Department of Mathematics COMSATS University Islamabad Attock Pakistan Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Electrical and Computer Engineering Air University Islamabad Pakistan

Current study aims to numerically investigate the bi-directional flow of nanofluids in the presence of Cattaneo–Christov double diffusion for two heat sources, namely, prescribed surface temperature (PST) along with prescribed heat flux (PHF). The conventional mathematical model in partial differential equations (PDEs) of the system have been reduced into an equivalent set of ordinary differential equations (ODEs). The system dynamics in the form of approximate solutions of the ODEs is presented by Adams and Explicit Runge Kutta numerical solvers. For better understanding the influence of physical parameter of interest including Brownian movement parameter, thermophoresis parameter, concentration relaxation parameter, Schmidt number, Prandtl number, thermal relaxation parameter, temperature ratio parameter, stretching ratio parameter and Deborah number on temperature, as well as concentration profiles are presented exhaustively. Appropriateness of the scheme is ascertained through close resemblance of results for different state of the art counterparts with negligible error around 10–05 to 10–09. © 2024 Taylor & Francis Group, LLC.

关键词： Ordinary differential equations

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NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

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National science Review 2024年第5期11卷 202-216页

作者： Weijie Zheng Huawei Mu Zhiyi Chen Jiajun Liu Debin Xia Yuxiao Cheng Qi Jing Pak-Ming Lau Jin Tang Guo-Qiang Bi Feng Wu Hao Wang AHU-IAI AI Joint Laboratory Anhui University Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science Center National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of China Division of Life Sciences and Medicine University of Science and Technology of China Interdisciplinary Center for Brain Information Brain Cognition and Brain Disease InstituteShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

Quantitative analysis of activated neurons in mouse brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it is challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of a large cohort of mice from many terabytes of volumetric imaging data. Here, we introduce NEATmap, a deep learning–based high-efficiency,high-precision and user-friendly software for whole-brain neuronal activity trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+neurons. We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.

关键词： whole-brain imagingneuronal activity trace mapping deep learning quantitative analysis

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Parameter Obfuscation and Restoration for Secure Federated Learning Aggregation

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International Journal of Network Security 2024年第1期26卷 116-124页

作者： Ma, Xiangxiang Gong, Linming Chen, Jian Wang, Daoshun The Shaanxi Key Laboratory of Clothing Intelligence School of Computer Science Xi’an Polytechnic University Xi’an710048 China State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services School of Computer Science Xi’an Polytechnic University Xi’an710048 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Federated Learning (FL) is a privacy-preserving solution that addresses the issue of data security. It achieves model aggregation by uploading only the locally trained parameters of each party to a central server, thus preserving the original privacy of each entity. However, recent studies indicate that the global parameters aggregated by the server and the local parameters submitted by users may inadvertently reveal private data. In this paper, we propose a secure FL aggregation protocol based on parameter confusion and restoration. Our protocol ensures the protection of local parameters uploaded by a single client by uploading only blurred parameters to the server. The global model aggregated by the server remains unaffected by the interference data, ensuring the high accuracy of the final model. Furthermore, the aggregation server remains unaware of any parameter information during training. We demonstrate the security of our protocol against a semihonest adversary environment and prove that even if n-2 participants collude with the server, it is impossible to learn the private information of the honest participants. Our tests on the MNIST and cifar10 datasets show that our system achieves accuracy comparable to the plaintext baseline. © (2023) All Rights Reserved.

关键词： Restoration

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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