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Retraction Note: Overlapping gait pattern recognition using regression learning for elderly patient monitoring

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 265-265页

作者： Youssef, Ahmed E. Kotb, Yasser Fouad, Hassan Mustafa, Ibrahim College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Computers and Systems Engineering Faculty of Engineering at Helwan Helwan University Cairo Egypt Computer Science Division Department of Mathematics Faculty of Science Ain Shams University Cairo Egypt Applied Medical Science Department Community College King Saud University Riyadh Saudi Arabia Biomedical Engineering Department Faculty of Engineering Helwan University Cairo Egypt

Thermal performance optimization of microchannel heat sinks with triangle wave fin designs and various heat transfer fluids using GA/RSM/TOPSIS

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Case Studies in Thermal engineering 2025年 72卷

作者： Amr S. Abouzied Ali Basem Talib K. Ibrahim Ahmad Almadhor Khalid Almutairi Hind Albalawi Salem Alkhalaf Dilsora Abduvalieva Ibrahim Elbadawy Samah G. Babiker Department of Pharmaceutical Chemistry College of Pharmacy University of Hail Hail 81442 Saudi Arabia Faculty of Engineering Warith Al-Anbiyaa University Karbala 56001 Iraq Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Applied College Mechanical Engineering Technology University of Hafr Al Batin Hafr Al Batin Saudi Arabia Department of Physics College of Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent 100070 Uzbekistan College of Engineering and Technology American University of the Middle East Kuwait Department of Electronic Physics Faculty of Applied Science Red Sea University Port Sudan Sudan

Microchannel heat sinks (MHSs) stand out as a promising solution for dissipating heat in high-performance systems. The development and optimization of these systems are vital to meeting the growing demands of modern, power-dense electronics. The present study introduces a novel approach to enhance heat dissipation in microchannel systems. The design incorporates two sets of fins with a distinctive triangle wave pattern within the microchannels. The acquired results for the introduced fins were compared with the data of a non-finned MHS and a device with straight fins. The maximum temperature of the non-finned MHS was 307.222 K, while the incorporation of the mentioned fins reduced the temperature by approximately 2 K. This reduction is significant, as lower maximum temperatures improve the reliability and longevity of electronic components. Response surface methodology (RSM) was utilized to develop predictive models, reducing reliance on complex simulations and costly prototypes. The effects of fin folding angle ( θ ) and spacing from side walls ( S ) on thermo-hydraulic performance were systematically analyzed. Optimization was achieved by integrating genetic algorithms with the predictive models. Three distinct optimization targets (maximizing the Nusselt number, minimizing the pressure drop, and maximizing the device's overall efficiency) were considered, and three optimum designs were proposed for each target. Additionally, Pareto front analysis and TOPSIS evaluation provided a comprehensive multi-objective assessment. In RSM-based anticipation models, the R 2 values were 0.971 for the Nusselt number and 0.977 for pressure drop. The remarkable R 2 values indicated that the models for both responses had a high level of accuracy. Based on the results, heat sink design 3 (HSD3) with geometric parameters of θ = 97.785° and S = 70 μm was recommended for enhancing the overall performance of the MHS. By utilizing just two triangle wave fins configured according to th

关键词： TOPSIS and Pareto fronts Optimization Response surface methodology Microchannel heat sink design Triangle wave fins Genetic Algorithm

Understanding viscoelastic flow instabilities: Oldroyd-B and beyond

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arXiv 2022年

作者： Sánchez, Hugo A. Castillo Jovanović, Mihailo R. Kumar, Satish Morozov, Alexander Shankar, V. Subramanian, Ganesh Wilson, Helen J. Department Of Applied Mathematics And Statistics Institute Of Mathematics And Computational Sciences University Of Sao Paulo Brazil Ming Hsieh Department Of Electrical And Computer Engineering University Of Southern California Los AngelesCA90089 United States Department Of Chemical Engineering And Materials Science University Of Minnesota MinneapolisMN55455 United States SUPA School Of Physics And Astronomy The University Of Edinburgh EdinburghEH9 3FD United Kingdom Department Of Chemical Engineering Indian Institute Of Technology Kanpur208016 India Engineering Mechanics Unit Jawaharlal Nehru Center For Advanced Scientific Research Bangalore560064 India Mathematics Department University College London Gower Street LondonWC1E 6BT United Kingdom

