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SSRN 2022年

作者： Rosdi, Mohd Farhan Mat Hussain, Eizah Mat Yasir, Ahmad Shah Hizam Md. Abdullah, Azlina Khoiry, Muhammad Azry School of Computer Science and Engineering Taylor's University Selangor Subang Jaya47500 Malaysia Malay Academic Studies University of Malaya Kuala Lumpur50603 Malaysia Rabdan Academy Abu Dhabi114646 United Arab Emirates Anthropology and Sociology Program Center for Research in Development Social and Environmental Studies Faculty of Social Sciences and Humanities Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Faculty of Engineering & Built Environment National University of Malaysia Selangor Bangi43600 Malaysia

This study analyzes the impacts of engineering-specific job demands on an engineer's professional commitment. This study utilized survey of 504 engineers in Malaysia. Structural equation modelling is used to test the hypothesized model. The result shows engineer's job demands and job resources have significantly impacted psychological well-being and job strain. Professional commitment is influenced by job strain and psychological well-being. The findings also indicate that addressing the "demand for technical knowledge" and "financial resources" is critical to achieving professional commitment. Result is useful for engineering managers and engineers in leading and planning routine work in an engineering organization. © 2022, The Authors. All rights reserved.

关键词： Engineers

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BPS2025 - Systematic embedding of biomechanics within dynamical graph grammars with application to actin networks in dendritic spine heads

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Biophysical Journal 2025年第3期124卷 206a-206a页

作者： Eric Mjolsness Matthew Hur Thomas M. Bartol Terrence J. Sejnowski Departments of Computer Science and Mathematics University of California Irvine Irvine CA USA Mathematical Computational and Systems Biology Program University of California Irvine Irvine CA USA Computational Neurobiology Laborartory Salk Institute for Biological Studies San Diego CA USA

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The 2020 power blackout in the brazilian state of amapá: Lessons learned and opportunities

The 2020 power blackout in the brazilian state of amapá: Le...

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2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021

作者： Do Prado, Josue Campos Santos, Marcos Vinicius Ramos, Lucas Feksa Canha, Luciane Neves Konzen, Gabriel School of Engineering Computer Science Washington State University VancouverWA98686 United States School of Engineering Newcastle University Newcastle Upon TyneNE1 7RU United Kingdom Federal University of Rondônia Department of Electrical Engineering Porto Velho Brazil Graduate Program in Electrical Engineering Federal University of Santa Maria Santa Maria Brazil Division of Energy Economics and Environmental Studies Rio de Janeiro Brazil

ISBN: (纸本)9781665444217

The Brazilian state of Amapa=-notable for having 70% of its area consisting of unexplored Amazonian forests-experienced a massive power blackout in November 2020. An internal short-circuit in a transformer led to an explosion followed by a fire at the Macapa substation. This event left about 90% of the Amapa's population without power for several days in the middle of the COVID-19 pandemic, disrupting the lives of hundreds of thousands of people and raising concerns from several electricity sector stakeholders. This paper discusses select lessons learned from the power blackout and presents future opportunities to improve power system reliability and resilience for the grids in Amapá, and other similar Brazilian states, with a special focus on the increasingly important role of of distributed energy resources and smart grid technologies. © 2021 IEEE.

关键词： Outages

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CoVimmune COVID-19 Immunity Calculator: Web Application Development and Validation Study

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JMIR Formative Research 2025年 9卷 e59467页

作者： Slotkin, Rebecca Kyriakides, Tassos C. Yu, Vinni Chen, Xien Kundu, Anupam Gupta, Shaili Department of Medicine Yale School of Medicine West Haven CT United States Cooperative Studies Program Coordinating Center VA CT Healthcare System West Haven CT United States Yale School of Public Health Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Yale Center for Analytical Sciences Yale School of Public Health New Haven CT United States Veterans Healthcare System of Connecticut West Haven CT United States

