Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces by Xiaoyu Xu, Hongbo Chen, Chenliang Zhang, Yanhui Duan, Guangxue Wang

“…In aerodynamic shape optimization, geometric parameterized variables have a significant impact on the flow field, thereby influencing both the effectiveness and efficiency of the optimization process. …”
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A domain free of the zeros of the partial theta function by V. Kostov

“…For each $q$ fixed, $\theta$ is an entire function of order $0$ in the variable~$x$. When $q$ is real and $q\in (0,0.3092\ldots )$, $\theta (q,.)$ is a function of the Laguerre-P\'olya class $\mathcal{L-P}I$. …”
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Multi-UAV Cooperative Suspension Control Strategy Considering Variable Rope Length and Load Posture Coupling Effect by Yi Wang, Kang Li, Xuefu Li, Jingyu Wang, Pengbo Yu

“…On this basis, the traditional dynamic model is improved, with a focus on the impact of variable rope length, analyzing the dynamic coupling relationship between rope length and swing angle, and constructing related coupling error functions to improve the stability and control accuracy of the system. …”
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Predicting Wear Rate and Friction Coefficient of Li₂Si₂O₅ Dental Ceramic Using Optimized Artificial Neural Networks by Marko Pantić, Saša Jovanović, Aleksandar Djordjevic, Suzana Petrović Savić, Milan Radenković, Živče Šarkoćević

“…A genetic algorithm (GA) was used to optimize the ANN’s hyperparameters, improving its ability to model complex, nonlinear relationships between input variables, including normal load and velocity and output properties such as wear rate and friction coefficients. …”
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Experimental Study on Desorption Characteristics of Coalbed Methane under Variable Loading and Temperature in Deep and High Geothermal Mine by Haifeng Ma, Lingjie Wang, Housheng Jia, Jucai Chang, YingMing Li, Xiangyang Zhang, Zuxiang Hu, Zhiqiang Yin

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Exploring patterns of heart rate variability in major depressive disorder: A short-term study in Jaipur, Rajasthan by Sunidhi sharma, Sudhanshu Kacker, Neha Saboo

“…Methods: This observational study was conducted at RUHS College of Medical Sciences and Associated Hospitals, Jaipur, from July 2022 to January 2023, on a major depressive disorder population of either sex in the age group of 20-40 years. Cognitive functions were assessed using a questionnaire, and AD instruments recorded heart rate variability variables (time & frequency domain) using a digital physiograph (MLT004/ST). …”
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THE PROBLEM OF ENERGY EFFICIENCY OF SYSTEMS BASED ON ARTIFICIAL INTELLIGENCE OF THE INTERNET OF THINGS (AIOT) by С. В. Войтко

“…The emphasis is on allocating conventionally fixed and conventionally variable costs for AIoT. It was determined that integrating artificial intelligence functionality into the IoT infrastructure provides the opportunity to store big data for processing using artificial intelligence elements capable of conducting predictive analysis to make informed management decisions. …”
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Correlation and path analysis of interaction between snap beans yield and its components with crop management by N. Shaban

“…Results showed that fresh weight of pod/studied variant (+++0.99), fresh weight of one pod (+++0.77), fresh weight of pods/ plant (+++0.67), calcium content in bean pods (++0.57), pods number /plant (++0.51), pollen fertility (++0.44) had positive impact on yield. The partial function of the studied parameters on variability of bean yield is 98.9%. …”
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Genetic and molecular basis of abnormal BOLD signaling variability in patients with major depressive disorder after electroconvulsive therapy by Siyu Fan, Yulin Zhang, Rui Qian, Jie Hu, Hao Zheng, Wentao Dai, Yang Ji, Yue Wu, Xiaohui Xie, Si Xu, Gong-Jun Ji, Yanghua Tian, Kai Wang

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An Efficient Coupled Genetic Algorithm-NLP Method for Heat Exchanger Network Synthesis

“…Solving such problems leads to difficulties in the optimization of continuous and binary variables. This paper presents a new efficient and robust method in which structural parameters are optimized by genetic algorithm (G.A.) and continuous variables are handled due to a modified objective function for maximum energy recovery (MER). …”
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The Role of Profitability and Liquidity in Meeting the Feasibility Standards of Sharia Banking with Capital Adequacy as A Moderating Variable in Indonesia by Padli Pawaid Yahya, Joko Setyono

