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SSRN 2023年

作者： Wang, Yangbo Zhou, Jie Lu, Jianglin Wan, Jun Gao, Can Lin, Qingshui College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China SMILE Lab Department of ECE College of Engineering Northeastern University Boston02115 United States School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Basic Teaching Department Liaoning Technical University Huludao125105 China

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using the raw data to represent each sample under the self-expression framework may not be ideal, as noise points are inevitably involved in the process of representing clean samples. To address this issue, this work proposes a novel self-expression model called robust Self-Expression learning with adaptive Noise Perception (SENP). SENP decomposes each sample into a clean part and a noisy part, and samples with large self-expression losses can be recognized as the noise points. A reliable coefficient matrix can then be learned by using only the clean points to reconstruct the clean part of each sample. By simultaneously detecting the noisy part of each sample and noise points, and adaptively mitigating their negative impacts, the representative ability of the generated coefficient matrix is improved. Moreover, inspired by the solution of non-negative matrix factorization (NMF), an effective algorithm is formed to optimize SENP. Extensive experiments on well-known benchmark datasets demonstrate the superiority of SENP compared to several state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment

arXiv

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arXiv 2025年

作者： Suvon, Mohammod N.I. Zhou, Shuo Tripathi, Prasun C. Fan, Wenrui Alabed, Samer Khanal, Bishesh Osmani, Venet Swift, Andrew J. Chen, Chen Lu, Haiping Department of Computer Science University of Sheffield Sheffield United Kingdom Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield Sheffield United Kingdom Department of Clinical Radiology Sheffield Teaching Hospitals Sheffield United Kingdom INSIGNEO Institute for in Silico Medicine University of Sheffield Sheffield United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science IITRAM Gujarat India Nepal Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computing Imperial College London London United Kingdom

Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMICs) primarily due to the scarcity of advanced diagnostic tools. Several studies in PH have applied machine learning to low-cost diagnostic tools like 12-lead ECG (12L-ECG), but they mainly focus on areas with limited resources, overlooking areas with no diagnostic tools, such as rural primary healthcare in LMICs. Recent studies have shown the effectiveness of 6-lead ECG (6L-ECG), as a cheaper and portable alternative in detecting various cardiac conditions, but its clinical value for PH detection is not well proved. Furthermore, existing methods treat 12L-/6L-ECG as a single modality, capturing only shared features while overlooking lead-specific features essential for identifying complex cardiac hemodynamic changes. In this paper, we propose Lead-Specific Electrocardiogram Multimodal Variational Autoencoder (LS-EMVAE), a model pre-trained on large-population 12L-ECG data and fine-tuned on task-specific data (12L-ECG or 6L-ECG). LS-EMVAE treats each lead in 12L-ECG as a separate modality and incorporates a novel hierarchical modality expert composition mechanism based on Mixture of Expert and Product of Expert to enable flexible, adaptive latent feature fusion among lead-specific and shared features. Unlike existing approaches, LS-EMVAE makes better predictions on both 12L-ECG and 6L-ECG at inference time, making it an equitable solution for areas with both limited and no diagnostic tools. We pre-trained LS-EMVAE on 800, 000 publicly available 12L-ECG samples and fine-tuned it for two tasks: 1) PH detection and 2) phenotyping pre-/post-capillary PH, on in-house datasets of 892 and 691 subjects across 12L-ECG and 6L-ECG settings. Extensive experiments show that LS-EMVAE outperforms existing baselines in both ECG settings, while 6L-ECG achieves performance comparable to 12L-ECG, unlocking its potential for global PH screening in areas without diagnostic tools. Copyright ©

关键词： Electrocardiography

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Emotions' influence on customers’ e-banking satisfaction evaluation in e-service failure and e-service recovery circumstances

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Social sciences and Humanities Open 2022年第1期6卷

作者： Nyagadza, Brighton Mazuruse, Gideon Muposhi, Asphat Chuchu, Tinashe Makoni, Tendai Kusotera, Brain Department of Marketing Marondera University of Agricultural Sciences and Technology (MUAST) Zimbabwe Teaching and Learning Institute (TLI) Marondera University of Agricultural Sciences and Technology (MUAST) Zimbabwe Department of Information & Marketing Sciences Midlands State University (MSU) Zimbabwe Division of Marketing School of Business Sciences University of Witwatersrand Johannesburg South Africa Department of Mathematics and Computer Science School of Natural Sciences Great Zimbabwe University (GZU) Masvingo Zimbabwe Department of Statistics and Mathematics Bindura University of Science Education (BUSE) Bindura Zimbabwe

