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arXiv 2024年

作者： Ahmad, Alwani Liyana Sanchez-Bornot, Jose Sotero, Roberto C. Coyle, Damien Idris, Zamzuri Faye, Ibrahima Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Perak Malaysia Department of Neurosciences Hospital Universiti Sains Malaysia Kelantan Malaysia Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee campus Derry~Londonderry BT48 7JL United Kingdom Department of Radiology Hotchkiss Brain Institute University of Calgary CalgaryAB Canada The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Universiti Teknologi PETRONAS Perak Malaysia

Background: Magneto/eletro-encephalography (MEG/EEG) are non-invasive neuroimaging techniques that offer excellent temporal and spatial resolution that can be used to study brain functioning. It has been proven effective in research on dementia and Alzheimer's disease (AD), where MEG/EEG analyses can identify changes in brain activity at various stages of AD progression, including preclinical and prodromal. research has shown differences in the power spectrum properties among healthy controls (HC) and individuals with mild cognitive impairment (MCI) or AD. It has also been conjectured that MEG/EEG analyses have the capability to identify pathological changes before first clinical symptoms and may provide biomarkers for interventions designed to reverse or slow the progression of AD. Objective: To conduct a comprehensive analysis by evaluating the performance of various classification techniques using features extracted by following different MEG methodologies. This study will focus on selecting MEG-based biomarkers/features that enhance performance in distinguishing between healthy control (HC) and mild cognitive impairment (MCI) participants from the BioFIND study. Furthermore, we will compare this with the effectiveness of using MRI-based anatomical features extracted from same participants, independently or in combination with the MEG features;thus, extending from previous study where similar analyses were performed using ADNI and OASIS-3 datasets. Methods: MEG and MRI data are obtained from the BioFIND dataset, comprising 324 participants with 158 individuals diagnosed as MCI and 166 HC. Sensor and source-based MEG analyses are performed using custom MATLAB code based on SPM12 and OSL toolboxes, for co-registration, preprocessing including filtering, and automatic artifact rejection. Source-based analyses were applied through different source localization methods as implemented in SPM12 and DAiSS toolboxes. We conducted analogous machine learning analyses on sen

关键词： Neurodegenerative diseases

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Prediction of Gas Utilization Ratio Under Different Working Conditions of Blast Furnace Based on Extreme Learning Machine

Prediction of Gas Utilization Ratio Under Different Working ...

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International Conference on control and Robots (ICCR)

作者： Tianen Ma Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Ministry of Education of Engineering Research Centre for Earth Exploration Intelligent Technology Wuhan China

ISBN: (数字)9781728175621

ISBN: (纸本)9781728175638

Since the working condition affects the chemical reactions in a blast furnace (BF), the relationship between the blast-supply parameters and the gas utilization ratio (GUR) varies with different working conditions. This paper presents a GUR prediction model based on working condition classification. First, this paper analyzes the influence of working conditions on the state parameters of a BF. Then, a fuzzy clustering method is used to identify the working condition by using some state parameters. Next, the GUR prediction model was designed based on an extreme learning machine under different working conditions. Finally, the simulation results verified the effectiveness of this method.

关键词： Employee welfare Extreme learning machines Simulation Clustering methods Predictive models Blast furnaces Robots

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The Causal Effect of Personality Traits on the Mental Health in Breast Cancer Patients Under the Occurrence of Negative Life Events 18th

The Causal Effect of Personality Traits on the Mental Hea...

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18th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2024

作者： Petrakis, Aristeidis Mylona, Eugenia Kourou, Konstantina Manikis, Georgios Kondylakis, Haridimos Marias, Kostas Poikonen-Saksela, Paula Simos, Panagiotis Karademas, Evangelos Mazzocco, Ketti Pat-Horenczyk, Ruth Sousa, Berta Fotiadis, Dimitrios I. Computational Biomedicine Laboratory FORTH-ICS Heraklion Greece Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina Ioannina Greece Foundation for Research and Technology-Hellas Institute of Molecular Biology and Biotechnology Department of Biomedical Research Ioannina Greece Helsinki University Hospital Comprehensive Cancer Center and Helsinki University Helsinki Finland School of Medicine University of Crete Heraklion Greece Department of Psychology University of Crete Rethymno Greece Applied Research Division for Cognitive and Psychological Science European Institute of Oncology Milan Italy Department of Oncology and Hemato-oncology University of Milan Milan Italy School of Social Work and Social Welfare The Hebrew University of Jerusalem Jerusalem Israel Breast Unit Champalimaud Clinical Centre/Champalimaud Foundation Champalimaud Research Lisboa Portugal

