A Data-Driven Approach Based on Deep Neural Network Regression for Predicting the Compressive Strength of Steel Fiber Reinforced Concrete by Nhat-Duc Hoang, Van-Duc Tran

“…Experimental results show that the L1 regularization helps achieve the most desired performance, with a coefficient of determination (R2) of roughly 0.96. Notably, an asymmetric loss function is used along with Nadam to decrease the percentage of overestimated cases from 50.83% to 27.08%. …”
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Enhanced Dung Beetle Optimizer-Optimized KELM for Pile Bearing Capacity Prediction by Bohang Chen, Mingwei Hai, Gaojian Di, Bin Zhou, Qi Zhang, Miao Wang, Yanxiu Guo

“…The model utilizes the pile length, pile diameter, average effective vertical stress, and undrained shear strength as input variables, with the bearing capacity serving as the output variable. …”
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Developing a novel layer network structure for a LSTM model to predict mean monthly river streamflow by Amin Gharehbaghi, Redvan Ghasemlounia, Shahaboddin Daneshvar, Farshad Ahmadi

“…For doing so, to select the most effective parameters on MRSF m , the Pearson’s correlation coefficient (PCC) and Cosine amplitude sensitivity (CAS) as features selection process are carried out for potential meteorological variables in the study area (i.e., average monthly temperature (T m ), evaporation (ET m ), and precipitation (P m )) and target (MRSF m ). …”
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ANALYSIS OF CONTACT LOADS CONCENTRATION IN PRESSURE COUPLINGS OF THIN-WALL COMPONENTS by S. V. Shishkin

“…Deviations of the shape of the contact surfaces from the straightness are taken into account by its respective pressure coupling function. The analysis of the findings suggests that the concentration coefficient value slumps as the contact compliance coefficient of the borderline layer increases. …”
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Cooperative Control of Interconnected Air Suspension Based on Energy Consumption Optimization by Guoqing Geng, Shuai Zeng, Liqin Sun, Zhongxing Li, Wenhao Yu

“…The optimal interval for suspension force is obtained through solving cost functions while satisfying a set of constraints on controlled variables and thereby reducing the coupling complexity of a multivariable control system. …”
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The association between age and long-term quality of life after curative treatment for prostate cancer: a cross-sectional study by Reidun Sletten, Ola Berger Christiansen, Line Merethe Oldervoll, Lennart Åstrøm, Håvard Kjesbu Skjellegrind, Jūratė Šaltytė Benth, Øyvind Kirkevold, Sverre Bergh, Bjørn Henning Grønberg, Siri Rostoft, Asta Bye, Paul Jarle Mork, Marit Slaaen

“…We found no statistically significant independent association between age at treatment and global QoL, PF or side effects, except for sexual function (regression coefficient [RC] −0.77; p < 0.001) and hormonal/vitality (RC 0.30; p = 0.006) function. …”
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Supplier Selection and Order Allocation in A Pharmaceutical Wholesaler by Ryan Hikmah Fadilla, Cucuk Nur Rosyidi, Wakhid Ahmad Jauhari

“…As part of the system modeling, a sensitivity analysis was performed to explore the effects of specific parameters on the objective function and decision variables, assessing variations in inventory costs, shortage costs, and demand. …”
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Shell and tube heat exchanger optimization: A critical literature assessment and fairness-based comparative performance analysis of meta-heuristic algorithms by Samet Gürgen

“…In order to make comparisons, the objective function, decision variables and their boundary values were taken as the same. …”
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Forecasting Electricity Production in a Small Hydropower Plant (SHP) Using Artificial Intelligence (AI) by Dawid Maciejewski, Krzysztof Mudryk, Maciej Sporysz

“…Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. …”
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Adsorption of Cd (II) Using Chemically Modified Rice Husk: Characterization, Equilibrium, and Kinetic Studies by Javier Montalvo-Andía, Warren Reátegui-Romero, Alexis D. Peña-Contreras, Walter F. Zaldivar Alvarez, María E. King-Santos, Víctor Fernández-Guzmán, José Luis Guerrero-Guevara, Jhon E. Puris-Naupay

