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2021 IEEE International Conference on Power, Electrical, Electronic and Industrial Applications, PEEIACON 2021

作者： Ahmed, Abdussalam Ali Ahmed, Alforjani Ali Alarga, Ahmed Salem Daw Alsharif, Abdulgader Bani Waleed University Mechanical Engineering Department Bani Waleed Libya College of Applied Sciences and Technology Al-Awata Libya Elmergib University Department of Computer Science Khums Libya College of Civil Aviation Technology and Meteorology Espiaa Libya

ISBN: (纸本)9781665478663

Yaw stability control is a critical component of lateral and longitudinal dynamics, allowing for the improvement and evaluation of both vehicle handling and stability performance. A fourteen-degree-of-freedom model of a car with front steering is constructed in order to examine and analyze ways to improve the vehicle's yaw stability. The vehicle control system's feedback signal, which is the vehicle's yaw rate, is viewed as an input signal;we present a yaw moment control algorithm based on the PID controller. Two steering conditions are simulated in the simulations: a lane change maneuver and step steering. The simulation results indicate that the controlled vehicle's yaw rate follows a route close to the desired yaw rate target value;this indicates the success of both the controller utilized in this study and the mathematical equations developed. © 2021 IEEE.

关键词： Three term control systems

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

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Correction to: Scalable fabrication of wide-bandgap perovskites using green solvents for tandem solar cells (Nature Energy, (2024), 10.1038/s41560-024-01672-x)

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Nature Energy 2025年

作者： Duan, Chenyang Gao, Han Xiao, Ke Yeddu, Vishal Wang, Bo Lin, Renxing Sun, Hongfei Wu, Pu Ahmed, Yameen Bui, Anh Dinh Zheng, Xuntian Wang, Yurui Wen, Jin Wang, Yinke Ou, Wennan Liu, Chenshuaiyu Zhang, Yuhong Nguyen, Hieu Luo, Haowen Li, Ludong Liu, Ye Luo, Xin Saidaminov, Makhsud I. Tan, Hairen National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences Frontiers Science Center for Critical Earth Material Cycling Nanjing University Nanjing China Co. Ltd Suzhou China Department of Chemistry University of Victoria VictoriaBC Canada Research School of Electrical Energy and Materials Engineering College of Engineering and Computer Science The Australian National University CanberraNSW Australia

Correction to: Nature Energyhttps://***/10.1038/s41560-024-01672-x, published online 15 November 2024. In the version of the article initially published, we mistakenly listed the environmental score for DMSO as 4 instead of the correct score of 5, based on the evaluation criteria outlined by CHEM21. This error, along with the transposition of the "NMP" and "DMPU" labels on the x axis, has been corrected in Fig. 1a, as seen below in Fig. 1. (Figure presented.) Original and corrected Fig. 1a. To ensure clarity and accuracy, we have revised our understanding of the combination rules for determining risk levels. Previously, our explanation might have been unclear or misleading to readers. To address this, we have included an excerpt from the CHEM21 solvent guide below, which provides a clear list of solvent risk levels and their ranking rules (Tables 1 and 2). (Table presented.) (Table presented.) CHEM21 solvent guide of "classical" solvents1 Ranking by default1 These two tables clearly illustrate the risk levels and comprehensive evaluation criteria for the solvents discussed in our article. To enhance reader understanding, we have also integrated this information into the corrected Fig. 1a, providing a visual representation of the risk levels associated with different solvents. Additionally, we have made two necessary revisions to the second paragraph of the article. The sentence "To address this challenge, the evaluation criteria outlined by CHEM21 offer a guide, categorizing solvents into four groups based on their impact on safety, health and environment10, namely recommended (no chemical incompatibility under process conditions), problematic (suitable for lab or kilo-scale lab use), hazardous (strong constraints on scale-up) and highly hazardous (solvents to be avoided even in the lab)" has been corrected to "To address this challenge, the evaluation criteria outlined by CHEM21 offer a guide, comprehensively categorizing solvents into three groups based on the

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Life Test Plan Optimization for Electrical Insulation During Temperature Load: A Semi-Optimal Approach

Life Test Plan Optimization for Electrical Insulation During...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Rahul Vishnoi Sanjeev Kumar Verma Vishal Sharma K. Yuvaraj S. Gopinath Deepika Dongre Department of ECE TMU Department of Applied Sciences Invertis University Bareilly Uttar Pradesh Film & Television Studies School of Media IIMT University Meerut Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore Vishwakarma Institute of Information Technology Pune

Considering that the Arrhenius equation between stress condition and lifetime is valid and that the proportional lifespan at an excessive pressure reflects some reliable statistical properties. It is essential to demonstrate the highest diagnostic strategy from the perspective of effectiveness under such living scenarios. Knowing the semi-optimal test plan, where effectiveness is similar to that of the maximum one and test conditions are straightforward, is additionally helpful. The goal of minimization is to determine the ideal amount of test subjects with each test stressors, and we assume that there are three sample stress levels. The root-average-squared error for the duration in use circumstance serves as the benchmark for optimization problems. We were using the hyperparameters in an actual trial situation to adjust for realism. If predictability of the Characteristic curve is necessary, we have ended up finding that there is just a subtle distinction between both the optimal results obtained and the mainstream test result when making comparisons it to the data achieved that use the mainstream tester, in which test samples are equitably represented to each experiment anxiety levels. The traditional testing phase is one of the partially ideal test plans, in our opinion. The agreement between the theory and computer findings has been examined.

