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COVID-19 in Malaysia and the trusted elements of mobile health records management issues

AIP Conference Proceedings 2023年第1期2827卷

作者： Fatin Nur Zulkipli Azman Ismail Nurussobah Hussain Mohd Fakhrul Fahmi Sarwani Saiful Farik Mat Yatin Information Science Studies College of Computing Informatics and Media Universiti Teknologi MARA Puncak Perdana Selangor Malaysia Information Science Studies College of Computing Informatics and Media Universiti Teknologi MARA (UiTM) Kelantan Branch Kelantan Malaysia Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Lumut Perak Malaysia Meridian Project Management Sdn Bhd Cyberjaya Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Selangor Malaysia

The novel Coronavirus (COVID-19) global pandemic has become a challenge for the healthcare industry and the digitalization era. Thus, it is high time for government hospitals to implement one of the Eleventh Malaysia Planning (11MP) strategies to integrate the transformation of the mobile application systems with future-ready interfaces. The reliability of a healthcare records system that includes vital information about patients like demographics and medical history requires high-security measures. The COVID-19 outbreak highlights the importance of the trustworthiness of the healthcare system through a stronger partnership between security and information technology. However, the lack of standards concerning the safety and storage of records and legal compliance are crucial issues for mobile health records management (MHRM). Since the outbreak, the Malaysian government has launched the “MySejahtera” mobile platform to educate citizens on effective information dissemination. However, the increased COVID-19 testing highlights the shortfall of “MySejahtera,” suggesting that enhancements to the existing system are much needed. Thus, this study aims to analyze mobile health records’ trustworthiness in Malaysian hospitals by creating a reliable framework. A qualitative approach using descriptive and comparative data will be employed. The study’s outcome is expected to be a guide for Malaysian healthcare in providing conducive environments for all.

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High-precision inversion of vegetation parameters in the AI era: Integrating hyperspectral remote sensing and deep learning

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Innovation 2025年第6期6卷

作者： Hu, Jinkang Peng, Dailiang Chen, Jing M. Huete, Alfredo R. Yu, Le Lou, Zihang Cheng, Enhui Yang, Xuan Zhang, Bing Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China College of Resource and Environment University of Chinese Academy of Sciences Beijing100094 China School of Geographical Sciences Fujian Normal University Fuzhou350117 China Department of Geography and Planning University of Toronto TorontoONM5S 3G3 Canada School of Life Sciences University of Technology Sydney UltimoNSW2007 Australia Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Institute of Agricultural Remote Sensing and Information Technology Application College of Environmental and Resource Sciences Zhejiang University Hangzhou310058 China China Remote Sensing Satellite Ground Station Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

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How likely is a random graph shift-enabled?

arXiv

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arXiv 2021年

作者： Chen, Liyan Cheng, Samuel Stankovic, Vladimir Stankovic, Lina Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhejiang Zhoushan316022 China Department of Computer Science and Technology Tongji University Shanghai201804 China School of Electrical and Computer Engineering University of Oklahoma OK74105 United States Department of Electronic and Electrical Engineering University of Strathclyde GlasgowG1 1XW United Kingdom

The shift-enabled property of an underlying graph is essential in designing distributed filters. This article discusses when a random graph is shift-enabled. In particular, popular graph models Erdős–Rényi (ER), Watts–Strogatz (WS), Barabási–Albert (BA) random graph are used, weighted and unweighted, as well as signed graphs. Our results show that the considered unweighted connected random graphs are shift-enabled with high probability when the number of edges is moderately high. However, very dense graphs, as well as fully connected graphs, are not shift-enabled. Interestingly, this behaviour is not observed for weighted connected graphs, which are always shift-enabled unless the number of edges in the graph is very low. © 2021, CC BY.

关键词： Signal processing

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Neural posterior estimation with guaranteed exact coverage: The ringdown of GW150914

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Physical Review D 2023年第4期108卷 044029-044029页

作者： Marco Crisostomi Kallol Dey Enrico Barausse Roberto Trotta SISSA Via Bonomea 265 34136 Trieste Italy and INFN Sezione di Trieste 34127 Trieste Italy IFPU—Institute for Fundamental Physics of the Universe Via Beirut 2 34014 Trieste Italy School of Physics Indian Institute of Science Education and Research Thiruvananthapuram Maruthamala PO Vithura Thiruvananthapuram 695551 Kerala India Physics Department Imperial College London Prince Consort Road SW7 2AZ London United Kingdom Centro Nazionale “High Performance Computer Big Data and Quantum Computing” Via Magnanelli 2 40033 Casalecchio di Reno (BO) Italy

We analyze the ringdown phase of the first detected black-hole merger, GW150914, using a simulation-based inference pipeline based on masked autoregressive flows. We obtain approximate marginal posterior distributions for the ringdown parameters, namely the mass, spin, and the amplitude and phases of the dominant mode and its first overtone. Thanks to the locally amortized nature of our method, we are able to calibrate our posteriors with injected simulations, producing posterior regions with guaranteed (i.e., exact) frequentist coverage of the true values. For GW150914, our calibrated posteriors provide only mild evidence (∼2σ) for the presence of an overtone, even if the ringdown is assumed to start at the peak of the amplitude.

