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University Students’ Attitudes toward Adoption of Mobile Health Application for Serving Older People

Sensors and Materials 2023年第7期35卷 2581-2601页

作者： Chao, Pei-Ju Cheng, Yu-Huei Liu, Yi-Hui Hsu, Tsu-Hsuan Travel & Tourism Program International College Ming Chuan University 5 De Ming Rd. Gui Shan District Taoyuan333 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology 168 Jifeng E. Rd. Wufeng District Taichung413 Taiwan Department of Senior Citizen Service Management National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. North District Taichung404 Taiwan Graduate Institute of Rehabilitation Counseling National Changhua University of Education 1 Jin-De Rd. Changhua City500 Taiwan

In this quantitative study, we examined the awareness of mobile health (mHealth) applications among Taiwan’s university students, particularly the government-operated My Health Bank application. The My Health Bank application allows users to access their personal health information simply through their smartphone’s sensor-assisted facial recognition camera and fingerprint sensor. Whether these university students might help older adults to use this application if they perceive it to be helpful was also explored. Two instruments were used in this study, namely, the Perceptions toward My Health Bank (PMHB) and Geriatric Attitudes Scale (GAS). Two hundred twenty-five (225) university students from the departments of Senior Citizen Service management (n = 136) and Nursing (n = 89) at two universities participated in this study. The findings indicated low adoption of My Health Bank and related mHealth applications among students even though they had high adoption rates of smartphones. However, they perceived that the functions of this application are useful and should be promoted to older adults. Thus, building a basic foundation of mHealth and related technology courses is recommended to assist healthcare students in being well-prepared for working with older adults in the digital society. © MYU K.K.

关键词： mHealth

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Skin-mimic hydrogel materials with water-perspiration control for soft robots developed by 3D printing

Skin-mimic hydrogel materials with water-perspiration contro...

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Pacific Rim Meeting on Electrochemical and Solid State Science 2020, PRiME 200

作者： Kanai, S. Watanabe, Y. Shiblee, M.N.I. Khosla, A. Ogawa, J. Kawakami, M. Furukawa, H. Department of Mechanical Systems Engineering Graduate School of Science and Engineering Yamagata University 4-3-16 Jonan Yonezawa Yamagata992-8510 Japan

ISBN: (纸本)9781607689089

Hydrogels are soft and wet materials with high biocompatibility, and are expected as a material for soft robots that have a touch similar to that of human skin. However, hydrogels have some technical drawbacks such as high-water evaporation rate, different touch and temperature from that of human skin. The purpose of this research is to reproduce a touch of hydrogels similar to the human skin. We designed a flow channel inside the hydrogel for supplying and circulating warm water to mitigate drying of the gel. With this mechanism mass loss due to drying was decreased by 43%. In addition, we found that coating hydrogels with solvent and polymer changes its surface friction coefficient. © The Electrochemical Society.

关键词： Hydrogels

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Gradient Boosting Model for Water Quality Forecasting

Gradient Boosting Model for Water Quality Forecasting

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International Conference on Data Analytics and management, ICDAM 2023

作者： Panda, Parnani Banerjee, Sushovan Mishra, Sushruta Shashidhar, Chitra de Albuquerque, Victor Hugo C. Reis, Marcello Singh, Pooja Kalinga Institute of Industrial Technology Deemed to Be University Bhubaneswar India Department of Commerce and Management Seshadripuram College Bengaluru560020 India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Ceará Brazil Fort Lauderdale United States Lead of the Biomedical Data Analytics Research Group Bengaluru India Federal Institute of Ceará Ceará Brazil Meteora Consultoria Fortaleza Brazil Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819965465

The aim of our study is to evaluate the quality of water using machine learning methods. Potability is a subjective word that quantifies water quality. In this research, a variety of metrics were used to measure general potability of the water. The prescribed elements are applied like variable matrix to denote water quality. The article used three different classification methods, with success rates ranging from 84.27 to 93.16%, to predict the water quality class including ensemble approach like Random Forest (RFC), SVM for classification (SVC), as well as XGBoost. The Gradient Boosting approach, out of all the ones used, received the greatest accuracy (93.16%), precision (96.3%), recall (92.76%) and F-score (94.54%). Also, it achieved the least computational delay with a mean latency of 1.34 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Water quality

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The impact of copper oxide nanoparticles reinforced nanocellulose acetate membrane on antibacterial and dye removal Potency

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Results in engineering 2024年 23卷

