Low-frequency rTMS modulates small-world network properties in an AVH-related brain network in schizophrenia by Lin Zhang, Li Guo, Xiaohui Liu, Jing Han, Yuanqiang Zhu, Chaozong Ma, Ye Li, Weiliang Ye

“…Resting-state fMRI data were collected before and after treatment to assess functional connectivity within the predefined 35-region AVH-related network. small-worldness (σ), normalized clustering coefficient (γ), and normalized characteristic path length (λ), as well as functional segregation (clustering coefficient [Cp], local efficiency [El]) and functional integration (global efficiency [Eg], characteristic path length [Lp])—were analyzed before and after rTMS. …”
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An Empirical Analysis of Herding Behavior in the Stock Market: Evidence from Various Economic and Social Conditions in Iran by Majid Aghaei

“…This approach models the response of the dependent variable to the independent variable at various quantiles, denoted as "τ," making it an effective tool for analyzing non-normal distributions. …”
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On the Nonlinear Forced Vibration of the Magnetostrictive Laminated Beam in a Complex Environment by Nicolae Herisanu, Bogdan Marinca, Vasile Marinca

“…The nonlinear differential equations were studied using an original, explicit, and very efficient technique, namely the optimal auxiliary functions method (OAFM). …”
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SCIENCE TEACHERS’ ICT USE FREQUENCY AND THE BARRIERS THEY WERE EXPOSED by Fatma TAŞKIN EKİCİ, Seçil DEMİRHAN, İzzet KARA, Erhan EKİCİ

“…There are no statistical differences by these variables. Solely, the teachers already taken courses related to ICT and their use are stated that more frequent use than teachers didn’t take significant in statistical mean.…”
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Nonlinearity Estimation and Compensation for Accurate PMSM Modeling and Voltage Prediction by Beichen Ding, Yuting Lu, Chunyan Lai, Weiwen Peng, Kaide Huang, Guodong Feng

“…Specifically, the offsets to the base model are modeled using nonlinear functions with variable coefficients to compensate saturation and core loss effect, which can achieve better accuracy without changing the model structure. …”
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Dimensions-Reduced Volterra Digital Pre-Distortion Based On Orthogonal Basis for Band-Limited Nonlinear Opto-Electronic Components by Hananel Faig, Yaron Yoffe, Eyal Wohlgemuth, Dan Sadot

“…However, naive implementation of the Volterra polynomial model usually introduces significant complexity due to the large number of model coefficients. Here, we propose the use of orthogonal polynomial basis functions for efficient DPD implementation. …”
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Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools by Shailendra Pawanr, Kapil Gupta

“…One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. …”
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Comparing machine learning approaches for estimating soil saturated hydraulic conductivity. by Ali Akbar Moosavi, Mohammad Amin Nematollahi, Mohammad Omidifard

“…Results revealed that all NN models particularly PSO-NNs were efficient in prediction of Kfs. However, further evaluations may be recommended for other soil conditions and input variables to quantify their potential uncertainties and wider potential and versatility before they are used in other geographical locations/soil conditions.…”
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Bankruptcy rules and sustainable water management: A MODSIM-NSGAII simulation multi-objective optimization framework for equitable transboundary water allocation by Bentolhoda Asl-Rousta, S. Jamshid Mousavi

“…This study addresses these challenges by introducing a new optimal bankruptcy rule (OpPro rule) through a simulation-optimization model (MODSIM-NSGAII), where MODSIM simulates the spatial and temporal variability of water resources, and the NSGA-II optimization algorithm includes two objective functions of maximizing basin-wide ecological sustainability and maximizing economic efficiency reflecting ''equitable and reasonable utilization'' of water. …”
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Magnetic Field Effects on Convective Heat Transfer of Ferrofluid from a Heated Sphere in Porous Media by Ayesha Aktar, Sharaban Thohura, Md. Mamun Molla

“…For increasing Ha, the figure of streamlines seems to depict functions with more gradual changes, and for isotherms, it represents functions with sharper, exponential-like increases. …”
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Removal of chromium from synthetic wastewater by electrocoagulation and using natural coagulant (blend of hen eggshell powder with lime): optimization of response surface methodolo... by Werkne Sorsa Muleta, Firomsa Bidira Abdi, Endrias Adane Bekele

