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The joint effects of physical activity and sleep duration on risk of osteoporosis in Taiwanese adult population: The Taiwan Biobank Study

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Osteoporosis International 2024年第3期35卷 523-531页

作者： Chen, Kun-Hui Su, Chen-Ming Liu, Wei-Ju Tzeng, Huey-En Lee, Chia-Lin Tsai, Chun-Hao Department of Orthopedic Surgery Taichung Veterans General Hospital Taichung 407219 Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung 402204 Taiwan Department of Computer Science & Information Engineering College of Computing and Informatics Providence University Taichung City 43301 Taiwan Department of Sports Medicine China Medical University Taichung 406040 Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung 407219 Taiwan Intelligent Data Mining Laboratory Department of Medical Research Taichung Veterans General Hospital Taichung 407219 Taiwan School of Medicine Taipei Medical University Taipei 11031 Taiwan Division of Transfusion Medicine /Hematology - Oncology Taichung Veterans General Hospital Taichung 40705 Taiwan Department of Public Health College of Public Health China Medical University Taichung 406040 Taiwan Department of Medicine School of Medicine National Yang Ming Chiao Tung University Taipei 112304 Taiwan Department of Orthopedics China Medical University Hospital Taichung 406040 Taiwan College of Biomedical Engineering China Medical University Taichung 406040 Taiwan

Summary: Most studies investigating the association between physical activity and osteoporosis prevention only focused on specific types of physical activity. This study’s evidence regarding the combined effects or interaction of sleep duration and physical activity. The findings emphasize the role of sleep duration and physical activity in association with osteoporosis. Purpose: The associations between physical activity, sleep duration, and prevalent osteoporosis in Taiwanese adults were studied in this cross-sectional study. Methods: The Taiwan Biobank enrolled a community-based cohort of ~ 120,000 volunteers (as of April 30, 2020) between 30 and 76 years of age with no history of cancer. Amongst, bone mineral density (BMD) measures by dual-energy X-ray absorptiometry (DXA) were available in 22,402 participants. After excluding individuals who had no complete data of BMI (n = 23), MET score (n = 207), T-score (n = 8,826), and sleep duration (n = 16), 13,330 subjects were included as the primary cohort. Univariate and multivariable regression analyses were performed to determine the associations between the presence of osteoporosis, physical activity level, sleep duration, and other variables. Results: The results showed that after adjustment, subjects with physical activity < 20 METs/week and ≥ 20 METs/week (aOR = 1.017 and 0.767, respectively) were associated with risk of osteoporosis than those with zero MET. The odds of osteoporosis were not significantly lower in subjects who slept for ≥ 8 h/day (aOR = 0.934,p=0.266). In addition, compared to short sleepers with no physical activity, adults with increased physical activity ≥ 20 METs/week and sleep ≥ 8 h/day had a significantly lowest likelihood of osteoporosis (aOR = 0.702). Those with medium physical activity (< 20 METs/week) plus average sleep duration (6.5–8 h/day) did not have significant higher odds of osteoporosis (aOR = 1.129,p=0.151). Conclusion: The findings emphasize the joint role of sleep duration

关键词： Bone mineral density (BMD) Exercise habit Osteoporosis Sleep duration Taiwan biobank

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Sensorless End-to-End Freehand Ultrasound with Physics Inspired Network

Sensorless End-to-End Freehand Ultrasound with Physics Inspi...

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IEEE Symposium (IUS) Ultrasonics

作者： Yimeng Dou Fangzhou Mu Yin Li Tomy Varghese Department of Electrical and Computer Engineering University of Wisconsin (UW)-Madison Madison WI USA Department of Medical Physics University of Wisconsin School of Medicine and Public Health Madison WI USA Department of Computer Science UW-Madison Madison WI USA Department of Biostatistics & Medical Informatics University of Wisconsin School of Medicine and Public Health Madison WI USA

Three-dimensional ultrasound (3D US) imaging finds broad applications in clinical practice. Compared to using 'wobbler' (motor embedded) or 'matrix array' transducers which suffers from a restricted field of view, freehand US offers more flexibility in mapping the 3D volume along the scanning path. Nevertheless, current methods for reconstructing 3D US volumes using freehand scanning are challenged by significant elevational shifts along the scanning trajectory. Previous research explored the integration of motion sensors to resolve transducer drift, yet frequent motion artifacts often compromise the benefits brought by these sensors. Recent work turned to deep neural networks (DNNs) for estimating the relative pose of imaging planes between frames in the scanning trajectory, allowing for sensorless freehand US. However, these data-driven models fall short in accuracy as they lack physical constraints, hence limiting their use in clinical practice. Inspired by the physical properties of US, we design a physics inspired DNN architecture that can better leverage the contextual cues between elevational frames for sensorless freehand 3D US reconstruction, and demonstrate substantially improving reconstruction accuracy.