作者： Yanuhar, Uun Suryanto, Heru Amin, Muhamad Maulana, Jibril Mohamad Lal, Mohammad Tamrin Caesar, Nico Rahman Study Program of Aquatic Resources Management Faculty of Fisheries and Marine Sciences Universitas Brawijaya East Java Indonesia Department of Mechanical and Industrial Engineering Faculty of Engineering Universitas Negeri Malang Jl. Semarang 5 Malang65145 Indonesia Department of Aquaculture Faculty of Fisheries and Marine Universitas Airlangga Jl. Dharmahusada Permai Surabaya60115 Indonesia Borneo Marine Research Institute Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Doctoral Program of Environment Science Post Graduate Program Universitas Brawijaya Jalan Veteran East Java Malang65145 Indonesia

With the rapid growth of the textile, leather, cosmetics, and pharmaceutical industries, concerns about pollutants in these sectors, especially dye pollutants, are increasing. This research characterizes bacterial nanocellulose acetate membranes made from Sargassum extract reinforced with copper oxide nanoparticles and tests their antibacterial properties and dye removal in water. Sargassum sp. extract was fermented using Acetobacter xylinum for 10 days, producing bacterial nanocellulose, which was then esterified to create bacterial nanocellulose acetate. Copper oxide nanoparticles were added to the bacterial nanocellulose acetate solution in varying concentrations, cast, and oven-dried. The membrane was analyzed using Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscope, antibacterial activity, and dye removal. Results showed uniform copper oxide nanoparticles distribution on the membrane surface. Structure analysis revealed diffraction peaks at 35.44° and 38.57°, indicating a reduced crystalline index but improved membrane effectiveness. Antibacterial tests showed a zone of inhibition against bacterial growth, increasing with higher copper oxide nanoparticle content. The addition of copper oxide nanoparticles also influenced the dye removal capacity for Metanil Yellow, Congo Red, Eosin Yellow, Methylene Blue, and Malachite Green. The kinetic analysis showed that the adsorption process follows a pseudo-first-order model, with higher correlation coefficients (R2) compared to the pseudo-second-order model. © 2024 The Author(s)

关键词： Fourier transform infrared spectroscopy

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Micropatterning of functional lipid bilayer assays for quantitative bioanalysis

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Biomicrofluidics 2023年第3期17卷 031302-031302页

作者： Montalbo, Reynaldo Carlos K. Tu, Hsiung-Lin Institute of Chemistry Academia Sinica Taipei11529 Taiwan Nanoscience and Technology Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Department of Engineering and System Science National Tsing-Hua University Hsinchu300044 Taiwan Genome and Systems Biology Degree Program Academia Sinica National Taiwan University Taipei10617 Taiwan

Interactions of the cell with its environment are mediated by the cell membrane and membrane-localized molecules. Supported lipid bilayers have enabled the recapitulation of the basic properties of cell membranes and have been broadly used to further our understanding of cellular behavior. Coupled with micropatterning techniques, lipid bilayer platforms have allowed for high throughput assays capable of performing quantitative analysis at a high spatiotemporal resolution. Here, an overview of the current methods of the lipid membrane patterning is presented. The fabrication and pattern characteristics are briefly described to present an idea of the quality and notable features of the methods, their utilizations for quantitative bioanalysis, as well as to highlight possible directions for the advanced micropatterning lipid membrane assays. © 2023 Author(s).

关键词： Lipid bilayers

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Characterizing basin-scale subsurface hydraulic heterogeneity with multiscale geological and hydrological measurements

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Journal of Hydrology 2023年第PartA期625卷

作者： Lin, Hong-Ru Wen, Jet-Chau Zhang, Zaiyong Wang, Yu-Li Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology Taiwan Research Center for Soil and Water Resources and Natural Disaster Prevention National Yunlin University of Science and Technology Taiwan Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region Chang'an University Ministry of Education China School of Water and Environment Chang'an University China Department of Bioenvironmental Systems Engineering National Taiwan University Taiwan

Mapping aquifer heterogeneity is a pressing need for managing regional groundwater resources. Many studies have shown that combining hydraulic tomographic surveys with point-scale geological information is viable for detailing meter-scale to kilometer-scale hydraulic heterogeneity of aquifers. However, due to the complexity of aquifer systems, few field investigations have been conducted on basin-scale systems. In this study, we collected datasets of stream stages, groundwater levels, and borehole logs from 35 wells in a river plain during the wet season, when flood events altered the groundwater flow fields. We then analyzed these datasets using correlation analysis and a geostatistical inverse model. The hydraulic transmissivity and storage coefficient distributions were inverted based on different numbers of geological zones. By validating the estimated parameter fields with independent datasets, we demonstrated that appropriate selection of the number of geological zones, using point-scale geological information and clustering, improved the estimated fields. Further increasing the number of zones beyond an appropriate number deteriorated the estimates at the basin-scale site. The proposed method offers a cost-effective approach for basin-scale characterization. The better knowledge of subsurface hydraulic heterogeneity can support water resource management decisions. © 2023 Elsevier B.V.