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Analyzing radiative heat on third-grade fluids over an expanding inclined sheet subject to higher-order chemical reaction: An overlapping grid spectral quasilinearization approach

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Partial Differential Equations in applied mathematics 2025年 13卷

作者： Agbaje, Titilayo M Oluwaseun, Folarin Mishra, S.R. Baithalu, Rupa Panda, Subhajit School of Computer Science and Applied Mathematics University of the Witwatersrand Private Bag 3 Braamfontein Johannesburg 2050 South Africa School of Mathematics Statistics and Computer Science University of the KwaZulu-Natal Private Bag X01 Scottsville Pietermaritzburg 3209 South Africa Department of Mathematics Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar 751030 India Centre for Data Science Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar 751030 India

Higher-order chemical reactions significantly enhance the concentration of the fluid indicating its vital role in processes involving radiative species. Therefore, the observation shows its important practical applications in industrial processes such as polymer extrusion, in which heat transfer properties are useful in the production of quality products. The proposed study emphases on the free convection of a third-grade fluid via an inclined expanding sheet for the influence of radiative heat and higher-order chemical reactions. However, the analysis incorporates the influence of viscous dissipation that is significant in determining the thermal behavior of the fluid. Further, the proposed flow phenomena governed by the set of partial differential equations are transformed into ordinary ones by the operating of suitable similarity rules. The transformed equations equipped with characterizing parameters are handled by a numerical technique known as the over lapping grid spectral quasilinearization method. The quantitative behavior of the factors intricate in it is deployed graphically and described briefly. However, the important outcomes of the study are;Richardson number is useful in enhancing the fluid momentum whereas the impact is reversed for the temperature and concentration distribution. Further, increasing heavier species i.e., Schmidt number attenuates the concentration profile. © 2025 The Author(s)

关键词： Free convection Higher-order chemical reaction Over lapping grid spectral quasilinearization method Radiative heat Third-Grade fluid Viscous dissipation

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Bioelectronic protein nanowire sensors for ammonia detection

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Nano Research 2020年第5期13卷 1479-1484页

作者： Alexander F.Smith Xiaomeng Liu Trevor L.Woodard Tianda Fu Todd Emrick Juan M.Jiménez Derek R.Lovley Jun Yao Department of Biomedical Engineering University of MassachusettsAmherstMA 01003USA Department of Electrical and Computer Engineering University of MassachusettsAmherstMA 01003USA Department of Microbiology University of MassachusettsAmherstMA 01003USA Department of Polymer Science and Engineering University of MassachusettsAmherstMA 01003USA Department of Mechanical and Industrial Engineering University of MassachusettsAmherstMA 01003USA Institute for Applied Life Sciences(IALS) University of MassachusettsAmherstMA 01003USA

Electronic sensors based on biomaterials can lead to novel green technologies that are low cost,renewable,and *** we demonstrate bioelectronic ammonia sensors made from protein nanowires harvested from the microorganism Geobacter *** nanowire sensor responds to a broad range of ammonia concentrations(10 to 10^6 ppb),which covers the range relevant for industrial,environmental,and biomedical *** sensor also demonstrates high selectivity to ammonia compared to moisture and other common gases found in human *** results provide a proof-of-concept demonstration for developing protein nanowire based gas sensors for applications in industry,agriculture,environmental monitoring,and healthcare.

关键词： nanowire protein nanowire biomaterial bioelectronics biosensor ammonia sensor

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A Novel U-Net Based CNN Algorithm for High-Accuracy Weather Forecasting

A Novel U-Net Based CNN Algorithm for High-Accuracy Weather ...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Thirumurugan Shanmugam Chirag Chandrashekar Arun Kumar Sivaraman Janakiraman Nithiyanantham Priya Ravindran S T Sarvesh Narayanan Department of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman School of CSE Vellore Institute of Technology Chennai India Digital Engineering Solution Centre Photon Interactive Inc. Ontario Canada Department of ECE K.L.N. College of Engineering Tamilnadu India School of Computer Science Dalhousie University Halifax Canada

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Accurate weather forecasting is essential for sectors like agriculture, aviation, and disaster management. However, deep learning algorithms face challenges in prediction accuracy due to issues like vanishing gradients, overfitting, and high computational demands. This research proposes a novel U-Net based architecture utilizing a Convolutional Neural Network (CNN) bottleneck layer to improve weather forecasting. Key features include a skip-connection mechanism, modified weight update rules, Gaussian-mutation operations, and the Adam optimizer for enhanced feature extraction and faster, more accurate predictions. The model was tested using precipitation data from Doppler Weather Radar (DWR) Chennai Radar and weather parameters from European Centre for Medium-Range Weather Forecasts (ECMWF). A dedicated GeoServer facilitates realtime data processing. Experimental results show the proposed algorithm achieves 97.5% accuracy, outperforming CNN and long short-term memory (LSTM) models by 5.84% and 2.41%, respectively.

