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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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Satellite-based InSAR Geodesy and Collocation with GNSS

Satellite-based InSAR Geodesy and Collocation with GNSS

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2023 International Conference on ENTERprise Information systems, CENTERIS 2023 - International Conference on Project management, ProjMAN 2023 - International Conference on Health and Social Care Information systems and Technologies, HCist 2023

作者： Papco, Juraj Bakon, Matus Kubica, Lukas Belicova, Gabika Droscak, Branislav Ferianc, Martin Rovnak, Martin Ruiz, Antonio M. Sousa, Joaquim J. Department of Theoretical Geodesy and Geoinformatics Faculty of Civil Engineering Slovak University of Technology in Bratislava Radlinskeho 11 Bratislava813 62 Slovakia Insar.sk Ltd Konstantinova 3 Presov080 01 Slovakia Department of Finance Accounting and Mathematical Methods Faculty of Management and Business University of Presov Konstantinova 16 Presov080 01 Slovakia Department of Engineering School of Sciences and Technology Universidade de Tras-os-Montes e Alto Douro Quinta de Prados Vila Real5000-801 Portugal Geodetic and Cartographic Institute Bratislava Chlumeckeho 4 Bratislava827 45 Slovakia Department of Cartographic Geodetic and Photogrammetry Engineering University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain Research Group RNM-282 Microgeodesia Jaen University of Jaen Campus Las Lagunillas s/n Jaen23071 Spain Institute for Systems and Computer Engineering Technology and Science INESC-TEC Porto4200-465 Portugal

Satellite radar interferometry (InSAR) enables large-scale monitoring of displacement with precision akin to ground-based methods. Using measurements from Sentinel-1 satellites, persistent scatterer networks cover areas up to hundreds of kilometers in size. One of the current challenges for the InSAR community is to tie these networks into a current and country-specific global terrestrial reference system through collocation with other geodetic techniques, especially Global Navigation Satellite systems (GNSS). In this work, an optimal procedure for assessing a suitable collocation properties for InSAR and GNSS techniques is discussed, along with the proposal for the methodology to determine the spatial relationship between the reference points of both techniques. The study analyzes three collocation stations in Slovakia using their radar backscattering characteristics. InSAR achieves an intrinsic accuracy of up to 1 mm but has uncertainties several orders of magnitude larger in 3D space. A collocation of InSAR measurements with GNSS can tie relative InSAR displacement estimates to an absolute reference frame. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Geodesy

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Investigating the interrelationships between the barriers of green computing adoption: TISM-MICMAC-DEMATEL method

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Quality and Quantity 2024年第1期59卷 507-570页

作者： Dalvi-Esfahani, Mohammad Nilashi, Mehrbakhsh Abumalloh, Rabab Ali Thurasamy, Ramayah Ahmad, Muhammad Shakil Blockchain Fintech and Cryptocurrency Research Group University of Southampton Malaysia Johor Iskandar Puteri 79100 Malaysia UCSI Graduate Business School UCSI University No. 1 Jalan Menara Gading UCSI Heights Cheras Kuala Lumpur 56000 Malaysia Department of Computer Science and Engineering Qatar University Doha 2713 Qatar School of Management Universiti Sains Malaysia Minden Penang 11800 Malaysia Department of Information Technology and Management Daffodil International University Birulia Bangladesh Department of Management Sunway Business School (SBS) Selangor Petaling Jaya 47500 Malaysia University Center for Research & amp Development (UCRD) Chandigarh University Punjab Ludhiana 140413 India Faculty of Economics and Business Universitas Indonesia (UI) West Java Depok City 16424 Indonesia Management Information Systems Department School of Business The University of Jordan (UJ) Amman 11942 Jordan TUIBS Leadership Management & amp Human Resources Teesside University Middlesbrough United Kingdom Faculty of Business Sohar University Sohar P.C 311 Oman

