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arXiv 2024年

作者： Gunning, Edward Golovkine, Steven Simpkin, Andrew J. Burke, Aoife Dillon, Sarah Gore, Shane Moran, Kieran O’Connor, Siobhan Whyte, Enda Bargary, Norma Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States MACSI Department of Mathematics and Statistics University of Limerick Ireland School of Mathematical and Statistical Sciences University of Galway Ireland School of Allied Health Faculty of Education and Health Science University of Limerick Limerick Ireland Centre for Injury Prevention and Performance Athletic Therapy and Training School of Health and Human Performance Dublin City University Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland

Biomechanics and human movement research often involves measuring multiple kinematic or kinetic variables regularly throughout a movement, yielding data that present as smooth, multivariate, time-varying curves and are naturally amenable to functional data analysis. It is now increasingly common to record the same movement repeatedly for each individual, resulting in curves that are serially correlated and can be viewed as longitudinal functional data. In this work, we present a new approach for modelling multivariate multilevel longitudinal functional data, with application to kinematic data from recreational runners collected during a treadmill run. For each stride, the runners’ hip, knee and ankle angles are modelled jointly as smooth multivariate functions that depend on subject-specific covariates. Longitudinally varying multivariate functional random effects are used to capture the dependence among adjacent strides and changes in the multivariate functions over the course of the treadmill run. A basis modelling approach is adopted to fit the model – we represent each observation using a multivariate functional principal components basis and model the basis coefficients using scalar longitudinal mixed effects models. The predicted random effects are used to understand and visualise changes in the multivariate functional data over the course of the treadmill run. In our application, our method quantifies the effects of scalar covariates on the multivariate functional data, revealing a statistically significant effect of running speed at the hip, knee and ankle joints. Analysis of the predicted random effects reveals that individuals’ kinematics are generally stable but certain individuals who exhibit strong changes during the run can also be identified. A simulation study is presented to demonstrate the efficacy of the proposed methodology under realistic data-generating scenarios. © 2024, CC BY.

关键词： Exercise equipment

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Influence of electric field orientation on the effect of ocular current stimulation using full field electroretinogram

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Current Directions in Biomedical Engineering 2021年第2期7卷

作者： Blum, Maren-Christina Leydolph, Lilly Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

Ocular current stimulation (CS) exhibits potential for the treatment of neurodegenerative ocular diseases. For a full field electroretinogram (ffERG) we found no CS effect on the characteristic waves (a-wave, b'-wave, and b-wave). To investigate whether the orientation of the generated electric field has an influence on the CS effect, this study repeated the previous ffERG study with changing one CS electrode position and compared the results of both studies. In the first study 15 (8 m, 27.5 ± 4.5 years) and in the second study 17 (7 m, 22.0 ± 1.9 years) healthy volunteers were stimulated with an anodal, cathodal, and sham direct CS of 800 μA for 5 min in three sessions (randomized, different days). For both studies, a cut-sized ring rubber electrode was placed around the eye. A square rubber electrode was placed for the first study at the ipsilateral temple and for the second study at the visual cortex. Before (ERG 1) and during (ERG 2) the CS, the ffERG was measured. For both studies, the difference between the ERG 1 and ERG 2 measurement (CS effect) for the three characteristic waves was analyzed and compared between the studies. For statistical analysis, the Mann-Whitney U test with Bonferroni correction was applied (α = 0.05). The comparison of the data distribution showed only slight differences between the studies. The Mann-Whitney U test found no significant difference of the CS effect between the studies for all amplitudes and CS groups (p ≥ 0.0055). In the mean, the latency differences were smaller than the time resolution, therefore no statements for latency effects were possible. It can be concluded that the retinal cells generating the ffERG are not affected by ocular CS in either electrode montages tested. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Rubber

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Lung Ultrasound for the Detection of Pulmonary Tuberculosis Using Expert- and AI-Guided Interpretation: A Prospective Cohort Study

SSRN

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SSRN 2025年

作者： Suttels, Véronique Brokowski, Trevor Wachinou, Ablo Prudence Wolleb, Julia Hada, Aboudou Rasisou Du Toit, Jacques Daniel Fiogbé, Arnauld Attannon Guendehou, Brice Alovokpinhou, Frederic Sefou, Fadyl Makpemikpa, Ginette Bessat, Cécile Roux, Alexia Garcia, Elena Brahier, Thomas Opota, Onya Doenz, Jonathan Vignoud, Julien Agodokpessi, Gildas Affolabi, Dissou Hartley, Mary-Anne Boillat-Blanco, Noémie Department of Medicine Infectious Diseases Lausanne University Hospital University of Lausanne Lausanne Switzerland Yale School of Medicine Department of Biomedical Informatics & Data Science New HavenCT06510 United States Cotonou Benin Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Emergency Department Lausanne University Hospital University of Lausanne Lausanne1011 Switzerland Institute of Microbiology University of Lausanne University Hospital Centre Lausanne Switzerland Lausanne1015 Switzerland National Reference Laboratory for Mycobacteriology Cotonou Benin Yale University United States Faculty of Health Sciences Benin Intelligent Global Health Machine Learning and Optimization Laboratory

