Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. The coordinates of the control points of the NURBS surface at the bulbous bow are taken as the design variables. …”
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Flow-based cytometric analysis of cell cycle via simulated cell populations. by M Rowan Brown, Huw D Summers, Paul Rees, Paul J Smith, Sally C Chappell, Rachel J Errington

“…We present a new approach to the handling and interrogating of large flow cytometry data where cell status and function can be described, at the population level, by global descriptors such as distribution mean or co-efficient of variation experimental data. …”
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Parametric Survival Models of Hemodialysis Patients in Relation with Patient-Related Factors by Reem Yousif MEKKI, Mohamed Hassan MUDAWI, Manahil Saidahmed MUSTAFA, Altaiyb Omer Ahmed MOHMMED, Ahmed Bakheet Abd ALLA, Abdel Rahman AHMED

“…Based on the Cox-Snell Residuals and AIC, BIC, and Gompertz (PH) model is an efficient model than other when the values of (AIC=662.21), (BIC=703.83) and (R2=0.211) where maintained Study assessed that the variables dealing with univariate models were signifi cant but had a signifi cant effect on hemodialysis survival. …”
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INVESTIGATION OF UV/TiO2-ZnO-Co PHOTOCATALITIC DEGRADATION OF AZO DYE (REACTIVE RED 120) BY RESPONSE SURFACE METHODOLOGY by MOHSEN MANSOURI, MARJAN TANZIFI, HOSSEIN LOTFI, MOHSEN NADEMI

“…Results were in agreement with empirical values and the sensitivity analysis showed above parameters as the most efficient variables in decolorization efficiency. Analysis of variance (ANOVA) revealed highly determination coefficient value (R2 = 0.9996 and adjusted-R2 = 0.999) and satisfactory prediction second-order regression model. …”
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A theory and data-driven method for rapid bottom hole pressure calculation in UGS by Yang Li, Haiwei Guo, Xianfeng Gong, Naixin Lu, Kairui Zhang

“…To enhance the operational and maintenance efficiency of UGS, this paper innovatively proposes a new method for calculating bottom hole pressure. …”
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A Knowledge-Driven Smart System Based on Reinforcement Learning for Pork Supply-Demand Regulation by Haohao Song, Jiquan Wang

“…Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. …”
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A LINEAR SIMULATION MODEL FOR OPTIMIZING CROP STRUCTURE IN ORDER TO MAXIMIZE INCOME IN A VEGETAL AGRICULTURAL FARM by Sorin IONITESCU

“…The model included: the 8 unknown variables for the cultivated area with 8 crops: wheat, rye, barley, peas, rape, soybean, maize and sunflower, 14 restrictions regarding Diesel fuel, fertilizers, herbicides, total surface, expenditures, income, and area per each crop, and objective - function f(Max) Income. …”
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Comparative Study on the Treatment Effect of Stretching and High-frequency Diathermy in Subjects With Gastrocnemius Tightness by Beom-Seop Kim, Yixin Wang, Ye-Jin Kim, Min-Young Kim, Hye-Seon Jeon

“…Background: Ankle flexibility is important for maintaining proper biomechanical function. Static stretching is used to improve flexibility with minimal risk; however, its effects are often temporary. …”

Cultivares de trigo submetidas a déficit hídrico no início do florescimento, em casa de vegetação Wheat cultivars submitted to water deficit at the beginning of flowering in greenh... by Diego dos Santos, Vandeir F. Guimarães, Jeferson Klein, Samuel L. Fioreze, Eurides K. Macedo Júnior

“…Reduction was found in the gravimetric soil moisture, in the relative levels of water and in all biometric variables, in function of the water deficit. The grain production showed difference only among the water regimes, in which the cultivar CD 111 is more efficient in the maintenance of the productive potential in conditions of water deficit, through the quick recovery in the relative content of water in leaves.…”
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Center of Largest Area Defuzzifier Vnit VLSI Architecture by Asim Majeed Murshid, Ahmed Chalak Shakir, Sarmed Nihad Mohammed