While a considerable body of research addresses firms’ endeavors toward environmental sustainability, the prominence of green computing has been somewhat eclipsed by impediments hindering its swift integration and development. To address this gap, this study conducted a comprehensive analysis, identifying twelve distinct barriers categorized into adoption costs, technological challenges, and internal obstacles. Through consultation with fifteen field experts, the study employed Interpretive Structural Modeling (ISM) and Matrice d’Impacts Croises Multipication Applique´ an Classment (MICMAC) methodologies to scrutinize the influences among these barriers. The findings illuminated that firm size and legal constraints emerge as pivotal and autonomous factors exerting substantial influence on the adoption. Additionally, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was deployed to ascertain the relative importance of these barriers and delineate their causal relationships. Notably, the DEMATEL results underscored that, according to experts, adoption cost barriers hold greater significance compared to other dimensions. Transactional risk, technological complexity, and switching cost were identified as pivotal barriers within their respective dimensions. The decision-making framework formulated in this study offers valuable insights for managers and practitioners, providing a nuanced understanding of the interplay between identified barriers and their implications for implementing green computing initiatives. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Barriers DEMATEL Green computing MICMAC Total interpretive structural modeling

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Deep Eutectic Solvent-Aqueous Two-Phase Leaching System for Direct Separation of Lithium and Critical Metals

ACS Sustainable Resource Management

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ACS Sustainable Resource management 2024年第11期1卷 2482-2491页

作者： Kevin Septioga Adroit T. N. Fajar Rie Wakabayashi Masahiro Goto Department of Applied Chemistry Graduate School of Engineering Kyushu University 744 Motooka Fukuoka 819-0395 Japan Center for Energy Systems Design International Institute of Carbon-Neutral Energy Research (WPI-I2CNER) Kyushu University 744 Motooka Fukuoka 819-0395 Japan

Developing an effective recycling process for reclaiming valuable metals from lithium-ion batteries is an urgent issue owing to increasing battery waste from electric vehicles. In this study, we developed a leaching method that enables the direct separation of lithium from other critical metals, namely, nickel and cobalt, using a two-phase system that consists of a deep eutectic solvent (DES) and water. The DES consisting of 4,4,4-trifluoro-1-phenyl-1,3-butadione and tri-n-octylphosphine oxide showed the highest leaching performance when combined with water. Several operational parameters, such as the aqueous fraction, solid-liquid ratio, reaction time, and operation temperature, were evaluated. The optimum results in the two-phase direct leaching system were obtained using a 1:1 DES-water ratio and 10 g/L solid-liquid ratio, reacted at 80 °C for 24 h. Anin-situstripping phenomenon was observed, revealing that lithium transferred from the DES phase to the aqueous phase. In the application of black mass leaching, the aqueous phase significantly enhanced Co, Ni, and Mn extraction into the DES phase and thus plays an important role in separating lithium from other metals. The efficiency of direct lithium leaching from the black mass reached 99% within 24 h.

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A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot

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Frontiers of Information Technology & Electronic engineering 2022年第8期23卷 1217-1228页

作者： Manjun TIAN Xiali LI Shihan KONG Licheng WU Junzhi YU First Research Institute of the Ministry of Public Security of PRC Beijing 100048China School of Information Engineering Minzu University of ChinaBeijing 100081China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

To tackle the problem of aquatic environment pollution,a vision-based autonomous underwater garbage cleaning robot has been developed in our *** propose a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object ***,the YOLOv4 algorithm is chosen as a basic neural network framework to perform object *** the purpose of further improvement on the detection accuracy,YOLOv4 is transformed into a four-scale detection *** improve the detection speed,model pruning is applied to the new *** virtue of the improved detection methods,the robot can collect garbage *** detection speed is up to 66.67 frames/s with a mean average precision(mAP)of 95.099%,and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

关键词： Object detection Aquatic environment Garbage cleaning robot Modified YOLOv4

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Multifunctional composite magnet realizing record-high transverse thermoelectric generation

arXiv

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arXiv 2024年

作者： Ando, Fuyuki Hirai, Takamasa Alasli, Abdulkareem Sepehri-Amin, Hossein Iwasaki, Yutaka Nagano, Hosei Uchida, Ken-Ichi National Institute for Materials Science Tsukuba305-0047 Japan Department of Mechanical Systems Engineering Nagoya University Nagoya464-8603 Japan Department of Advanced Materials Science Graduate School of Frontier Sciences The University of Tokyo Kashiwa277-8561 Japan

