Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…The MM–BO–XGBoost model demonstrates excellent training efficiency and prediction performance.Conclusion The analysis of the mechanism of the VD furnace refining process and Pearson correlation analysis are conducted, and the input variables for the prediction model of VD furnace endpoint liquid steel temperature are identified. …”
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Estimation of micro-extraction process hippuric acid for toluene exposure monitoring in urine samples by Behnaz Sarani, Mashaallah Rahmani, Ahmad Reza Abbasian

“…Abstract This study employed a quick, efficient, sensitive, and easy-to-use micro-extraction process to monitor biomarker hippuric acid (BIHA) in urine samples as a bioindicator of exposure to toluene with an HPLC-DAD instrument. …”
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Parameter Calculation and Rotor Structure Optimization Design of Solid Rotor Induction Motors by Hao Xu, Jinghong Zhao, Sinian Yan

“…On this basis, with efficiency, power factor, starting torque and starting current as the optimization objectives, and the number of slots, slot depth, slot width, squirrel cage material and end ring thickness as the optimization variables, the optimization schemes of slotted rotor and squirrel cage rotor are obtained by using the three-dimensional finite element method. …”
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Sustitución parcial de alimento balanceado comercial por saccharina rústica en pollo de engorde by Dixon Fabián Flórez-Delgado, Jesús Esteban Fonseca-García, Dubel Reinaldo Cely-Leal

“…During the development of the study, productive indicators such as feed intake, weight gain (WG), feed efficiency (FE), feed conversion ratio (FC), carcass yield (CY), productivity index (PI), European production efficiency factor (EPF), and feed costs were measured. …”
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Domain knowledge-driven decomposition-based large-scale optimization for ship cabin structures by Puyu JIANG, Jun LIU, Qiangjun LUO, Yuansheng CHENG

“…Each girder size variable is separately grouped, and the corresponding subproblem's objective function is the weight of the cabin structure. …”
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Optimization of Material Flow and Product Allocation in Inter-Unit Operations: A Case Study of a Refrigerator Manufacturing Facility by Selman Karagoz, Yasin Karagoz

“…<i>Background</i>: Logistics operations are integral to manufacturing systems, particularly in the transportation processes that occur not only between facilities and stakeholders but also between warehouses and workstations within a facility. The design of functional areas and allocating goods to appropriate zones within the warehouse management system (WMS) are critical activities that substantially influence the efficiency of manufacturing logistics operations. …”
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Thermo-economic optimization of a hybrid solar-wind energy system for the production of clean hydrogen and electricity by Ali Bedakhanian, Akbar Maleki, Siavash Haghighat

“…The objective functions included total cost rate and total exergy efficiency. …”
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Mitigating Load Imbalance and Enhancing Distribution Transformer Performance via Fuzzy Inference System by Althoff Hibban, Azriyenni Azhari Zakri, Dirman Hanafi, Boy Ihsan, Umair Ali

“…To address this issue, this study proposes a fuzzy logic-based approach using a Mamdani-type Fuzzy Inference System (FIS). The input variables, current magnitudes in phases R, S, and T, are represented by trapezoidal membership functions classified into several categories. …”
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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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Pareto Optimal Solutions for Stochastic Dynamic Programming Problems via Monte Carlo Simulation by R. T. N. Cardoso, R. H. C. Takahashi, F. R. B. Cruz

“…Instead of using the expected values of the objective function, the randomness nature of the decision variables is kept along the process, while Pareto fronts weighted by all quantiles of the objective function are determined. …”
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Optimization of power output in plateau photovoltaic power stations using a hybrid Kepler and Gaussian quantum particle swarm algorithm by Shuang Gan, Heng Hu, Shaoshuai Li, Qian Peng, Taidong Yan, Huasheng Gong, Yuancheng Zhang

“…The optimization process is modeled with key decision variables, including panel configurations, snow removal strategies, and system efficiency adjustments. …”
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Response Surface Optimization of CuS Photocatalytic Process Using UV‐vis Irradiation for Wastewater Treatment by Caressa Munien, Emmanuel Kweinor Tetteh, Sudesh Rathilal

“…Thus, this study aimed to optimize the operating parameters of the photocatalytic degradation process using copper sulphide (CuS) and UV‐visible irradiation via response surface methodology (RSM) to determine the relationship that exists between the most significant factors and the water quality parameters. The efficiency of the CuS photocatalyst was evaluated as a function of three input variables viz. …”
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Production of Cyanobacterial Microscale Adsorbent by Hydrothermal Method for Pre-concentration of Cadmium from Water by T. Mohammadi Arian, G. Rahimi, R. Khavari Farid

“…Solid-phase extraction using carbon adsorbents is the most efficient and common method of pre-concentration of heavy metals from environmental samples. …”
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Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study by Leila Zeinolabedini, Forough Ameli, Abdolhossein Hemmati-Sarapardeh

“…In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. …”
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Numerical and Experimental Study of a Hydrodynamic Analysis of the Periodical Fluctuation of Bio-Inspired Banded Fins by Chonglei Wang, Qihang Liu, Junhao Yang, Chunyu Guo

“…By using a method of controlling variables, such as wave number, swing angle, and frequency, where only one independent variable is changed at a time while the others remain constant, the impact on thrust coefficient function and the obtained periodic variation laws governing hydrodynamic performance are studied. …”
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Encapsulation Based Method for Natural Frequency Identification of Deployable Solar Arrays with Multiple Plates by Chunjuan Zhao, Xiangyu Zhao, Shanbo Chen, Jisong Yu, Lei Zhang

“…However, for the batch production of small satellites, the accuracy and efficiency of traditional ground modal testing methods are limited. …”
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Greening concept in inventory system for deteriorating items with preservation investment and price and stock dependent demand via marine predators algorithm by Hachen Ali, Gobinda Chandra Panda, Adel Fahad Alrasheedi, Ali Akbar Shaikh, Jeonghwan Gwak

“…The resulting objective function is highly nonlinear and complex. To solve it efficiently, this study employs the Marine Predators Algorithm (MPA)—a newly developed metaheuristic algorithm well-suited for continuous, nonlinear optimization. …”
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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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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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Optimum Design of RC Beams Strengthened with Externally Bonded FRP Using Pattern Search by Noor Mohammed Sheet, Baraa J. M. AL-Eliwi, Rabi M. Najem

“…The design process involved an objective function that represents the cost function of the optimum solution and design variables that define the optimum dimensions of the beam and FRP material, such as beam width, effective beam depth, FRP strip thickness, FRP strips spacing, and FRP strip width, according to the design constraints of the ACI 440-2R-17 code. …”
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