“…In optimal experimental conditions, the maximum adsorption efficiency was 92.65%. Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM) were used allowing the identification of the main functional groups and morphology of rice husk and treated rice husk, and the results showed an improvement of adsorption characteristics after rice husk treatment with NaOH. …”
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Torsional Vibration Suppression During Mode Transition Process in a Parallel Hybrid Electric Vehicle Based on Multiple Model Predictive Control by Cheng Huang, Changqing Du, Longjian Li, Xiangyu Gongye, Yifan Zhao

“…At the same time, the influence of the cost function, the weight coefficient, and the predictive time domain on the torsional vibration suppression effect during the mode transition process are explored in the mMPC strategy design, and the solution time is controlled within 10ms, which meet the real-time requirements. …”
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Enhancing wireless applications through reconfigurable electro-mechanical reflectarray antenna design for beam steering by Behrokh Beiranvand, Rashid Mirzavand

“…The proposed design involves multiple reflective strips at variable heights, enabling significant adaptability to incident waves at various angles. …”
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Advances in nursing care for post-stroke limb dysfunction rehabilitation by Jie Bai, Kecheng Chen

“…Clinically validated assessment tools demonstrate variable utility across recovery phases, with the Pittsburgh Rehabilitation Participation Scale offering efficiency in acute settings and the Motivation in Stroke Patients for Rehabilitation Scale providing comprehensive evaluation during subacute and chronic phases. …”
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Process engineering by Ahmed Mohamed Farid Shaaban, Azza Ibrahim Hafez, Mona Amin Abdel-Fatah, Nabil Mahmoud Abdel-Monem, Mohamed Hanafy Mahmoud

“…The solution diffusion model was used to develop power correlations to calculate the permeate side solute mass transfer coefficient as a function of effective cross-flow Reynolds number. …”
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Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys by Uma Maheshwera Reddy Paturi, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, Nagireddy Gari Subba Reddy

“…This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. Among the ML methods explored, a backpropagation neural network (BPNN) model with a sigmoid activation function exhibited superior predictive accuracy compared to other algorithms. …”
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Comparative Study of the Impact of Corruption on the Human Development Index by T. M. Zharlygassinov, A. Zh. Panzabekova, M. S. Dosmanbetova

“…The reason for this is the significant negative impact of this phenomenon on the standard of living of citizens and the efficiency of the functioning of state bodies. However, the strength of such interaction may differ depending on the country and how conscientiously the employees of its state apparatus carry out their work. …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Current non-invasive screening modalities rely predominantly on acoustic immittance measurements, which demonstrate variable diagnostic performance. Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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Correlation of Immune Parameters in Breast Cancer Patients Undergoing General Anesthesia: post-hoc Analysis of the TeMP Study by Kristina K. Kadantseva, Valery V. Subbotin, Mikhail Y. Yadgarov, Elizaveta M. Korolenok, Levan B. Berikashvili, Roman A. Akchulpanov, Nikolay S. Karnaukhov, Ksenia S. Korchagina, Polina I. Kukina, Oksana A. Svitich, Artem N. Kuzovlev, Anna S. Barmina, Valery V. Likhvantsev

“…In the TIVA group, changes were variable: immunoglobulin levels increased at 1 hour (P&lt;0.001) but decreased at 24 hours (P&lt;0.001). …”
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Modeling and Regulation of Dynamic Temperature for Layer Houses Under Combined Positive- and Negative-Pressure Ventilation by Lihua Li, Min Li, Yao Yu, Yuchen Jia, Zhengkai Qian, Zongkui Xie

“…Additionally, a temperature control method for a layer house is designed using a variable universe fuzzy PID control algorithm (VFPID). …”
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Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin by Prashant Parasar, Akhouri Pramod Krishna

“…The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m3/s, Nash–Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R2 values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs), specific humidity (huss), and temperature (tas) as key predictors, while (pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN’s robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. …”
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