“…Control variables include Net Profit Margin (NPM), Operational Efficiency (BOPO), and Loan to Deposit Ratio (LDR). …”
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Orchard variable-rate spraying method integrating GNSS and wind-excited audio-conducted leaf area density by Hangxing Zhao, Hangxing Zhao, Hangxing Zhao, Shenghui Yang, Shenghui Yang, Shenghui Yang, Wenwei Li, Wenwei Li, Han Feng, Han Feng, Shijie Jiang, Shijie Jiang, Weihong Liu, Weihong Liu, Jingbin Li, Yongjun Zheng, Yongjun Zheng, Songchao Zhang

“…As a result, few fully functional variable-rate spraying systems have been developed based on this parameter.MethodsThis study presents a variable-rate spraying method that integrates global navigation satellite system (GNSS) positioning with wind-excited audio-conducted estimation of canopy leaf area density. …”
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Evaluation of the Efficiency of Metaheuristic Algorithms in the Optimal Design of Pile Wall Retaining Systems by Farshad Taiyari

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Optimization of non-smooth functions via differentiable surrogates. by Shikun Chen, Zebin Huang, Wenlong Zheng

“…These models are commonly used to predict outputs based on a combination of fixed parameters and adjustable variables. A key transition in optimization involves moving beyond simple prediction to determine optimal variable values. …”
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Scalable Processing of Glass with Multi‐Functional Cu Coating by Dan Wang, Xuelu Zhang, Yan Xing, Guanghua Liu, Hui Wu, Wei Pan

“…Abstract Functional glass has been intensively studied as a future material with improved comfort, safety, and practicality across a variety of settings, including industrial, and residential environments. …”
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Weighted average ensemble for Cholesky-based covariance matrix estimation by Xiaoning Kang, Zhenguo Gao, Xi Liang, Xinwei Deng

“…Our key idea is to obtain different weights for different candidate estimates by minimizing an appropriate risk function with respect to the Frobenius norm. Different from the existing ensemble estimation based on the MCD, the proposed method provides a sparse weighting scheme such that one can distinguish which variable orderings employed in the MCD are useful for the ensemble matrix estimate. …”
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Beyond Global Metrics: The U-Smile Method for Explainable, Interpretable, and Transparent Variable Selection in Risk Prediction Models by Katarzyna B. Kubiak, Agata Konieczna, Anna Tyranska-Fobke, Barbara Więckowska

“…Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. …”
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Investigating Attitudes towards Refugee Students based on Several Variables: Revised Version of Refugee Student Attitude Scale by Ahmet Göçen, Ragıp Terzi, Bilal Altun

“…At this stage, the contribution of hosting teachers andschool administrators to attitudes towards migrants and inclusive educationpolicies is a key to the efficient functioning of the process. In this study,attitudes towards refugee students of teachers from Sanliurfa, one of Turkey'smost populated-refugee cities, is investigated based on several variables. …”
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Model-Based Control Allocation During State Transitions of a Variable Recruitment Fluidic Artificial Muscle Bundle by Jeong Yong Kim, Matthew Bryant

“…FAMs can be bundled together in parallel to exhibit variable recruitment functionality, which is an activation strategy inspired by how motor units (MUs) in skeletal muscle are recruited. …”
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On a geometric approach to the estimation of interpolation projectors by Mikhail V. Nevskii, Alexey Y. Ukhalov

“…Here $\sigma S$ is the result of homothety of $S$ with respect to the center of gravity with coefficient $\sigma$. Let $d\geqslant n+1,$ $\varphi_1(x),\ldots,\varphi_d(x)$ be linearly independent monomials in $n$ variables, and $\varphi_1(x)\equiv 1,$ $\varphi_2(x)=x_1,\ \ldots, \varphi_{n+1}(x)=x_n.$ Put $\Pi:=$lin$(\varphi_1,\ldots,\varphi_d).$ The interpolation projector $P: C(\Omega)\to \Pi$ with a set of nodes $x^{(1)},\ldots, x^{(d)} \in \Omega$ is defined by equalities $Pf\left(x^{(j)}\right)=f\left(x^{(j)}\right).$ Denote by $\|P\|_{\Omega}$ the norm of $P$ as an operator from $C(\Omega)$ to $C(\Omega)$ . …”
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