Experimental and theoretical analysis of ultrasonic separation in bioethanol purification by Junli Liu, Hao Feng

“…This model is more accurate than previous models because of considering the effects of temperatures and composition on the droplet size and using a more accurate model, modified Myers-Scott model, to predict the surface tension precisely. …”
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Calibration and establishment for the discrete element simulation parameters of pepper stem during harvest period by Jiaxuan Yang, Jin Lei, Xinyan Qin, Zhi Wang, Jianglong Zhang, Lijian Lu

“…The Central-Composite and Box-Behnken tests were then used to formulate quadratic regression equations and optimal parameter combinations for the significant factors were derived using an optimization solver to validate the accuracy of the model. …”
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Research and Structural Optimizationof Hydrogel-based Desiccant-coated Heat Exchanger by LEI Xiaoping, ZHANG Hua, WANG Jiayun

“…Therefore, this paper developed an efficient hydrogel composite material, set up a dehumidification experimental platform, and established a physical model of hydrogel-based desiccant-coated heat exchanger in COMSOL based on material characteristics. …”
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Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

“…While ductility can be engineered through alloying composition and processing condition optimization, this remains challenging due to the virtually infinite and vast unexplored compositional space. …”
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Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel by Xiaochang Xie, Mutong Liu, Ping Yang, Zenan Yang, Chengbo Pan, Chenchong Wang, Xiaolu Wei

“…In addition, the optimal model was combined with a genetic algorithm to obtain the best composition and process scheme with high-creep-performance potential, providing guidance for the design of austenitic heat-resistant steel.…”
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Application of Response Surface Methodology and Artificial Neural Network in the Adsorption of Methylene Blue Using Enhansed Chitosan Beads by Ephraim Igberase, Innocentia G. Mkhize

“…For prediction and optimization, response surface methodology/central composite design (RSM-CCD) and artificial neural network (ANN) were applied for MB adsorption. …”
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16S rRNA metabarcoding applied to the microbiome of insect products (novel food): a comparative analysis of three reference databases by Gabriele Spatola, Alice Giusti, Laura Gasperetti, Roberta Nuvoloni, Alessandra Dalmasso, Francesco Chiesa, Andrea Armani

“…In this study, 42 insect-based food products were chosen as food models to find out how reference database choice could affect the microbiome results in food matrices. …”
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Machine learning assisted design of reactor steels with high long-term strength and toughness by Ivan Trofimov, Pavel Korotaev, Ivan Ivanov, Anton Malginov, Allen Tokhtamyshev, Alexey Yanilkin, Ivan Kruglov

“…In order to find steels with optimal properties, we collected the database of steels and their properties, trained machine learning models and developed optimization algorithm. …”
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TO THE QUESTION OF DEVELOPMENT CURRICULUM OF GENERATION 3 FOR THE IT PROFESSION by N. V. Lapickaja, S. П Shuldova, G. F. Sarkisjan

“…The model makes it possible to determine the trajectory and labor intensity of forming competencies, and also to optimize the curriculum of the specialty.…”
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RATIONALE FOR THE SELECTION OF AUXILIARY COMPONENTS FOR THE NALTREXONE HYDROCHLORIDE NASAL SPRAY by Y. M. Domnina, S. A. Kedik, V. V. Suslov, E. A. Shnyak

“…To prevent leakage of the drug from the nasal cavity, the thermosensitive poloxamer (Kolliphor® P 407) was introduced into its composition. The dynamic viscosity was studied of model samples in the temperature range of 7-37 °C. …”
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Mespilus germanica (MG) and Tribulus terrestris (TT) Used as Biosorbents for Lead Removal from Aqueous Solutions: Adsorption Kinetics and Mechanisms by Majid Ehteshami, Hamidreza Zolfaghari, Marjan Salari, Ehsan Teymouri

