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arXiv 2024年

作者： Suvon, Mohammod N.I. Tripathi, Prasun C. Fan, Wenrui Zhou, Shuo Liu, Xianyuan Alabed, Samer 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 Information School University of Sheffield Sheffield United Kingdom Department of Computer Science IITRAM Gujarat India Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computing Imperial College London London United Kingdom

Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite their potential, these approaches often fall short especially when the size of labeled patient data is limited, a common challenge in the medical domain. Furthermore, only a few studies have explored multimodal methods to study CHDI, which mostly rely on costly modalities such as cardiac MRI and echocardiogram. In response to these limitations, we propose a novel multimodal variational autoencoder (CardioVAEX,G) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset. Specifically, CardioVAEX,G introduces a novel tristream pre-training strategy to learn both shared and modality-specific features, thus enabling fine-tuning with both unimodal and multimodal datasets. We pre-train CardioVAEX,G on a large, unlabeled dataset of 50, 982 subjects from a subset of MIMIC database and then fine-tune the pre-trained model on a labeled dataset of 795 subjects from the ASPIRE registry. Comprehensive evaluations against existing methods show that CardioVAEX,G offers promising performance (AUROC = 0.79 and Accuracy = 0.77), representing a significant step forward in non-invasive prediction of CHDI. Our model also excels in producing fine interpretations of predictions directly associated with clinical features, thereby supporting clinical decision-making. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Generative AI as a Catalyst for Transforming Transnational engineering Education: Opportunities, Challenges, and Future Directions

Generative AI as a Catalyst for Transforming Transnational E...

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IEEE Education engineering (EDUCON)

作者： Sami Ahmed Haider Khwaja Mutahir Ahmad Jehan Akbar Mukesh Soni Ismail Keshta Azzah AlGhamdi Hafiza Mahrukh Shahzadi School of Engineering and Physical Sciences Heriot-Watt University EH144AS Edinburgh United Kingdom School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu P.R. China Glasgow College University of Glasgow Glasgow United Kingdom Centre for Research & Development Chandigarh University Mohali Punjab India College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

Generative Artificial Intelligence (GAI) is emerging as a transformative force that empowers transnational education (TNE) in engineering. Recent trends indicate a significant shift in the application of generative AI in engineering policies, academic research, business practices, and educational settings throughout TNE. Governments and organizations are transitioning from restrictive stances to developing guiding frameworks for its application, enabling cross-border collaboration in TNE. Numerous universities have permitted and even promoted the utilization of GAI. Furthermore, academic research around the world is looking into the pros and cons of GAI in engineering education, focusing on how it can help teachers and keep students interested. Industrial applications are diversifying, extending across disciplines, and TNE is occurring in engineering contexts, including cross-border programs. GAI possesses the capacity to transform TNE by revolutionizing talent development, reformulating engineering models, and facilitating scientific assessment across multinational frameworks. However, problems like the generative illusion, ethical and ideological risks, lack of trust between teachers and students, and new threats to TNE in engineering equity in global settings require substantial focus. This study examines these concerns and outlines potential strategies to leverage GAI for transnational education in engineering, offering stakeholders the opportunity to prioritize AI literacy among educators and learners. This work emphasizes that cross-disciplinary and collaborative R&D, following national and international standards, should tackle application hurdles while guaranteeing safety and inclusion. This study also addresses several future directions that can contribute to creating a unified framework and cost-effective solutions. These solutions, integrated with platforms like the National Smart Education Platform, can bridge digital divides, ensuring equitable access a

关键词： Ethics Generative AI Catalysts Collaboration Transforms Market research Safety Stakeholders engineering education Research and development

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Analysis of Risk Factors and Diagnosis for Anxiety Disorder in Older People with the Aid of Artificial Intelligence: Observational Study

Analysis of Risk Factors and Diagnosis for Anxiety Disorder ...

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Artificial Intelligence and Cognitive science (AICS), Irish Conference on

作者： Jinling Wang Michaela Black Debbie Rankin Jonathan Wallace Catherine F Hughes Leane Hoey Adrian Moore Joshua Tobin Mimi Zhang James Ng Geraldine Horigan Paul Carlin Kevin McCarroll Conal Cunningham Helene McNulty Anne M Molloy School of Computing Engineering and Intelligent Systems Ulster University Derry~Londonderry United Kingdom School of Computing Ulster University Belfast United Kingdom School of Biomedical Sciences Nutrition Innovation Centre for Food and Health Ulster University Coleraine United Kingdom School of Geography and Environmental Sciences Ulster University Coleraine United Kingdom School of Computer Science & Statistics Trinity College Dublin Dublin Ireland School of Health Wellbeing and Social Care The Open University Belfast United Kingdom Mercers Institute for Research on Ageing St James's Hospita Dublin Ireland School of Medicine Trinity College Dublin Dublin Ireland

