Non-invasive liver fibrosis markers are increased in obese individuals with non-alcoholic fatty liver disease and the metabolic syndrome by Anders Askeland, Rikke Wehner Rasmussen, Mimoza Gjela, Jens Brøndum Frøkjær, Kurt Højlund, Maiken Mellergaard, Aase Handberg

“…We used MRI (T1 relaxation times (T1) and liver stiffness), circulating biomarkers (CK18, PIIINP, and TIMP1), and algorithms (FIB-4 index, Forns score, FNI, and MACK3 score) to assess their potential in predicting liver fibrosis risk. …”
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Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning by Sizhang Wang, Xiaoyan Wang, Jing Xia, Qiang Mu

“…Then, four overlapping M1 macrophage infiltration-related genes (M1 MIRGs), namely CCDC69, PPP1R16B, IL21R, and FOXP3, were obtained using five machine-learning algorithms. Subsequently, nomogram models were constructed to predict the incidence of Her2-positive breast cancer patients. …”
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Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing by Guolan Xian, Jiangang Liu, Yongxin Lin, Shuang Li, Chunsong Bian

“…Timely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destructive sampling, UAV remote sensing provides a timely and efficient strategy for estimating biomass. …”
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Oxidative balance is associated with diabetic kidney disease and mortality in adults with diabetes mellitus: Insights from NHANES database and Mendelian randomization by Li Jiang, Jie Jian, Xulin Sai, Hongda Yu, Wanxian Liang, Xiai Wu

“…The physical activity was identified as the core variable predicting DKD risk by two machine learning algorithms. …”
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A Reproducible Method for Donor Site Computed Tomography Measurements in Abdominally Based Autologous Breast Reconstruction by Damini Tandon, MD, Arthur Sletten, MD, PhD, Austin Ha, MD, Gary B. Skolnick, BA, MBA, Paul Commean, BEE, Terence Myckatyn, MD

“…Larger patient cohorts must be leveraged to determine correlations between abdominal CT scan findings and donor site outcomes using machine learning algorithms that generate models for predicting abdominal donor site complications.…”
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Evaluation of Time-Domain Acoustic Signature in TIG Welding of 5083 Aluminum Alloy: A Methodological Comparison of Feature Reduction Approaches by V M Gautham, A Sumesh, E V Jithin, K Rameshkumar, Dinu Thomas Thekkuden

“…The findings of the study are promising for developing online monitoring systems to predict weld quality with high accuracy.…”
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Thermal boundary condition optimization of feed system based on RSM-IRIME by Haiyang Liu, Xianying Feng, Ming Yao, Anning Wang, Yandong Liu

“…And for the first time, the results are demonstrated that the simulation results solved by the optimized thermal boundary conditions can still achieve the dynamic following of the temperature rise and deformation data under the variable rotational speed, and thus maintain a high predictive accuracy. The effectiveness of the proposed optimization method is verified. …”
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Resonant solitary states in complex networks by Jakob Niehues, Serhiy Yanchuk, Rico Berner, Jürgen Kurths, Frank Hellmann, Mehrnaz Anvari

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A multi-model approach for estimation of ash yield in coal using Fourier transform infrared spectroscopy by Sameeksha Mishra, Anup K. Prasad, Arya Vinod, Anubhav Shukla, Shailayee Mukherjee, Bitan Purkait, Atul K. Varma, Bhabesh C. Sarkar

“…Multiple algorithms, including piecewise linear regression (PLR), artificial neural networks (ANN), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), were utilized to predict the ash yield in coal. …”
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Dose-Weighted Network Pharmacology: Evaluating Traditional Chinese Medicine Formulations for Lumbar Disc Herniation by Zhou C, Xiang T, Yu Y, Ma H, Liu C, Yang F, Yang L