ISBN: (纸本)9783031855719

Using longitudinal data from Portugal, Italy, Finland, and Israel, this article attempts to explore the causal relationship between mental health and the Big Five personality traits of breast cancer patients with the appearance of adverse life events. The main conclusion is that patients with high scores in the Big Five personality traits index enjoy better mental health status than patients with low scores in that index. In addition, there is a significant negative correlation between mental health deterioration and personality traits as an index and as individual traits. The negative impact on the mental health of cumulative adverse life events is significant and cannot be moderated by personality traits. Single negative life events have a positive correlation with mental health status deterioration, though not all of them are significant and only work-related problems can moderated significantly by high scores in the personality trait index. Moreover, the most important control variable are the state of exercise, which if it changes from active to not active causes a significant drop in the mental health score and cancer stage. Lastly, this causal relationship is validated by the prediction accuracy of AI/ML models. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Active learning

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intelligent Synthesis of Individual Rehabilitation Plans for Patients after Joint Endoprosthesis Based on Computer Vision Technologies

Intelligent Synthesis of Individual Rehabilitation Plans for...

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International Conference on Information Technologies in engineering Education (Inforino)

作者： D.G Arseniev V.V. Borisov A.E. Misnik E.A Gomzhina M.A. Shalukhova Scientific Innovation Institute “Mathematical Modeling and Intelligent Control Systems” Peter the Great St. Petersburg Polytechnic University St. Petersburg Russian Federation Department of Computer Engineering National Research University “MPEI” Branch in Smolensk Smolensk Russia Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University” Mogilev Belarus Early Medical Rehabilitation FSBI “FCTOE” of the Ministry of Health of the Russian Federation Smolensk Russian Federation Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University Peter the Great St. Petersburg Polytechnic University Mogilev Belarus

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The article presents an innovative approach to the rehabilitation process for patients with joint endoprosthetics, focusing on the integration of a specialized software module based on computer vision technologies and the ontological approach. Developed for the Federal centre for Traumatology, Orthopaedics, and Endoprosthesis in Smolensk, Russia, this system is designed to improve the quality of life for patients by offering personalized rehabilitation plans. The core of the study lies in the application of ontological models and meta-associative graphs for synthesizing individual rehabilitation plans, which streamline the recovery process and reduce the workload on healthcare professionals. This method allows for the processing of large amounts of information, providing detailed conclusions and recommendations for functional correction of the musculoskeletal system. The software utilizes computer vision to analyze patient movement and develop tailored rehabilitation strategies, overcoming the limitations of traditional diagnostic tools like MRI, which may not be suitable for all patients. By incorporating artificial intelligence and augmented reality, the system enhances the accuracy of patient assessments and the effectiveness of rehabilitation plans. Furthermore, the article discusses the application of this technology in the educational process at the Belarusian-Russian University, where students are introduced to cutting-edge software tools that assist in creating personalized rehabilitation plans. This educational integration not only enriches the students’ learning experience but also fosters innovation in the development of new applications for computer vision in medical rehabilitation. The ontological approach, combined with modern technology, represents a significant advancement in the management of musculoskeletal system diseases, offering a more efficient, personalized approach to rehabilitation and recovery.

关键词： Computer vision Musculoskeletal system Technological innovation Computational modeling Medical services Propulsion Trajectory

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Dynamic Security Evaluation Method for Power systems Considering Cascading Failures

Dynamic Security Evaluation Method for Power Systems Conside...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ren Liu Xingye Xu Wei Du Kaishun Xiahou Yihua Zhu Dongxu Chang Hengdao Guo CSG Key Laboratory for Power System Simulation Electric Power Research Institute of China Southern Power Grid Guangzhou China School of Electric power engineering South China University of Technology Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute of China Southern Power Grid Guangzhou China National Energy Power Grid Technology R&D Centre China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of China Southern Power Grid Intelligent Operation and Control for New Energy Power System Guangzhou China

With the increasing level of informatization and interconnection in the electric power system, the risk of external attacks on power systems is increasing. In response to the drawbacks of traditional static security assessment methods, this paper establishes two security metrics, which are voltage and power flow amplitude deviation security metric and power flow entropy security metric. Based on these security metrics, a dynamic security evaluation model that considers cascading failure is proposed. Simulation results demonstrate that the proposed security metrics can effectively reflect the security of cascading failure propagation, thereby providing valuable insights for decision-making by power system operators.

