Binary composite crossover genetic algorithm for locating critical slip surface by Wei Qin, Jiancheng Zhao

“…A reasonable combination of parameters for the binary composite crossover (BCC) operator in the RGA-BCC is determined through six benchmark function experiments. On this basis, the stability of five soil slopes from the literature was analysed using the RGA-BCC in combination with the Morgenstern and Price method, and the results obtained were compared with those in the published literature to demonstrate that the RGA-BCC is able to efficiently determine the CSSs of slopes and their minimum factors of safety. …”
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On Mixed Problems for Quasilinear Second-Order Systems by Rita Cavazzoni

“…The proof of the main theorem relies on two preliminary results: existence of the solution to mixed problems for linear second-order systems with smooth coefficients, and existence of the solution to initial-boundary value problems for linear second-order operators whose coefficients depend on the variables 𝑥 and 𝑡 through a function 𝑣∈𝐻𝑠(ℜ𝑑+1). …”
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Algorithm for Dynamic Reactive Power Optimization of Regional Power Grid Based on Interior Point Method and Neighborhood Search Decoupling Dynamic Programming Method by Jie ZHANG, Hengfeng WANG, Shengchun LIU, Huabiao WANG, Canghai WANG, Yao RAN, Xianmin WANG, Yongfei MA, Wei YAN

“…The two-stage method could not only ensure the quality of the optimal solution, but also avoid solving the state combination explosion problem with discrete variables, which greatly improves the computational efficiency. …”
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Multi-objective optimal scheduling of cascade reservoirs in complex basin systems: Case study of the Jinsha River-Yalong River confluence basin in China by Zhaocai Wang, Zhihua Zhu, Hualong Luan, Tunhua Wu

“…Benchmark function tests demonstrate that IMOSSA outperforms others in terms of optimization capability and stability. …”
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Shunt active power filter using model predictive control with stability guarantee by Jhon Pérez-Ramírez, Diego Montoya-Acevedo, Walter Gil-González, Oscar Danilo Montoya, Carlos Restrepo

“…The cost function exhibits convex characteristics, ensuring a unique control law. …”
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Numerical Investigation and Optimization of Transpiration Cooling Plate Structures with Combined Particle Diameter by Dan Wang, Yaxin Liu, Xiang Zhang, Mingliang Kong, Hanchao Liu

“…An optimization analysis of CPD−PS is conducted using response surface methodology (RSM), and the influence of design variables on the objective function (<i>η</i>_ave and <i>p</i>) is analyzed. …”
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Sustainable PV-hydrogen-storage microgrid energy management using a hierarchical economic model predictive control framework by Xinyu Guo, Faying Gu, Hongxu Liu, Yongcheng Yu, Runjie Li, Juan Wang

“…First, a precise nonlinear model of the PHS microgrid is established and the logic variables are introduced to capture the hydrogen devices’ short-term properties, i.e., the start-up/shut-down of electrolyzers and fuel cells. …”
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Safety Assessment of Loop Closing in Active Distribution Networks Based on Probabilistic Power Flow by Wenchao Cai, Yuan Gao, Xiping Zhang, Qin Si, Jiaoxin Jia, Bingzhen Li

“…By modeling DGs and loads as random variables, their cumulants are efficiently obtained through LHS. …”
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A non-dominated sorting based multi-objective neural network algorithm of ethylene glycol hydrogenation reactor in energy reduction by Fakhrony Sholahudin Rohman, Sharifah Rafidah Wan Alwi, Dinie Muhammad, Muhamad Nazri Murat, Ashraf Azmi

“…Artificial intelligence (AI) can be applied to various ethylene glycol (EG) production aspects to improve efficiency, quality, and overall process optimization. …”
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Solving 0-1 Knapsack and Bin Packing Problem Using Logical Social Group Optimization by Rhiddhi Prasad Das, Junali Jasmine Jena, Suresh Chandra Satapathy, Naeem M. S. Hannoon

“…This prominent research gap has been addressed in this paper by introducing Logical Social Group Optimization (LSGO), a logic-based binarized variant of Social Group Optimization (SGO) that leverages Boolean logic for improved efficiency. In LSGO, after the binarization by replacing the arithmetic operators with logical operators, one of the variables gets cancelled out. …”
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ABOUT SOME FEATURES OF TRANSMISSION MODE FOR ACTIVE POWER OF ELECTRICAL LINE by Paţiuc V., Berzan V.P., Anisimov V., Cabac S., Postotonca S.

