Predictive Modeling and Optimization of Hot Forging Parameters for AISI 1045 Ball Joints Using Taguchi Methodology and Finite Element Analysis by Naiyanut Jantepa, Nattarawee Siripath, Surasak Suranuntchai

“…The ANOVA revealed that the billet temperature played a significant role in influencing the preforming force, finishing force, and mean stress, with a maximum impact of 62.30%, 59.50%, and 94.20% on the variation in the response variable, respectively. Additionally, the friction factor significantly affected the preforming and finishing forces, contributing 36.19% and 38.28%. …”
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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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Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data by Ryo Murakami, Taisuke T. Sasaki, Hideki Yoshikawa, Yoshitaka Matsushita, Keitaro Sodeyama, Tadakatsu Ohkubo, Hiroshi Shinotsuka, Kenji Nagata

“…We then applied Bayesian linear regression to the maximum energy product [Formula: see text], using the extracted peak features as the explanatory variable. The outcomes yielded reasonable and robust regression results. …”
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Fast 3D localization of nano-objects in wide-field interferometric scattering microscopy via vectorial diffraction model-derived analytical fitting by Liaoliao Wei, Caroline Livan Anyi, Huakun Li, Haitao Nie, Bihan Wen, Tong Ling

“…The simplified analytic function includes a quadratically scaling amplitude term and a linearly scaling phase term, both of which change with the nano-object’s axial position. …”
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Design of Hall power supply based on full bridge half bridge switching digital LLC resonant converter by Xu Tinghao, Wang Shaoning, Liu Aohui

“…The LLC resonant converter is studied for half bridge full bridge dynamic topology switching and soft start, achieving high efficiency and wide range output. By establishing a fundamental model of LLC resonant converter, analyzing the characteristic curves of frequency and gain, selecting the values of K and Q, variable frequency control output voltage is achieved, and a low peak full bridge half bridge switching mode based on simultaneous frequency conversion and variable duty cycle is designed. …”
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同轴全铰链双输出脉冲发生机构尺度综合与优化 by 杜力, 邹昌平, 冯俊, 向成宣, 黄茂林

“…In order to enhance the efficiency,stability and simplify the structure of the mechanism,the dimension synthesis of the full hinge coaxial two-output function generator is performed by using group-synthesis scheme and regarding the six-bar mechanism as the parallel mechanism of a set of basic four-bar mechanism and a set of equivalent four-bar mechanism.Firstly,a basic four-bar mechanism synthetic model is established and solved based on two position synthesis and equal instantaneous transmission ratio.Then the dimensional parameters of an equivalent four-bar mechanism（a five-bar loop） is solved by using equivalent principle of a variable link-length four-bar mechanism based on the basic four-bar mechanism.By this scheme,the dimensional synthesis of high order of motion parameter can be solved with good results,and the results show that the pulse data of the full hinge coaxial two-output function generator is only six percent.…”
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A non-dominated sorting based multi-objective neural network algorithm of ethylene glycol hydrogenation reactor in energy reduction by Fakhrony Sholahudin Rohman, Sharifah Rafidah Wan Alwi, Dinie Muhammad, Muhamad Nazri Murat, Ashraf Azmi

“…Artificial intelligence (AI) can be applied to various ethylene glycol (EG) production aspects to improve efficiency, quality, and overall process optimization. …”
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Deep Multi-Component Neural Network Architecture by Chafik Boulealam, Hajar Filali, Jamal Riffi, Adnane Mohamed Mahraz, Hamid Tairi

“…The architecture’s efficiency is further demonstrated by its reduced trainable parameters and robust handling of multimodal and variable-length inputs, making it a versatile solution for classification tasks.…”
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AESware: Developing AES-enabled low-power multicore processors leveraging open RISC-V cores with a shared lightweight AES accelerator by Eunjin Choi, Jina Park, Kyuseung Han, Woojoo Lee

“…AESware supports variable key lengths and ensures minimal power consumption with a compact design. …”
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COMPARATIVE ANALYSIS OF THE ACADEMIC PERFORMANCE IN THE AGRICULTURAL UNIVERSITIES FROM ROMANIA AND REPUBLIC OF MOLDOVA USING THE NONPARAMETRIC METHOD OF DATA ENVELOPMENT ANALYSIS by Ilie CERCEL, Gina FÎNTÎNERU, Elena TOMA, Augustin VOLCONOVICI, Alla CHIRSANOVA, Anatol RACUL

“…As a result of analyzing the academic performance rating by option variable returns to scale, it is possible to determine the resources for improving staff activity by evaluating the distance function for each causal factor.…”
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Incorporation of Mean/Maximum Stress Effects in the Multiaxial Racetrack Filter by Marco Antonio Meggiolaro, Jaime Tupiassú Pinho de Castro, Hao Wu

“…The MRF efficiency is evaluated from tension-torsion experiments in 316L stainless steel tubular specimens under non-proportional (NP) load paths, showing it can robustly filter out nondamaging events even under multiaxial NP variable amplitude loading histories. …”
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Record Keeping Systems and Service Delivery in Uganda: Case Of Kabale District Local Government by Turyasingura John Bosco, agaba moses, Kabagambe Jesse David, Barigye Godfrey

“…The purpose of this study was to determine how the Kabale District Local Government's service delivery was impacted by its record-keeping procedures. The study's end variable was the provision of services, and its predictor factors were the efficiency with which the records management systems function. …”
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Reliability analysis of landing gear operation based on improved support vector machine-based decomposed-coordinated by ZHANG Lei, CHEN Junyu, FENG Jun, FENG Yunwen, LIU Lei

“…The case results show that the proposed ISVM-DC can accurately decompose and coordinate the architecture of landing gear operational failure, and efficiently construct and solve the reliability performance function.…”
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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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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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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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A Study of Some Properties of Fuzzy Laplace Transform with Their Applications in Solving the Second-Order Fuzzy Linear Partial Differential Equations by Elhassan Eljaoui, Said Melliani

“…Then, some properties dealing with the partial derivatives of fuzzy Laplace transform for a fuzzy function of two real variables have been proved. Afterwards, an algorithm of fuzzy Laplace transform for solving second-order fuzzy partial differential equations has been proposed. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

“…Results: The Generalized Linear Model Boosting combined with Random Forest (glmBoost + RF) algorithm using 14 variables achieved comparable performance (Area Under the Curve [AUC] = 0.981) to the more complex Least Absolute Shrinkage and Selection Operator combined with Random Forest (Lasso + RF) algorithm using 25 variables (AUC = 0.985). …”
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