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Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment

arXiv

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arXiv 2024年

作者： Ahmad, Alwani Liyana Sanchez-Bornot, Jose Sotero, Roberto C. Coyle, Damien Idris, Zamzuri Faye, Ibrahima Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Perak Malaysia Department of Neurosciences Hospital Universiti Sains Malaysia Kelantan Malaysia Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee campus Derry LondonderryBT48 7JL United Kingdom Department of Radiology Hotchkiss Brain Institute University of Calgary CalgaryAB Canada The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Universiti Teknologi PETRONAS Perak Malaysia Universiti Teknologi PETRONAS Perak32610 Malaysia

Background. Alzheimer's Disease (AD) represents a significant challenge in neurodegenerative disorders, necessitating early detection for effective intervention. Among neuroimaging methods, magnetic resonance imaging (MRI) is widely used because it is easy to apply in clinical practice and cost-effective, making it crucial for studying AD. Objective. This study aims to perform a comprehensive analysis of machine learning (ML) methods used in MRI-based biomarker selection and classification analysis. The goal is to study AD-related early cognitive decline by discriminating between healthy control (HC) participants who stayed stable and those unstable (uHC) who developed mild cognitive impairment (MCI) within five years. Methods. We utilized 3-Tesla (3T) MRI data from the Alzheimer's Disease Neuroinformatic Initiative (ADNI) and the Open Access Series of Imaging Studies 3 (OASIS-3), focusing on HC and uHC. Freesurfer’s recon-all, among other tools, was used to extract MRI-based anatomical biomarkers corresponding to semi-automatic segmented subcortical and cortical brain regions. We applied various ML techniques to select features and classify the data. These included methods from preliminary analysis performed in the MATLAB Classification Learner (MCL) app and more sophisticated methods like nested cross-validation and Bayesian optimization implemented in a customized pipeline to enhance classification performance for balanced and imbalanced datasets. Our pipeline was applied to both original imbalanced and randomly balanced datasets within a Monte Carlo analysis. Moreover, we implemented data harmonization approaches based on polynomial regression that enhanced the performance of ML and statistical methods. Complementary performance metrics, such as Accuracy (Acc), area under receiver operating characteristic curve (AROC), F1 score, and Matthew’s correlation coefficient (MCC), were used to evaluate the assessed methodologies. Results. In feature selection analyses,

关键词： Neurodegenerative diseases

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An overview of robust maritime situation awareness methods

An overview of robust maritime situation awareness methods

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International Conference on Transportation Information and Safety (ICTIS)

作者： Xueqian Xu Xinqiang Chen Bing Wu Tsz Leung Yip Intelligent Transport Systems Research Centre (ITSC) Wuhan University of Technology Wuhan china Institute of Logistics Science and Engineering Shanghai Maritime University shanghai china Department of Logistics and Maritime Studies Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

ISBN: (纸本)9781665497145

Maritime situation awareness is the core of ship navigation and determines the development direction of smart ships. Visual perception becomes an active yet important research task in maritime traffic. To aim that, this paper introduces three situation awareness methods based on visual images. The first method uses kernelized correlation filtering algorithms and Kalman filter models to solve ship occlusion in the image and achieve accurate ship tracking tasks; The second method uses kernelized correlation filtering and logarithmic polar coordinate transformation to solve the imaging size changes during ship navigation; The third method uses yolo algorithms to obtain the high-quality distance information from port videos. The experimental results and quantitative indicators show that each method can effectively achieve reliable maritime situational awareness tasks. The comprehensive realization of maritime situation awareness can better reduce risks and ensure the safety of ships.

关键词： Visualization Correlation Navigation Interference Cameras Safety Reliability

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Environmental and health impacts of reduced PM2.5 and trace metals from ship emissions under low-sulfur fuel oil policy in Shanghai, China

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Environmental Pollution 2025年 377卷 126409页

作者： Dong, Xuyang Zhang, Yan Yu, Guangyuan Xiong, Yuqi Han, Zimin Huo, Juntao Huang, Cheng Kan, Haidong Zheng, Mei Ning, Zhi Xie, Bing National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary Department of Environmental Science and Engineering Fudan University Shanghai200438 China Shanghai200062 China MOE Laboratory for National Development and Intelligent Governance Shanghai Institute for Energy and Carbon Neutrality Strategy IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health Fudan University Shanghai200433 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China Shanghai200235 China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment Shanghai Institute of Infectious Disease and Biosecurity Fudan University Shanghai200032 China Children's Hospital of Fudan University National Centre for Children's Health Shanghai China SKL-ESPC and SEPKL-AERM College of Environmental Sciences and Engineering and Centre for Environment and Health Peking University Beijing100871 China Division of Environment and Sustainability The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste School of Ecological and Environmental Sciences East China Normal University Shanghai200241 China

