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A comprehensive review on natural convection in trapezoidal cavities with mono and hybrid nanofluids

International Journal of Thermofluids 2025年 27卷

作者： Rashid, Farhan Lafta Alkhekany, Zainab Abdul Karim Eleiwi, Muhammad Asmail Bouabidi, Abdallah Ahmad, Shabbir Chibani, Atef Kezzar, Mohamed Kadhim, Saif Ali Askar, Ali Habeeb Hammoodi, Karrar A. Department of Petroleum Engineering College of EngineeringmUniversity of Kerbala Karbala56001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala56001 Iraq Electromechanical Engineering Department College of Engineering University of Samarra Samarra Iraq University of Gabes Avenue of Omar Ib-Elkhattab Gabes Zrig6023 Tunisia Graduate Program of Ocean Engineering School of Engineering Universidade Federal do Rio Grande Italia Avenue km 8 Rio Grande96201-900 Brazil Institute of Geophysics and Geomatics China University of Geosciences Wuhan430074 China Research Center in Industrial Technologies CRTI P.O.Box 64 Algiers Cheraga16014 Algeria Department of Mechanical Engineering University of 20 Aout 1955 El Hadaiek Road B. O. 26 Skikda21000 Algeria Technology Department Faculty of Technology 20 Aout 1955 University of Skikda PO Box 26 Skikda21000 Algeria Mechanical Engineering Department University of Technology- Iraq Baghdad Iraq Department of Fluid and Heat Engineering University of Miskolc Miskolc Hungary Institute of Physics and Electrical Engineering University of Miskolc Miskolc Hungary College of Engineering University of Al Maarif Al Anbar Iraq

This review synthesises advancements in natural convection within trapezoidal cavities using mono- and hybrid nanofluids, emphasising their geometric advantages for thermal management. Trapezoidal cavities promote asymmetric flow patterns that enhance heat transfer compared to conventional geometries, as demonstrated in applications like solar absorbers and electronic cooling. Through analysis of 50+ previous studies, the review identifies key trends: Hybrid nanofluids like Cu-Al₂O₃/water consistently outperform mono nanofluids in Nusselt number improvement, with gains exceeding 20 %. Inclined walls mitigate stagnant flow zones, though exact reduction rates vary with aspect ratio and nanoparticle concentration. Magnetic fields and porous media further modulate thermal performance, but trade-offs emerge between conductivity enhancement and viscosity penalties. This review provides a framework to optimise trapezoidal cavities with nanofluids for industrial deployment. © 2025 The Author(s)

关键词： Natural convection

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Simulation Optimization-Based model for Decision-Making in the stroke clinical pathway

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Computers and Industrial engineering 2025年 204卷

作者： Boareto, Pedro A Moretti, Leonardo N Safanelli, Juliana Liberato, Rafaela B Moro, Carla HC Pécora Junior, José E Moro, Claudia MCB Coelho, Leandro dos S Loures, Eduardo FR Deschamps, Fernando Portela Santos, Eduardo A Pontifícia Universidade Católica do Paraná 1155 Imaculada Conceição St. PR Curitiba80215-901 Brazil Electrical Engineering Graduate Program Universidade Federal do Paraná Paraná Curitiba Brazil Joinville Stroke Registry 238 Getúlio Vargas Av. Santa Catarina Joinville89202-000 Brazil Municipal Hospital São José 238 Getúlio Vargas Av. Santa Catarina Joinville89202-000 Brazil Federal University of Paraná 632 Prefeito Lothário Meissner Av. PR Curitiba80210-170 Brazil Health Technology Graduate Program Pontifícia Universidade Católica do Paraná 1155 Imaculada Conceição St. PR Curitiba80215-901 Brazil Department of Mechanical Engineering Universidade Federal do Paraná 230 Cel. Francisco H. dos Santos Av. Polytechnic Center Bloc IV PR Curitiba81530-000 Brazil