Permanent magnets are used in various products and essential for human society. If omnipresent permanent magnets could directly convert heat into electricity, they would lead to innovative energy harvesting and thermal management technologies. However, achieving such "multifunctionality" has been difficult due to poor thermoelectric performance of conventional magnets. In this work, we develop a multifunctional composite magnet (MCM) that enables giant transverse thermoelectric conversion while possessing permanent magnet features. MCM comprising alternately and obliquely stacked SmCo5/Bi0.2Sb1.8Te3 multilayers exhibits an excellent transverse thermoelectric performance at room temperature by optimizing its anisotropic structure. Owing to the extremely low interfacial electrical and thermal resistivities, the experimentally determined figure of merit zxyT reaches 0.20 close to the analytically calculated ideal value. The MCM-based thermopile module generates 204 mW in maximum at a temperature difference of 152 K, whose power density normalized by heat transfer area and temperature gradient is not only record-high among transverse thermoelectric modules but also comparable to that of commercial thermoelectric modules utilizing the Seebeck effect. The multifunctionality of our MCM provides unprecedented opportunities for energy harvesting and thermal management everywhere permanent magnets are currently used. © 2024, CC BY.

关键词： Permanent magnets

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Characteristics of Heavy Metal Distributions in the Coastal Areas of Yangtze River 32nd

Characteristics of Heavy Metal Distributions in the Coastal ...

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32nd International Ocean and Polar engineering Conference, ISOPE 2022

作者： Zhou, Xiaosheng Lou, Sha Wang, Hao Radnaeva, Larisa Dorzhievna Nikitina, Elena Department of Hydraulic Engineering Tongji University Shanghai China Key Laboratory of Yangtze RiverWater Environment Ministry of Education Tongji University Shanghai China Laboratory of chemistry of natural systems Baikal Institute of Nature Management of Siberian branch of the Russian Academy of sciences Ulan-Ude Russia

ISBN: (纸本)9781880653814

With the rapid development of economy and the concentrated populations, more and more pollutants entry into the water environment in coastal areas of the Yangtze River Estuary. Field investigations were conducted twice in this study to evaluate the characteristics of heavy metals in the coastal areas of the Yangtze River Estuary in January 2021. Eight sampling locations were selected as Liu Estuary (LHK), Shi Dong Kou (SDK), Wu Song Kou (WSK), Zhu Yuan (ZY), San Jia Gang (SJG), Chao Yang Nong Chang (CYNC), Da Zhi River (DZH), Nan Hui (NH). Eight sediment samples and eight water samples were collected, and the heavy metal distribution characteristics were analyzed. The results have shown that the concentrations of Cr, Cu, Zn, Cd, and Pb in the surface sediment sample were 51.96-97.25, 18.40-28.03, 46.47-94.45, 0.185-3.095, 11.28-32.08 mg/kg. The Sediment Quality Guideline was used to evaluate the biological effect of sediment. The results show that the most negative impact of heavy metals in these areas were Cr and Cu. They have acute or chronic toxicity to organisms in water, which will reduce the number of organisms. © 2022 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Heavy metals

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Analysis of Improving Sales Process Efficiency with Salesforce Industries CPQ in CRM 7th

Analysis of Improving Sales Process Efficiency with Salesfor...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Pathak, Pritesh Pal, Souvik Maity, Saikat Jeyalaksshmi, S. Adhikari, Saurabh Akila, D. Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Management Information Systems Saveetha College of Liberal Arts And Sciences Saveetha Institute of Medical And Technical Sciences Chennai India Kolkata India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Chennai India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Applications Saveetha College of Liberal Arts And Sciences Saveetha Institute of Medical And Technical Sciences Chennai India

