A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

“…This study will have made attempt to address the complexities involves in the concrete mix designs process with the aim of achieving the desired 28-day compressive strength for FAGP. …”
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Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning by Junge Wang, Jie Chai, Li Chen, Tinghuan Zhang, Xi Long, Shuqi Diao, Dong Chen, Zongyi Guo, Guoqing Tang, Pingxian Wu

“…Machine learning (ML) techniques, which can process high-dimensional data, offer promising solutions. …”
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REGIONAL DEVELOPMENT'S PERSPECTIVES IN EUROPE by Mihai, OPRESCU

“…Regional development is an evolving process that aims at reducing socio-economic disparities at the level of a certain territory, while boosting its potential. …”
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Technical Evolution and Intelligent Development of Rail Transit Equipments by FENG Jianghua

“…Rail transit has been evolving as a vehicle equipment-centric complex system. During the process of past industrial revolutions, rail transit equipment kept standing at the forefront of technical revolutions and boosting the process of technical application and industrialization. …”
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Carbon Sequestration Strategies in Regenerative Agricultural Systems by Leveraging Wireless Sensor Networks for Precision Carbon Management by Al-Jawahry Hassan M., AI_Sadi Hafidh l., Rajasekhar Boddu

“…Then the digital twin model is fed with the collected data, that is, the soil carbon processes in the real world are mirrored in a virtual platform. …”
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Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. …”
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Barlow Twins deep neural network for advanced 1D drug–target interaction prediction by Maximilian G. Schuh, Davide Boldini, Annkathrin I. Bohne, Stephan A. Sieber

“…The use of our hybrid approach of deep learning and gradient boosting machine as the underlying predictor ensures fast and efficient predictions without the need for substantial computational resources. …”
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Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai by V. A. Nevzorova, T. A. Brodskaya, K. I. Shakhgeldyan, B. I. Geltser, V. V. Kosterin, L. G. Priseko

“…The χ2, Fisher and MannWhitney tests, univariate logistic regression (LR) were used for data processing and analysis. To build predictive models, we used following machine learning (ML) methods: multivariate LR, Weibull regression, and stochastic gradient boosting.Results. …”
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Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study by Jie Zhang, Xinyi Feng, Wenhe Wang, Shudan Liu, Qin Zhang, Di Wu, Qin Liu

“…Five models, (a) random forest, (b) extreme gradient boosting (XGBoost), (c) logistic regression, (d) neural network, and (e) support vector machine were applied to predict loneliness. …”
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Predictive potential of cardiovascular risk factors and their associations with arterial stiffness in people of European and Korean ethnic groups by T. A. Brodskaya, V. A. Nevzorova, K. I. Shakhgeldyan, B. I. Geltser, D. A. Vrazhnov, Yu. V. Kistenev

“…Developed using modern machine learning technologies, the assessment aortic PWV models taking into account the ethnic factor can be a useful tool for processing and analyzing data in predictive studies.…”
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Accelerated cGMP production of near-native HIV-1 Env trimers following electroporation transfection and immunogenicity analysis by Shridhar Bale, Elena Gustchina, Javier Guenaga, Victor Ayala, Wen-Hsin Lee, Gabriel Ozorowski, Stephen Whitney, Richard Wilson, Sabyasachi Baboo, Jolene K. Diedrich, Esmeralda D. Doyle, Lauren Hudacik, Elana Ben-Akiva, Kristen A. Rodrigues, Darrell J. Irvine, John R. Yates, James C. Paulson, Andrew B. Ward, Timothy Fouts, Richard T. Wyatt

“…Following additional downstream processing steps, purified trimer was vialed, frozen and stored at –80 °C. …”
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Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data by Oussama Arab, Soufiana Mekouar, Mohamed Mastere, Roberto Cabieces, David Rodríguez Collantes

“…Regarding this challenge, our research proposes a new approach in the signal processing chain and feature extraction from microtremor data that focuses mainly on the Horizontal-to-Vertical Spectral Ratio (HVSR) so as to assess liquefaction potential as a natural hazard using AI. …”
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Land cover classification using Land Parcel Identification System (LPIS) data and open source Eo-Learn library by Fatih Fehmi Şimşek, Süleyman Savaş Durduran

“…In the classification process, the Light Gradient Boosting Machines (LightGBM) algorithm in the Eo-Learn library and the physical blocks produced within the scope of the Land Parcel Identification System (LPIS) project were used as ground truth data; arable land, bare land, forest, artificial surface, shrubland, tree crops and water classes were created. …”
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A Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data by Teresa Rubio, Maria Chernigovskaya, Susanna Marquez, Cristina Marti, Paula Izquierdo-Altarejos, Amparo Urios, Carmina Montoliu, Vicente Felipo, Ana Conesa, Victor Greiff, Sonia Tarazona

“…The provided computational pipeline functions as a resource to facilitate TCR profiling from RNA-seq data boosting immunophenotype analyses of immunological diseases.…”
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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors by Elham Raiesi Nafchi, Pedram Fadavi, Sepideh Amiri, Susan Cheraghi, Maryam Garousi, Mansoureh Nabavi, Iman Daneshi, Marzieh Gomar, Malihe Molaie, Ali Nouraeinejad

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Recent advances of P38MAPK signaling pathway in diabetic nephropathy by Ma Dong-mei, Zhang Xiang-gui

“…As one of the most serious chronic complications of diabetes, diabetic nephropathy is a major cause of chronic renal failure.P38 MAPK is an important signal transduction molecule in MAPK signal pathway.It accelerates the process of diabetic nephropathy by stimulating the production of reactive oxygen species, boosting the release of inflammatory mediators, regulating renin-angiotensin system and affecting the formation and degradation of glomerular mesangial matrix.And p38 MAPK inhibitor and traditional Chinese medicine with p38 MAPK as therapeutic target are expected to become new pharmaceutical preparations for diabetic nephropathy.…”
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Transition metal vacancy and position engineering enables reversible anionic redox reaction for sodium storage by Congcong Cai, Xinyuan Li, Jiantao Li, Ruohan Yu, Ping Hu, Ting Zhu, Tianyi Li, Sungsik Lee, Nuo Xu, Hao Fan, Jinsong Wu, Liang Zhou, Liqiang Mai, Khalil Amine

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A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite by Prashant Anerao, Atul Kulkarni, Yashwant Munde, Namrate Kharate

“…The dataset has been carefully prepared to facilitate machine learning for both training and testing, and it contains the experimental results and associated process parameters. Four distinct machine learning algorithms have been selected for predictive modeling: Linear Regression, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). …”
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Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring by Jie Song, Yujun Yi

“…Abstract Estuarine wetlands, formed by the interaction of fluvial and tidal processes, exhibit complex spatiotemporal variations in soil moisture and salinity. …”
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Development and Validation of Multi-zone Non-equilibrium Steam-gas Pressurizer Model by ZHU Junzhi1, QU Caoqing2, SHI Weili1, YANG Jue1, GOU Junli2, FAN Jie2, FANG Yukun1, HUANG Kai1

“…The results show that, in the boosting stage, the multi-zone non-equilibrium steam-gas pressurizer model can accurately simulate the pressure response characteristics. …”
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