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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Improving the Performance of Job Backfilling Based on Job's Weight and Back Propagation Neural Network

Improving the Performance of Job Backfilling Based on Job's ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hazem Alnajjar Nadia Al-Rousan Syed Sahal Nazli Alhady Syed Hassan Junita Mohamad-Saleh Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of computer engineering College of Engineering AlBalqa Applied University Salt Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University Amman Jordan Colleage of Engineering and Architecture School of Electrical & Electronic Engineering Engineering Branch Campus Universiti Sains Malaysia Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Nowadays, the grid computing environment faces many difficulties executing new jobs, especially jobs requiring large resource requirements and long execution times. This motivates researchers and scholars to find cheap and fast methods to improve the efficiency of grid environments. One of the cheap and fast methods is to implement job scheduling algorithms based on cheap and fast techniques. This paper proposes a new job ranking backfilling algorithm based on the job's weight and back propagation neural network. To define the weight of the job, first, the proposed model will use a clustering algorithm to cluster the job's dataset into groups, and then the groups will be ranked using an experimental ranking equation. A discrete event simulator is used to validate the proposed algorithm's capability and robustness. The average results revealed that the new algorithm outperforms previous algorithms. The improvement of the studied metrics is between 1.19 and 6.30, respectively. The results proved that the proposed model is efficient and can be used with low overhead in a real environment.

关键词： Backpropagation Measurement Scheduling algorithms Computational modeling Neural networks Clustering algorithms Grid computing Mathematical models Robustness Faces

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IoT-Based Healthcare Monitoring System

IoT-Based Healthcare Monitoring System

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Marwa Abdulrahman Al-Hadi Ghaleb Hamoud Al-Gaphari Ibrahim Ahmed Al-Baltah Francisco Bacallao Julian Ahmed Abdulrahman Al-Hadi Department of Computer Science Faculty of Computer Science and IT Sana’a University Sana’a Yemen Department of Information Technology Faculty of Computer Science and IT Sana’a University Faculty of Science and Engineering Al-Hikma University Sana’a Yemen Consultant Cardiovascular Surgeon in Cardiac-Center Military Hospital Yemen Consultant Cardiovascular Surgeon in Cardiac-Center Military Hospital Spain Department of Science and Technologies Faculty of Engineering and Technology Mohamed Boudiaf University M'sila Algeria Department of Industrial Electronic and Automatic Engineering Faculty of Higher Polytechnic Technology Burgos University Burgos Spain

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

In an era when healthcare was becoming increasingly crucial, many developing nations, including Yemen, struggled to provide basic medical services. Nearly half of Yemen's population lacked access to adequate healthcare, with the situation being even more dire in rural and remote areas. While sectors such as industry, agriculture, and environmental science rapidly embraced technological advancements, healthcare systems in these regions lagged, with limited research addressing affordable and accessible solutions. This gap was further highlighted by the global healthcare crisis and the urgent need for innovative, cost-effective approaches. In response, this study outlined the creation and deployment of a cost-effective health monitoring system utilizing Internet of Things (IoT) technology to address these challenges. The system was designed to monitor vital signs, specifically heart rate (HR), using an Arduino Uno and ECG sensor, at a meager total cost of $27.25. It provided real-time monitoring for situations requiring immediate intervention. Power consumption tests demonstrated that the system operated efficiently, consuming between 1.061 and 1.35 watts, making it practical and affordable. Additionally, the potential integration of deep learning techniques promises to enhance the system's accuracy and efficiency. Although this study focused on IoT-based health monitoring, its potential extended beyond this, offering broader implications for future healthcare technology solutions.

关键词： Heart rate Deep learning Power demand Costs Medical services Electrocardiography Real-time systems Internet of Things Biomedical monitoring Monitoring

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Blockchain technology, Structure, and Applications: A Survey

Blockchain Technology, Structure, and Applications: A Survey

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2023 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2023

作者： Moosavi, Nazanin Taherdoost, Hamed Mohamed, Nachaat Madanchian, Mitra Farhaoui, Yousef Khan, Inam Ullah Research and Development Departement Hamta Business Corporation Vancouver Canada Department of Arts Communications and Social Sciences University Canada West Vancouver Canada QMinded | Quark Minded Technology Inc Vancouver Canada College of Technology and Engineering Westcliff University Irvine United States Abu Dhabi United Arab Emirates Fairleigh Dickinson University Vancouver Canada Department of Computer Science Faculty of Sciences and Technic Moulay Ismail University Morocco Department of Electronic Engineering School of Engineering and Applied Science Isra University Pakistan

