Robust Optimization Research of Cyber–Physical Power System Considering Wind Power Uncertainty and Coupled Relationship by Jiuling Dong, Zilong Song, Yuanshuo Zheng, Jingtang Luo, Min Zhang, Xiaolong Yang, Hongbing Ma

“…Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology–function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. …”
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Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model by Moses Oluwatobi Fajobi, Olumuyiwa Ajani Lasode, Adekunle Akanni Adeleke, Peter Pelumi Ikubanni, Ayokunle Olubusayo Balogun, Prabhu Paramasivam

“…The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. …”
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Retrieval of carbon and inorganic phosphorus during hydrothermal carbonization: ANN and RSM modeling by Abolfazl Shokri, Mohammad Amin Larki, Ahad Ghaemi

“…Next, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were used to compare the results and improve the model fit. …”
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Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data by Dongwang Tao, Shizhe Fang, Haixu Liu, Jianqi Lu, Jiang Wang, Qiang Ma

“…Our results demonstrate that SVR-MDR model outperform other machine learning models such as kernel ridge regression and decision tree models, and SVR-MDR and RSVR-MDR models outperform conventional loglinear regression and multinomial models, because SVR can map the complex nonlinear function of multiple variables and consider the available information of buildings especially the fundamental frequency. …”
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Accurate Sugarcane Detection and Row Fitting Using SugarRow-YOLO and Clustering-Based Spline Methods for Autonomous Agricultural Operations by Guiqing Deng, Fangyue Zhou, Huan Dong, Zhihao Xu, Yanzhou Li

“…This model introduces the WTConv convolutional modules to expand the sensory field and improve computational efficiency, adopts the iRMB inverted residual block attention mechanism to enhance the modeling capability of crop spatial structure, and uses the UIOU loss function to effectively mitigate the misdetection and omission problem in the region of dense and overlapping targets. …”
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Biomechanical Analysis of <i>Camellia oleifera</i> Branches for Optimized Vibratory Harvesting by Rui Pan, Ziping Wan, Mingliang Wu, Shikui Lu, Lewei Tang

“…The nonlinear least squares method, based on the hyperbolic tangent function, was employed to fit the bending load–deflection curves of the branches. …”
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The Structural Model of Happiness Based on Mindfulness with Mediator Role of Emotional Intelligence, Self-Compassion and Resilience by Mohammad Reza Tamannai far, Faezeh Taraffo

“…Pearson's correlation coefficient method and structural equation modeling were used to analyze the data. …”
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Influence of Migratory Strategy, Group Size, and Environmental Conditions on the Movements of Caribou in Eastern Alaska by Kyle Joly

“…Migration is a diverse behavior exhibited by a wide array of organisms. Variability in the type of movements is rooted in their purpose, environmental factors, demographics, and individual physiological condition. …”
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Mechanical and Civil Engineering Optimization with a Very Simple Hybrid Grey Wolf—JAYA Metaheuristic Optimizer by Chiara Furio, Luciano Lamberti, Catalin I. Pruncu

“…The proposed SHGWJA was tested very successfully in seven “real-world” engineering optimization problems taken from various fields, such as civil engineering, aeronautical engineering, mechanical engineering (included in the CEC 2020 test suite on real-world constrained optimization problems) and robotics; these problems include up to 14 optimization variables and 721 nonlinear constraints. Two representative mathematical optimization problems (i.e., Rosenbrock and Rastrigin functions) including up to 1000 variables were also solved. …”
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Safety Assessment of Loop Closing in Active Distribution Networks Based on Probabilistic Power Flow by Wenchao Cai, Yuan Gao, Xiping Zhang, Qin Si, Jiaoxin Jia, Bingzhen Li

“…By modeling DGs and loads as random variables, their cumulants are efficiently obtained through LHS. …”
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Investigation of a chest radiograph-based deep learning model to identify an imaging biomarker for malnutrition in older adults by Ryo Sasaki, Yasuhiko Nakao, Fumihiro Mawatari, Takahito Nishihara, Masafumi Haraguchi, Masanori Fukushima, Ryu Sasaki, Satoshi Miuma, Hisamitsu Miyaaki, Kazuhiko Nakao