Healthcare is highly complex, sensitive and needs constant improvements. Several works have already been developed to support those processes. However, finding the optimum solution takes much work and time. Multi-Objective Genetic Algorithms (MOGA) improve the results by finding the optimal trade-off between multiple conflicting objectives and exploring the problem space more thoroughly. This study presents an enhanced framework that integrates Process Mining (PM), Discrete Event Simulation (DES), and Multi-Objective Genetic Algorithms (MOGAs) into an optimized, end-to-end pipeline. This framework builds upon an existing non-optimized approach to enable decision-makers to explore and implement Key Performance Indicator (KPI)-oriented solutions directly from raw log data. The framework navigates vast and complex solution spaces by embedding MOGAs into the KPI-oriented simulation process, delivering optimized scenarios with improved performance, boosting decision-making efficiency. The clinical stroke pathway, covering symptoms’ onset to hospital discharge, was utilized as a case. This research demonstrates how optimization techniques with classical techniques into one unified framework can accelerate healthcare improvements, offering scalable applications to other domains beyond stroke care. The results demonstrate that using MOGA leads to improved solutions compared to the non-optimized framework, and this approach can be evaluated in short periods due to its performance. The findings underscore the solutions’ sensitivity to changes in simulation parameters, emphasizing the importance of considering multiple objectives when dealing with complex decision-making problems in the healthcare industry. Future studies are suggested to extend the model, compare the effectiveness of different optimization methods within the framework, and test the framework's applicability to other domains. © 2025 Elsevier Ltd

关键词： Clinical research

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A security model for DNS tunnel detection on cloud platform 7

A security model for DNS tunnel detection on cloud platform

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7th Workshop on Communication Networks and Power Systems, WCNPS 2022

作者： De Souza Bezerra Borges, Lorena De Oliveira Albuquerque, Robson De Sousa Junior, Rafael Timoteo Professional Graduate Program in Electrical Engineering - PPEE University of Brasília-UnB Faculty of Tecnology Electrical Engineering Departament Brazil

ISBN: (数字)9781665474566

ISBN: (纸本)9781665474566

DNS tunneling uses DNS protocol features to establish command and control channels thus being possibly exploited as a malicious tool for data exfiltration. DNS tunneling security threats affect crossplatform systems within local and cloud computing resources. This article proposes an effective DNS tunnel detection methodology integrating cloud-based resources. The proposed detection methods compose an unsupervised machine-learning model execution for anomaly identification. The validation uses a collected DNS traffic dataset and shows the practical approach for C2, data exfiltration, and heartbeat tunnel test situations, as high levels of anomaly detection are obtained even for those lightweight data during the transfer process. This study has an operational approach and could be adapted to compose security control systems for organizations. © 2022 IEEE.

关键词： Cloud computing

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Quantum-resistant Cryptography in FPGA 7

Quantum-resistant Cryptography in FPGA

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7th Workshop on Communication Networks and Power Systems, WCNPS 2022

作者： Policarpo, Renata C. Nery, Alexandre S. Albuquerque, Robson De O. University of Brasília Professional Post-Graduate Program in Electrical Engineering Department of Electrical Engineering Brasilia70910-900 Brazil

ISBN: (数字)9781665474566

ISBN: (纸本)9781665474566

The use of cryptography techniques to guarantee confidentiality, authenticity and integrity of sensitive data has become mandatory. Besides, advancements in quantum computers are gradually posing a threat to public key encryption technology. Without proper security measures, fraudsters may easily gain access to one's personal and sensitive data. The result of the third round of NIST's Post-Quantum Cryptography (PQC) process for standardization of public-key cryptography systems brought CRYSTALS-Kyber as the first mechanism selected for key encapsulation. The aim of this study is to provide the specification of a reconfigurable CRYSTAL-Kyber accelerator using High-Level Synthesis (HLS) technology. Our architecture requires about 2200 LUTs, 3001 FFs and 28 DSP on a low-cost Zynq FPGA (XC7Z020-1 CLG400C). The total time spent by the accelerator in a key exchange simulation is approximately 0.84 ms, operating at 100 MHz, and the estimated power consumption in this process is 1.695W. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Multiband Patch Antenna Design Using Nature-Inspired Optimization Method

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 151-162页

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Pierezan, Juliano Mariani, Viviana C. Coelho, Leandro S. Sarigiannidis, Panagiotis Koulouridis, Stavros Goudos, Sotirios K. ELEDIA@AUTH Research Group School of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Electrical Engineering Federal University of Parana Curitiba80060-000 Brazil Industrial and Systems Engineering Graduate Program and Mechanical Engineering Graduate Program Pontifical Catholic University of Parana Curitiba80215-901 Brazil Department of Electrical and Computer Engineering University of Western Macedonia Kozani501 00 Greece Electrical and Computer Engineering Department University of Patras Patras26504 Greece

Radio frequency energy harvesting has attracted considerable interest as a technique of enabling self-powered wireless networks. This technique faces several challenges, such as the receiving and the rectifying modules of a rectenna system. Multiband antennas provide several comparative advantages against the goal of maximizing the amount of energy harvesting. In this work, we present a multiband microstrip patch antenna with three slits operating in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. A feasible solution of the antenna is obtained by the application of a recently introduced nature-inspired optimization technique, namely the Coyote Optimization Algorithm. The proposed antenna operates satisfactorily in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. Measured results of the proposed antenna exhibit an acceptable performance (multiband frequency operation, maximum gain of 3.94 dBi, broadside operation) and demonstrate features of operation, which make it a strong candidate for various RF energy harvesting applications. © 2020 IEEE.