Background: This study illustrates the development of a simple web-based application, which demonstrates the relationship between serum anti-SARS-CoV-2 S1/receptor-binding domain immunoglobulin G (IgG) and anti-SARS-CoV-2 neutralizing antibody (nAb) half-maximal inhibitory concentration (IC50) titers in a vaccinated US adult population and compares them to prior data on nAb titers at different time points after vaccination. Objective: The objective of this study is to create an easily accessible calculator that uses the results of commercially available anti-SARS-CoV-2 serum IgG to approximate the underlying ability to neutralize SARS-CoV-2. Methods: Our web-based application leveraged two previously published datasets. One dataset demonstrated a robust correlation between nAb and serum IgG. The other dataset measured nAb titers at specific time periods over a year-long interval following a messenger RNA vaccination primary series and booster vaccine dose. Clinical factors that were statistically significant on a forward linear regression model examining the prediction of nAb from serum IgG were incorporated in the application tool. Results: By combining the datasets described above, we developed a publicly available web-based application that allows users to enter a serum IgG value and determine their estimated nAb titer. The application contextualizes the estimated nAb titer with the theoretical distance from the corresponding vaccine-mediated antibody protection. Using the clinical variables that had a significant impact on how well IgG values predict nAb titers, this application allows for a patient-centered, nAb titer prediction. Conclusions: This application offers an example of how we might bring the advances made in scientific research on protective antibodies post-SARS-CoV-2 vaccination into the clinical sphere with practical tools. © Rebecca Slotkin, Tassos C Kyriakides, Vinni Yu, Xien Chen, Anupam Kundu, Shaili Gupta.

关键词： COVID-19 immunity immunoglobulin G neutralizing antibody patient-centered care SARS-CoV-2 vaccination vaccine hesitancy vaccine timing web application

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Mining GIS data to predict urban sprawl

arXiv

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arXiv 2021年

作者： Pampoore-Thampi, Anita Varde, Aparna S. Yu, Danlin Department of Computer Science Montclair State University MontclairNJ United States Department of Earth and Environmental Studies Montclair State University MontclairNJ United States Environmental Management PhD Program Montclair State University MontclairNJ United States

This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term "urban sprawl" refers to overgrowth and expansion of low-density areas with issues such as car dependency and segregation between residential versus commercial use. Sprawl has impacts on the environment and public health. In our work, spatiotemporal features related to real GIS data on urban sprawl such as population growth and demographics are mined to discover knowledge for decision support. We adapt data mining algorithms, Apriori for association rule mining and J4.8 for decision tree classification to geospatial analysis, deploying the ArcGIS tool for mapping. Knowledge discovered by mining this spatiotemporal data is used to implement a prototype spatial decision support system (SDSS). This SDSS predicts whether "urban sprawl" is likely to occur. Further, it estimates the values of pertinent variables to understand how the variables impact each other. The SDSS can help decision-makers identify problems and create solutions for avoiding future sprawl occurrence and conducting urban planning where sprawl already occurs, thus aiding sustainable development. This work falls in the broad realm of geospatial intelligence and sets the stage for designing a large scale SDSS to process big data in complex environments, which constitutes part of our future work. © 2021, CC BY-SA.

关键词： Association rules

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities

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AJPM Focus 2023年第1期2卷 100062-100062页

作者： Levanti, Danielle Monastero, Rebecca N. Zamani, Mohammadzaman Eichstaedt, Johannes C. Giorgi, Salvatore Schwartz, H. Andrew Meliker, Jaymie R. Undergraduate Studies Cornell University Ithaca NY United States Renaissance School of Medicine Stony Brook University Stony Brook NY United States Department of Computer Science College of Engineering and Applied Sciences Stony Brook University Stony Brook NY United States Department of Psychology School of Humanities and Sciences Stanford University Palo Alto CA United States Department of Computer and Information Science University of Pennsylvania Philadelphia PA United States Program in Public Health Department of Family Population & Preventive Medicine Renaissance School of Medicine Stony Brook University Stony Brook NY United States

Introduction: Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data. Methods: We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine learning‒based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google Trends data using search query frequencies. A qualitative evaluation of trends is presented. Results: Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all the 7 locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual states. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results. Conclusions: Our study shows the feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends. © 2022 The Author(s)

关键词： coronavirus mental health Social media stay-at-home order

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Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

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International Journal of Medical Informatics 2025年 202卷 105989-105989页

