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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Multi-Objective Optimization Methods for University Campus Planning and Design—A Case Study of Dalian University of Technology by Lin Qi, Chaoran Chen, Jun Dong

“…A five-dimensional objective system—comprising energy efficiency, spatial quality, economic cost, ecological benefits, and cultural expression—was established, alongside the identification and standardization of 29 key variables to construct mapping relationships among objective functions. …”
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Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…Abstract Graph-based methods have made significant progress in addressing the dependent correlations among ECG time series variables. However, most existing graph structures primarily focus on local similarity while overlooking global semantic correlation. …”
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Structural strength analysis and optimization of converter lug based on Kriging model by HE Guanqiang, LIU Yongjiang, LI Hua, CHEN Liming

“…In this paper, machine learning methods were considered to approximate the functional relationships between design variables and responses. …”
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An Optimization Model for Shell Plate Seam Landing Using Minimum Manufacturing Cost and a Solution by Genetic Algorithm by Ho Rim Pae, Min Hyok Jon, Chol Jun Pak

“…In this paper, a new optimization model and its solution method for the landing of seams and butts (for convenience, seam and butt are simply called seam) on the ship hull surface are proposed in order to improve the shipbuilding efficiency. The minimum manufacturing cost of ship hull shell plates (SHSPs) is the objective function, the rules and requirements for the seam position and the size of a single shell plate are the constraints, and the position and shape of the seam are the design variables. …”
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Wafer‐Scale Synthesis of Topological Insulator Sb2Te3 Thin Films by Ali Shafiei, Ahmad Fathi Hafshejani, Rehab M. G. Ahmed, Alessio Lamperti, Emanuele Longo, Lorenzo Locatelli, Christian Martella, Alessandro Molle, Graziella Tallarida, Carlo Zucchetti, Claudia Wiemer, Massimo Longo, Roberto Mantovan

“…Furthermore, comprehensive information on the variability of the functional properties of Sb2Te3 thin films with their morphological, chemical, and structural properties, as probed by scanning electron microscopy, X‐ray diffraction/reflectivity, atomic force microscopy, Raman spectroscopy, time‐of‐flight secondary ion mass spectrometry, and energy‐dispersive X‐ray analyses is reported. …”
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A simple model of the rheological curve of HPAM solutions at different temperatures by Eduar Pérez, Dario Alviso, Mauricio Carmona, Eduardo Manrique, Guillermo Artana

“…The solvent viscosity and relaxation time-employed as references to define these parameters-are functions of temperature. On the master surface, while the power coefficient of Carreau-Yasuda (n) exhibits only a slight dependency on temperature, the relative viscosity depends monotonically on this variable. …”
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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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IRAQ'S AGRICULTURAL ECONOMY AND THE REALITY OF THE VARIABLRS AND THE RESULTS OBJECTIVE CURRICULUM AND QUANTITATIVE ANALYSIS FOR THE PERIOD (1990-2011) by Salim Al-Nuaimy, Mahasen. M Al-Jeboury

“…The research aims to formulate sober policies and practical actions contribute to adjust the course of the agricultural sector and to achieve its goals, which will make it easier to raise production and productivity, Variables of agricultural sector were agricultural work, capital accumulation agricultural, technical mechanism, technical chemical, agricultural loans, agricultural export, import of agricultural, national income, domestic consumption, the cultivatedareas, that have been expressed as (policies of productivity and investment, marketing and price) where its impact was deare on the growth of the agricultural sector and overall productivity during (1990-2010) and the development process and increase production and productivity in the agricultural sector associated with economic, social and institutional multiple and overlapping factors that effects the forms and varying proportions in its impact on agricultural output, which had been measured under function producing an aggregate of variables agricultural policy and its contribution to productivity growth as well as measuring productivity elements total (TFP) by using index (Malmaquist) depending on the (DEAP). …”
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Quality control of DEMs using check surfaces by Francisco Javier Ariza-López, José Rodríguez-Avi