“…The predicted data were evaluated by computing the correlation coefficients and area under the curve (AUC). Results: As a numerical variables analysis, albumin and hemoglobin predictions were relatively accurate (R=0.71, 0.74). …”
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Clinical diagnostic and prognostic value of homocysteine combined with hemoglobin [f (Hcy-Hb)] in cardio-renal syndrome caused by primary acute myocardial infarction by Min-Juan Zeng, Yan-Quan Liu, Ping Pang, Xiao-Jun Chen, He Huang, Rui-Na Huang, Shi-Sen Li, He-Hui Zhang, Yue Yin, Chang-Sen Wang, Jin-Da Lin, Lin Lin, Shao-Peng Chen, Li Xu, Liang-Qing Zhang

“…Conclusion The newly constructed function [f (Hcy-Hb)], integrating Hcy and Hb levels, exhibits robust diagnostic and prognostic value in CRS. …”
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Investigating the relationship between the monetary policy shock through the exchange rate channel on the management quality index in the banking system: by examining the productiv... by Farhad Sharifi Bagha, Jafar Haqiqat, Zahra Karimi Takanloo

“…During this time, the number of changes in the target variables by the variables themselves decreases and the changes in the variables caused by the shock of the exchange rate channel increase. …”
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TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluat... by S. Schmitt, S. Schmitt, S. Schmitt, F. J. Fischer, J. G. C. Ball, N. Barbier, M. Boisseaux, D. Bonal, B. Burban, X. Chen, G. Derroire, G. Derroire, G. Derroire, J. W. Lichstein, D. Nemetschek, N. Restrepo-Coupe, S. Saleska, G. Sellan, P. Verley, G. Vincent, C. Ziegler, J. Chave, I. Maréchaux

“…The simulated canopy height distribution showed a high correlation coefficient (CC) with observed aerial and satellite data (CC <span class="inline-formula">&gt;</span> 0.92), while the species and functional composition were well represented (CC <span class="inline-formula">&gt;</span> 0.75). …”
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Modelación de la Solubilidad de Panela Granulada y Otros Edulcorantes en Agua by Nelly Lara, Alejandra Clavijo, Verónica Barrera

“…The goodness of the constants of fitting solubility models evaluated in terms of regression statistics and coefficients. Only, the sanitary condition of sugarcane showed significant effect on the solubility constant b. …”
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Modelación de la Solubilidad de Panela Granulada y Otros Edulcorantes en Agua by Nelly Lara, Alejandra Clavijo, Verónica Barrera

“…The goodness of the constants of fitting solubility models evaluated in terms of regression statistics and coefficients. Only, the sanitary condition of sugarcane showed significant effect on the solubility constant b. …”
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Modelación de la Solubilidad de Panela Granulada y Otros Edulcorantes en Agua by Nelly Lara, Alejandra Clavijo, Verónica Barrera

“…The goodness of the constants of fitting solubility models evaluated in terms of regression statistics and coefficients. Only, the sanitary condition of sugarcane showed significant effect on the solubility constant b. …”
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Characterizing Performance on a Suite of English-Language NeuroUX Mobile Cognitive Tests in a US Adult Sample: Ecological Momentary Cognitive Testing Study by Emily W Paolillo, Jessica Bomyea, Colin A Depp, Ashley M Henneghan, Anunay Raj, Raeanne C Moore

“…The tests tapped the domains of executive function, processing speed, reaction time, recognition memory, and working memory. …”
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Engineering safe anti-CD19-CD28ζ CAR T cells with CD8a hinge domain in serum-free media for adoptive immunotherapy by Muthuganesh Muthuvel, Muthuganesh Muthuvel, Thamizhselvi Ganapathy, Trent Spencer, Sunil S. Raikar, Saravanabhavan Thangavel, Alok Srivastava, Alok Srivastava, Alok Srivastava, Sunil Martin

“…CARs are modular synthetic antigen receptors integrating the single-chain variable fragment (scFv) of an immunoglobulin molecule to the TCR signaling. …”
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Deep Learning Approaches for Morphological Classification of Intestinal Organoids by Giovanni Cicceri, Sebastiano Di Bella, Simone Di Franco, Giorgio Stassi, Matilde Todaro, Salvatore Vitabile

“…The use of deep learning (DL) in organoid image analysis becomes crucial to handle complexity, variability, and large amounts of data efficiently and accurately, overcoming the limitations of traditional image processing approaches. …”
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