“…TSD, YJD, and PQT were evaluated in animal experiments, and the algorithm’s feasibility was validated through GO and KEGG pathway analysis, Thermal Hyperalgesia Test, H&E staining, Western blotting (WB), RT-PCR, and ELISA assays.Results: The computational model indicated that YJD and PQT had higher predicted efficacy compared to TSD. …”
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Multi-source data-driven Bayesian network for risk analysis of maritime accidents in the high sea by Xiaotong Qu, Chengbo Wang, Chengbo Wang, Ruijia Zhao, Mingxing Fang, Mingxing Fang, Mingxing Fang, Xinlian Xie

“…The DDBN model achieves an accident prediction accuracy of 84.0%, with both precision and recall exceeding 75%. …”
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Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit by Joaquin Gonzalez, Liliana Avelar Sosa, Gabriel Bravo, Oliverio Cruz-Mejia, Jose-Manuel Mejia-Muñoz

“…Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. …”
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Diagnostic performance of YEARS criteria combined with point-of-care lung and venous ultrasound in suspected pulmonary embolism by Chiara Gigli, Giovanni Volpicelli, Antonio Annovi, Ángel Sánchez Garrido-Lestache, Maria Cristina Vedovati, Cecilia Becattini, Simone Vanni, Peiman Nazerian

“… Clinical prediction rules and bedside lung and venous ultrasound are frequently used in the diagnostic workup of suspected pulmonary embolism (PE). …”
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Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios by Hongwei Wang, Wei Liu, Chenghui Wang, Kao Guo, Zihao Wang

“…To address this, a novel self-organizing prediction method for spatio-temporal resource allocation is proposed. …”
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UAV-Based Intelligent Sensing Over CF-mMIMO Communications: System Design and Experimental Results by Jian Sun, Wei Shi, Jiacheng Yao, Wei Xu, Dongming Wang, Yang Cao

“…Moreover, we propose an enhanced target tracking algorithm, which is further integrated with Kalman filter-based trajectory prediction for performance improvement. …”
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A Preemptive Scan Speed Control Strategy Based on Topographic Data for Optimized Atomic Force Microscopy Imaging by Thi Thu Nguyen, Oyoo Michael Juma, Luke Oduor Otieno, Thi Ngoc Nguyen, Yong Joong Lee

“…The calculated mean square errors (MSEs) show that the combination of speed adjustments and preemptive surface topography prediction has successfully allowed us to suppress the potential oscillations during the speed adjustment process, thereby enhancing the stability of the adaptive AFM system as well.…”
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Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease by Hsin-Yueh Liang, Hsin-Yueh Liang, Kai-Cheng Hsu, Kai-Cheng Hsu, Kai-Cheng Hsu, Shang-Yu Chien, Chen-Yu Yeh, Ting-Hsuan Sun, Meng-Hsuan Liu, Kee Koon Ng

“…A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. …”
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The Significance of MAPK Signaling Pathway in the Diagnosis and Subtype Classification of Intervertebral Disc Degeneration by Yong Liu, Xueyan Chen, Jingwen Chen, Chao Song, Zhangchao Wei, Zongchao Liu, Fei Liu

“…Additionally, we applied PPI networks, LASSO analysis, the RF algorithm, and the SVM‐RFE algorithm to identify core MAPK‐related genes. …”
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In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure by Lamiae Elkhattabi, Imane Morjane, Hicham Charoute, Soumaya Amghar, Hind Bouafi, Zouhair Elkarhat, Rachid Saile, Hassan Rouba, Abdelhamid Barakat

“…Stability analysis predicted 9 nsSNPs (I32S, C51Y, G58E, G58R, C78S, G79C, W98C, C103G, and C104Y) which can decrease protein stability with at least three out of the four algorithms used in this study. …”
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Modelling flame-to-fuel heat transfer by deep learning and fire images by Caiyi Xiong, Zilong Wang, Xinyan Huang

“…Results show that the proposed AI algorithm trained by flame images can predict both the convective and radiative heat flux distributions on the condensed fuel surface with a relative error below 20%, based on the input of real-time flame morphology that can be captured by a larger grid size. …”
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