Background: Point-of-care lung ultrasound (LUS) is a promising tool for portable sputum-free tuberculosis (TB) triage. We investigate the diagnostic performance of LUS to detect TB using expert and artificial intelligence (AI) guided interpretation. We introduce ULTR-AI (Ultrasound-led TB recognition using AI), a suite of deep learning (DL) models designed to automate TB prediction from LUS images. Methods: In this prospective cohort study in a tertiary center in urban Benin, a standardized 14-point sliding scan LUS protocol was performed for symptomatic patients by a trained operator. LUS images were reviewed by two blinded and independent readers. Same-day single sputum Xpert MTB/RIF Ultra® was the microbiological reference standard. The suite comprises three AI models, ULTR-AI, ULTR-AI[signs] and ULTR-AI[max]. ULTR-AI predicts TB directly from images using DL, ULTR-AI[signs] first generates human-recognizable pathological signs before TB risk prediction in a machine learning (ML) model. Finally, ULTR-AI[max] takes the maximal TB risk score predicted by these two models. Findings: Out of 760 patients screened, 504 were analyzed. Median age was 40 (IQR 30-52), 196 (39%) were female, 78 (15%) were people with HIV and 66 (13%) had previous TB. Overall, 192 (38%) had bacteriologically confirmed TB. Human expert interpretation of lung ultrasound achieved a sensitivity of 0·90 (95%CI: 0·89, 0·92), specificity of 0·61 (95%CI: 0·54, 0·67) [AUROC 0·84, 95% CI: 0.82-0.85]. AI-guided interpretation with ULTRA-AI[max] reached a sensitivity of 0·91 (95% CI, 0·90–0·96) and a specificity of 0·85 (95% CI, 0·74–0·88) [AUROC 0·93, 95% 0·92-0·95]. Interpretation: In this cohort, AI-guided LUS meets the WHO requirements for a sputum-free TB triage test, enabling point-of-care testing with minimal infrastructure. ULTR-AI could further help decentralize TB diagnostics in LMICs, improving timely detection. Validation in diverse populations is crucial to confirm clinical utility. © 2025,

关键词： Lung cancer

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Optimal half-metal band structure for large thermoelectric performance

arXiv

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arXiv 2023年

作者： Rahangiar, Finantius E.M. Cahaya, Adam B. Muntini, Melania S. Anshori, Isa Hasdeo, Eddwi H. Department of Physics Universitas Gadjah Mada Sekip Utara BLS 21 Yogyakarta55281 Indonesia Research Center for Quantum Physics National Research and Innovation Agency South Tangerang Indonesia Department of Physics Faculty of Mathematics and Natural Sciences University of Indonesia Depok16424 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology West Java Bandung Indonesia Bandung Institute of Technology West Java Bandung Indonesia Department of Physics and Material Science University of Luxembourg LuxembourgL-1511 Luxembourg

Half-metal ferromagnets were predicted [in IEEE Trans. Mag. 51, 1 (2015)] to give large thermoelectric performance in anti-parallel spin valve configuration. Despite being metals that suffer from the Wiedemann-Franz law, the additional spin degrees of freedom allow for tuning of the thermoelectric properties due to the spin-valve enhancement factor (SVEF). We test this theory and find a mismatch of parameters that gives large TE performance and large SVEF. As a result, we show that the spin-valve setup is useful only for gapless HMF with initially poor TE performance. To obtain the largest TE performance, one still needs to open the band gap. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy gap

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A Comparative Study of Job-Related Stress in Long-Term Care Workers in Japan and China 9

A Comparative Study of Job-Related Stress in Long-Term Care ...