“…Using the Kolmogorov–Smirnov statistic, the observed distribution functions of the results for different sample sizes (20, 50, 100) and different patch sizes (3 × 3, 5 × 5, 9 × 9, 11 × 11, 15 × 15, 19 × 19) and for a sample of points are compared against the true population distribution function. …”
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Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease by Sudharshan Putha, Swaroop Reddy Gayam, Bhavani Prasad Kasaraneni, Krishna Kanth Kondapaka, Sateesh Kumar Nallamala, Praveen Thuniki

“…The least absolute shrinkage and selection operator (LASSO) algorithm was applied to identify highly correlated clinical variables influencing cognitive function. Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. …”
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A United Probabilistic Approach to Minimising the Sum of Absolute Values by Changyong Feng, Honghong Liu, Ethan Poon, Ge Feng

“…Methodology: The sum of absolute values can be standardized so that the sum of the coefficients equals 1. In this case, the sum of absolute values takes the form 𝐸𝐸|𝑋𝑋−𝑎𝑎|, where 𝑋𝑋 is a random variable. …”
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Unraveling novel variants in the NF1 gene and investigating potential therapeutic strategies by Jianmei Huang, Ke Yang, Yaoping Wang, Xinrui Ma, Wenke Yang, Xiaodong Huo, Jie Bai, Hongjie Zhu, Jinming Wang, Yibing Lv, Shixiu Liao

“…A retrospective analysis of 49 clinical trials over the past 20 years revealed that NF1 therapies predominantly target neurofibromin’s GTPase function. Gene therapies aiming to restore neurofibromin by transducing the truncated NF1-GRD gene have been developed but faced pre-clinical challenges, including cloning capacity, transduction efficiency, and immunogenicity caused by gene delivery. …”
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Thermoelastic damping in piezothermoelastic nanobeam resonators under the strain gradient theory with micro-inertia and non-fourier heat conduction by Olha Hrytsyna, Maryan Hrytsyna

“…To account for small-scale effects, the Helmholtz free energy density is defined as a function of the strain tensor, strain gradient tensor, electric field vector, and temperature field, considering these parameters as independent state variables. …”
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Design and evaluation of BOOGIE: a collector for the analysis of cloud composition and processes by M. Vaitilingom, M. Vaitilingom, C. Bernard, M. Ribeiro, C. Verhaege, C. Verhaege, C. Gourbeyre, C. Berthod, A. Bianco, L. Deguillaume, L. Deguillaume

“…<p>In situ cloud studies are fundamental to study the variability in cloud chemical and biological composition as a function of environmental conditions and assess their potential for transforming chemical compounds. …”
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The Macroeconomic Framework of Support Analysis for Sustainable Businesses Development by Constantin Mitrut, Cristina Bălăceanu, Mihaela Gruiescu, Daniela Serban

“…The present research is based on a multifactorial liniar regression model which outlines the direct correlation between the dependent variable welfare and the independent variable of concentration measured by the Gini coefficient of wealth concentration, on the one hand, and by the GDP level, on the other hand, at the level of year 2012. …”
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A Novel Structural Modification Method for Vibration Reduction: Stiffness Sensitivity Analysis with Principal Strain Application by Jiajun Hong, Takuya Yoshimura

“…The sensitivity is evaluated as a differential coefficient of the target response with respect to the design variable, e.g., stiffness or mass. …”
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Non-Similar Analysis of Boundary Layer Flow and Heat Transfer in Non-Newtonian Hybrid Nanofluid over a Cylinder with Viscous Dissipation Effects by Ahmed Zeeshan, Majeed Ahmad Yousif, Muhammad Imran Khan, Muhammad Amer Latif, Syed Shahzad Ali, Pshtiwan Othman Mohammed

“…Non-similar system of PDEs are obtained with efficient conversion variables. The dimensionless PDEs are truncated using a local non-similarity approach up to third level and numerical solution is evaluated using MATLAB built-in-function bvp4c. …”
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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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