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Analysis of flow and heat transport between converging channel

The European Physical Journal Special Topics 2023年第13期233卷 2335-2351页

作者： Boudjemline, Attia Rehman, Sohail Hashim Khedher, Nidhal Ben Industrial Engineering Department College of Engineering University of Ha’il Ha’il Saudi Arabia Department Mechanical Engineering School of Material Sciences and Engineering Georgia Institute of Technology Atlanta USA Department of Mathematics University of Peshawar Peshawar Pakistan Department of Mathematics and Statistics University of Haripur Haripur Pakistan Department of Mechanical Engineering College of Engineering University of Ha’il Ha’il Saudi Arabia Laboratory of Thermal and Energy Systems Studies National School of Engineering of Monastir University of Monastir Monastir Tunisia

Heat transport analysis for non-Newtonian fluid flows between non-parallel wall channels has sustainable significance in high-performance thermal engineering processes. In recent years, this analysis is extensively used in numerous natural flows and industrial processes, for instance, blood flow through human veins, lubrication systems, automobile radiators, thermal pumps, and water purification processes, etc. Therefore, this research, it is targeted to enhance thermal performance with the addition of ultrafine metallic nanoparticles into working fluids. With this goal in mind, this research work presents a numerical investigation for buoyancy-driven flow of Carreau nanofluids confined in a vertical converging enclosure. In addition, heat and mass transport analysis with non-linear thermal radiation and activation energy are mathematically formulated via Buongiorno’s model. A new formulation is developed for purely radial flow inside this converging channel and appropriate non-dimensional variables are utilized for problem simplification. These transformed equations are then numerically tackled with the help of a versatile numerical method, bvp4c function in MATLAB. The simulated results are portrayed by virtue of nanofluid velocity, temperature, and concentration distributions with variation in governing dimensionless parameters. The results indicate that the velocity was significantly reduced with higher activation energy parameter. Moreover, the higher values of the Grashof number yields increasing conduct in velocity distributions.

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Online Distributed Optimization in Radial Power Distribution Systems: Closed-Form Expressions

Online Distributed Optimization in Radial Power Distribution...

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2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2021

作者： Sadnan, Rabayet Asaki, Tom Dubey, Anamika Department of Electrical Engineering and Computer Science Washington State University United States Department of Mathematics and Statistics Washington State University United States

ISBN: (纸本)9781665415026

Managing optimal operations for power distribution systems centrally have significant limitations that motivates distributed computational paradigm. However, for managing fast varying phenomena - resulting from highly variable distributed energy resources (DERs), the existing distributed optimization approaches are inefficient. Related online distributed control methods are equally limited in their applications. They require thousands of time-steps to track the network-level optimal solutions, resulting in slow performance. We have previously developed an online distributed controller that leverages the system's radial topology to achieve network-level optimal solutions within a few time steps. However, it requires solving a node-level nonlinear programming problem at each time step. This paper analyzes the solution space for the node-level optimization problem and derives the analytical closed-form solutions for the decision variables. The theoretical analysis of the node-level optimization problem and obtained closed-form optimal solutions eliminate the need for embedded optimization solvers at each distributed agent and significantly reduce the computational time and optimization costs. © 2021 IEEE.

关键词： Electric load flow

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Assessment of Axial Power Peaking Factors in GHARR-1 LEU Core: A Decadal Simulation Analysis

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World Journal of Nuclear Science and Technology 2024年第1期14卷 72-85页

作者： Emmanuel Kwame Ahiave Emmanuel Ampomah-Amoako Rex Gyeabour Abrefah Mathew Asamoah Department of Mathematics and Statistics Ho Technical University Ho Ghana Department of Nuclear Engineering Graduate School of Nuclear and Allied Sciences University of Ghana Accra Ghana Nuclear Regulatory Authority Accra Ghana National Nuclear Research Institute Accra Ghana

This study aims to thoroughly investigate the axial power peaking factors (PPF) within the low-enriched uranium (LEU) core of the Ghana Research Reactor-1 (GHARR-1). This study uses advanced simulation tools, like the MCNPX code for analysing neutron behavior and the PARET/ANL code for understanding power variations, to get a clearer picture of the reactor’s performance. The analysis covers the initial six years of GHARR-1’s operation and includes projections for its whole 60-year lifespan. We closely observed the patterns of both the highest and average PPFs at 21 axial nodes, with measurements taken every ten years. The findings of this study reveal important patterns in power distribution within the core, which are essential for improving the safety regulations and fuel management techniques of the reactor. We provide a meticulous approach, extensive data, and an analysis of the findings, highlighting the significance of continuous monitoring and analysis for proactive management of nuclear reactors. The findings of this study not only enhance our comprehension of nuclear reactor safety but also carry significant ramifications for sustainable energy progress in Ghana and the wider global context. Nuclear engineering is essential in tackling global concerns, such as the demand for clean and dependable energy sources. Research on optimising nuclear reactors, particularly in terms of safety and efficiency, is crucial for the ongoing advancement and acceptance of nuclear energy.