Anxiety disorders are the most common mental health problems particularly in older people who suffer from loneliness and social isolation, chronic health conditions, financial insecurity and other factors that can lead to anxiety disorders. The high prevalence and health risks of anxiety disorders, and the requirement for effective mental care, coupled with recent advances in artificial intelligence, has resulted in an increase exploration of how machine learning can aid the diagnosis and prediction of mental health problems. Data from the Trinity-Ulster-Department of Agriculture (TUDA) study will be utilized to identify risk factors for anxiety in community dwelling older adults using machine learning techniques. The TUDA study includes detailed information on biochemical, clinical, nutritional, lifestyle, and sociodemographic factors in 5186 older people recruited from the Republic of Ireland and Northern Ireland. These characteristics could foster the prediction of anxiety disorders using supervised machine learning methods. Biomarker risk factor analysis was conducted to facilitate feature engineering. In this observational study, several classical machine learning models have been trained to predict anxiety disorders. Comparing the accuracy results and determining the impact of features on the predictions of each method. The models' performance was assessed on a held-out test set and achieved an accuracy of 85.4% (sensitivity: 67.0%, specificity: 90.3%) and 83.4% (sensitivity: 81.5%, specificity: 83.9%) for two best performing methods i.e., random forest and support vector machine respectively, using the standard Synthetic Minority Oversampling Technique. Risk factors such as female sex, loneliness, separated/divorced conditions, lifestyle-related, socio-economic low status, chronic diseases, and family related diseases were identified. These results will aid in the early detection of anxiety disorder in future studies.

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Sustainable and intelligent time-series models for epidemic disease forecasting and analysis

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Sustainable Technology and Entrepreneurship 2024年第2期3卷

作者： Chhabra, Anureet Singh, Sunil K. Sharma, Akash Kumar, Sudhakar Gupta, Brij B. Arya, Varsha Chui, Kwok Tai Department of Computer Science Chandigarh College of Engineering and Technology Chandigarh 160019 India International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon School of Computing Skyline University College United Arab Emirates Department of Business Administration Asia University Taiwan Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Hong Kong Metropolitan University (HKMU) 695014 Hong Kong

There is an increasing risk of outbreaks escalating into epidemics, despite huge advances in medical science. Epidemics like COVID-19, Monkeypox, Influenza and HIV have been affecting people and public health infrastructure at an alarming rate around the world. COVID-19 alone has infected more than 500 million people out of which 6 million have died over 100 countries. HIV is also a major global public health issue and has claimed 85.6 million lives till 2023. Forecasting the trends of these epidemics is important in order to efficiently manage national and global health risks by improving early warning systems. Therefore an intelligent framework to forecast epidemic diseases is proposed and a detailed comparative analysis is conducted using different time-series models. This study contributes to (Sustainable Development Goal) SDG-3 by predicting epidemics disease trends precisely using ARIMA, Polynomial Regression, SARIMA, Holt's, Fb-Prophet time-series models, which can decrease the burden on healthcare systems. Using the best-suited models, the Mean Absolute Percentage Error (MAPE) values for Monkeypox, HIV, COVID-19 and Influenza forecasting were 0.0129, 0.0035, 0.0011, and 0.024 respectively. © 2023 The Author(s)

关键词： ARIMA COVID-19 Epidemic disease Fb-prophet Holt's model Influenza MonkeyPox SDG-3 Time-series forecasting

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A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks

A Heuristic Sparsification Method to Improve the Performance...

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International Conference on Information science and Technology (ICIST)

作者： Fangyu Zhang Yi Pan Yanjie Wei Jin Liu Zhenli Lin Centre for High Performance Computing Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China School of Computer Science and Engineering Central South University Changsha China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced.

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Quasi-Phase-Matching Enabled by van der Waals Stacking

arXiv

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arXiv 2024年

作者： Tang, Yilin Sripathy, Kabilan Qin, Hao Lu, Zhuoyuan Guccione, Giovanni Janousek, Jiri Zhu, Yi Hasan, Md Mehedi Iwasa, Yoshihiro Lam, Ping Koy Lu, Yuerui School of Engineering College of Engineering Computing and Cybernetics The Australian National University CanberraACT2601 Australia ARC Centre for Quantum Computation and Communication Technology The Australian National University CanberraACT2601 Australia Research School of Physics College of Science The Australian National University CanberraACT2601 Australia Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Clarendon Laboratory University of Oxford OxfordOX1 3PU United Kingdom RIKEN Center for Emergent Matter Science Hirosawa 2-1 Wako Saitama351-0198 Japan 2 Fusionopolis Way Innovis #08-03 Singapore138634 Singapore

