Predicting the Probability of Failure in Truss Structures Using Artificial Neural Networks by Mohammadreza Gholami, Seyed Ahmad Mobinipour, Mohammad Javad Haji Mazdarani, Seyed Rohollah Hoseini Vaez

“…The limitation of node displacement is considered as a limit state function. The data was split as 60% for training and 40% was used for testing and validation. …”
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Cardio-Ankle Vascular Index and Indices of Diabetic Polyneuropathy in Patients with Type 2 Diabetes by Akihiko Ando, Michiaki Miyamoto, Kazuhiko Kotani, Kenta Okada, Shoichiro Nagasaka, Shun Ishibashi

“…The cardio-ankle vascular index (CAVI) is used to test vascular function and is an arterial stiffness marker and potential predictor of cardiovascular events. …”
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Adaptive Fault-Tolerant Control of Mobile Robots with Fractional-Order Exponential Super-Twisting Sliding Mode by Hao Wu, Shuting Wang, Yuanlong Xie, Hu Li

“…To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. …”
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Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. by Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari

“…Yield is a complex trait and reliant on various related variables called yield components. In this study, the five most important yield component traits in soybean were measured using a panel of 250 genotypes grown in four environments. …”
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Performance Analysis of Reconfigurable Intelligent Surface-Assisted Underwater Wireless Optical Communication Systems by Qi Zhang, Dian-Wu Yue, Xian-Ying Xu

“…In the RIS-assisted UWOC systems with intensity modulation and direct detection (IM-DD), we apply the approximate distribution of the sum of Gamma-Gamma random variables to derive the probability density function (PDF) of the instantaneous received signal-to-noise ratio (SNR) for the first time. …”
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An Investigation of Knee Injury Prevalence and its Mechanism among Premier League Soccer Referees in Iran by Hamid Mahdavi Mohtasham, Shahnaz Shahrbanian

“…Using the Pearson correlation coefficient, this study examined the relationships between different variables including KOS subscales as well as the current level of self-reported knee joint function and proprioception. …”
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Analysis of Mobile JKN Application Service Quality With E-GovQual Method and IPA by Randy Rizki Ananda, Indriani Noor Hapsari

“…The results of the coefficient of determination (R-Square) show that overall, the variables of ease of use, reliability, trust, content and appearance, functionality and interaction and citizen support significantly affect service quality by 88.6%. …”
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A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization by Yongfa Chen, Yingjie Zhu, Jie Wang, Meng Li

“…Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. …”
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Donor Parameters and Platelet Yield during Plateletpheresis: A Cross-sectional Prospective Study by Ruchi Singh, Ganga S. Pilli, Ramadevi Vivek Wani

“…A relationship between pre-donation donor variables and platelet yield was studied using the Pearson correlation coefficient (r). …”
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Modeling Overall Survival in Patients With Pancreatic Cancer From a Pooled Analysis of Phase II Trials by Eva Rahman Kabir, Faruque Azam, Tanisha Tabassum Sayka Khan, Hasina Yasmin, Namara Mariam Chowdhury, Syeda Maliha Ahmed, Baejid Hossain Sagar, Nasrin Ahmed Tahrim

“…The relationship between predictors and OS was explored by a gamma generalized linear model (GLM) with a log‐link function and compared with linear models. Results The Spearman rank correlation coefficient between PFS/TTP and OS was 0.88 (95% confidence interval [CI] 0.85–0.89; p < 0.0001; n = 610) and between ORR and OS was 0.58 (0.52–0.64; p < 0.0001; n = 514). …”
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A Predictive Model for Estimating Reservoir Impounding Time Based on the Net Inflow and Storage Volume by Harimukti Rosita R., Pitojo T. Juwono, Lily M. Limantara, Emma Yuliani

“…The initial impounding of a reservoir represents the first test following the completion of dam construction, during which the reservoir begins to function as intended. Therefore, the expected outcome of this research is to formulate a model that estimates the duration required to fill the reservoir to its designated storage level. …”
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Solving Linear Programming Problems by Reducing to the Form with an Obvious Answer by Gleb D. Stepanov

“…From the coefficients of the mentioned functionals, a matrix is formed, which is called the LPP matrix. …”
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Analysis of Heat Transfer Performance in a Brayton Cycle Recuperator by Elvis Falcão de Araújo, Guilherme Borges Ribeiro, Lamartine Nogueira Frutuoso Guimarães

“…The characteristic curves of heat transfer rate, effectiveness, convection coefficient and Colburn factor are built for each of the studied geometries in function of the Reynolds number. …”
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An Optimal Hybrid Control Method for Energy-Saving of Chilled Water System in Central Air Conditioning by Yan Zhang, Xiaoli Chu, Yongqiang Liu

“…Here, an optimum control method is proposed with the principle of the minimum, by setting the minimum power consumption as the performance function in fixed time, which realizes variable control of pumps and accurate adjustment of temperature inside room. …”
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Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites by Feng Bin, Shahab Hosseini, Jie Chen, Pijush Samui, Hadi Fattahi, Danial Jahed Armaghani

“…We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. …”
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Cognitive performance classification of older patients using machine learning and electronic medical records by Monika Richter-Laskowska, Ewelina Sobotnicka, Adam Bednorz

“…Various ML techniques are evaluated to classify cognitive performance levels based on input features such as sociodemographic variables, lab results, comorbidities, Body Mass Index (BMI), and functional scales. …”
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Improved Sliding Mode Active Disturbance Rejection Control for Single-inductance Dual-output Buck Converter by HUANG Jinfeng, ZHOU Jie

“…Based on this insight, the CRESO is formulated to estimate both state variables and total internal/external disturbances. …”
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Clinical Outcome Prediction Model in Tetralogy of Fallot Without Pulmonary Valve Replacement Using Contraction Fraction From the SCOUT‐TOF Registry by Mark A. Fogel, Jennifer A. Faerber, Abdullah Mahmood, Keerthi Reddy, Xuemei Zhang, Elizabeth Goldmuntz, Matthew A. Harris, David Biko, Sara Partington, Stephen Paridon, Michael Mcbride, Victor Ferrari, Kevin K. Whitehead, Laura Mercer‐Rosa

“…An elastic net was used to identify significant variables to enter a best subsets logistic regression. …”
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Decentralized queue control with delay shifting in edge-IoT using reinforcement learning by Viacheslav Kovtun

“…Analytical expressions for key QoS indicators (delay, variability, loss, energy consumption) as functions of the shift parameter are derived, and a multi-factor reward function is constructed. …”
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A Quasi-Convex RKPM for 3D Steady-State Thermomechanical Coupling Problems by Lin Zhang, D. M. Li, Cen-Ying Liao, Li-Rui Tian

“…A meshfree, second-order, quasi-convex reproducing kernel scheme is employed to approximate field variables for solving the linear Poisson equation and the elastic thermal stress equation in sequence. …”
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