“…Four main parameters were selected for experimental design, namely, pH, time of contact, the ratio of adsorbent to solution volume, and initial concentration of lead in solution, and the optimization of effective parameters on lead adsorption was performed based on central composite design (CCD) under response surface methodology (RSM). …”
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Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation by Li Feng, Jiangnan Li, Qiong Lu, Yuanqi You, Zunyan Xu, Liyuan Liu, Li Fu, Peng Gao, Jianhong Yi, Caiju Li

“…To address this challenge, this study applied a machine learning approach: a Support Vector Regression (SVR) based “composition-conductivity” model was constructed to predict the impact of individual elements on the alloy’s electrical conductivity. …”
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A Novel Approach to Strengthening Cryptography Using RSA, Efficient Domination and Fuzzy Logic by Ghulam Muhiuddin, Annamalai Meenakshi, Janusz Kacprzyk, Ganesan Ambika, Hossein Rashmamlou

“…The intractability of factoring huge composite numbers is a prerequisite for RSA’s security. …”
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Study on the characteristics of Cd2+ adsorption of mineral processing wastewater by micro-organisms supported by hydrothermal carbon by Gang ZHOU, Xinyuan ZHANG, Shuailong LI, Qunzhi MENG, Jianjun YAO, Jinhao ZHANG, Hui ZHEN

“…In addition, the adsorption kinetics experiment and adsorption isotherm model experiment found that the adsorption of Cd2+ by HC-PVA-SA-MOI was more consistent with the quasi-second-order kinetic model and Langmuir model, indicating that the adsorption mechanism of Cd2+ by HC-PVA-SA-MOI was mainly homogeneous monolayer chemisorption. …”
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Nonlinear Trajectory Controller with Improved Performances for Waveriders by LiuQing Yang, YanBin Liu, Yong Zhang

“…This paper presents a nonlinear trajectory controller with improved performances for a general model of the waverider based on feedback linearization theory and composite nonlinear feedback (CNF) technique. …”
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Exploration of the Ignition Delay Time of RP-3 Fuel Using the Artificial Bee Colony Algorithm in a Machine Learning Framework by Wenbo Liu, Zhirui Liu, Hongan Ma

“…These results consistently outperformed the baseline model that lacked ABC optimization. On an entirely independent external test set comprising 70 low-pressure shock tube samples, the model still exhibited strong generalization capability, achieving R² = 0.976 and MAPE = 2.18%, thereby confirming its robustness across datasets with different sources. …”
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Development of technology of centrifugal electroslag casting by A. G. Janishevskaja

“…There are given the optimal parameters of the process and chemical composition ofthe offered slag skull.…”
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Extractive distillation of tetrahydrofuran–ethyl acetate–water mixture in schemes including columns with side sections and side draws by D. G. Rudakov, S. M. Kharlamov, P. S. Klauzner, E. A. Anokhina, A. V. Timoshenko

“…The main research method consisted of a computational experiment with the Aspen Plus v. 12 software package. The local composition UNIQUAC equation model was used for describing vapor–liquid equilibrium. …”
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Analysis of the Pelletability of Vegetable Crop Foliage Using a Commercial Flat Die Pellet Mill by Omid Gholami Banadkoki, Shahab Sokhansanj, Anthony Lau

“…Pelletization energy requirements varied significantly, with cucumber (21.8 kWh/t) and summer squash (18.7 kWh/t) requiring the lowest energy input, whereas soybean (49.6 kWh/t) and sawdust (47.3 kWh/t) exhibited the highest energy demands due to greater resistance to densification. A predictive model was developed to correlate single pellet density and durability with bulk pellet properties—yielding high predictive accuracy, with R² = 0.936 for bulk density (<i>B</i><i>D</i><i>_e</i>) and R² = 0.861 for bulk durability (<i>B</i><i>D</i><i>_u</i>)—thereby facilitating process optimization for large-scale pellet production. …”
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Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning by Kang Xu, Zhengming Sun, Jian Tu, Wenwang Wu, Huihui Yang

“…Multi-principal element alloys (MPEAs), distinguished by their complex compositions and exceptional mechanical properties, pose significant challenges for conventional predictive approaches in mechanical property optimization. …”
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