Quasi-phase matching (QPM) is a technique extensively utilized in nonlinear optics for enhancing the efficiency and stability of frequency conversion processes. However, the conventional QPM relies on periodically poled ferroelectric crystals, which are limited in availability. The 3R phase of molybdenum disulfide (3R-MoS2), a transition metal dichalcogenide (TMDc) with the broken inversion symmetry, stands out as a promising candidate for QPM, enabling efficient nonlinear process. Here, we experimentally demonstrate the QPM at nanoscale, utilizing van der Waals stacking of 3R-MoS2 layers with specific orientation to realize second harmonic generation (SHG) enhancement beyond the non QPM limit. We have also demonstrated enhanced spontaneous parametric down-conversion (SPDC) via QPM of 3R-MoS2 homo-structure, enabling more efficient generation of entangled photon pairs. The tunable capacity of 3R-MoS2 van der Waals stacking provides a platform for tuning phase-matching condition. This technique opens interesting possibilities for potential applications in nonlinear process and quantum technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Van der Waals forces

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Human-cyber-physical systems:concepts,challenges,and research opportunities

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Frontiers of Information Technology & Electronic engineering 2020年第11期21卷 1535-1553页

作者： Zhiming LIU Ji WANG RISE-Centre for Research and Innovation in Software Engineering School of Computer and Information ScienceSouthwest UniversityChongqing 400715China State Key Laboratory for High Performance Computing School of ComputerNational University of Defense TechnologyChangsha 410073China

In this perspective article,we first recall the historic background of human-cyber-physical systems(HCPSs),and then introduce and clarify important *** discuss the key challenges in establishing the scientific foundation from a system engineering point of view,including(1)complex heterogeneity,(2)lack of appropriate abstractions,(3)dynamic black-box integration of heterogeneous systems,(4)complex requirements for functionalities,performance,and quality of services,and(5)design,implementation,and maintenance of HCPS to meet *** we propose four research directions to tackle the challenges,including(1)abstractions and computational theory of HCPS,(2)theories and methods of HCPS architecture modelling,(3)specification and verification of model properties,and(4)software-defined *** article also serves as the editorial of this special section on cyber-physical systems and summarises the four articles included in this special section.

关键词： Abstractions Architecture modelling Evolution Software-defined technology

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Digital Health during COVID-19: Informatics Dialogue with the World Health Organization

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Yearbook of Medical Informatics 2021年第1期30卷 13-16页

作者： Koch, Sabine Hersh, William R. Bellazzi, Riccardo Leong, Tze Yun Yedaly, Moctar Al-Shorbaji, Najeeb Health Informatics Centre Department of Learning Informatics Management and Ethics Karolinska Institutet Stockholm Sweden Department of Medical Informatics & Clinical Epidemiology School of Medicine Oregon Health & Science University Portland OR United States Department of Electrical Computer and Biomedical Engineering University of Pavia Italy IRCCS ICS Maugeri Pavia Italy Department of Computer Science School of Computing National University of Singapore and AI Singapore Singapore Information Society Division African Union Commission Addis Ababa Ethiopia eHealth Development Association Amman Jordan

Background: On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic. Objectives: The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs). Methods: The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants’ consent and summarized in this paper. Results: The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general;(b) ethics and artificial intelligence;(c) digital divide;(d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic. Conclusions: Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly. © 2021 Thieme Medical Publishers, Inc.. All rights reserved.

关键词： Artificial Intelligence COVID-19 Global Health Medical Informatics World Health Organization

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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Towards practical and massively parallel quantum computing emulation for quantum chemistry

arXiv

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arXiv 2023年

作者： Shang, Honghui Fan, Yi Shen, Li Guo, Chu Liu, Jie Duan, Xiaohui Li, Fang Li, Zhenyu Institute of Computing Technology Chinese Academy of Sciences Beijing China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Software Shandong University Jinan China National Research Center of Parallel Computer Engineering and Technology Beijing China

Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, quantum resource is too scarce to support these explorations. Therefore, it is valuable to emulate quantum computing on classical computers for developing quantum algorithms and validating quantum hardware. However, existing simulators mostly suffer from the memory bottleneck so developing the approaches for large-scale quantum chemistry calculations remains challenging. Here we demonstrate a high-performance and massively parallel variational quantum eigensolver (VQE) simulator based on matrix product states, combined with embedding theory for solving large-scale quantum computing emulation for quantum chemistry on HPC platforms. We apply this method to study the torsional barrier of ethane and the quantification of the protein-ligand interactions. Our largest simulation reaches 1000 qubits, and a performance of 216.9 PFLOPS is achieved on a new Sunway supercomputer, which sets the state-of-the-art for quantum computing emulation for quantum chemistry. © 2023, CC BY.

关键词： Computational chemistry

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