Machine learning-assisted analysis of serum metabolomics and network pharmacology reveals the effective compound from herbal formula against alcoholic liver injury by Jiamu Ma, Peng Wei, Xiao Xu, Ruijuan Dong, Xixi Deng, Feng Zhang, Mengyu Sun, Mingxia Li, Wei Liu, Jianling Yao, Yu Cao, Letian Ying, Yuqing Yang, Yongqi Yang, Xiaopeng Wu, Gaimei She

“…It was postulated that the effective compounds would bind with key targets from the PI3K-AKT signaling pathway, as indicated by the prediction model of compound-target interaction (R2 > 0.95). …”
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Radiomics in the differential diagnosis of glioblastoma under the primary neurooncoimaging conditions by Nikita E. Maslov, Daria A. Valenkova, Alexander M. Sinitсa, Gennadiy E. Trufanov, Vladimir M. Moiseenko, Alexander Yu. Efimtsev, Vera V. Chernobrivtseva

“…The aim of our study is to develop a radiomics model for IDH mutation status prediction, which can be applied to primary diagnostic imaging in patients with suspected adult-type diffuse gliomas. …”
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Classifying schizophrenia using functional MRI and investigating underlying functional phenomena by Yangyang Liu, Bi Wan, Zixuan Liu, Shuaiqi Zhang, Pei Liu, Ningning Ding, Yuxin Wang, Jun Dong, Moiz Kabeer Ahmad, Haisan Zhang

“…Results: The average prediction accuracy of various ML classifiers reached 0.9241 by fALFF, ReHo, and DC values. …”
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Remote Embedded System for Agricultural Field Monitoring: Enhancing Resource Allocation in Agriculture by Ronald J. Contijo, Lucas C. de Camargo, Renan O. A. Takeuchi, André L. S. Moscato, Lafaiete H. R. Leme, Wenderson N. Lopes

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Combining methylated RNF180 and SFRP2 plasma biomarkers for noninvasive diagnosis of gastric cancer by Zhihao Dai, Jin Jiang, Qianping Chen, Minghua Bai, Quanquan Sun, Yanru Feng, Dong Liu, Dong Wang, Tong Zhang, Liang Han, Litheng Ng, Jun Zheng, Hao Zou, Wei Mao, Ji Zhu

“…Area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were determined. …”
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On the Extrapolation of Generative Adversarial Networks for Downscaling Precipitation Extremes in Warmer Climates by Neelesh Rampal, Peter B. Gibson, Steven Sherwood, Gab Abramowitz

“…For extreme precipitation (99.5th percentile), RCM simulations predict a robust end‐of‐century increase with future warming (∼5.8%/°C on average from five simulations). …”
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Physics-Informed Neural Network for Load Margin Assessment of Power Systems with Optimal Phasor Measurement Unit Placement by Murilo Eduardo Casteroba Bento

“…The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction.…”
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Chaotic Behaviors and Coexisting Attractors in a New Nonlinear Dissipative Parametric Chemical Oscillator by Y. J. F. Kpomahou, A. Adomou, J. A. Adéchinan, A. E. Yamadjako, I. V. Madogni

“…The performed numerical simulations confirm the obtained analytical predictions. Second, the prediction of coexisting attractors is investigated by solving numerically the new nonlinear parametric ordinary differential equation via the fourth-order Runge–Kutta algorithm. …”
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The Multiobjective Based Large-Scale Electric Vehicle Charging Behaviours Analysis by Yimin Zhou, Zhifei Li, Xinyu Wu

“…The residential traveling historical data of EVs are analyzed and fitted to predict their probability distribution, so that the models of the traveling patterns can be established. …”
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An Early Warning Method of Unbalanced Power Battery Capacity Attenuation Based on ARIMA Model by KANG Zuchao, XIONG Gang, WANG Wenming, ZHONG Xiongwu, ZHOU Yanhui