The Oldroyd-B model has been used extensively to predict a host of instabilities in shearing flows of viscoelastic fluids, often realized experimentally using polymer solutions. The present review, written on the occasion of the birth centenary of James Oldroyd, provides an overview of instabilities found across major classes of shearing flows. These comprise (i) the canonical rectilinear shearing flows including plane Couette, plane and pipe Poiseuille flows;(ii) viscometric shearing flows with curved streamlines such as those in the TaylorCouette, cone-and-plate and parallel-plate geometries;(iii) non-viscometric shearing flows with an underlying extensional flow topology such as the flow in a cross-slot device;and (iv) multilayer shearing flows. While the underlying focus in all these cases is on results obtained using the Oldroyd-B model, we also discuss their relation to the actual instability, and as to how the shortcomings of the Oldroyd-B model may be overcome by the use of more realistic constitutive models. All the three commonly used tools of stability analysis, viz., modal linear stability, nonmodal stability, and weakly nonlinear stability analyses are discussed, with supporting evidence from experiments and numerical simulations as appropriate. Despite only accounting for a shear-rate-independent viscosity and first normal stress coefficient, the Oldroyd-B model is able to qualitatively predict the majority of instabilities in the aforementioned shearing flows. The review also highlights, where appropriate, open questions in the area of viscoelastic stability. © 2022, CC BY.

关键词： Stability

Automated Grain Boundary Detection for Bright-Field Transmission Electron Microscopy Images via U-Net

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arXiv 2023年

作者： Patrick, Matthew J. Eckstein, James K. Lopez, Javier R. Toderas, Silvia Asher, Sarah A. Whang, Sylvia I. Levine, Stacey Rickman, Jeffrey M. Barmak, Katayun Department of Applied Physics and Applied Mathematics Columbia University 200 S.W. Mudd Building 500 W. 120 Street New YorkNY10027 United States Department of Physics University of Illinois 1110 W. Green Street UrbanaIL61801 United States Department of Mechanical Engineering Columbia University 210 S.W. Mudd Building 500 W. 120 Street New YorkNY10027 United States Department of Physics Barnard College 504A Altschul Hall 3009 Broadway New YorkNY10027 United States Department of Mathematics and Computer Science Duquesne University 440 College Hall 1100 Locust Street PittsburghPA15282 United States Department of Materials Science and Engineering Lehigh University Whitaker Lab 244 5 E. Packer Avenue BethlehemPA18015 United States

Quantification of microstructures is crucial for understanding processing-structure and structure-property relationships in polycrystalline materials. Delineating grain boundaries in bright-field transmission electron micrographs, however, is challenging due to complex diffraction contrast in images. Conventional edge detection algorithms are inadequate;instead, manual tracing is usually required. This study demonstrates the first successful machine learning approach for grain boundary detection in bright-field transmission electron micrographs. The proposed methodology uses a U-Net convolutional neural network trained on carefully constructed data from bright-field images and hand tracings available from prior studies, combined with targeted postprocessing algorithms to preserve fine features of interest. The image processing pipeline accurately estimates grain boundary positions, avoiding segmentation in regions with intragrain contrast and identifying low-contrast boundaries. Our approach is validated by directly comparing microstructural markers (i.e., grain centroids) identified in U-Net predictions with those identified in hand tracings;furthermore, the grain size distributions obtained from the two techniques show notable overlap when compared using t-test, Kolmogorov-Smirnov test, and Cramér-von Mises test. The technique is then successfully applied to interpret new microstructures having different image characteristics from the training data, with preliminary results from platinum and palladium microstructures presented, highlighting the versatility of our approach for grain boundary identification in bright-field micrographs. © 2023, CC BY-NC-ND.