作者： Matsumoto, Koutarou Suzuki, Masahiro Ishihara, Kazuaki Tokunaga, Koki Matsuda, Katsuhiko Chen, Jenhui Yamashiro, Shigeo Soejima, Hidehisa Nakashima, Naoki Kamouchi, Masahiro Department of Health Care Administration and Management of Medical Sciences Kyushu University Fukuoka Japan Graduate Degree Program of Applied Data Sciences Sophia University Tokyo Japan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan Division of Neurosurgery Saiseikai Kumamoto Hospital Kumamoto Japan Institute for Medical Information Research and Analysis Saiseikai Kumamoto Hospital Kumamoto Japan Medical Information Center Kyushu University Hospital Fukuoka Japan Department of Medical Informatics of Medical Sciences Kyushu University Fukuoka Japan Center for Cohort Studies of Medical Sciences Kyushu University Fukuoka Japan

Background: We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict poor functional outcomes and in-hospital mortality in patients with intracerebral hemorrhage (ICH). These models were designed to assist non-specialists in emergency settings with limited access to stroke specialists. Methods: A retrospective analysis of 527 patients with ICH admitted to a Japanese tertiary hospital between April 2019 and February 2022 was conducted. Deep learning techniques were used to extract features from three-dimensional CT images and unstructured data, which were then combined with tabular data to develop an L1-regularized logistic regression model to predict poor functional outcomes (modified Rankin scale score 3–6) and in-hospital mortality. The model's performance was evaluated by assessing discrimination metrics, calibration plots, and decision curve analysis (DCA) using temporal validation data. Results: The multimodal model utilizing both imaging and text data, such as medical interviews, exhibited the highest performance in predicting poor functional outcomes. In contrast, the model that combined imaging with tabular data, including physiological and laboratory results, demonstrated the best predictive performance for in-hospital mortality. These models exhibited high discriminative performance, with areas under the receiver operating curve (AUROCs) of 0.86 (95% CI: 0.79–0.92) and 0.91 (95% CI: 0.84–0.96) for poor functional outcomes and in-hospital mortality, respectively. Calibration was satisfactory for predicting poor functional outcomes, but requires refinement for mortality prediction. The models performed similar to or better than conventional risk scores, and DCA curves supported their clinical utility. Conclusion: Multimodal prediction models have the potential to aid non-specialists in making informed decisions regarding ICH cases in emergency departments as part of clinical dec

关键词： Logistic regression

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Decision Support Systems Selection of Soang Superior Brood Using Weighted Product (WP) and Simple Additive Weighting (SAW) Method 4

Decision Support Systems Selection of Soang Superior Brood U...

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4th International Conference on Energy, Environment, Epidemiology and Information System, ICENIS 2019

作者： Ramadiani Kurniawan, Reynaldi Kridalaksana, Awang Harsa Jundillah, Muhammad Labib Faculty of Computer Science and Information Technology Mulawarman University Samarinda Indonesia Magister Program of Information System School of Post Graduate Studies Diponegoro University Semarang Indonesia

Gurame Soang Latin name Osphronomus Labirynthici gouramy including fish, that as the fish have gills and breathing apparatus in the form of additional gills (Labyrinth). One important factor in fish farming Soang Gurame is holding elections. Superior brood has characteristics identical, to distinguish it needs to experience since it is quite difficult. Some of the assessment factors in selecting a superior brood that could be considered in the cultivation of the fish farmers Gurame Soang as ideal weight, physical disability, movement of fish, the shape of the head, and the body color. All of these factors combined to get an assessment that could help the decision making process fish Gurame Soang superior sires. This study used two methods that have selected the calculation accuracy of the decision-making process. The method used is the Weighted Product and Simple Additive weighting. The results of the research that has been done, it can be concluded the total number of alternative Gurame Soang superior sires data as many as 10 fish, and obtained the results of testing the level of accuracy by using the results of data from a breeder Gurame Soang superior sires, Method of Weighted Product got an accuracy of 80% and methods Simple Additive weighting got an accuracy of 60%. © The Authors, published by EDP Sciences, 2019.

关键词： Fish

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