关键词： GHARR-1 Power Peaking Factor Nuclear Reactor Safety Low Enriched Uranium Core Operational Longevity Thermal Hydraulics

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Critical Tokunaga model for river networks

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Physical Review E 2022年第1期105卷 014301-014301页

作者： Yevgeniy Kovchegov Ilya Zaliapin Efi Foufoula-Georgiou Department of Mathematics Oregon State University Corvallis Oregon 97331 USA Department of Mathematics and Statistics University of Nevada Reno Nevada 89557 USA Department of Civil and Environmental Engineering and Department of Earth System Science University of California Irvine California 92697 USA

The hierarchical organization and self-similarity in river basins have been topics of extensive research in hydrology and geomorphology starting with the pioneering work of Horton in 1945. Despite significant theoretical and applied advances, however, the mathematical origin of and relation among Horton's laws for different stream attributes remain unsettled. Here we capitalize on a recently developed theory of random self-similar trees to elucidate the origin of Horton's laws, Hack's laws, basin fractal dimensions, power-law distributions of link attributes, and power-law relations between distinct attributes. In particular, we introduce a one-parametric family of self-similar critical Tokunaga trees that includes the celebrated Shreve's random topology model and extends to trees that approximate the observed river networks with realistic exponents. The results offer tools to increase our understanding of landscape organization under different hydroclimatic forcings, and to extend scaling relationships useful for hydrologic prediction to resolutions higher than those observed.

关键词： Scaling laws of complex systems Random graphs River networks Tree network Self-similarity

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Evaluating efficiency in water and sewerage services: An integrated DEA approach with DOE and PCA

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Science of the Total Environment 2025年 959卷 178288页

作者： Pishini, Khodarahm Abdolazimi, Omid Shishebori, Davood Rezaee, Mustafa Jahangoshai Sepehrifar, Mohammad Department of Industrial Engineering Yazd University Yazd Iran Department of Industrial and Systems Engineering Mississippi State University Mississippi StateMS United States University of Quebec Montreal Canada Department of Industrial Engineering Urmia University of Technology Urmia Iran Department of Mathematics and Statistics Mississippi State University Mississippi StateMS United States

Evaluating the performance of service organizations like Water and Sewerage companies is essential for optimal operations, high-quality service, and cost efficiency. This paper introduces a model using data envelopment analysis (DEA) to assess the efficiency of operational units within such companies. The selection of key performance indicators is complicated by the numerous inputs and outputs, each affecting systems and activities differently. To enhance DEA model performance due to the imbalance between the number of inputs/outputs and the number of units under evaluation, this research integrates design of experiments (DOE) and principal component analysis (PCA) for variable screening and data reduction, creating new linear combinations with minimal information loss. These methods represent a new direction in handling numerous variables in DEA models. Addressing unit heterogeneity by removing environmental factors from inputs reduces research errors. A case study showed that some units can achieve high efficiency with fewer inputs and more valuable outputs. The findings offered managerial insights for informed decision-making and strategic planning, optimizing resources in line with the company's mission and vision. This methodology ultimately improves service reliability, customer satisfaction, and environmental sustainability. The graphical abstract has been simplified to enhance readability and focus on the primary methodological advances. It emphasizes the integration of PCA for dimensionality reduction, DOE for variable scereening, and DEA for efficiency evaluation. © 2024 The Authors

关键词： Customer satisfaction

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A Sensor-Based Deep Learning Approach for Recognizing Daily and Work Activities in Open Environments for Sanitation Workers 9

A Sensor-Based Deep Learning Approach for Recognizing Daily ...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Sanitation workers play a crucial role in maintaining public hygiene and cleanliness. Understanding their daily routines and job responsibilities is essential for improving their work environment, task distribution, and overall supervision. However, tracking sanitation workers in open environments remains a persistent challenge. This paper introduces a deep learning model named CNN-BiGRU-CBAM, designed to recognize sanitation workers' daily and working activities using smartwatch sensors. The model leverages advanced convolutional neural networks to extract highly informative features from multi-modal sensor data, including acceleration information, collected from smartwatches. The dataset used in this study is carefully curated and collected from sanitation workers during their daily work routines, encompassing activities such as walking, running, sweeping, lifting, and driving vehicles. These activities are systematically categorized to cover both work-related tasks and non-work activities. Experimental results demonstrate that the proposed model consistently achieves an average F1-score of over 94.50% across seven activity groups, outperforming baseline deep learning methods by an impressive margin of 5.62%. The model excels in accurately identifying operations in diverse and dynamic work settings. This research highlights the potential of smartwatches and deep learning in continuously analyzing sanitation workers' duties and developing improved working regulations that consider their well-being and daily tasks. © 2024 IEEE.