“…It is of important significance to study a timely warning algorithm of the unbalance of power battery capacity attenuation. …”
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Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks by Xiangwu Deng, Zhiping Peng, Delong Cui

“…Motivated by this, this article has studied soft-sensing technology for measuring diene yields. A diene yield prediction method based on a deep belief network algorithm network is proposed, and the regularity of historical diene yield data is fully explored by the method. …”
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Optimization of Adversarial Reprogramming for Transfer Learning on Closed Box Models by Alexander Bott, Moritz Siems, Alexander Puchta, Jurgen Fleischer

“…In this work, we optimise a transfer learning approach for predicting the Remaining Useful Life (RUL) of ball bearings, particularly in scenarios with limited data availability. …”
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sedInterFoam 1.0: a three-phase numerical model for sediment transport applications with free surfaces by A. Mathieu, Y. Kim, T.-J. Hsu, C. Bonamy, J. Chauchat

“…Using sedInterFoam, four test cases are successfully reproduced to validate the free-surface evolution algorithm's implementation, mass conservation of sediment and fluid phases, and predictive capabilities and to demonstrate its potential in modeling a broader range of coastal applications with sediment transport dominated by surface waves.…”
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Integration of Accelerometers and Machine Learning with BIM for Railway Tight- and Wide-Gauge Detection by Jessada Sresakoolchai, Chayutpong Manakul, Ni-Asri Cheputeh

“…These data are processed and analyzed using supervised machine-learning algorithms to classify and predict potential tight- and wide-gauge events. …”
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An accuracy-privacy optimization framework considering user’s privacy requirements for data stream mining by Waruni Hewage, R. Sinha, M. Asif Naeem

“…Additionally, a data fitting module using kernel regression is integrated, a unique approach that predicts accuracy levels based on user-defined privacy thresholds. …”
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Design of Trabecular Bone Mimicking Voronoi Lattice-Based Scaffolds and CFD Modelling of Non-Newtonian Power Law Blood Flow Behaviour by Haja-Sherief N. Musthafa, Jason Walker

“…A Finite Element Method-based computational fluid dynamics (CFD) simulation was performed on all designed Voronoi scaffolds to predict the pressure drops and permeability of non-Newtonian blood flow behaviour using the power law material model. …”
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Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption by Manar Ahmed Hamza, Maha M. Althobaiti, Fahd N. Al-Wesabi, Rana Alabdan, Hany Mahgoub, Anwer Mustafa Hilal, Abdelwahed Motwakel, Mesfer Al Duhayyim

“…In the existing system, random forest and ANN methods were used for TC and SMX for predicting the quantities of antibiotics in the CBMs. …”
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Topology aware multitask cascaded U-Net for cerebrovascular segmentation. by Pierre Rougé, Nicolas Passat, Odyssée Merveille

“…This loss requires computing the skeletons of both the manual annotation and the predicted segmentation in a differentiable way. Currently, differentiable skeletonization algorithms are either inaccurate or computationally demanding. …”
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Higgs boson production in association with massive bottom quarks at NNLO+PS by Christian Biello, Javier Mazzitelli, Aparna Sankar, Marius Wiesemann, Giulia Zanderighi

“…We present an extensive phenomenological analysis both at the inclusive level and considering bottom jets using flavour-tagging algorithms. By comparing four-flavour and five-flavour scheme predictions at NNLO+PS, we find that the NNLO corrections in the four-flavour scheme resolve the long-standing tension between the two schemes. …”
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Proteína LIC10494 de Leptospira interrogans serovar Copenhageni: modelo estructural y regiones funcionales asociadas by Orlando Emilio Acevedo, George Emílio Barreto, Janneth González-Santos

“…Protein LIC10494 of Leptospira interrogans serovar Copenhageni: structural model and associated functional regions. Objective.Predict by computational means the 3D structure of the antigenic protein LIC10494 and report associated important functional regionsfor its pathogenicity and immunogenicity. …”
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