关键词： Accuracy Computational modeling Atmospheric modeling Weather forecasting Predictive models Feature extraction Prediction algorithms Convolutional neural networks Long short term memory Meteorology

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Accurate Loss Prediction of Realistic Hollow-core Anti-resonant Fibers Using Machine Learning

arXiv

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arXiv 2024年

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Amezcua-Correa, Rodrigo Habib, Md Selim The Department of Data Science and Business Analytics Florida Polytechnic University 4700 Research Way LakelandFL33805 United States The Faculty of Electrical Engineering and Information Technology University of Applied Sciences KarlsruheBW-76133 Germany The CREOL University of Central Florida OrlandoFL32816 United States The Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States

Hollow-core anti-resonant fibers (HC-ARFs) have proven to be an indispensable platform for various emerging applications due to their unique and extraordinary optical properties. However, accurately estimating the propagation loss of nested HC-ARFs remains a challenging task due to their complex structure and the lack of precise analytical and theoretical models. To address this challenge, we propose a supervised machine-learning framework that presents an effective solution to accurately predict the propagation loss of a 5-tube nested HC-ARF. Multiple supervised learning models, including random forest, logistic regression, quadratic discriminant analysis, tree-based methods, extreme gradient boosting, and K-nearest neighbors are implemented and compared using a simulated dataset. Among these methods, the random forest algorithm is identified as the most effective, delivering accurate predictions. Notably, this study considers the impact of random structural perturbations on fiber geometry, encompassing random variations in tube wall thicknesses and tube gap separations. In particular, these perturbations involve randomly varying outer and nested tube wall thicknesses, tube angle offsets, and randomly distributed non-circular, anisotropic shapes within the cladding structure. It is worth noting that these specific perturbations have not been previously investigated. Each tube exhibits its unique set of random values, leading to longer simulation times for combinations of these values compared to regular random variables in HC-ARFs with similar tube characteristics. The comprehensive consideration of these factors allows for precise predictions, significantly contributing to the advancement of HC-ARFs for many emerging applications. © 2024, CC BY.

关键词： Fibers

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Semi-Implicit Lagrangian Voronoi Approximation for Compressible Viscous Fluid Flows

arXiv

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arXiv 2024年

作者： Kincl, Ondřej Peshkov, Ilya Boscheri, Walter Laboratory of Applied Mathematics DICAM University of Trento Trento38123 Italy Laboratoire de Mathématiques UMR 5127 CNRS Université Savoie Mont Blanc Le Bourget du Lac73376 France Department of Mathematics and Computer Science University of Ferrara Ferrara44121 Italy

This paper contributes to the recent investigations of Lagrangian methods based on Voronoi meshes. The aim is to design a new conservative numerical scheme that can simulate complex flows and multi-phase problems with more accuracy than SPH (Smoothed Particle Hydrodynamics) methods but, unlike diffuse interface models on fixed grid topology, does not suffer from the deteriorating quality of the computational grid. The numerical solution is stored at particles, which move with the fluid velocity and also play the role of the generators of the computational mesh, that is efficiently re-constructed at each time step. The main novelty stems from combining a Lagrangian Voronoi scheme with a semi-implicit integrator for compressible flows. This allows to model low-Mach number flows without the extremely stringent stability constraint on the time step and with the correct scaling of numerical viscosity. The implicit linear system for the unknown pressure is obtained by splitting the reversible from the irreversible (viscous) part of the dynamics, and then using entropy conservation of the reversible sub-system to derive an auxiliary elliptic equation. The method, called SILVA (Semi-Implicit Lagrangian Voronoi Approximation), is validated in a variety of test cases that feature diverse Mach numbers, shocks and multi-phase flows. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Optimization Design for Federated Learning in Heterogeneous 6G Networks

arXiv

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arXiv 2023年

作者： Luo, Bing Ouyang, Xiaomin Sun, Peng Han, Pengchao Ding, Ningning Huang, Jianwei The Data Science Research Center the Division of Natural and Applied Sciences Duke Kunshan University Jiangsu Kunshan China The department of Information Engineering The Chinese University of Hong Kong Hong Kong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for innovative applications and intelligent services. However, traditional machine learning (ML) approaches require centralizing the training data in the data center or cloud, raising serious user-privacy concerns. Federated learning, as an emerging distributed AI paradigm with privacy-preserving nature, is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. However, there are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenging heterogeneity issues from three aspects: incentive mechanism design, network resource management, and personalized model optimization. We also present some open problems and promising directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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