关键词： Sanitation

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A Hybrid Residual CNN with Channel Attention Mechanism for Continuous User Identification Using Wearable Motion Sensors 47

A Hybrid Residual CNN with Channel Attention Mechanism for C...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350365597

Recognizing individuals continuously using wearable devices has several applications in personalized services and security. This paper presents a hybrid deep network architecture called SE-ResBiLSTM that integrates a squeeze-and-excitation (SE) mechanism with residual bidirectional LSTM layers. The goal is to enhance the accuracy of person identification using data from wearable motion sensors. The SE blocks facilitate channel-wise feature refinement, highlighting helpful information. Residual connections enable the training of deeper LSTM architectures. The proposed method's accuracy in identifying individuals across increasing periods between training and testing is evaluated using the SP-SW HAR dataset, which contains activity data collected from wristwatch sensors. By leveraging representation learning and exhibiting resilience against sensor noise and temporal fluctuations, SE-ResBiLSTM improves recognition accuracy compared to regular and convolutional LSTM networks. According to the trial data, SE-ResBiLSTM achieved a maximum identification accuracy of 98.71 %. The experimental results demonstrate that this approach significantly boosts the performance of user identification using wristwatch sensors. © 2024 IEEE.

关键词： Wearable sensors

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Exact analytical solution for the Sakiadis boundary layer

arXiv

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arXiv 2024年

作者： Barlow, Nathaniel S. Cade Reinberger, W. Weinstein, Steven J. School of Mathematics & Statistics Rochester Institute of Technology RochesterNY14623 United States Department of Chemical Engineering Rochester Institute of Technology RochesterNY14623 United States

It has recently been established [Naghshineh et al., IMA J. of Appl. Math., 88, 1 (2023)] that a convergent series solution may be obtained for the Sakiadis boundary layer problem once key parameters are determined iteratively using the series itself. Here, we provide exact and explicit analytical expressions for these parameters, including that associated with wall shear, thus completing the exact analytical solution. The complete exact analytical solution to the Sakiadis problem is summarized here for direct use. © 2024, CC BY-SA.

关键词： Boundary layers

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Predictive Powered Inference for Healthcare;Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression 9

Predictive Powered Inference for Healthcare;Relating Optical...

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9th Machine Learning for Healthcare Conference, MLHC 2024

作者： Schultz, Jacob Filippatou, Angeliki McCormack, Brenna Fitzgerald, Kathryn C. Wei, Shuwen Johnson, Evan Kalaitzidis, Grigorios Vasileiou, Elena Sotirchos, Elias Saidha, Shiv Calabresi, Peter Prince, Jerry Jedynak, Bruno M. Fariborz Maseeh Department of Mathematics and Statistics Portland State University PortlandOR United States Department of Neurology Johns Hopkins University BaltimoreMD United States Whiting School of Engineering Johns Hopkins University BaltimoreMD United States Boston University Boston University Medical Campus BostonMA United States Department of Neurology Mount Sinai Hospital New YorkNY United States

Prediction-powered inference (PPI and PPI++) is a recently developed statistical method for computing confidence intervals and tests. It combines observations with machine-learning predictions. We use this technique to measure the association between the thickness of retinal layers and the time from the onset of Multiple Sclerosis (MS) symptoms. Further, we correlate the former with the Expanded Disability Status Scale, a measure of the progression of MS. In both cases, the confidence intervals provided with PPI++ improve upon standard statistical methodology, showing the advantage of PPI++ for answering inference problems in healthcare. © 2024 J. Schultz et al.

关键词： Optical coherence tomography

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Fault Detection Filtering for Unmanned Surface Vehicles with Markov Switching and Random Packet Losses

Fault Detection Filtering for Unmanned Surface Vehicles with...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Meng-Jie Hu Ju H. Park Jun Cheng School of Information and Control Engineering China University of Mining and Technology Xuzhou Department of Electrical Engineering Yeungnam University Gyeongsan Republic of Korea School of Mathematics and Statistics Guangxi Normal University Guilin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper explores the dissipative fault detection filtering (FDF) issue for unmanned surface vehicles (USVs) within a network environment, considering switching channels and random occurrence of packet losses. Specifically, a multi-channel transmission mechanism is adopted to enhance the reliability of the system, and the switching channel is orchestrated by a Markov chain. The random intermittent data dropouts in each channel between the system and FDF modeled as a Bernoulli process are considered. Leveraging the Lyapunov theory and slack matrix technique, switching-channel-dependent FDF is crafted to detect faults for USVs and ensure that the augmented system keeps stochastic stability while attaining a strictly dissipative performance. Finally, an illustrative example is presented to validate the effectiveness of the proposed method for USVs.

关键词： Sufficient conditions Filtering Fault detection Simulation Stochastic processes Packet loss Switches Reliability theory Stability analysis Autonomous vehicles

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