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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第1期2卷 40-51页

作者： Ferrando, Robert Pagnier, Laurent Mieth, Robert Liang, Zhirui Dvorkin, Yury Bienstock, Daniel Chertkov, Michael University of Arizona Program in Applied Mathematics and Department of Mathematics TucsonAZ85721 United States Department of Industrial and Systems Engineering at Rutgers University PiscatawayNJ08854 United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218-2625 United States Elec. and Comp. Eng. and Ralph o'Connor Sustainable Energy Institute Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218-2625 United States Columbia University IEOR New YorkNY10027 United States

This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels. © 2023 IEEE.

关键词： Costs

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Mathematical modeling of malaria transmission dynamics in humans with mobility and control states

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Infectious Disease Modelling 2023年第4期8卷 1015-1031页

作者： Gbenga Adegbite Sunday Edeki Itunuoluwa Isewon Jerry Emmanuel Titilope Dokunmu Solomon Rotimi Jelili Oyelade Ezekiel Adebiyi Covenant University Bioinformatics Research Covenant UniversityOtaNigeria Department of Computer and Information Sciences Covenant UniversityOtaNigeria Department of Mathematics Covenant UniversityOtaNigeria Department of Biochemistry Covenant UniversityOtaNigeria Covenant Applied Informatics and Communications-African Centre of Excellence Covenant UniversityOtaOgun StateNigeria Division of Applied Bioinformatics German Cancer Research Center(DKFZ)HeidelbergGermany

Malaria importation is one of the hypothetical drivers of malaria transmission dynamics across the *** studies on malaria importation focused on the effect of the use of conventional malaria control strategies as approved by the World Health Organization(WHO)on malaria transmission dynamics but did not capture the effect of the use of traditional malaria control strategies by vigilant *** order to handle the aforementioned situation,a novel system of Ordinary Differential Equations(ODEs)was developed comprising the human and the malaria vector *** of the system was carried out to assess its quantitative *** novel computational algorithm used to solve the developed system of ODEs was implemented and benchmarked with the existing Runge-Kutta numerical solution ***,simulations of different vigilant conditions useful to control malaria were carried *** novel system of malaria models was well-posed and epidemiologically meaningful based on its quantitative *** novel algorithm performed relatively better in terms of model simulation accuracy than *** the best model-fit condition of 98%vigilance to the use of conventional and traditional malaria control strategies,this study revealed that malaria importation has a persistent impact on malaria transmission *** lieu of this,this study opined that total vigilance to the use of the WHO-approved and traditional malaria management tools would be the most effective control strategy against malaria importation.

关键词： Malaria importation Traditional malaria control Ordinary differential equation Quantitative properties Novel algorithm Runge-Kutta

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Attractive Multistep Reproducing Kernel Approach for Solving Stiffness Differential Systems of Ordinary Differential Equations and Some Error Analysis

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computer Modeling in engineering & Sciences 2022年第1期130卷 299-313页

作者： Radwan Abu-Gdairi Shatha Hasan Shrideh Al-Omari Mohammad Al-Smadi Shaher Momani Department of Mathematics Faculty of ScienceZarqa UniversityZarqa13132Jordan Department of Applied Science Ajloun CollegeAl-Balqa Applied UniversityAjloun26816Jordan Department of Physics and Basic Sciences Faculty of Engineering TechnologyAl-Balqa Applied UniversityAmman11134Jordan Nonlinear Dynamics Research Center(NDRC) Ajman UniversityAjman20550United Arab Emirates Department of Mathematics Faculty of ScienceThe University of JordanAmman11942Jordan

In this paper,an efficient multi-step scheme is presented based on reproducing kernel Hilbert space(RKHS)theory for solving ordinary stiff differential *** solution methodology depends on reproducing kernel functions to obtain analytic solutions in a uniform formfor a rapidly convergent series in the posed Sobolev *** the Gram-Schmidt orthogonality process,complete orthogonal essential functions are obtained in a compact field to encompass Fourier series expansion with the help of kernel properties ***,by applying the standard RKHS method to each subinterval,approximate solutions that converge uniformly to the exact solutions are *** this purpose,several numerical examples are tested to show proposed algorithm’s superiority,simplicity,and *** gained results indicate that themulti-step RKHSmethod is suitable for solving linear and nonlinear stiffness systems over an extensive duration and giving highly accurate outcomes.

