Mask-Pix2Pix Network for Overexposure Region Recovery of Solar Image by Dong Zhao, Long Xu, Linjie Chen, Yihua Yan, Ling-Yu Duan

“…This paper makes effort to retrieve/recover missing information of overexposure by exploiting deep learning for its powerful nonlinear representation which makes it widely used in image reconstruction/restoration. …”
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An Experimental Investigation on the Mechanical Properties of Gangue Concrete as a Roadside Support Body Material for Backfilling Gob-Side Entry Retaining by Peng Gong, Zhanguo Ma, Xiaoyan Ni, Ray Ruichong Zhang

“…The experimental results show that the compressive strength of gangue concrete increases with age, and that the strength of gangue concrete demonstrates a nonlinear decreasing trend with the increase of the cementing material’s water-cement ratio. …”
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The Correlation between Pore Structure and Macro Durability Performance of Road Concrete under Loading and Freeze-Thaw and Drying-Wetting Cycles by Sheng-bo Zhou, Jun-lin Liang, Wei-an Xuan, Ye Qiu

“…The correlation between strength and pore parameters can be represented with multiple nonlinear equations. A negative correlation is shown between strength and fractal dimension and most probable pore size. …”
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Mesoscopic Numerical Simulation of Fracture Process and Failure Mechanism of Concrete Based on Convex Aggregate Model by Yijiang Peng, Xiyun Chen, Liping Ying, Ying Chen, Lijuan Zhang

“…A multistage linear damage constitutive model that can describe nonlinear behavior of concrete under mechanical stress was proposed. …”
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A Uniform Robust Exact Differentiator Based Neuro-Fuzzy Fractional Order Sliding Mode Control for Optimal Standalone Solar Photovoltaic System by Safeer Ullah, Ahmed S. Alsafran, Ambe Harrison, Ghulam Hafeez, Abouobaida Hassan, Baheej Alghamdi, Habib Kraiem

“…The neural network accurately predicts nonlinear reference voltage trajectories, whereas the differentiator estimates unmeasurable states and external disturbances. …”
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Modeling and Seismic Performance Analysis of Grid Shear Walls by Weijing Zhang, Caiwang Li, Xiao Chu

“…In this paper, based on an earthquake engineering simulation open system (OpenSees), a new modeling approach for grid shear walls is proposed, and nonlinear analysis of two grid walls with different grid sizes under cyclic load is carried out. …”
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Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks by Peter Appiahene, Yaw Marfo Missah, Ussiph Najim

“…Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). …”
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INTERNATIONAL RELATIONS AND DIPLOMATIC SERVICE: RETROSPECTIVE ANALYSIS AND PROSPECTS OF THE NEW WORLD ORDER by E. V. Ohotskii

“…The author focuses readers attention on the regularities of formation, development and peculiarities of legal regulation of international relations, considers these relations as an ongoing, highly controversial and multidirectional developing process of the formation of the world system of States and international relations, explores the driving forces, events and phenomena, who had in his time, and many still have a decisive influence on international policy the leading powers of the world in the framework of nonlinear processes of globalization and the current, seriously flawed by today's standards, world order and system of international law. …”
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SSA-ELM Hydrological Time Series Forecast Model Based on Wavelet Packet Decomposition and Phase Space Reconstruction by LI Lude, CUI Dongwen

“…Considering the nonlinear and multi-scale characteristics of hydrological time series,this paper proposes a squirrel search algorithm (SSA)-extreme learning machine (ELM) forecasting model based on wavelet packet decomposition (WPD) and phase space reconstruction.It is then applied to the Shangguo Hydrological Station in Yunnan Province for monthly runoff and precipitation forecasting.Specifically,WPD is performed to decompose the runoff and precipitation time series data,and the Cao method is applied to reconstruct the phase space of each subseries component.Then,the principle of SSA is outlined,and objective functions are constructed through the training samples of each component.The objective functions are optimized by SSA,and the results are compared with the optimization results of the whale optimization algorithm (WOA),the gray wolf optimization (GWO) algorithm,and the particle swarm optimization (PSO) algorithm.Finally,the weight of the ELM input layer and the hidden layer bias obtained by optimization based on SSA,WOA,GWO algorithm,and PSO algorithm,respectively,are utilized to build SSA-ELM,WOA-ELM,GWO-ELM,and PSO-ELM models,which,in addition to the unoptimized ELM models,are applied to forecast each subseries component,and the forecast results are summed and reconstructed to obtain the final forecasting results.The results show that SSA outperforms WOA,GWO algorithm,and PSO algorithm in optimizing the objective functions of each component and that it offers better optimization accuracy.The mean relative error,mean absolute error,mean square root error,and forecast pass rate of the proposed SSA-ELM model for monthly runoff and monthly precipitation forecast are 5.32% and 3.84%,0.078 m³/s and 0.169 mm,0.103 m³/s and 0.209 mm,97.5% and 95.8%,respectively,indicating that its forecasting accuracy is higher than that of other models such as the WOA-ELM model.…”
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Effect of friction on levelling of the maxillary canine with NiTi superelastic wire - an in silico experiment using a finite element method by Rikou Miyawaki, Yukiko Yokoi