关键词： Grain boundaries

Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion

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arXiv 2024年

作者： Xing, Xiaodan Tang, Chunling Murdoch, Siofra Papanastasiou, Giorgos Guo, Yunzhe Xiao, Xianglu Cross-Zamirski, Jan Schönlieb, Carola-Bibiane Liang, Kristina Xiao Niu, Zhangming Fang, Evandro Fei Wang, Yinhai Yang, Guang Bioengineering Department and Imperial-X Imperial College London London United Kingdom Centre for Craniofacial & Regenerative Biology King’s College London London United Kingdom Archimedes Unit Athena Research Centre Athens Greece School of Computer Science and Electronic Engineering The University of Essex Essex United Kingdom Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom University of Bergen Bergen Norway MindRank AI Ltd. Hangzhou China Department of Clinical Molecular Biology University of Oslo Akershus University Hospital Lørenskog Norway Data Sciences and Quantitative Biology Discovery Sciences AstraZeneca R&D Cambridge United Kingdom National Heart and Lung Institute Imperial College London London United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom

Immunofluorescent (IF) imaging is crucial for visualizing biomarker expressions, cell morphology and assessing the effects of drug treatments on sub-cellular components. IF imaging needs extra staining process and often requiring cell fixation, therefore it may also introduce artefects and alter endogenouous cell morphology. Some IF stains are expensive or not readily available hence hindering experiments. Recent diffusion models, which synthesise high-fidelity IF images from easy-to-acquire brightfield (BF) images, offer a promising solution but are hindered by training instability and slow inference times due to the noise diffusion process. This paper presents a novel method for the conditional synthesis of IF images directly from BF images along with cell segmentation masks. Our approach employs a Residual Diffusion process that enhances stability and significantly reduces inference time. We performed a critical evaluation against other image-to-image synthesis models, including UNets, GANs, and advanced diffusion models. Our model demonstrates significant improvements in image quality (p © 2024, CC BY-SA.

关键词： Image segmentation

Migration in Multi-Population Differential Evolution for Many Objective Optimization

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Migration in Multi-Population Differential Evolution for Man...

Congress on Evolutionary Computation

作者： Pratyusha Rakshit Archana Chowdhury Amit Konar Atulya K. Nagar Basque Center for Applied Mathematics Bilbao Spain Department of Computer Science Engineering Christian College of Engineering Bhilai India Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Mathematics and Computer Science Liverpool Hope University United Kingdom

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

The paper proposes a novel extension of many objective optimization using differential evolution (MaODE). MaODE solves a many objective optimization (MaOO) problem by parallel optimization of individual objectives. MaODE involves N populations, each created for an objective to be optimized using MaODE. The only mode of knowledge transfer among populations in MaODE is the modified version of mutation policy of DE, where every member of the population during mutation is influenced by the best members of all the populations under consideration. The present work aims at further increasing the communication between the members of the population by communicating between a superior and an inferior population, using a novel migration strategy. The proposed migration policy enables poor members of an inferior population to evolve with a superior population. Simultaneously, members from the superior population are also transferred to the inferior one to help it improving its performance. Experiments undertaken reveal that the proposed extended version of MaODE significantly outperforms its counterpart and the state-of-the-art techniques.

关键词： Sociology Statistics Optimization Linear programming Convergence computer science

Global well-posedness for fractional Sobolev-Galpern type equations

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arXiv 2021年

作者： Nguyen, Huy Tuan Tuan, Nguyen Anh Yang, Chao Department of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Division of Applied Mathematics Thu Dau Mot University Binh Duong Province Viet Nam College of Mathematical Sciences Harbin Engineering University 150001 China

This article is a comparative study on an initial-boundary value problem for a class of semilinear pseudo-parabolic equations with the fractional Caputo derivative, also called the fractional Sobolev-Galpern type equations. The purpose of this work is to reveal the influence of the degree of the source nonlinearity on the well-posedness of the solution. By considering four different types of nonlinearities, we derive the global well-posedness of mild solutions to the problem corresponding to the four cases of the nonlinear source terms. For the advection source function case, we apply a nontrivial limit technique for singular integral and some appropriate choices of weighted Banach space to prove the global existence result. For the gradient nonlinearity as a local Lipschitzian, we use the Cauchy sequence technique to show that the solution either exists globally in time or blows up at finite time. For the polynomial form nonlinearity, by assuming the smallness of the initial data we derive the global well-posed results. And for the case of exponential nonlinearity in two-dimensional space, we derive the global well-posedness by additionally use of Orlicz *** Codes 35K20, 35K58 © 2021, CC BY.