Blockchain-based decentralized technology has gained a lot of attractions and interest in academia and industry. This peer-to-peer and transparent blockchain network makes it an acceptable technology. This decentralized network has many great specifications such as transparency, immutability, irreversibility and auditability. Bitcoin, the first and most successful blockchain application, achieves much success in recent years. This survey article attempts to outline an extensive survey of blockchain technology, investigating its structures, various protocol layers, features, value, challenges, and applications. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Blockchain

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Deep Coupling Network For Multivariate Time Series Forecasting

arXiv

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arXiv 2024年

作者： Yi, Kun Zhang, Qi He, Hui Shi, Kaize Hu, Liang An, Ning Niu, Zhendong School of Computer Science Beijing Institute of Technology no. 5 Zhongguancun South Street Haidian Beijing100081 China School of Electronic and Information Engineering Tongji University China School of Medical Technology Beijing Institute of Technology China Faculty of Engineering and Information Technology University of Technology Sydney Australia School of Computer Science and Information Engineering HeFei University of Technology China

Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time series data, which can seriously degrade forecasting accuracy. In this paper, we reexamine intra- and inter-series relationships from the perspective of mutual information and accordingly construct a comprehensive relationship learning mechanism tailored to simultaneously capture the intricate multi-order intra- and inter-series couplings. Based on the mechanism, we propose a novel deep coupling network for MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated to explicitly exploring the multi-order intra- and inter-series relationships among time series data concurrently, a coupled variable representation module aimed at encoding diverse variable patterns, and an inference module facilitating predictions through one forward step. Extensive experiments conducted on seven real-world datasets demonstrate that our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines. © 2024, CC BY.

关键词： Forecasting

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A Compact Highly-Selective Planar Triple-band Bandpass Filter 4

A Compact Highly-Selective Planar Triple-band Bandpass Filte...

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4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Zhao, Bing-Xiong Guo, Jian-Hui Wong, Sai-Wai Xie, Ze-Ming Zhang, Long Zhu, Lei He, Yejun College of Electronic and Information Engineering Shenzhen University Shenzhen China College of Electronic and Information Engineering South China University of Technology Guangzhou China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9781733509619

This paper present a compact and highly-selective planar triple-band bandpass filter. This filter consists of two pairs of quarter-wavelength short-circuited resonators and a dual-mode resonator. And one pair of short-circuited resonators is embedded in the other pair. The two pairs of short-circuited resonators form the first and second passband, and the dual-mode resonator which used to form the third passband. Moreover, due to the existence of hybrid electromagnetic coupling and source-load coupling, there is a transmission zero on both sides of each passband of the filter. At last, this filter is manufactured and measured, and the mensuration results agree well with the simulation results. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Bandpass filters

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The Iterative Dichotomiser 3 method improves soft skills training decisions using survey data

The Iterative Dichotomiser 3 method improves soft skills tra...

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2024 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2024

作者： Gupta, Subir Hoang, Quang-Trung Roy, Bhaswati Chowdhury, Subrata Mukherjee, Sandip Pathak, Aditya Kumar Duc, Diep Can Haldia Institute of Technology West Bengal 721657 India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam Dept. of FMS Dr.B.C. Roy Engineering College WB Durgapur India Dept. of Computer Science and Machine Learning Sreenivasa Inst. of Technology and Management Studies Andhra Pradesh Chittoor India Dept. of IT Haldia Institute of Technology West Bengal 721657 India Electric Power University Hanoi Viet Nam

ISBN: (纸本)9798350389180

This investigation explores student expectations and perceptions regarding higher education institutions, focusing on aligning professional programs with student career aspirations. The study evaluates how effectively academic institutions understand and meet training needs essential for student success in professional fields. Students were categorized using the Iterative Dichotomiser 3 methodology to forecast their training requirements. Analytical tools such as the confusion matrix and the ROC curve assessed the precision of these predictions. The results show a specificity rate of 74.1%, a sensitivity of 78.6%, and an overall accuracy of 76.7%. The combined sensitivity and specificity score is 3.527, and the empirical area under the ROC curve is 0.76, suggesting the model’s reasonable efficacy. The findings indicate a significant student demand for enhanced instruction in soft skills, underscoring the need for educational institutions to adapt their curricula to better prepare students for professional challenges. © 2024 IEEE.