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Adaptive belief discretization for POMDP planning

arXiv

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arXiv 2021年

作者： Grover, Divya Dimitrakakis, Christos Department of Computer Science and Engineering Chalmers University Sweden Department of Informatics University of Oslo Norway

Partially Observable Markov Decision Processes (POMDP) is a widely used model to represent the interaction of an environment and an agent, under state uncertainty. Since the agent does not observe the environment state, its uncertainty is typically represented through a probabilistic belief. While the set of possible beliefs is infinite, making exact planning intractable, the belief space’s complexity (and hence planning complexity) is characterised by its covering number. Many POMDP solvers uniformly discretize the belief space and give the planning error in terms of the (typically unknown) covering number. We instead propose an adaptive belief discretization scheme, and give its associated planning error. We furthermore characterise the covering number with respect to the POMDP parameters. This allows us to specify the exact memory requirements on the planner, needed to bound the value function error. We then propose a novel, computationally efficient solver using this scheme. We demonstrate that our algorithm is highly competitive with the state of the art in a variety of scenarios. © 2021, CC BY.

关键词： Markov processes

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Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis

arXiv

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arXiv 2023年

作者： Ahmed, Aram M. Rashid, Tarik A. Hassan, Bryar A. Majidpour, Jaffer Noori, Kaniaw A. Rahman, Chnoor Maheadeen Abdalla, Mohmad Hussein Qader, Shko M. Tayfor, Noor Mohammed, Naufel B. Department of Information Technology College of Science and Technology University of Human Development Sulaimani Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Department of Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani46023 Iraq Department of Computer Science University of Raparin Sulaimani Iraq Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq

Agents of any metaheuristic algorithms are moving in two modes, namely exploration and exploitation. Obtaining robust results in any algorithm is strongly dependent on how to balance between these two modes. Whale optimization algorithm as a robust and well recognized metaheuristic algorithm in the literature, has proposed a novel scheme to achieve this balance. It has also shown superior results on a wide range of applications. Moreover, in the previous chapter, an equitable and fair performance evaluation of the algorithm was provided. However, to this point, only comparison of the final results is considered, which does not explain how these results are obtained. Therefore, this chapter attempts to empirically analyze the WOA algorithm in terms of the local and global search capabilities i.e. the ratio of exploration and exploitation phases. To achieve this objective, the dimension-wise diversity measurement is employed, which, at various stages of the optimization process, statistically evaluates the population's convergence and diversity. © 2023, CC BY.

关键词： Optimization

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Hierarchical Multi-Modal Fusion on Dynamic Heterogeneous Graph for Health Insurance Fraud Detection

Hierarchical Multi-Modal Fusion on Dynamic Heterogeneous Gra...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhang, Jian Yang, Fan Lin, Kaibiao Lai, Yongxuan Xiamen University Department of Automation Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Shenzhen Research Institute of Xiamen University Shenzhen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In this paper, we devote to aggregating longitudinal and multi-modal information from heterogeneous neighbors to obtain an accurate node embedding on the dynamic heterogeneous graph. Recently, Heterogeneous Graph Neural Networks (GNNs) have attracted extensive attention in fraud detection. However, when faced with longitudinal and multi-modal data such as health insurance records, existing GNN-based fraud detectors always discard the multi-modal information (e.g., medication and treatment) of heterogeneous neighbors and ignore the inconsistent claimer behavior in the longitudinal records. To fully utilize the information, we represent the records in the form of a dynamic heterogeneous graph, and propose Hierarchical Multi-modal Fusion Graph Neural Network (HMF-GNN) which learns not only topological information, but also embeddings of longitudinal and multi-modal entities to improve the performance of fraud detection. Experimental results on two real-world health insurance datasets demonstrate that HMF-GNN outperforms state-of-the-art graph embedding methods and GNN-based fraud detectors. © 2022 IEEE.

关键词： Health insurance

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Network on Privacy-Aware Audio-and Video-Based Applications for Active and Assisted Living: GoodBrother Project

Network on Privacy-Aware Audio-and Video-Based Applications ...