Purpose: The growth in electronic banking platforms resulted in the unintended effect of minimising the customer-service provider physical interactions that were instrumental in managing negative emotions emanating from service failure. This study investigates the influence of emotions related to e-service failure and e-service recovery strategies on customer satisfaction. Design/methodology/approach: Cross sectional data was collected from 433 e-banking customers using a structured, respondent administered questionnaire. Respondents were randomly intercepted as they moved out of personal service platforms of four selected banks. Hypotheses were tested using structural equation modelling. Findings: Emotions were found to have a direct positive influence on customers’ e-banking satisfaction evaluation during e-service failure and e-service recovery. Research limitations/implications: Research was conducted in Marondera, an agro-based emerging town geographically located in Mashonaland East province of Zimbabwe. The generalisation of the study findings in Zimbabwe may be enhanced by extending the study to other cities. Practical implications: During the formulation of e-service marketing strategies, bank managers are encouraged to consider the influence of emotions on customers’ e-banking satisfaction in order to enhance the effectiveness of e-service recovery initiatives. Originality/value: The findings of this study contribute to efforts towards effective management of emotions related to e-service failure and evaluation of e-service recovery strategies, a study area which remains under researched in Zimbabwe's banking sector. © 2022 The Authors

关键词： Customer satisfaction e-banking e-service failure and e-service recovery Emotions Zimbabwe

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Semi-supervised deep neural networks for left atrial segmentation from cardiac MRI

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European Journal of Preventive Cardiology 2023年第SUPPLEMENT_1期30卷

作者： Qiu, Y Meng, J Loughborough University Department of Computer Science Loughborough United Kingdom of Great Britain & Northern Ireland Lancashire Teaching Hospitals NHS Foundation Trust Preston United Kingdom of Great Britain & Northern Ireland

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Waste heat harness in a thermal energy system using TEGs, and SCO 2 brayton cycle driven by renewable sources for electricity and liquid hydrogen Production: Thermo-Economic optimization using ANNs

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Case Studies in Thermal Engineering 2025年 72卷

作者： Amr S. Abouzied Sarminah Samad Pradeep Kumar Singh Ahmed H. Janabi Mohamed Shaban Asma Ahmed A. Mohammed Shoira Formanova H. Elhosiny Ali Samah G. Babiker Abdulrahman M. Alansari Department of Pharmaceutical Chemistry College of Pharmacy University of Hail Hail 81442 Saudi Arabia Department of Management College of Business Administration Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Mechanical Engineering Institute of Engineering & Technology GLA University Mathura U.P. 281406 India Computer Techniques Engineering Department College of Engineering & Technology Al-Mustaqbal University Babylon Iraq Department of Physics Faculty of Science Islamic University of Madinah Madinah 42351 Saudi Arabia Department of Computer Science University of Tabuk Saudi Arabia Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Physics Department Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Department of Electronic Physics Faculty of Applied Science Red Sea University Port Sudan Sudan Department of Mechanical Engineering College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia

This research introduces an innovative thermal energy system that combines solar and wind energy to produce electricity, generate hydrogen, and facilitate liquefaction. This system includes a parabolic trough solar collector (PTSC) that heats nitrate salts, transferring the thermal energy to a supercritical carbon dioxide Brayton cycle (SCO 2 -BC). Furthermore, thermoelectric generators (TEG) are integrated to capture energy from waste heat sources. Additionally, this study breaks new ground by incorporating solar and wind power with a supercritical CO 2 cycle alongside hydrogen liquefaction, a field that is still relatively uncharted. A detailed techno-economic and environmental model is utilized to assess the system's performance, concentrating on critical indicators such as second law efficiency, total cost rate, hydrogen production rate, net power output, levelized costs, and the rate of CO 2 emission reduction. Following this, an optimization process is carried out using a genetic algorithm to investigate two different scenarios. Finally, the LINMAP method is applied to identify optimal solutions for each scenario. The study reveals that the system generated a grid power output of 461.2 kW and produced 8.3 kg of liquid hydrogen per hour. The overall cost of operation was established at 103.8 $/h with an exergy efficiency of 16.2 %. Further refinements resulted in values of 19.33 % for second-law efficiency, 124.80 $/h for cost rate, and 1021.64 kW for grid power.