关键词： Classical black holes General relativity Gravitational waves

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Classification of cow diet based on milk mid infrared spectra: a data analysis competition at the "International workshop of spectroscopy and chemometrics 2022"

arXiv

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arXiv 2022年

作者： Frizzarin, Maria Visentin, Giulio Ferragina, Alessandro Hayes, Elena Bevilacqua, Antonio Dhariyal, Bhaskar Domijan, Katarina Khan, Hussain Ifrim, Georgiana Le Nguyen, Thach Meagher, Joe Menchetti, Laura Singh, Ashish Whoriskey, Suzy Williamson, Robert Zappaterra, Martina Casa, Alessandro Teagasc Animal & Grassland Research and Innovation Centre Moorepark Ireland School of Mathematics and Statistics University College Dublin Ireland Department of Veterinary Medical Sciences University of Bologna Italy Teagasc Food Research Centre Ashtown Ireland Teagasc Food Research Centre Moorepark Ireland School of Computer Science University College Dublin Ireland Department of Mathematics and Statistics National University of Ireland Maynooth Ireland Insight Centre for Data Analytics University College Dublin Ireland School of Biosciences and Veterinary Medicine University of Camerino Italy School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Department of Agricultural and Food Sciences University of Bologna Italy Faculty of Economics and Management Free University of Bozen-Bolzano Italy

In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics – Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows’ diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows’ diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model. © 2022, CC BY.

关键词： Machine learning

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Robust Spatial Cell-Type Deconvolution with Qualitative Reference for Spatial Transcriptomics (Small Methods 5/2025)

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Small Methods 2025年第5期9卷

作者： Qishi Dong Yi Yang Ziye Luo Haipeng Shen Xingjie Shi Jin Liu College of Big Data and Internet Shenzhen Technology University Shenzhen 518118 China The Key Laboratory of Developmental Genes and Human Disease School of Life Science and Technology Southeast University Nanjing 210018 China Department of Biometrics Astrazeneca Global R&D (China) CO. Ltd. Shanghai 200085 China Faculty of Business and Economics Hong Kong University Pokfulam Hong Kong SAR China KLATASDS-MOE Academy of Statistics and Interdisciplinary Sciences School of Statistics East China Normal University Shanghai 200062 China School of Data Science The Chinese University of Hong Kong-Shenzhen Shenzhen 518172 China

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Investigation of changes in ankylosing spondylitis disease activity through 2021 COVID-19 wave in Taiwan by using electronic medical record management system

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Scientific Reports 2023年第1期13卷 1-8页

作者： Huang, Pei-Ju Chen, Yun-Wen Yen, Tsai-Hung Liu, Yen-Tze Lin, Shih-Ping Chen, Hsin-Hua Department of Family Medicine Changhua Christian Hospital Changhua Taiwan Division of General Medicine Department of Internal Medicine Taichung Veterans General Hospital 1650 Boulevard Sect. 4 Taichung 40705 Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Division of Infection Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan School of Medicine National Yang-Ming Chiao Tung University Taipei Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Institute of Biomedical Science and Rong Hsing Research Center for Translational Medicine & Big Data Center Chung Hsing University Taichung Taiwan

We aim to investigate the alteration in disease activity of ankylosing spondylitis (AS) individuals before, during, and after the COVID-19 wave in Taiwan by using electronic medical-record management system (EMRMS). We identified 126 AS individuals from the single center, and gathered data of the three disease activities (Bath Ankylosing Spondylitis Disease Activity Index [BASDAI], Ankylosing Spondylitis Disease Activity Score with erythrocyte sedimentation rate [ASDAS-ESR], and Ankylosing Spondylitis Disease Activity Score with C-Reactive Protein [ASDAS-CRP]) by using EMRMS before (7 February to 1 May, 2021), during (2 May to 24 July, 2021), and after the COVID-19 wave (25 July to 16 October, 2021). We compared the disease activity measures of the three phases through a paired t test. Among the 126 individuals, CRP was significantly higher during the COVID-19 wave (0.2 (0.1, 0.5) mg/dl, p = 0.001) than before the wave (0.2 (0.1, 0.4) mg/dl), ESR (8.0 (4.0, 15.0) mm/h, p = 0.003) and ASDAS-ESR (1.4 (1.0, 1.9), p = 0.032) were significantly higher after the wave than during the wave (6.0 (3.0, 12.0) mm/h and 1.2 (0.9, 1.8) mm/h) e. ESR, CRP, ASDAS-ESR and ASDAS-CRP were all significant higher after COVID-19 wave than before. The disease activities of AS individuals in Taiwan worsened after 2021 COVID-19 wave in Taiwan. © 2023, The Author(s).