“…The functional analysis has revealed that the proposed architecture is implementing COLA based defuzzifier efficiently and accurately. …”
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Inference of Impulse Responses via Bayesian Graphical Structural VAR Models by Daniel Felix Ahelegbey

“…Impulse response functions (IRFs) are crucial for analyzing the dynamic interactions of macroeconomic variables in vector autoregressive (VAR) models. …”
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Unsteady flow and heat transfer optimization of viscous fluid with bioconvection over a rotating stretchable disk and gyrotactic motile microorganisms by Mazhar Hussain, Samya Shahid, Noreen Sher Akbar, Mohammed Kbiri Alaoui

“…The model combined energy and concentration equation of microorganisms along with modified momentum equations, considering impact of internal heat generation and nanoparticles diffusion. Impact of unsteady variable (S), disk stretching variable (α), Prandtl number (Pr), Lewis number (Le) are analyzed graphically to understand the thermal and flow behavior. …”
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Research on Constraint Processing Method of High-dimensional Optimization Operation Problem of Cascade Reservoirs by Zhongzheng HE, Shuliang LI, Wei HUANG, Feng YAN, Jisi FU, bin XIONG

“…The intelligent optimization algorithm is a multidimensional linkage random search, which boasts a vast optimization space but suffers from low optimization efficiency. Therefore, this study introduces a constraint processing approach that integrates a penalty function with nested DPSA–POA and intelligent algorithms and applies it to the optimal flood control operation problem of cascade reservoirs in the middle reaches of the Ganjiang River, with decision variables extending to 2196 dimensions. …”
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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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Triangular Fuzzy Finite Element Solution for Drought Flow of Horizontal Unconfined Aquifers by Christos Tzimopoulos, Nikiforos Samarinas, Kyriakos Papadopoulos, Christos Evangelides

“…The initial water table is assumed to be curvilinear, following the form of an inverse incomplete beta function. To account for uncertainties in the system, the hydraulic parameters—hydraulic conductivity (K) and porosity (S)—are treated as fuzzy variables, considering sources of imprecision such as measurement errors and human-induced uncertainties. …”
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Multi-period optimisation of flexible natural gas production network infrastructure with an operational perspective: A mixed integer linear programming approach by Noor Yusuf, Roberto Baldacci, Ahmed AlNouss, Tareq Al-Ansari

“…Notably, the case considering fixed and operating cost variables together as a single cost variable in the objective function, referred to as annualised cost (case C), offered optimal cost quantification with relaxed technical constraints. …”
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The impact of digital economy on urban-rural income gap under government intervention by LE Yang, GAO Dongxi, ZHANG Yi, GUO Wei, CHEN Yi

“…Ordinary least squares (OLS) and instrumental variables-two stage least squares (IV-2SLS) estimation methods are used to empirically test the above mentioned-theoretical inferences from the empirical level based on the mediation effect model. …”
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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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Optimization of Adaptive I²H ∞ Control Method Based on Multiple Input Sensors by Yu Gu, Hanyang Li, Zeting Mei, Hao Wen, Yuanxiong Jin, Wenxuan Dong

“…The core contributions of this study include: 1) Designing a multi-sensor current reference estimator to dynamically generate the optimal electromagnetic torque through state variables such as wheel speed, acceleration, slope, and human factor database (heart rate, subjective score, fatigue index) to achieve real-time prediction of rider demand; 2) Proposing an adaptive current reference value estimation algorithm that integrates feedforward compensation and error feedback to ensure smooth switching of assistance modes and suppress sensor noise; 3) Developing an intention-induced H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> robust current tracking controller that significantly enhances the system&#x2019;s robustness to parameter fluctuations and external disturbances by optimizing the H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> norm of the closed-loop transfer function, while supporting personalized riding assistance.…”
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APPLICATION AND PERFORMANCE COMPARISON OF MULTI-OUTPUT MACHINE LEARNING FOR NUMERICAL-NUMERICAL AND NUMERICAL-CATEGORICAL OUTPUTS by Karin Joan, Robyn Irawan, Benny Yong

“…Multi-Output Machine Learning is an advancement of traditional machine learning, designed to predict multiple output variables simultaneously while considering the relationships between these output variables. …”
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