关键词： Multi-step approach reproducing kernel Hilbert space method stiffness system error analysis numerical solution

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MILP-Aided Cryptanalysis of the FUTURE Block Cipher 1

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15th International Conference on Security for Information Technology and Communications, SECITC 2022

作者： İlter, Murat Burhan Selçuk, Ali Aydın Institute of Applied Mathematics Middle East Technical University Ankara Turkey Aselsan Inc. Ankara Turkey Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey

ISBN: (数字)9783031326363

ISBN: (纸本)9783031326356

FUTURE is a recently proposed, lightweight block cipher. It has an AES-like, SP-based, 10-round encryption function, where, unlike most other lightweight constructions, the diffusion layer is based on an MDS matrix. Despite its relative complexity, it has a remarkable hardware performance due to careful design decisions. In this paper, we conducted a MILP-based analysis of the cipher, where we incorporated exact probabilities rather than just the number of active S-boxes into the model. Through the MILP analysis, we were able to find differential and linear distinguishers for up to 5 rounds of FUTURE, extending the known distinguishers of the cipher by one round. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Integer programming

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Many-Objective Multi-Verse Optimizer (MaOMVO): A Novel Algorithm for Solving Complex Many-Objective engineering Problems

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Journal of The Institution of Engineers (India): Series C 2024年第6期105卷 1467-1502页

作者： Kalita, Kanak Jangir, Pradeep Pandya, Sundaram B. Shanmugasundar, G. Chohan, Jasgurpreet Singh Abualigah, Laith Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Avadi600062 India Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Ostrava Ostrava Ostrava70800 Czech Republic Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Jadara University Research Center Jadara University Irbid Jordan Department of Electrical Engineering Shri K.J. Polytechnic Bharuch392001 India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai600044 India Department of Mechanical Engineering and University Centre for Research and Development Chandigarh University Mohali140413 India Computer Science Department Al-Bayt University Mafraq25113 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura140401 India Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

research has demonstrated the effectiveness of metaheuristic algorithms in addressing optimization challenges involving multi-objectives. These algorithms excel in generating a range of solutions that not only approach the Pareto front but are also evenly distributed. Multi-objective algorithms are effective for problems with two or three objectives. However, their efficiency diminishes in scenarios with many objectives, as the diversity selection and convergence pressure become less effective. Balancing convergence and diversity in multi-objective optimization pose a significant challenge. In response, this study introduces a novel many-objective multi-verse optimizer algorithm named MaOMVO for addressing many-objective problems. It integrates reference point and niche preserve to improve convergence and diversity and employs an innovative information feedback mechanism technique for population renewal. The superiority of the MaOMVO algorithm is evident in tests with MaF problems having 5, 8 and 15 objectives, as well as five real-world problems (RWMaOP1—RWMaOP5). It outperforms four leading algorithms Many-objective moth flame optimization, many-objective particle swarm optimization, non-dominated sorting genetic algorithm-III and reference vector guided evolutionary algorithm in terms of generational distance by 70%, inverted generational distance by 52%, spacing by 46.66%, spread by 55.55%, hypervolume by 52% and running time by 52% with concave, convex and mixed pareto fronts, confirming its robustness in diverse optimization scenarios. © The Institution of Engineers (India) 2024.

关键词： Genetic algorithms

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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CONVERGENCE OF DISCONTINUOUS GALERKIN METHODS FOR QUASICONVEX AND RELAXED VARIATIONAL PROBLEMS

arXiv

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arXiv 2025年

作者： Grekas, Georgios Koumatos, Konstantinos Makridakis, Charalambo Vikelis, Andreas Computer Electrical Mathematical Sciences and Engineering Division KAUST Saudi Arabia Department of Mathematics University of Sussex United Kingdom Institute of Applied & Computational Mathematics Foundation for Research & Technology-Hellas Greece Department of Mathematics and Applied Mathematics University of Crete Greece Faculty of Civil Engineering Czech Technical University of Prague Czech Republic

In this work, we establish that discontinuous Galerkin methods are capable of producing reliable approximations for a broad class of nonlinear variational problems. In particular, we demonstrate that these schemes provide essential flexibility by removing inter-element continuity while also guaranteeing convergent approximations in the quasiconvex case. Notably, quasiconvexity is the weakest form of convexity pertinent to elasticity. Furthermore, we show that in the non-convex case discrete minimisers converge to minimisers of the relaxed problem. In this case, the minimisation problem corresponds to the energy defined by the quasiconvex envelope of the original energy. Our approach covers all discontinuous Galerkin formulations known to converge for convex energies. This work addresses an open challenge in the vectorial calculus of variations: developing and rigorously justifying numerical schemes capable of reliably approximating nonlinear energy minimization problems with potentially singular solutions, which are frequently encountered in materials science. © 2025, CC BY.