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9th International Congress on Advanced Applied informatics, IIAI-AAI 2020

作者： Yu, Yating Taki, Seiko Takanokura, Masato Kawakami, Masaru Yamada, Tetsuo Liang, Shuyu Kitami Institute of Technology Regional Management Engineering Course Kitami Japan Chiba Institute of Technology Faculty of Social Systems Science Department of Management Information Science Chiba Japan Kanagawa University Faculty of Engineering Department of Industrial Engineering and Management Yokohama Japan Jichi Medical University Faculty of Nursing Tochigi Japan The University of Electro-Communications Department of Informatics Tokyo Japan Shandong University Data Science Institute Jian China

ISBN: (纸本)9781728173979

Japan revised and implemented the Occupational Safety and Health Act in December 2015 to mandate that businesses with 50 or more workers have all of their workers tested for stress annually. In China, one effect of the 'one child policy' and the nuclearization of the family has been that the number of children has decreased, while the number of elderly people living alone continues to grow. This growth is increasing the demand for long-term care workers. However, because the aging of the Chinese population has been more rapid than in Japan, and China has yet to develop a long-term care system to cope with the aging of its population, the burden on long-term care staff appears to be increasing. Although the resulting need for more support for long-term care staff has become an issue, China has yet to develop a mental health policy like that of Japan that mandates the monitoring of mental health, for example, through annual testing of workers for stress. As a result, the factors underlying long-term care worker stress have yet to be *** study attempted to discover, through a comparison of long-term care worker stress in Japan and China, the kinds of job-related stress care workers tended to have and what the principal underlying factors resulting in high levels of stress may be in each country. To that end, data were collected on workers' psychological and physical stress using the Brief Job Stress Questionnaire (BJSQ) and analyzed to find out how stress may differ by country, gender, job responsibilities, having to work night shifts, or having certain qualifications. © 2020 IEEE.

关键词： Health

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Efficient load profiling and forecasting in large electric power systems 19

Efficient load profiling and forecasting in large electric p...

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19th Conference Information Technologies - Applications and Theory, ITAT 2019

作者： Lendák, Imre Horváth, Tomáš Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Budapest Hungary

The goal of this paper is to present an efficient load forecasting algorithm for large electric power systems. It uses a combination of nearest neighbor-based load profile clustering and rule-based load forecasting. The load data was sliced into daily load curves, which were K-Means-clustered, thereby compressing data and simplifying the solution. K-Means was chosen in the proof of concept phase and will be substituted with more precise solutions later. In the forecasting phase the daily load profile is predicted based on the forecast date, day type (e.g. weekday or weekend) and historical consumption data for similar days in the past. The solution was tested on a large dataset consisting of one year-long, 5-minute measurement data in a 1900-power-line system. The solution showed excellent performance in both the training and forecast phases. It produced meaningful forecasts even when the input data contained significant amounts of anomalies. An additional advantage of the presented solution is that it can be used for medium and long-term forecasting with limited and/or missing input data. Copyright © 2019 for this paper by its authors.

关键词： Electric power plant loads

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Hybrid AI- and Blockchain-Powered Secure Internet Hospital Communication and Anomaly Detection in Smart Cities

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Processes 2025年第5期13卷 1466-1466页

作者： Xiaofeng Wang Xiaoguang Yue Noshina Tariq Ahthasham Sajid College of Management Shenzhen University Shenzhen 518060 China Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad 44000 Pakistan Department of Information Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad 46000 Pakistan Faculty of Computing and Informatics Multimedia University Cyberjaya 63100 Malaysia

Internet of Things (IoT) devices have revolutionized real-time monitoring and distant patient care in smart cities’ healthcare systems. However, this advancement has come with several issues, such as data security, scalability, operational efficiency, and fault tolerance. Previous approaches are not well suited to the real-time processing of IoT data in healthcare, given the low latency, high throughput, and effective anomaly detection needed for such a task. Given these challenges, this paper proposes a hybrid Artificial Intelligence (AI)- and blockchain-based IoT governance framework for Internet hospitals using Proof-of-Authority (PoA) in smart cities. It encompasses application of the enhanced RSA for secure data transmission, real-time anomaly detection through the Isolation Forest algorithm, and a private blockchain architecture designed for high scalability. It effectively detects tampering and replay attacks to minimize illegitimate and unauthorized access or manipulation of patients’ data. The proposed framework achieves relatively significant improvements over a state-of-the-art baseline model. It has cut the transaction response time by 50%, doubled the Throughput Per Second (TPS), and attained a 100% detection performance in anomalies. Comparative analysis reveals its linear scalability with increasing workload, ensuring consistent performance under varying transaction volumes. This study’s findings highlight the proposed framework’s potential to mitigate key issues in IoT-enabled Internet hospitals in smart cities.

