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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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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Dynamic Model and Dynamic Response of Automobile Dual-Mass Flywheel with Bifilar-Type Centrifugal Pendulum Vibration Absorber by Lei Chen, Jianming Yuan, Hang Cai, Jinmin Hu

“…Involving the moment of inertia of the centrifugal pendulum, the model considers the nonlinearities of DMF and bifilar CPVA. Afterward, the dynamic model of the automobile power transmission system equipped with the DMF with bifilar-type CPVA is built, and the dynamic responses of the system are investigated under idling and driving conditions. …”
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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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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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Finite Element Method-Based Spherical Indentation Analysis of Jute/Sisal/Banana-Polypropylene Fiber-Reinforced Composites by Nitish Kaushik, Ch. Sandeep, P. Jayaraman, J. Justin Maria Hillary, V. P. Srinivasan, M. Abisha Meji

“…The analyses results showed that as the distance between the fiber’s center increases, the bearing load capacity of all composite increases nonlinearly. The jute fiber composite shows predominate load-carrying capacity compared to other composites at all L/D ratios and interference ratios. …”
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Analysis of Surrounding Rock Pressure of Deep Buried Tunnel considering the Influence of Seepage by Qingchen Yao, Yukun Ma, Zongyang Xiao, Zudi Zhang, Yaxin Lu, Chenyang Liu

“…Based on the elastic-plastic solution, the nonlinear elastic-plastic solution of the interaction between surrounding rock and lining structure considering the effect of seepage force is proposed, and the radius of surrounding rock plastic zone is obtained. …”
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Computationally Efficient Minimum-Time Motion Primitives for Vehicle Trajectory Planning by Mattia Piccinini, Simon Gottschalk, Matthias Gerdts, Francesco Biral

“…Finally, the motion primitives of this paper achieve similar maneuver times as minimum-time economic nonlinear model predictive control (E-NMPC), but with significantly lower computational load (two orders of magnitude). …”
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Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling by Ziqiang Chen, Peng Ju, Zhe Wang, Du Huang, Lei Shi, Kangyao Deng

“…However, the multi-parameter coupling and nonlinear increase in the combustion process make the model and controller design more difficult. …”
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Stability and computational analysis of Influenza-A epidemic model through double time delay by Ateq Alsaadi, Ali Raza, Muhammed Bilal Riaz, Umar Shafique

“…Additionally, the results show that the nonstandard finite difference approximation is an efficient, cost-effective method, independent of time step size, to solve such highly nonlinear and complex real-world problems.…”
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Comparison of Kolmogorov–Arnold Networks and Multi-Layer Perceptron for modelling and optimisation analysis of energy systems by Talha Ansar, Waqar Muhammad Ashraf

“…KAN models are embedded in the optimisation framework of nonlinear programming and feasible optimal solutions are estimated, maximising thermal efficiency up to 42.17 ± 0.88 % and minimising turbine heat rate to 7487 ± 129 kJ/kWh corresponding to power generation of 500 ± 14 MW for the thermal power plant. …”
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BIPE: A Bi-Layer Predictive Ensemble Framework for Forest Fire Susceptibility Mapping in Germany by Ling Hu, Volker Hochschild, Harald Neidhardt, Michael Schultz, Pegah Khosravani, Hadi Shokati

“…Our results confirm that BIPE outperforms traditional high-performance models like Support Vector Machine (SVM), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Convolutional Neural Network (CNN), showcasing its practical effectiveness and reliability on the data of nonlinear, high-dimensional, and complex interactions. …”
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Normalized difference vegetation index prediction using reservoir computing and pretrained language models by John Olamofe, Ram Ray, Xishuang Dong, Lijun Qian

“…Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset, we designed and implemented Reservoir Computing (RC) models and transformer-based models including pretrained language model, and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression, Decision Tree, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and DLinear. …”
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