关键词： Waste heat recovery Thermal energy utilization Parabolic trough solar collectors Thermoelectric generators Claude liquid hydrogen production Artificial neural networks and genetic algorithm

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Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

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Heliyon 2024年第21期10卷 e39735页

作者： Williams, Kathleen A. Numssen, Ole Guerra, Juan David Kopal, Jakub Bzdok, Danilo Hartwigsen, Gesa Lise Meitner Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Methods and Development Group “Brain Networks” Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany The Neuro - Montreal Neurological Institute (MNI) McConnell Brain Imaging Centre Department of Biomedical Engineering Faculty of Medicine School of Computer Science McGill University Montreal Canada Mila - Quebec Artificial Intelligence Institute Montreal QC Canada Wilhelm Wundt Institute for Psychology Leipzig University Germany

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition. © 2024 The Authors

关键词： Cognition cTBS fMRI Functional connectivity Resting state TMS

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An Efficient Polynomial-Time Approximation Scheme for Parallel Multi-Stage Open Shops

SSRN

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SSRN 2022年

作者： Dong, Jianming Jin, Ruyan Lin, Guohui Su, Bing Tong, Weitian Xu, Yao School of Management and E-Business Contemporary Business and Trade Research Center Zhejiang Gongshang University Zhejiang Hangzhou310018 China Public course teaching and Research Office Taizhou Vocational and Technical College Zhejiang Taizhou318000 China Department of Computing Science University of Alberta EdmontonABT6G 2E8 Canada School of Economics and Management Xi’an Technological University Xi’an China Department of Computer Science Georgia Southern University StatesboroGA30458 United States

Various new scheduling problems have been arising from real-world applications and spawning new research areas in the scheduling field. We study the parallel multi-stage open shops problem, which generalizes the classic open shop scheduling and parallel machine scheduling problems. Given m identical k-stage open shops and a set of n jobs, we aim to process all jobs on these open shops with the minimum makespan, i.e., the completion time of the last job, under the constraint that job preemption is not allowed. We present an efficient polynomial-time approximation scheme (EPTAS) for the case when both m and k are constant. The main idea for our EPTAS is the combination of several categorization, scaling, and linear programming rounding techniques. Jobs and/or operations are first scaled and then categorized carefully into multiple types so that different types of jobs and/or operations are scheduled appropriately without increasing the makespan too much. © 2022, The Authors. All rights reserved.

关键词： Linear programming

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An exponential stability test for a messenger rna–micro rna ode model

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第4期82卷 11-16页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

In this paper we obtain a numerically tractable test (sufficient condition) for the exponential stability of the unique positive equilibrium point of an ODE system. The result (Theorem 3.1) is based on Lyapunov theory and Linear Ma-trix Inequalities techniques. The ODE model is related to the messengerRNA-microRNA interaction. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： RNA

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Glucose Quantification from Absorption Spectroscopy: A Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

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Heliyon 2024年第10期10卷

作者： Heydar Khadem Hoda Nemat Jackie Elliott Mohammed Benaissa Department of Electronic and Electrical Engineering University of Sheffield UK Department of Computer Science University of Manchester Manchester UK Engineering and Development Department KultraLab London UK Department of Oncology and Metabolism University of Sheffield UK Sheffield Teaching Hospitals Diabetes and Endocrine Centre Northern General Hospital Sheffield UK

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.

关键词： near-infrared mid-infrared spectroscopy machine learning artificial intelligence glucose signal processing

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Earf-YOLO:A Model for Recognizing Zhuang Minority Patterns Based on YOLOv3

Research Square

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Research Square 2021年

作者： Wang, Xin Yan, Jingke Wang, Qin Qin, Qin Wang, Jun Shi, Zhuo Li, Huanghe School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Marine Engineering Guilin University of Electronic Technology Beihai536000 China Basic Teaching Department Guilin University of Electronic Technology Beihai536000 China

With reference to the limitations of YOLOv3 in recognizing symbols on Zhuang pattern, such as slow detection speed, unable to detect small object, and inaccurate positioning of bounding box, we propose a new model: Earf-YOLO (Efficient Attention Receptive Field You only look once) in this paper. In EarF-YOLO, we present an attention module: CBEAM (Convolution Block Efficient Attention Module) at first, which provides feature maps from channel and spatial dimensions. In CBEAM module, a local cross-channel interaction strategy without reducing dimensionality is used to improve the performance of the convolutional neural network. Besides, we put forward the SRFB (Strength Receptive Field Block) structure. During its training, more branch structures will be generated to enrich the feature space of the convolutional block. During its prediction, the multi-branched structures will be reparametrized and fused into one main branch to improve the performance of the model. Finally, we adopt some advanced training techniques to improve the detection performance. Experiments on the dataset of Zhuang patterns and the COCO dataset show that the Earf-YOLO model can effectively reduce the error of the prediction box and the ground-truth box, and decrease the calculation time. The mAP value of this model on the dataset of Zhuang patterns and on the COCO dataset reaches 82.1 (IoU=0.5) and 62.14 (IoU=0.5) respectively. © 2021, CC BY.

关键词： Convolution

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