Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine) by Chuin-Hen Liew, Song-Quan Ong, David Chun-Ern Ng

“…However, machine learning (ML) models to predict COVID-19 hospitalization in Asian children are lacking. …”
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High Order QCD Predictions for Inclusive Production of W Bosons in pp Collisions at s=13 TeV by Hasan Ogul, Kamuran Dilsiz, Emrah Tiras, Ping Tan, Yasar Onel, Jane Nachtman

“…It is found that the CT14 and NNPDF 3.0 predictions with NNLO QCD corrections are about 4% higher than the NLO CT14 and NNPDF 3.0 predictions while MMHT 2014 predictions with NLO QCD corrections are smaller than its NNLO QCD predictions by approximately 6%. …”
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A New Model for Predicting Flow in Fractured-Vuggy Carbonate Reservoirs under Pseudosteady State Condition by Nai Cao, Gang Lei, Yuan Su, Hong Li, Jiali Zhang, Ci Zhang

“…The present work is aimed at developing a new analytical model for predicting flow in fractured-vuggy carbonate reservoirs under pseudosteady state condition. …”
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A New Network-Based Strategy for Predicting the Potential miRNA-mRNA Interactions in Tumorigenesis by Jiwei Xue, Fanfan Xie, Junmei Xu, Yuan Liu, Yu Liang, Zhining Wen, Menglong Li

“…To facilitate the clinical cancer research, we proposed a network-based strategy to identify the cancer-related miRNAs and to predict their targeted genes based on the gene expression profiles. …”
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Development of a Hybrid Attention Transformer for Daily PM_2.5 Predictions in Seoul by Hyun S. Kim, Kyung M. Han, Jinhyeok Yu, Nara Youn, Taehoo Choi

Subjects: “…daily PM_2.5 prediction…”
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Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression Analysis by Yufeng Qian

“…Due to the time limitations and concerns of finishing resources, it is obligate to develop some relationships for predicting compaction parameters such as maximum dry unit weight (γdmax) and optimum moisture content (ωopt) from easily estimated index properties. …”
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Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning by Jian Wang, Chengsong Duan, Qiao Yu, Cheng Yang

Subjects: “…Sky wave propagation prediction…”
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Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network by S. Karthiyaini, K. Senthamaraikannan, J. Priyadarshini, Kamal Gupta, M. Shanmugasundaram

“…The present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28 days. …”
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Analytical Model to Predict Residual Flexural Capacity of Recycled Aggregate Concrete Beams with Corroded Longitudinal Rebars by Zhenghao Zou, Guojiao Yang, Tian Su

“…Based on theoretical analysis and experiment results, the influence of corrosion on flexural cracking moment was analysed and a model to predict the residual flexural capacity of RAC beams with corroded longitudinal rebars was established. …”
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Comment on “A Sustained Reduction in Serum Cholinesterase Enzyme Activity Predicts Patient Outcome following Sepsis” by Carlo Chiarla, Ivo Giovannini

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Risk prediction models for post-intensive care syndrome of ICU discharged patients: A systematic review by Pengfei Yang, Fu Yang, Qi Wang, Fang Fang, Qian Yu, Rui Tai

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Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning by Qiqiang Liang, Xin Xu, Shuo Ding, Jin Wu, Man Huang

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Enhancing Predictive Models in E-Commerce: A Comparative Study Using XGBoost Across Diverse Scenarios by Zhang Jianhao

“…Our strategy involves continuously feeding historical data into selected models to predict future sales volumes. By identifying patterns in the data, we aim to make the predictions more tangible and assess the validity of our approach through a statistical linear regression model. …”
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Predicting Energy and Emissions in Residential Building Stocks: National UBEM with Energy Performance Certificates and Artificial Intelligence by Carlos Beltrán-Velamazán, Marta Monzón-Chavarrías, Belinda López-Mesa

“…This study addresses the challenge by developing a national-scale urban building energy model (nUBEM) using artificial intelligence to predict non-renewable primary energy consumption and associated GHG emissions for residential buildings. …”
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Systematic review and meta‑analysis of factors predicting postoperative lung function after lung cancer resection by Hongling Wang, Lihong He, Xiaoyun Hu, Gongxue Xian

“…The meta-analysis showed that computed tomography (CT) volume and density measurement had the highest level of accuracy for predicting postoperative FEV1, with a mean difference between the predicted and actual value of 83 ml (95% CI, 41–116). …”
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Predicting the Development of a Low Gas-Saturated Zone of the Medvezhye Field Based on Geological and Hydrodynamic Modeling by Artem A. Shirjaev, Kirill E. Vinogradov, Y. V. Vaganov, Vitaliya O. Naumenko

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The role of intestinal ultrasound with colour Doppler in predicting the response to biologic therapy in inflammatory bowel disease patients by Mohamed Ali El-Nady, Nour Eldeen El-Hefny, Hayam Fathy, Wageeh Ali, Ahmed Mohammed Ashmawy

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Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests by Jichong Zhu, Yong Zhao, Chengqian Huang, Chenxing Zhou, Shaofeng Wu, Tianyou Chen, Xinli Zhan

“…Conclusion: We developed a diagnostic model using multiple ML methods to predict AFB results, achieving satisfactory diagnostic performance.…”
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Exploring the genetic profiles linked to senescence in thyroid tumors: insights on predicting disease progression and immune responses by Baoliang Zhang, Yanping Pang

“…Kaplan-Meier survival curves were plotted, and Receiver Operating Characteristic (ROC) curves rigorously confirmed the accuracy of model predictions.ResultsTo evaluate the predictive power of prognostic models across different phenotypic traits, we performed survival analysis, Gene Set Enrichment Analysis (GSEA), and immune-related differential analysis. …”
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Applying Stimulus-Organism-Response (Sor) Adoption for Predicting Generation Z’s Intention to Visit Tourism in Indonesia by Farah Putri Wenang Lusianingrum, Widya Nur Bhakti Pertiwi

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