“…It enteritis that for the active character load of the active power input and power output of the line, so functions , and functions that characterize developments efficiency , power factor at the input line and the load power factor when the variable z. …”
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Techno-economic assessment of low-carbon ammonia as fuel for the maritime sector by Wouter Schreuder, J. Chris Slootweg, Bob van der Zwaan

“…We incorporate three different cost levels for ammonia and a variable engine efficiency ranging from 35 % to 55 %. …”
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On a Solution of a Third Kind Mixed Integro-Differential Equation with Singular Kernel Using Orthogonal Polynomial Method by Ahmad Alalyani, M. A. Abdou, M. Basseem

“…While when using the separation method, we are able to obtain FIE with time coefficients, and these functions are described as an integral operator in time. …”
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Parameter Sensitivity Analysis and Algorithm Improvement of Optimization Scheduling Model for Cascade Pumping Stations by LIU Xiaolian, LI Zhenrong, WANG Xue-ni, ZHAI Yu, ZHANG Lei-ke, GUO Weiwei, TIAN Yu

“…Additionally, a Tent boundary mapping strategy was employed to regenerate the boundary value, resulting in improved the optimization efficiency of RUN. Six benchmark functions, including unimodal, multimodal and fixed dimension, were used to verify the performance and improvement strategies of TRUN. …”
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Optimization Design of CO2 Air Cooler by Liu Bin, Su Yun, Dong Xiaoyong, Wang Congfei, Cai Jinghui

“…Volume and the heat transfer coefficient of the heat exchanger are taken as an object function respectively, and fin-pitch, tube distance, tube rows, row numbers, diameter of heat pipe and the lengths of single pipe are selected as decision variables. …”
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Associations between milk infrared-predicted plasma biomarkers of stress resilience and fertility in dairy cattle: Insights for enhancing breeding programs and herd management by Alessio Cecchinato, Hugo Toledo-Alvarado, Lucio Flavio Macedo Mota, Vittoria Bisutti, Erminio Trevisi, Riccardo Negrini, Sara Pegolo, Stefano Schiavon, Luigi Gallo, Giovanni Bittante, Diana Giannuzzi

“…Pregnancy rate at first service, however, was analyzed using logistic regression and the same explanatory variables. The metabolites linked to liver function and damage, such as aspartate aminotransferase, total bilirubin, and alkaline phosphatase, had a relevant influence on iCF and DO in terms of the hazard ratio (HR). …”
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Neural Network-Based Design of a Buck Zero-Voltage-Switching Quasi-Resonant DC–DC Converter by Nikolay Hinov, Bogdan Gilev

“…The converter is a piecewise linear system mathematically described by Kirchhoff’s laws and represented through switching functions. In this way, a mathematical model is used to generate data on the behavior of the state variables obtained under various design parameters. …”
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Multiobjective Optimization of Stress-Release Boot of Solid Rocket Motor under Vertical Storage Based on RBF Model by Qiuwen Miao, Huihui Zhang, Zhibin Shen, Weiyong Zhou

“…Stress-release boot can effectively improve the structural integrity of SRM (solid rocket motor), but it will also influence the loading fraction and interior ballistic performance, so the purpose of this paper is to propose a multiobjective optimization method for stress-release boot. The design variables are the front and rear depth of the stress-release boot, and four optimization variables were determined according to the analysis of SRM performance. …”
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Comparative Analysis of ANFIS and State-ANFIS for Forecasting Cooking Oil Prices Based on Processed Palm Oil Yield (Crude Palm Oil) by Brodjol Sutijo Suprih Ulama, Fausania Hibatullah, Mochammad Reza Habibi, null Makkulau

“…Our analysis underscores S-ANFIS’s superiority over ANFIS, particularly with Gaussian membership functions, as it reduces RMSE and MAPE values by half while requiring fewer nodes, thereby improving computational efficiency. …”
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Machine learning-based prediction of pervaporation permeation using physicochemical properties of permeant-membrane and process conditions by Muhammad Mujiburohman, Marwen Elkamel, Farzad Hourfar, Ali Elkamel

“…Two types of inputs were evaluated: regular variables (Type I) and dimensionless groups derived from these regular variables (Type II). …”
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