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Euromicro Symposium on Digital System Design

作者： Nicolas Sklavos Maria Pantopoulou Francisco Florez-Revuelta Computer Engineering and Informatics Department (CEID) SCYTALE Group University of Patras Hellas School of Nuclear Engineering Purdue University West Lafayette Indiana USA Department of Computing Technology University of Alicante Alicante Spain

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

Active and Assisted Living (AAL) systems have a purpose to improve the lives of older or impaired people in various aspects. However, the use of equipment for data acquisition in these systems can be considered intrusive in some cases. Although de-identification may provide the needed protection to some extent, it is not always preferred, as it could affect the quality and utility of any obtained data. It is therefore crucial to establish methodologies for protecting the privacy of those monitored and thus affected by AAL systems. The purpose of GoodBrother is to a) analyze any issues arising from the use of monitoring AAL systems, regarding the users' privacy; b) establish proper guidelines for the use of these systems; c) develop privacy-aware methodologies for data handling; d) increase the systems' robustness and reliability; e) create databases to use towards benchmarking. Each one of these objectives are handled by separate interdisciplinary working groups.

关键词： Data privacy Privacy Ethics Digital systems Cameras Robustness Assisted living

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Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

arXiv

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arXiv 2024年

作者： Yang, Bo Zhang, Xueyao Yu, Zhiwen Cao, Xuelin Huang, Chongwen Alexandropoulos, George C. Zhang, Yan Debbah, Mérouane Yuen, Chau School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Informatics University of Oslo Oslo0316 Norway Center for 6G Technology The Khalifa University of Science and Technology Abu Dhabi United Arab Emirates School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Modelling and Use of Adaptive Control Using Petri Nets

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Procedia computer Science 2023年 225卷 1438-1447页

作者： Zoltán Balogh Kristián Fodor Jan Francisti Martin Drozda Maroš Čavojský Department of Computer Science Constantine the Philosopher University in Nitra 949 01 Nitra Slovakia Kándó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary Institute of Informatics and Mathematics Slovak University of Technology in Bratislava 811 07 Bratislava Slovakia

The issue of modeling holds significant importance for the applicability of control theory, particularly at higher control levels where the verbal description of control objects becomes increasingly crucial. However, a notable disadvantage of such verbal descriptions is the inherent ambiguity and vagueness of the obtained results. This article aims to address this challenge by proposing an approach that utilizes Petri nets (PN) to model and control a specific component within an intelligent home. Additionally, the Simulink modeling tool and Petri net analysis tools are introduced as aids in the modeling process. The article focuses on creating a model that accurately represents the temperature regulation system in the home, considering the constant rise in energy consumption and costs. Furthermore, the transfer of the model to the Simulink environment using IF THEN rules is demonstrated, allowing for an evaluation of its performance. Through the comprehensive application of this approach, the aim is to optimize energy usage and reduce costs within household environments.

关键词： Petri nets Simulation Matlab Smart homes

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Kalman-based Adaptive Moment Estimation Optimisation Algorithm to Enhance GPT in LLMs for Medical Sentiment Analysis of Patient Health-related Feedback

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Chen, Xingchi Ma, Wenxin Li, Dazhou Zhu, Fa Routray, Sidheswar Guduri, Manisha Margala, Martin The Pengcheng Laboratory Shenzhen518000 China The College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China The College of Information Science and Technology College of Artificial Intelligence Nanjing Forestry University Nanjing China The Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India The School of Computing and Informatics University of Louisiana LafayetteLA70503 United States

The progress in Natural Language Processing (NLP) using Large Language Models (LLMs) has greatly improved medical sentiment analysis of patient feedback extraction from health-related question and answer. However, using LLMs to analyze such data often requires significant training data and computational resources, resulting in considerable increases in training costs and durations, which is one of the primary issues in applying LLMs to real-world healthcare scenarios. To tackle these challenges, a novel optimization algorithm named KAdamEnGPT4LLM, based on Kalman filters and Adaptive Moment Estimation, is proposed to enhance training efficiency and reduce training costs of LLMs for analyzing patient feedback sentiment. Furthermore, the optimization algorithm KAdam-EnGPT4LLM is employed in training the LLM model GPT4ALL for medical sentiment analysis, resulting in the development of GPT4ALLMediSentAly-KAdam, which leds to faster convergence and more stable training specifically for medical questions and answer in the context of healthcare. The results show that our GPT4ALLMediSentAly-KAdam with the optimization algorithm KAdamEnGPT4LLM achieved better performance that include the best Accuracy, Recall, F1-score, and Runtime for both datasets, outperforming traditional fine-tuned LLMs such as the classic GPT4ALL, Ada, Babbage, Curie, and Duvinci. © 2013 IEEE.

关键词： Adaptive filters

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