关键词： Banach spaces

Exploring accelerated expansion in the universe: A study of f(Q,T) gravity with parameterized EoS and cosmological constraints

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arXiv 2024年

作者： Koussour, M. Myrzakulov, N. Rayimbaev, J. Errehymy, A. Donmez, Orhan Department of Physics University of Hassan II Casablanca Morocco L. N. Gumilyov Eurasian National University Astana010008 Kazakhstan New Uzbekistan University Mustaqillik Ave. 54 Tashkent100007 Uzbekistan University of Tashkent for Applied Sciences Gavhar Str. 1 Tashkent100149 Uzbekistan National University of Uzbekistan Tashkent100174 Uzbekistan Astrophysics Research Centre School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Private Bag X54001 Durban4000 South Africa College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait

The study conducted in this research paper utilizes the f(Q, T) gravity, where Q represents non-metricity and T represents the trace of the energy-momentum tensor, to investigate the accelerated expansion of the universe. To complete the study, an effective EoS with one parameter α, is parameterized as ωeff = − 3/α(1+z)3+3 . The linear version of f(Q, T) = −Q + σT is also considered, where σ is a constant. By constraining the model with six BAO points, 57 Hubble points, and 1048 Pantheon sample datasets, the parameters α and σ are determined to best match the data. The cosmological parameters and energy conditions for the model are derived and examined. The results show that the model is in good agreement with observations, and can serve as a valuable starting point for analyzing FLRW models in the f(Q, T) theory of gravity. Copyright © 2024, The Authors. All rights reserved.

关键词： Expansion

Hesitant bipolar fuzzy set-based decision system for electric vehicle charging station location planning

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Sustainable Energy, Grids and Networks 2025年 43卷

作者： Chakkarapani Sumathi Thilagasree Thippan Jayakumar Lakshmanaraj Ramya Krishnan Suvitha Dragan Pamucar Witold Pedrycz Joseph Varghese Kureethara Department of Mathematics Sri Ramakrishna Mission Vidyalaya College of Arts and Science Coimbatore India Department of Mathematics Bharathiar University Coimbatore 641 046 India Centre for Nonlinear Systems Chennai Institute of Technology Chennai Tamilnadu India Department of Applied Mathematical Science College of Science and Technology Korea University Sejong Republic of Korea Yuan Ze University College of Engineering Department of Industrial Engineering and Managemente Taoyuan City Taiwan Industrial Engineering Department Faculty of Engineering and Natural Sciences İstinye University İstanbul Türkiye Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Mathematics CHRIST (Deemed to be University) Bangalore India

The selection of electric vehicle (EV) charging station locations is a critical challenge that significantly affects the growth and acceptance of the EV industry. As EVs offer a sustainable solution to fossil fuel depletion and environmental pollution, identifying optimal charging station sites involves dealing with uncertain, inconsistent, and conflicting criteria. To address these challenges, this paper presents an innovative decision-making framework based on Hesitant Bipolar-Valued Fuzzy Sets (HBVFSs), which account for both positive and negative hesitant membership values to better model uncertainty in expert judgments. A novel hybrid Multi-Criteria Decision-Making (MCDM) technique is proposed, combining the Step-wise Weight Assessment Ratio Analysis (SWARA) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods to determine robust criteria weights within the HBVFS environment. The Preference Ranking Organization METHod for Enrichment Evaluation II (PROMETHEE-II) is employed for the final site ranking. This integrated approach enables a more comprehensive and reliable evaluation of potential locations by incorporating both qualitative and quantitative factors. The proposed methodology has practical applications in real-world infrastructure planning and supports more resilient decision-making in sustainable transportation networks. The results demonstrate the model’s effectiveness and adaptability in addressing the site selection problem under uncertainty.

关键词： Bipolar fuzzy set Electric vehicle Hesitant fuzzy set Multi-criteria decision-making Site selection