“…The canine, alveolar bone, and bracket were rigid bodies, while the periodontal ligament (PDL) was a nonlinear elastic material. Kinetic friction was caused by contact forces acting on the wire and the bracket slot. …”
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Study of the Effects of Different Dielectric Environments on the Characteristics of Electro-Explosive Discharge of Metal Wires and Shock Waves by Jiawei Liu, Jin Wang, Qilong Xue, Yuanyuan Zhang, Hufeng Li, Song Fang

“…Notably, the energy deposition efficiency shows a nonlinear relationship with fragmentation effectiveness, influenced by factors such as energy release modes, dielectric composition, and bubble dynamics. …”
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The Transformation and Leachability of Fly Ash/Cement Waste Forms Subjected to the Simultaneous Effect of Heat and Chemistry by Zhao Zheng, Hua Wen, Yuxiang Li, Min Qin, Yao Wang

“…The relationship of apparent diffusion coefficient between Sr2+ and Ca2+ was quadratic nonlinear, while the relationship between Cs+ and Ca2+ showed a linear relationship. …”
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Stagnation Point Flow of CoFe2O4/TiO2-H2O-Casson Nanofluid past a Slippery Stretching/Shrinking Cylindrical Surface in a Darcy–Forchheimer Porous Medium by Kifle Adula Duguma, Oluwole Daniel Makinde, Lemi Guta Enyadene

“…This paper examines the combined effects of Darcy–Forchheimer porous medium-resistant heating and viscous dissipation on stagnation point flow of a Casson nanofluid (CoFe2O4-H2O and TiO2-H2O) towards a convectively heated slippery stretching/shrinking cylindrical surface in a porous medium. The governing nonlinear model equations are obtained, analysed, and tackled numerically via the shooting technique with the Runge–Kutta–Fehlberg integration scheme. …”
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Warming alters plankton body-size distributions in a large field experiment by Dania Albini, Emma Ransome, Alex J. Dumbrell, Samraat Pawar, Eoin J. O’Gorman, Thomas P. Smith, Thomas Bell, Michelle C. Jackson, Guy Woodward

“…We addressed this gap by conducting an extensive freshwater mesocosm experiment across 36 large field mesocosms exposed to intergenerational warming treatments of up to +8 °C above ambient levels. We found a nonlinear decrease in the overall mean body size of zooplankton with warming, with a 57% reduction at +8 °C. …”
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Mathematical Modeling of Wax Deposition in Field-Scale Crude Oil Pipeline Systems by Francis Oketch Ochieng, Mathew Ngugi Kinyanjui, Jeconia Okelo Abonyo, Phineas Roy Kiogora

“…The novelty of this work is to develop a mathematical model that incorporates water-in-oil emulsions, wax precipitation kinetics, molecular diffusion, and shear dispersion to enable accurate predictions of both the wax deposit growth rate and aging of the deposit. The coupled nonlinear partial differential equations governing the flow are discretized in time by a second-order semi-implicit time discretization scheme based on the Adams-Bashforth and Crank-Nicolson methods, which completely decouples the computation of the governing equations. …”
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Asymptotic analysis of mathematical model describing a new treatment of breast cancer using AZD9496 and palbociclib by Ophir Nave, Ophir Nave

“…The mathematical model that described the interaction between the cancer cells, the treatment, and the immune system cells includes a system of nonlinear ordinary differential equations of the firs order. …”
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Optimal Containment Control for Unknown Active Heterogeneous MASs via Model-Free Recursive Reinforcement Learning by Lina Xia, Qing Li, Ruizhuo Song, Gaofu Yang

“…Finally, the effectiveness of the proposed improved algorithm is validated using a heterogeneous nonlinear multi-agent model.…”
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Enhancing Buck-Boost Converter Efficiency and Dynamic Responses with Sliding Mode Control Technique by Salah Hilo Mohammed Al-Attwani, Mustafa Teke, Ethar Sulaiman Yaseen Yaseen, Enes Bektaş, Nurettin Gökşenli

“…The research focuses on a sliding mode control approach to overcome the challenges of nonlinear dynamics and susceptibility to external disturbances. …”
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Robust Prediction of Healthcare Inflation Rate With Statistical and AI Methods in Iran by Mohammad Javad Shaibani, Ali Akbar Fazaeli

“…Using monthly time series data of HCIR in Iran, we developed various forecasting techniques based on classical smoothing methods, decomposition ETS (error, trend, and seasonality) approaches, autoregressive (AR) integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and a multilayer nonlinear AR artificial neural network (NARANN) with several training algorithms including Bayesian regularization (BR), Levenberg–Marquardt (LM), scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton, conjugate gradient with Powell–Beale restarts (CGB), conjugate gradient with Fletcher–Reeves updates (CGF), and resilient propagation (RPROP) algorithms. …”
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Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization by Asrul Adam, Mohd Ibrahim Shapiai, Mohd Zaidi Mohd Tumari, Mohd Saberi Mohamad, Marizan Mubin

“…The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. …”
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