ISBN: (纸本)9789819995615

The successful execution of Configure, Price, Quote (CPQ) protocols is of utmost importance for businesses that handle intricate product portfolios within their sales processes. The objective of these approaches is to optimise the generation of competitive quotations by leveraging data from diverse business systems, leading to a decrease in processing time and enhancement of operational efficiency. Salesforce's Industries CPQ for Communications Cloud, previously referred to as Vlocity CPQ, assumes a crucial function within the Salesforce platform by facilitating the automation of the quotation creation process for sales teams. Designed specifically for the communications industry, this solution enables communications service providers (CSPs) to effectively provide a wide array of products and services to their customers in a streamlined manner. By implementing Industries CPQ, Communication Service Providers (CSPs) have the potential to save expenses associated with customisation and maintenance, while also accelerating their time-to-market. In order to assess the practical ramifications of Salesforce Industries CPQ, the researcher intends to deploy the aforementioned system within ABC Insurance Company. The objective of the study is to evaluate the program's influence on sales efficiency and profitability by conducting interviews with a targeted cohort of employees who have employed the software in their sales endeavours. Furthermore, the study will include hypothesis testing to analyse the timing of quotation production across various policy configurations utilising Salesforce Industries CPQ, aiming to provide further validation of its benefits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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Distributional Utility Preference Robust Optimization Models in Multi-Attribute Decision Making

arXiv

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arXiv 2022年

作者： Hu, Jian Zhang, Dali Xu, Huifu Zhang, Sainan Department of Industrial and Manufacturing Systems Engineering University of Michigan - Dearborn DearbornMI48128 United States Sino-US Global Logistics Institute Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200030 China Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong N.T. Shatin Hong Kong

Utility preference robust optimization (PRO) has recently been proposed to deal with optimal decision-making problems where the decision maker’s (DM’s) preference over gains and losses is ambiguous. In this paper, we take a step further to investigate the case that the DM’s preference is random. We propose to use a random utility function to describe the DM’s preference and develop distributional utility preference robust optimization (DUPRO) models when the distribution of the random utility function is ambiguous. We concentrate on data-driven problems where samples of the random parameters are obtainable but the sample size may be relatively small. In the case when the random utility functions are of piecewise linear structure, we propose a bootstrap method to construct the ambiguity set and demonstrate how the resulting DUPRO can be solved by a mixed-integer linear program. The piecewise linear structure is versatile in its ability to incorporate classical non-parametric utility assessment methods into the sample generation of a random utility function. Next, we expand the proposed DUPRO models and computational schemes to address general cases where the random utility functions are not necessarily piecewise linear. We show how the DUPRO models with piecewise linear random utility functions can serve as approximations for the DUPRO models with general random utility functions and allow us to quantify the approximation errors. Finally, we carry out some performance studies of the proposed bootstrap-based DUPRO model and report the preliminary numerical test results. The paper is the first attempt to use distributionally robust optimization methods for PRO problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

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An AI-Driven VM Threat Prediction Model for Multi-Risks Analysis-Based Cloud Cybersecurity

arXiv

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arXiv 2023年

作者： Saxena, Deepika Gupta, Ishu Gupta, Rishabh Singh, Ashutosh Kumar Wen, Xiaoqing Department of Computer Science & Engineering The University of Aizu Japan Bangalore India Indian Institute of Information Technology Bhopal India The Department of Computer Applications National Institute of Technology Kurukshetra India the Department of Creative Informatics The Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka8208502 Japan

Cloud virtualization technology, ingrained with physical resource sharing, prompts cybersecurity threats on users’ virtual machines (VM)s due to the presence of inevitable vulnerabilities on the offsite servers. Contrary to the existing works which concentrated on reducing resource sharing and encryption/decryption of data before transfer for improving cybersecurity which raises computational cost overhead, the proposed model operates diversely for efficiently serving the same purpose. This paper proposes a novel Multiple Risks Analysis based VM Threat Prediction Model (MR-TPM) to secure computational data and minimize adversary breaches by proactively estimating the VMs threats. It considers multiple cybersecurity risk factors associated with the configuration and management of VMs, along with analysis of users’ behaviour. All these threat factors are quantified for the generation of respective risk score values and fed as input into a machine learning based classifier to estimate the probability of threat for each VM. The performance of MR-TPM is evaluated using benchmark Google Cluster and OpenNebula VM threat traces. The experimental results demonstrate that the proposed model efficiently computes the cybersecurity risks and learns the VM threat patterns from historical and live data samples. The deployment of MR-TPM with existing VM allocation policies reduces cybersecurity threats up to 88.9%. © 2023, CC BY.

关键词： Risk analysis

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