ship emissions can significantly exacerbate air pollution in coastal cities, threatening public health;however, a low-sulfur fuel oil policy, which restricts sulfur content in marine fuels, can effectively mitigate such pollution-driven challenges. This study employed a high-resolution ship emission inventory to assess the impacts of the fuel oil switch on air quality and public health in Shanghai from 2017 to 2021. Results showed a 37.3 % reduction in primary PM2.5 emissions from ships, with even steeper declines of 46.7 and 91.6 % in Ni and V emissions, respectively, leading to notable air quality improvements. Health assessments revealed a reduction in premature mortality attributable to long-term exposure to the contribution of ships to atmospheric PM2.5 concentrations, with deaths decreasing from 630 cases in 2017 to 481 in 2021. Similarly, short-term exposure-related deaths fell from 43 to 29. The port and waterfront areas experienced the most pronounced health benefits. The non-carcinogenic risks posed by trace metals (Ni and V), which were detected along the Huangpu River in 2017, dropped to 0.1 by 2021. Nonetheless, the carcinogenic risk from V persisted as a concern for adults in 2021. An analysis of ship-influenced episodes showed that population-weighted concentrations and short-term premature mortality decreased by over 50 % in densely populated areas and key ports. Despite the low population density in port areas such as Wusongkou and Waigaoqiao, the human health risks linked to ship emissions remained significant. This study demonstrates the effectiveness of low-sulfur fuel oil policy in reducing emissions and health risks, providing a scientific basis for refined pollution control strategies in port cities. © 2025 Elsevier Ltd

关键词： Air quality

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Size-induced axial segregation of binary mixtures in rotating drum of different lengths

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Chemical engineering Science 2025年 316卷

作者： Jielong He Yuelei Wang Liuyimei Yang Jin Xu Qijun Zheng Sida Liu Zongyan Zhou Jiangxi Provincial Key Laboratory of Particle Technology Jiangxi University of Science and Technology Nanchang 330013 China Research Centre for Intelligent Mineral Processing & Metallurgy International Institute for Innovation Jiangxi University of Science and Technology Nanchang 330013 China Ganjiang Innovation Academy Chinese Academy of Sciences Ganzhou 341000 China Centre for Simulation and Modelling of Particulate Systems Monash-Southeast University Joint Research Institute Suzhou 215123 China ARC Research Hub for Smart Process Design and Control Department of Chemical and Biological Engineering Monash University Melbourne VIC 3800 Australia

This study investigates the size-induced axial segregation of binary mixtures in rotating drums, particularly focusing on the effect of drum length. The results indicate that long drums require more time to achieve stable segregation and form more segregation bands. The core region under the free bed surface remains predominantly filled with small particles, indicating that radial segregation occurs simultaneously with the axial segregation. The angle of repose of large particle bands is consistently greater than that of small particle bands, which drives the migration of large particles. Short drums are more sensitive to changes in rotational speed, while long drums with moderate rotational speed promote the formation of segregation bands. The width of small particle bands shows an increasing–decreasing-stabilizing trend with the drum length increasing. An analysis of particle velocities reveals that the characteristics of segregation bands are closely related to the relative velocity difference between large and small particles.

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General-purpose machine-learned potential for 16 elemental metals and their alloys

arXiv

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arXiv 2023年

作者： Song, Keke Zhao, Rui Liu, Jiahui Wang, Yanzhou Lindgren, Eric Wang, Yong Chen, Shunda Xu, Ke Liang, Ting Ying, Penghua Xu, Nan Zhao, Zhiqiang Shi, Jiuyang Wang, Junjie Lyu, Shuang Zeng, Zezhu Liang, Shirong Dong, Haikuan Sun, Ligang Chen, Yue Zhang, Zhuhua Guo, Wanlin Qian, Ping Sun, Jian Erhart, Paul Ala-Nissila, Tapio Su, Yanjing Fan, Zheyong Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China School of Materials Science and Engineering Hunan University Changsha410082 China MSP group QTF Centre of Excellence Department of Applied Physics Aalto University P.O. Box 15600 Aalto EspooFI-00076 Finland Chalmers University of Technology Department of Physics Gothenburg41926 Sweden National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Civil and Environmental Engineering George Washington University WashingtonDC20052 United States Department of Electronic Engineering and Materials Science and Technology Research Center The Chinese University of Hong Kong N.T. Shatin999077 Hong Kong Department of Physical Chemistry School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Institute of Zhejiang University-Quzhou Quzhou324000 China College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Mechanics and Control of Mechanical Structures Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education Institute for Frontier Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong School of Science Harbin Institute of Technology Shenzhen518055 China College of Physical Science and Technology Bohai University Jinzhou121013 China Interdisciplinary Centre for Mathematical Modelling Department of Mathematical Sciences Loughborough University Leicestershire LoughboroughLE11 3TU United Kingdom

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science. Copyright © 2023, The Authors. All rights reserved.

关键词： Radiation damage

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