关键词： Groundwater flow

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Deep Neural Network Framework for Predicting Cardiovascular Diseases from ECG Signals

Recent Advances in Computer Science and Communications

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Recent Advances in Computer Science and Communications 2025年

作者： Soni, Tanishq Gupta, Deepali Uppal, Mudita Juneja, Sapna Gulzar, Yonis Ghafoor, Kayhan Zrar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India KIET Group of Institutions Ghaziabad India Department of Management Information Systems College of Business Administration King Faisal University Al Ahsa 31982 Saudi Arabia Department of Information and Communication Technology Eng Erbil Polytechnic University Iraq

Introduction: Cardio Vascular Disease (CVD), a primary cause of death worldwide, includes a variety of heart-related disorders like heart failure, arrhythmias, and coronary artery disease (CAD), where plaque buildup narrows the heart muscle's blood vessels and causes angina or heart attacks. Genetics, congenital anomalies, bad diet, lack of exercise, smoking, and chronic diseases including hypertension and diabetes can cause cardiac disease. Method: The symptoms can range from chest pain and shortness of breath to exhaustion and palpitations and diagnosis usually involves a medical history, physical examination, and electrocardiograms (ECGs), and stress testing. Lifestyle adjustments, medicines, angioplasty, and bypass grafts or heart transplants are possible treatments. Preventive measures include healthy living, risk factor management, and frequent checkups, which are few measures, whereas advanced algorithms can analyze massive volumes of ECG and MRI data to find patterns and anomalies that humans may overlook. Results: The deep learning models increase arrhythmia, coronary artery disease, and heart failure diagnosis accuracy and speed. They enable predictive analytics, early intervention, and personalized treatment programs, increasing cardiac care results. The proposed DNN model consists of a 3-layer architecture having input, hidden, and output layers. In the hidden layer, 2 layers, namely, the dense layer and batch normalization layer are added to enhance its accuracy. Conclusion: Three different optimizers namely Adam, AdaGrad, and AdaDelta are tested on 50 epochs and 32 batch sizes for predicting cardiovascular disease. Adam optimizer has the highest accuracy of 85% using the proposed deep neural network. © 2024 Bentham Science Publishers.

关键词： Personalized medicine

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Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

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Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights

arXiv

引用

arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan The Department of Computer Science National Chengchi University Taipei11529 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity. © 2024, CC BY.

关键词： Audiovisual

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Optimal PV inverter control in distribution systems via data-driven distributionally robust optimization

Optimal PV inverter control in distribution systems via data...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Bai, Linquan Xu, Guanglin Xue, Yaosuo University of North Carolina at Charlotte Department of Systems Engineering and Engineering Management Charlotte United States Oak Ridge National Laboratory Electrical and Electronics Systems Research Division Oak Ridge United States

ISBN: (纸本)9781728155081

Distribution systems with high penetration of uncertain solar generation call for advanced control strategies of photovoltaics (PVs) inverters. This paper proposes a data-driven distributionally robust optimization (DDDRO) approach to optimally controlling the PV inverters to improve the system operation performance under solar power uncertainties. In the proposed DDDRO approach, a Wasserstein ball-based method is proposed to construct the distributional ambiguity set to model the uncertainties of PV generation through partial observations of historical data without knowing exact probability distributions. We further reformulate the computationally intractable DDDRO model to a mixed integer second order cone programming (MISOCP) problem. The effectiveness and out-of-sample performance of the proposed approach have been demonstrated on a modified IEEE 33-node system. We conduct a comparative study to compare the proposed method with traditional chance constrained programming (CCP). It shows that the proposed DDDRO approach can provide a less conservative yet robust solution to minimize the worse-case expectation of the total network loss while maintaining nodal voltages in a secure range. © 2020 IEEE.

关键词： Second-order cone programming

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