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Prevention of external force damage to overhead power lines based on network and intelligent positioning

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第3期6卷 551-583页

作者： Xie, Bo-Lin Pan, Jeng-Shyang Lin, Yuh-Chung Xie, Yu-Hong Pan, Tien-Szu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Information Science and Technology Sanda University Shanghai201209 China Guangdong Power Grid Heyuan Power Supply Bureau Transmission Management Office China Southern Power Grid Heyuan517000 China Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung807 Taiwan

In the research on the prevention of external damage to power transmission lines, it is a difficult problem to prevent large machinery vehicles from damaging the overhead transmission lines. After the emergence of video surveillance technology, the accident rate has dropped significantly. But still unable to completely eliminate such accidents. The anti-external damage solution based on network and intelligent positioning is very effective for early warning of accidents. This gives us the opportunity to completely eliminate such accidents. In this paper we utilize the satellite positioning data of the power towers to establish a warning zone along the power line. When the system detects that some large machinery vehicles enter the warning zone, early warning messages will be sent to these vehicles. Afterward, the connection between vehicle drivers and management center can be established such that further instructions can be ordered to avoid accidents. According to our simulations, it presents our proposed mechanism can effectively prevent the damage occurrences which may be caused by large machinery vehicles. © 2021 Journal of Network Intelligence.

关键词： Accidents

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Monthly methane emissions in Chinese mainland provinces from 2013-2022

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Scientific data 2025年第1期12卷 948页

作者： Duo Cui Siyao Yang Xuanren Song Xiaoting Huang Cuncun Duan Mingrui Ji Zhongyan Li Siqi Yu Zhu Deng Piyu Ke Xinyu Dou Taochun Sun Zhu Liu International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. duocui@hku.hk. Department of Earth Sciences The University of Hong Kong Hong Kong SAR China. duocui@hku.hk. Department of Earth System Science Tsinghua University Beijing 100084 China. Faculty of Information Technology Monash University Melbourne 3168 Australia. College of Water Sciences Beijing Normal University Beijing 100875 China. College of Environmental & Resource Sciences Zhejiang University Hangzhou Zhejiang 310058 China. Department of Civil and Environmental Engineering Stanford University Stanford CA 94305 USA. Department of Geography The University of Hong Kong Hong Kong SAR China. Department of Earth System Science Stanford University Stanford CA 94305 USA. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. zhuliu@***. Department of Earth System Science Tsinghua University Beijing 100084 China. zhuliu@***.

As the world's largest source of methane emissions, accurately measuring and tracking China's emissions across various sectors is essential for global climate change efforts. Methane, a potent greenhouse gas, is emitted from diverse anthropogenic and natural sources, many of which exhibit pronounced temporal variability. In particular, emissions from rice cultivation, energy use, and livestock management show strong seasonal patterns, yet high-frequency and spatially detailed methane emission inventories have been lacking. This study introduces the Monthly Methane Emission Inventory for China's Provinces (MMCP), a comprehensive dataset covering the period from January 2013 to December 2022. The dataset includes emissions data from eight key sectors: coal mining, oil and gas systems, energy combustion, rice cultivation, livestock, solid waste, wastewater, and wetlands. By offering sector-specific and temporally resolved emission estimates, MMCP serves as a valuable resource for scientific research, policy evaluation, and emission mitigation planning. This inventory facilitates improved understanding of emission trends and supports more accurate modeling of atmospheric methane concentrations and climate feedbacks.

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DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

arXiv

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arXiv 2019年

作者： Zhao, Yue Hryniewicki, Maciej K. Department of Computer Science University of Toronto Toronto Canada Data Assurance & Analytics PricewaterhouseCoopers Toronto Canada

Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth. In this paper, an unsupervised outlier detector combination framework called DCSO is proposed, demonstrated and assessed for the dynamic selection of most competent base detectors, with an emphasis on data locality. The proposed DCSO framework first defines the local region of a test instance by its k nearest neighbors and then identifies the top-performing base detectors within the local region. Experimental results on ten benchmark datasets demonstrate that DCSO provides consistent performance improvement over existing static combination approaches in mining outlying objects. To facilitate interpretability and reliability of the proposed method, DCSO is analyzed using both theoretical frameworks and visualization techniques, and presented alongside empirical parameter setting instructions that can be used to improve the overall performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Anomaly detection

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