Prognostic assessment of early-stage liver cirrhosis induced by HCV using an integrated model of CX3CR1-associated immune infiltration genes by Haozheng Cai, Jing Zhang, Chuwen Chen, Junyi Shen, Xiaoyun Zhang, Wei Peng, Chuan Li, Haopeng Lv, Tianfu Wen

“…The developed combined risk model effectively predicted the prognosis of these individuals.…”
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Using computer modeling to find new LRRK2 inhibitors for parkinson’s disease by María C. García, Sebastián A. Cuesta, José R. Mora, Jose L. Paz, Yovani Marrero-Ponce, Frank Alexis, Edgar A. Márquez

“…This study aims to create a detailed dataset to build strong predictive models with various machine learning algorithms. …”
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Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling by Kabiruddin Khan, Ramin Abdullayev, Gopala Krishna Jillella, Varun Gopalakrishnan Nair, Mahmoud Bousily, Supratik Kar, Agnieszka Gajewicz-Skretna

“…Three machine learning methods MLR, PLS, and kNN were employed to develop predictive models. Further, q-RASTR models were developed to enhance the predictive power by similarity measures concept of the studied cyanides by integrating features from QSTR and ScSD models. …”
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Production, Transport, and Destruction of Dust in the Kuiper Belt: The Effects of Refractory and Volatile Grain Compositions by Thomas Corbett, Alex Doner, Mihály Horányi, Pontus Brandt, Will Grundy, Carey M. Lisse, Joel Parker, Lowell Peltier, Andrew R. Poppe, Kelsi N. Singer, S. Alan Stern, Anne J. Verbiscer

“…Models based on early SDC measurements predicted a peak dust number density at a heliocentric distance of ∼40 au, followed by a rapid decline. …”
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How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences by Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, Alexander Brenning, Wantong Li, Markus Reichstein, Joachim Denzler, Wei Shangguan, Guo Yu, Feini Huang, Jakob Zscheischler

“…IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. …”
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Experimental study on the drag reduction performance of sodium alginate in saline solutions by Zhensong Cheng, Xin Zhang, Xiaodong Dai, Hengli Zhai, Xinwang Song, Xudong Wang, Liang Gao, Guoxin Zhang, Yuan Lu, Lei Li, Xiu Yan, Jianhua Zhang

“…By comparing the predicted results with the experimental outcomes, we found that the accuracy of the predictive model is high, with the error controlled within ± 20%. …”
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Comprehensive analysis of ESCRT transcriptome-associated signatures and identification of the regulatory role of LMO7-AS1 in osteosarcoma by Shibing Zhao, Dasheng Tian, Fei Huang, Lei Wang, Jinhao Cheng, Zhitao He, Qitian Shen, Shuai Liang, Deliang Gong, Jun Liu, Chengfeng Yi, Chun Zhang, Erbao Bian, Juehua Jing, Tao Wang

“…First, we built a prognostic signature using 7 ESCRT-related genes (ERGs) to predict OS patient prognosis. Analysis of internal and external datasets revealed that the ERG signature has good predictive ability and reproducibility. …”
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SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models by Gazi Husain, Daniel Nasef, Rejath Jose, Jonathan Mayer, Molly Bekbolatova, Timothy Devine, Milan Toma

“…This bias can significantly undermine accurate predictions in real-world scenarios, highlighting the importance of the robust handling of imbalanced data for dependable results. …”
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Stress intensity factor solutions for CTS mixed mode specimen by F.V. Antunes, R. Branco, J.A.M. Ferreira, L.P. Borrego

“…A total number of 1120 cracked geometries were studied numerically with the finite element method and analytical solutions were fitted to the numerical predictions. An average difference of 0.53 % was found between numerical predictions and the analytical solution proposed for KI. …”
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FRACTURE CRITERION OF THE THEORY OF CRITICAL DISTANCE FOR NOTCHES by LIU XiaoMei, BIAN YongMing, LIANG YongCheng, LI AnHu

“…The values of fracture loading for all notches under mode I were predicted and also compared with the experimental values. …”
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Automated and highly parallelized Bayesian optimization scheme for direct drive fusion experiments on OMEGA by V. Gopalaswamy, A. Lees, R. Ejaz, C. A. Thomas, T. J. B. Collins, K. S. Anderson, W. Ebmeyer, R. Betti

“…We use this algorithm to find a markedly improved design for the performance implosions on OMEGA that is predicted to hydroequivalently scale to ignition at 2.15 MJ.…”
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Active Suppression of Rotating Stall Inception with Distributed Jet Actuation by Huu Duc Vo

“…Active control experiments with proportional feedback control show that the predicted stall precursors are suppressed to give a 5.5% range extension in compressor flow coefficient. …”
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Performance Evaluation and Estimation of Energy Measures of Grid-Connected PV Module by R. Srimathi, J. Meenakshi, R. Vijayabhasker, Semagn Shifere Belay

“…These predictions are then contrasted to the outcomes of the actual measurements. …”
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PREDICCIÓN DE TOXICIDAD ACUÁTICA MEDIANTE QSAR TOOLBOX: UN ENFOQUE IN SILICO PARA LA EVALUACIÓN DE RIESGOS QUÍMICOS EN LA FRACTURACIÓN HIDRÁULICA by Adolfo E. Ensuncho, Diana B. Ramírez, Jesús M. López

“…This method implements the quantitative structure-activity relationship to determine the toxicity of chemicals and predict their endpoints in biological systems, such as the mean lethal concentration (LC50). …”
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Contextualized embeddings for semantic change detection: Lessons learned by Andrey Kutuzov, Erik Velldal, Lilja Øvrelid

“…This method is used as a basis for an in-depth analysis of the degrees of semantic change predicted for English words across 5 decades. Our findings show that contextualized methods can often predict high change scores for words which are not undergoing any real diachronic semantic shift in the lexicographic sense of the term (or at least the status of these shifts is questionable). …”
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A priori physical information to aid generalization capabilities of neural networks for hydraulic modeling by Gianmarco Guglielmo, Andrea Montessori, Jean-Michel Tucny, Michele La Rocca, Pietro Prestininzi

“…Consequently, many purely data-driven Neural Networks have shown limited capabilities when tasked with predicting new scenarios. In this work, we propose introducing physical information into the training phase in the form of a regularization term. …”
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LOAD TRANSFER ANALYSIS IN BONDED-BOLTED HYBRID JOINTS by CHEN XiangMing, CHAI YaNan, YUAN Fei, YU Fei, CAO Su

“…Meanwhile,experimental data of the bonded joints,the bolted joints and the bonded/bolted hybrid joints are obtained and compared with the predictions. A good agreement between experimental results and numerical predictions is observed. …”
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FATIGUE LIFE ANALYSIS OF SIMULATED AIRCRAFT BEAM STRUCTURE CONNECTION by ZHENG Jie, LIU Yang, TONG MingBo

“…Experimental and simulation methods were used to predict the fatigue life of the connecting part of the aircraft beam structure. …”
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Extension of the TOPSIS Method for Decision Making Problems under Complex Fuzzy Data Based on the Central Point Index by Salameh Barbat, Mahnaz Barkhordariahmadi, Vahidmomenaei Kermani

“…Quantum mechanics wave functions could not be analyzed, nor could signals or time series or stock exchange transactions predict factors of a multiperiod alternation, nor could predictions be made about any of these variables. …”
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Two-Dimensional CFD Modeling of Hysteresis Behavior of MR Dampers by Pengfei Guo, Jing Xie

“…The main advantage of the proposed 2D model of MR dampers lies in that it can be used to predict dynamic behavior of MR devices of arbitrary geometries. …”
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