“…The central composite design of the response surface methodology was employed to estimate and optimize process variables, such as initial Cr concentration (225–475 mg/L), solution pH (5–9), and current density (0.35–045 A/m2), and treatment time (30–40 min) with an electrode distance (ED) of 0.5 and 1 cm, respectively. 99.90% and 99.74% of removal efficiencies were observed at initial Cr concentration of 456.11 mg/L, a solution pH of 5.45, with current density of 0.47 A/m2, and treatment time of 36.84 min. …”
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Optimum System Design Using Rough Interval Multi-Objective De Novo Programming by Iftikhar Hussein, Hegazy Zaher, Naglaa Ragaa Saeid, Hebaa Sayed Roshdy

“…This paper suggested a new approach for solving uncertainty of De novo programming problems (DNP) using a combination model consisting of a rough interval multi-objective programming (RIMOP) and DNP, where coefficients of decision variables of objective functions and constraints are rough intervals (RIC). …”
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A New Modeling Method for Meteorological Information of Regional Distributed Photovoltaic Power Generation Based on Multi‐Source Information Fusion by Yuhang Wang, Dengxuan Li, Wenwen Ma, Xi Zhang, Honglu Zhu

“…The Current challenges in DPV meteorological information fusion computation include feature engineering, the reasonable selection of input variables and preliminary establishment of mapping relationships. …”
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Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

“…This study investigates the operational efficiency of the lab-scale oxidation ditch (OD) functioning in simultaneous nitrification and denitrification modes, focusing on forecasting biochemical oxygen demand (BOD₅) concentrations over a five-day horizon. …”
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Study on the Photosynthetic Physiological Responses of Greenhouse Young Chinese Cabbage (<i>Brassica rapa</i> L. <i>Chinensis Group</i>) Affected by Particulate Matter Based on Hyp... by Lijuan Kong, Siyao Gao, Jianlei Qiao, Lina Zhou, Shuang Liu, Yue Yu, Haiye Yu

“…Particulate matter affects both the light environment and air quality in greenhouses, obstructing normal gas exchange and hindering efficient physiological activities such as photosynthesis. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Under the BIA, an alpha coefficient (α) of 15% is applied, whereas in the SA, each business line has a specific beta coefficient (β) ranging between 12% and 18%. …”
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Optimizing Load Frequency Control of Multi-Area Power Renewable and Thermal Systems Using Advanced Proportional–Integral–Derivative Controllers and Catch Fish Algorithm by Saleh A. Alnefaie, Abdulaziz Alkuhayli, Abdullah M. Al-Shaalan

“…Additionally, by contrasting the performance of the PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers, the efficiency of the CFA is confirmed. Additionally, a sensitivity analysis that considers simultaneous modifications of the frequency bias coefficient (B) and speed regulation (R) within a range of ±25% validates the efficacy and dependability of the suggested CFA-tuned PI(1+DD). …”
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Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer by Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Maria Porta, Alberto Concardi, Arnaud Gerard Michel Ceol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco

“…The DL model exhibited high accuracy in tissue quantification and TP53 status prediction, outperforming traditional methods in terms of precision and efficiency. DL-based approaches offer significant promise for enhancing biomarker testing and precision oncology by reducing intra- and inter-observer variability, but further validation is required to optimize their integration into real-world clinical workflows. …”
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A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP by Hongqi Li, Yujuan Wang, Peirong Fu

“…Steady-state visual evoked potential (SSVEP)-based brain—computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. …”
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Performance Analysis and Computational Interface for <italic>X &#x2013; R</italic> Intuitionistic Fuzzy Control Chart by Amanda Dos Santos Mendes, Tulio S. Almeida, Marcela A. G. Machado, Paloma Maria Silva Rocha Rizol

“…This work showed that intuitionistic fuzzy control charts are efficient at detecting special causes, and that the computer interface developed is capable of performing the proposed functions.…”
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