关键词： Students

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Deploying an Efficient Free Space Optical Network in the Presence of Different Environmental Turbulences

Deploying an Efficient Free Space Optical Network in the Pre...

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Communication engineering and Emerging Technologies (ICoCET), IEEE International Conference on

作者： Dalal Abdulmohsin Hammood Basim Galeb Aqeel Al-Hilali Mustafa Bashar Norizan Mohamed Nawawi Nur Hakimah Shukron Cybersecurity Technology Engineering Electrical Engineering Technical College Middle Technical University Baghdad Iraq Computer Technician Engineering Al Hikma University College Baghdad Iraq Medical Instrumentation Engineering Al-farahidi university Baghdad Iraq Uruk University Baghdad Iraq Advanced Communication Engineering Centre of Excellence Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Faculty Electronic Engineering Technology Universiti Malaysia Perlis Arau Perlis Malaysia

ISBN: (数字)9798331504144

ISBN: (纸本)9798331504151

This paper aims to develop a reliable FSO device that can transfer information quickly and effectively, making it ideal for short-to-medium distances in metropolitan areas lacking radio recurrence groups or linkages. The proposed system will be tested in light air, light rain, medium rain, and heavy rain, with attenuation values of 0.47, 1.988, 5.844, and 9.29 dB/km. Optisystem software version 21 will be used to evaluate the system's QF. Results obtained show that the implemented system can deliver signals 25 km distant in clear air, 12 km in light rain, 10 km in medium rain, and less than 6 km in severe rain. The method's performance with NRZ and RZ modulation formats is also examined. NRZ modulation is simpler to set up and requires less equipment than RZ modulation, and it reduces optical cable dispersion from light rain. NRZ is effective within 10 kilometers, while RZ handles dispersion and maintains synchronization better for extended broadcasts.

关键词： Rain Urban areas Attenuation Wavelength division multiplexing Software Synchronization Reliability Optical signal processing Optical fiber dispersion Optical pulses

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Diffusion inverse square-root recursive least square-based algorithm with adaptive network 18

Diffusion inverse square-root recursive least square-based a...

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18th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2021

作者： Sitjongsataporn, Suchada Prongnuch, Sethakarn Wiangtong, Theerayod Faculty of Engineering and Technology Dept. of Electronic Engineering Bangkok Thailand Suan Sunandha Rajabhat University Faculty of Industrial Technology Dept. of Computer Engineering Bangkok Thailand King Mongkut's Institute of Technology Ladkrabang Faculty of Engineering Dept. of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9780738111278

In this paper, a Combine-Then-Adapt diffusion strategy on adaptive recursive least square algorithm (RLS) is proposed with adaptive network. We apply the inverse autocorrelation matrix based on the QR-decomposition method in order to reduce the complexity. The Combine-Then-Adapt diffusion strategy is modified on the inverse square-root recursive least square (iQR-RLS) algorithm including with the combination and adaptation steps. By applying an idea of diffusion strategy and mixed cost function, we derive a diffused version of an inverse square-root recursive least square algorithm, named as DiQR-RLS. Experimental results demonstrate that the proposed DiQR-RLS can achieve clearly the better performance than the conventional recursive least square algorithm. © 2021 IEEE.

关键词： Cost functions

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A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

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Advances in Atmospheric Sciences 2025年第1期42卷 36-52页

作者： Bofei ZHANG Haipeng YU Zeyong HU Ping YUE Zunye TANG Hongyu LUO Guantian WANG Shanling CHENG Key Laboratory of Cryospheric Science and Frozen Soil Engineering Nagqu Plateau Climate and Environment Observation and Research Station of Tibet Autonomous RegionNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China University of the Chinese Academy of Sciences Beijing 100049China Key Open Laboratory of Arid Climate Change and Disaster Reduction of CMA Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu ProvinceInstitute of Arid MeteorologyChina Meteorological AdministrationLanzhou 730000China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China

Seasonal precipitation has always been a key focus of climate *** a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal ***,solving a nonlinear problem through linear regression is significantly *** study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in *** model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached *** PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,*** observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.

关键词： observational constraint LightGBM seasonal prediction summer precipitation machine learning

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