关键词： anomaly detection blockchain distributed role-based access control electronic health records internet hospital internet of things isolation forest algorithm smart cities proof-of-authority Rivest–Shamir–Adleman (RSA)

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A Secure Federated data-Driven Evolutionary Multi-objective Optimization Algorithm

arXiv

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arXiv 2022年

作者： Liu, Qiqi Yan, Yuping Ligeti, Péter Jin, Yaochu Trustworthy and General AI Lab School of Engineering Westlake University China Faculty of Technology Bielefeld University Bielefeld33619 Germany Faculty of Informatics Department of Computeralgebra Eötvös Loránd University Hungary Smart Data Group E-Group ICT Software Zrt Hungary Department of Computer Science University of Surrey Surrey GuildfordGU2 7XH United Kingdom

data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving many complex real-world optimization problems. However, most data-driven evolutionary algorithms are centralized, causing privacy and security concerns. Existing federated Bayesian optimization algorithms and data-driven evolutionary algorithms mainly protect the raw data on each client. To address this issue, this paper proposes a secure federated data-driven evolutionary multiobjective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server. We select the query points on a randomly selected client at each round of surrogate update by calculating the acquisition function values of the unobserved points on this client, thereby reducing the risk of leaking the information about the solution to be sampled. In addition, since the predicted objective values of each client may contain sensitive information, we mask the objective values with Diffie-Hellman-based noise, and then send only the masked objective values of other clients to the selected client via the server. Since the calculation of the acquisition function also requires both the predicted objective value and the uncertainty of the prediction, the predicted mean objective and uncertainty are normalized to reduce the influence of noise. Experimental results on a set of widely used multi-objective optimization benchmarks show that the proposed algorithm can protect privacy and enhance security with only negligible sacrifice in the performance of federated data-driven evolutionary optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Acquiring breathing movement and heart rate from inertial measurement units 12

Acquiring breathing movement and heart rate from inertial me...

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12th IADIS International Conference e-Health 2020, EH 2020, Part of the 14th Multi Conference on Computer science and Information Systems, MCCSIS 2020

作者： László, Martin Csongor Tarcsi, Ádám Istenes, Zoltán Eötvös Loránd University Hungary IoT&Data Innovation Lab Budapest Hungary Eötvös Loránd University Faculty of Informatics Data Science and Engineering Department IoT&Data Innovation Lab Hungary Eötvös Loránd University Hungary 3in Research Group Budapest Martonvásár Hungary

ISBN: (纸本)9789898704184

Wearable devices can raise suspicion about disorders, so action can taken in a timely fashion. However, wearable devices are still not widespread and inaccessible for many. Using smartphone's and smartwatch's inertial measurement units people's breathing movement and heart rate can be extracted. Breathing movement can be measured by device rotation heart rate can be measured by abdominal vibration. The devices are capable of acquiring signals very similar to Siesmocardiography, Cardiography and slightly similar to Ballistocardiography. © Proceedings of the 12th IADIS International Conference e-Health 2020, EH 2020 - Part of the 14th Multi Conference on Computer science and Information Systems, MCCSIS 2020. All rights reserved.

关键词： Smartphones

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Through the Lens of Patient Partners: Challenges in Accrual of Older Adults to NCI Clinical Trials

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Journal of the National Cancer Institute - Monographs 2022年第60期2022卷 125-134页

作者： BrintzenhofeSzoc, Karlynn Canin, Beverly Casas-Silva, Esmeralda Denicoff, Andrea Braun-Inglis, Christa Okado, Izumi Bakos, Alexis Oncology Social Work Kent School of Social Work University of Louisville Louisville KY United States SCOREboard Patient Advocate Board The Cancer and Aging Research Group United States Center for Biomedical Informatics and Information Technology Informatics and Data Science Program National Institutes of Health National Cancer Institute Rockville MD United States Division of Cancer Treatment and Diagnosis Cancer Therapy and Evaluation Program National Institutes of Health National Cancer Institute Rockville MD United States Clinical Faculty UH Nancy Atmospera-Walch School of Nursing University of Hawaii Cancer Center Hawaii M U NCORP Honolulu HI United States University of HawaiiCancer Center Honolulu HI United States Division of Cancer Prevention Community Oncology and Prevention Trials Research Group National Institutes of Health National Cancer Institute Rockville MD United States

The workshop “Engaging Older Adults in Cancer Clinical Trials Conducted in the NCI Clinical Trials Network: Challenges and Opportunities” included a Patient Stakeholder Workgroup that explored the needs and concerns of older adults with cancer regarding clinical trials. To accomplish this, the workgroup conducted patient focus groups in which participants were interviewed, recorded conversations were analyzed and coded, and salient themes were identified. The focus groups identified general barriers to accrual such as complex consent forms, general communication, restrictive eligibility, nonreferrals, patient costs, cultural insensitivity, limited accessibility in community settings, and transportation issues. They also identified the influence of knowledgeable information presenters, improved care, family or caregiver support, and the desire to help others as drivers or reasons to participate in clinical trials. The workshop concluded that multi-level interventions could be used to increase the accrual of older adults to National Cancer Institute clinical trials as well as others. © The Author(s) 2022. Published by Oxford University Press. All rights reserved.

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