关键词： Microstrip antennas

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DETERMINATION OF HARMONIC LEVELS IN NON-LINEAR NETWORKS BY COMPUTER-SIMULATION

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 1982年第1期24卷 50-52页

作者： GUTMANN, RJ CAMNITZ, LH Solid-State and Microstructures Engineering Division National Science Foundation Electrical Engineering Graduate Program Cornell University Ithaca NY USA

A method of accurately determining harmonic levels in nonlinear networks with periodic excitation is described. Timedomain omain transient analysis is combined with ac steady-state analysis using standard computer-simulation circuit-analysis programs to evaluate low levels of harmonic distortion with appreciably reduced numerical error. The technique is useful in a variety of applications such as mixers, rectifiers, power amplifiers, and switching power supplies.

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Identifying irregularity electricity usage of customer behaviors using logistic regression and linear discriminant analysis 3

Identifying irregularity electricity usage of customer behav...

引用

3rd International Conference on Science in Information Technology, ICSITech 2017

作者： Lawi, Armin Wungo, Supriyadi La Manjang, Salama Department of Computer Science Post-graduate Program of Electrical Engineering Dept. Electrical Engineering Universitas Hasanuddin Makassar Indonesia

ISBN: (纸本)9781509058662

This study aims to implement a machine learning technique in identifying the irregularities of customer behavior on the use of prepaid electricity pulses. The methods used are Linear Discriminant Analysis and Logistic Regression. The performance of the classification system will be evaluated using the 10-fold cross-validation technique. Validation results are measured using accuracy, precision and recall values. In this research shows that the use of machine learning technique has a good performance in classification of electrical consumption behavior. Experimental results with the different amount of data testing indicate that Logistic Regression method has high accuracy, precision, and recall value when compared with Linear Discriminant Analysis that is 100%. This is due to Logistic Regression method can predict irregularities accurately because the addition of the amount of data does not affect the performance of the method. © 2017 IEEE.

关键词： Regression analysis

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Evolving granular classification neural networks

Evolving granular classification neural networks

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2009 International Joint Conference on Neural Networks, IJCNN 2009

作者： Leite, Daniel F. Costa Jr., Pyramo Gomide, Fernando Faculty of Electrical and Computer Engineering University of Campinas Brazil Graduate Program in Electrical Engineering Catholic University of Minas Gerais Brazil

ISBN: (纸本)9781424435531

The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The suggested eGNN are neural models supported by granule-based learning algorithms whose aim is to tackle classification problems in continuously changing environments. eGNN are constructed from streams of data using fast incremental learning algorithms. eGNN models require a relatively small amount of memory to perform classification tasks. Basically, they try to find information occurring in the incoming data using the concept of granules and T-S neurons as basic processing elements. The main characteristics of eGNN models are continuous learning, self-organization, and adaptation to unknown environments. Association rules and parameters can be easily extracted from its structure at any step during the evolving process. The rule base gives a granular description of the behavior of the system in the input space together with the associated classes. To illustrate the effectiveness of the approach, the paper considers the Iris and Wine benchmark problems. © 2009 IEEE.

关键词： Learning algorithms

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Fast saliency detection using sparse random color samples and joint upsampling 29

Fast saliency detection using sparse random color samples an...

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29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016

作者： Lie, Maiko Min Ian Borba, Gustavo Benvenutti Neto, Hugo Vieira Gamba, Humberto Remigio Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná Curitiba Brazil Graduate Program in Biomedical Engineering Federal University of Technology - Paraná Curitiba Brazil

ISBN: (纸本)9781509035687

The human visual system employs a mechanism of visual attention, which selects only part of the incoming information for further processing. Through this mechanism, the brain avoids overloading its limited cognitive capacities. In computer vision, this task is usually accomplished through saliency detection, which outputs the regions of an image that are distinctive with respect to its surroundings. This ability is desirable in many technological applications, such as image compression, video quality assessment and content-based image retrieval. In this paper, a saliency detection method based on color distance with sparse random samples and joint upsampling is presented. This approach computes full-resolution saliency maps with short runtime by leveraging both edge-preserving smoothing and joint upsampling capabilities of the Fast Global Smoother. The proposed method is assessed through precision-recall curves, F-measure and average runtime on the MSRA1K dataset. Results show that the method is competitive with state-of-the-art algorithms in both saliency detection accuracy and runtime. © 2016 IEEE.

关键词： Image compression

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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