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Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 717-732页

作者： Marwani, Maher Kaddoum, Georges École de Technologie Supérieure Electrical Engineering Department MontrealQCH3C 1K3 Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of Computer Science and Mathematics Beirut797751 Lebanon

The proliferation of wireless technologies and the escalating performance requirements of wireless applications have led to diverse and dynamic wireless environments, presenting formidable challenges to existing radio resource management (RRM) frameworks. researchers have proposed utilizing deep learning (DL) models to address these challenges to learn patterns from wireless data and leverage the extracted information to resolve multiple RRM tasks, such as channel allocation and power control. However, it is noteworthy that the majority of existing DL architectures are designed to operate on Euclidean data, thereby disregarding a substantial amount of information about the topological structure of wireless networks. As a result, the performance of DL models may be suboptimal when applied to wireless environments due to the failure to capture the network's non-Euclidean geometry. This study presents a novel approach to address the challenge of power control and spectrum allocation in an N-link interference environment with shared channels, utilizing a graph neural network (GNN) based framework. In this type of wireless environment, the available bandwidth can be divided into blocks, offering greater flexibility in allocating bandwidth to communication links, but also requiring effective management of interference. One potential solution to mitigate the impact of interference is to control the transmission power of each link while ensuring the network's data rate performance. Therefore, the power control and spectrum allocation problems are inherently coupled and should be solved jointly. The proposed GNN-based framework presents a promising avenue for tackling this complex challenge. Our experimental results demonstrate that our proposed approach yields significant improvements compared to other existing methods in terms of convergence, generalization, performance, and robustness, particularly in the context of an imperfect channel. © 2023 CCBY.

关键词： Wireless networks

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Deep Learning Advances in Brain Tumor Classification: Leveraging VGG16 and MobileNetV2 for Accurate MRI Diagnostics 4

Deep Learning Advances in Brain Tumor Classification: Levera...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Alex David, S. Begum, Almas Kalilulah, S. Ismail Ruth Naveena, N. Rajathi, K. Hemalatha, D. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Dr. M. G. R. Educational and Research Institute Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798331508845

These modern deep learning models, such as VGG16 and MobileNetV2 will be utilized in this study to classify Brain tumors from a unique specialized MRI dataset The dataset consists of brain tumor original and augmented images as a two-class (yes/no) classification problem domains, where yes indicates the presence of a tumor while learns no corresponds to its absence. Two models VGG16 and MobileNetV2 support deep convolutional layer operations, well known for their very deep architecture;the other one is optimized over efficiency with depth-wise separable convolutions and is used to evaluate their performance in accurately classifying MRI images. The models use similar data preprocessing and regularization techniques;however, their performance metrics are different. Accuracy, precision, recall, and F1-score are the evaluation metrics for each of the five models to understand how well these differentiate between tumor images versus non-tumor images. This study is designed to improve the ability of brain tumor detection, thereby assisting in more reliable diagnosis and clinical decision-making. © 2024 IEEE.

关键词： Convolutional neural networks

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Influence of Heat on Fractionalized Nanofluid with Magnetic Field

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International Journal of applied and Computational mathematics 2025年第1期11卷 1-15页

作者： Ramzan, Muhammad Amir, Muhammad Abbas, Shajar Nazar, Mudassar Jan, Rashid Al Agha, Afnan AL Garalleh, Hakim Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan Department of Civil Engineering College of Engineering Institute of Energy Infrastructure (IEI) Universiti Tenaga National (UNITEN) Putrajaya Campus Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia Mathematics Research Center Near East University TRNC Nicosia 99138 Turkey Department of Mathematical Science College of Engineering Universty of Business and Technology Jeddah 21361 Saudi Arabia

Numerous researchers have been drawn to the rheology of non-Newtonian fluids because of their diverse uses in the engineering and manufacturing fields, such as lubrication, plastic processing, and mining. Additionally, the characteristics of magnetohydrodynamics non-Newtonian fluids permit its widespread application in computer hard drives, loudspeakers, magnetic resonance imaging, the administration of magnetic medicines, and magnetic hyperthermia. The novelty of present study is the use of fourier’s and fick’s laws. The current work is focused on the analysis of heat and mass transfer in a magnetohydrodynamics Maxwell nanofluid flows across an inclined vertical plate because of these possible uses. The values of angle of inclination is chosen in the range 0 to π3. From Table 1, the values of nanoparticles lies in the range of 0.012–0.18. Using suitable non-dimensional variables, ordinary differential equations are created from the modeling equations, and the Laplace transform method is used to solve these equations. Semi-analytical solutions for temperature, concentration, Bio-Convection, and velocity are found after employing the Laplace transform approach to address the issue. The study highlights several key outcomes likes velocity of fluid is increased with increasing values of Gr but decreased with increasing values of Pr, M, and maxwell parameter. The effect of increasing values of parameters R, L1, and N1 are seen to suppress the concentration profile. The bioconvection concentration profile increases with Φ but reduced with increasing values of L2. Furthermore, the comparison between ordinary and fractionalized maxwell fluid has been drawn. The application of the unsteady flow of nanofluid over a plate with the CPC operator has the potential to impact a wide range of solar energy-related technologies and innovations, leading to increased efficiency, sustainability, and the advancement of renewable energy solutions. © The Author(s), under exclusive licence

关键词： Chemical reaction CPC derivative Free convection Magnetic field Maxwell fluid

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