Characterization of Moisture Diffusion in Cured Concrete Slabs at Early Ages by Xiao Zhang, Hongduo Zhao

“…Lower w/c ratio tends to result in larger RH reduction. RH reduction considering both effect of diffusion and self-desiccation in early-age concrete is not sensitive to w/c ratio, but to curing method. …”
Get full text

High concentration formulation developability approaches and considerations by Jonathan Zarzar, Tarik Khan, Maniraj Bhagawati, Benjamin Weiche, Jasmin Sydow-Andersen, Alavattam Sreedhara

“…In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.…”
Get full text

Method for Determining Igneous Rock Mineral Content Using Element Logging Data Based on Variational AutoEncoder by JIA Ruilong, PAN Baozhi, WANG Qinghui, LI Yan, GUAN Yao, WANG Xinru

“…It employs a VAE (Variational AutoEncoder) approach to predict mineral content and reconstruct the elemental weight content. …”
Get full text

High-performance sentiment classification of product reviews using GPU(parallel)-optimized ensembled methods by Annaluri Sreenivasa Rao, Yeruva Jaipal Reddy, Guggilam Navya, Neelima Gurrapu, Jala Jeevan, M. Sridhar, Desidi Narasimha Reddy, Siva Kumar Pathuri, Dama Anand

“…The GPU-CUDA-ENSEMBLED algorithm is a GPU-accelerated method for sentiment classification, enhancing predictive performance by minimizing variances and biases. …”
Get full text

A Hybrid Automata-Driven Machine Learning Framework for Real-Time Energy Optimization in Smart Buildings by Rikame Rajashri, Ranjan Mritunjay Kr., Jadhav Monali, Wankhede Shrushti, Bankar Sakshi, Bachhav Pranjal

“…These inputs train the predictive models like Random Forest, XGBoost, LightGBM, and Neural Networks to predict energy requirements. …”
Get full text

Classification of Vaginal Cleanliness Grades through Surface‐Enhanced Raman Spectral Analysis via The Deep‐Learning Variational Autoencoder–Long Short‐Term Memory Model by Jia‐Wei Tang, Xin‐Ru Wen, Hui‐Min Chen, Jie Chen, Kun‐Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang

“…The signal‐to‐noise ratio of the generated spectra increased from the original 2.58–11.13. Among all algorithms, the VAE–LSTM algorithm demonstrates the best prediction ability and time efficiency. …”
Get full text

Using machine learning methods to investigate the role of volatile organic compounds in non-alcoholic fatty liver disease by Chih-Hao Shen, Ruei-Hao Huang, Yaw-Kuen Li, Ta-Wei Chu, Ta-Wei Chu, Dee Pei, Dee Pei

“…Ten Mach-L techniques were employed to evaluate two predictive models: Model 1 (demographic, lifestyle, and biochemical data), and Model 2 (Model 1 + VOCs), assessed using area under the receiver operating characteristic curve (AUC).ResultsSubjects with NAFLD had significantly higher values for age, BMI, blood pressure, and other biomedical markers, except for eGFR and HDL-C. …”
Get full text

Investigating the hydrogen renaissance in the global energy transition with AI integration by Abderrahim Lakhouit

“…AI plays a pivotal role in optimizing hydrogen production methods, such as electrolysis, by enhancing process efficiencies through machine learning models that predict and optimize operational parameters. In storage, AI-driven algorithms are improving the management of hydrogen in large-scale storage systems, helping to mitigate issues such as leakage and optimizing storage conditions. …”
Get full text

Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…Abstract This study presents a comprehensive approach to predicting solubility of recombinant protein in four E. coli samples by employing machine learning techniques and optimization algorithms. …”
Get full text

Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patients by Ran Du, Lijun Jing, Denggang Fu

“…The signature showed strong predictive power with AUC values of 0.68, 0.73, and 0.76 for 2-, 3-, and 5-year survival, respectively. …”
Get full text

A Mitochondria‐Related Signature in Diffuse Large B‐Cell Lymphoma: Prognosis, Immune and Therapeutic Features by Zhen‐Zhong Zhou, Jia‐Chen Lu, Song‐Bin Guo, Xiao‐Peng Tian, Hai‐Long Li, Hui Zhou, Wei‐Juan Huang

“…Conclusions We established a novel prognostic mitochondria‐related signature by machine learning algorithm, which also demonstrated outstanding predictive value in tumor microenvironment and responses to therapies.…”
Get full text

High accuracy inverse design of reconfigurable metasurfaces with transmission-reflection-integrated achromatic functionalities by Jiang Xiao-Qiang, Fan Wen-Hui, Chen Xu, Zhao Lv-Rong, Qin Chong, Yan Hui, Wu Qi, Ju Pei

“…Artificial intelligence algorithms based on deep neural network (DNN) have become an effective tool for conceiving metasurfaces recently. …”
Get full text

Neural network pruning based on channel attention mechanism by Jianqiang Hu, Yang Liu, Keshou Wu

“…Furthermore, it effectively balances pruning efficiency and prediction accuracy.…”
Get full text

An Intelligent System for Management of Medical Equipment Maintenance by Abbas Izadi, Mohamad Amin Bakhshali, Hadi Ghasemifard, Omid Sarrafzadeh

“…The system utilizes machine learning algorithms and data analytics to predict equipment failures, schedule maintenance tasks, and manage spare parts inventory efficiently. …”
Get full text

Research on High Arch Dam Deformation Monitoring Model with Deep Capturing Related Features in Factor-time Dimensions by XUE Jianghan, ZHANG Pengtao, TIAN Jichen, LU Xiang, CHEN Jiankang, Guo Yinju

“…However, at the present stage, the dam prediction model based on machine learning mostly adopts the means of data preprocessing, using optimization algorithm, and using the model's characteristics to stack multiple models, lacking in in-depth consideration of the physical mechanism of dam deformation. …”
Get full text

Seismic Failure Probability of a Curved Bridge Based on Analytical and Neural Network Approaches by K. Karimi-Moridani, P. Zarfam, M. Ghafory-Ashtiany

“…This study focuses on seismic fragility assessment of horizontal curved bridge, which has been derived by neural network prediction. The objective is the optimization of structural responses of metaheuristic solutions. …”
Get full text

Non-Destructive Early Detection of Drosophila Suzukii Infestation in Sweet Cherries (c.v. <i>Sweet Heart</i>) Based on Innovative Management of Spectrophotometric Multilinear Corre... by Giuseppe Altieri, Mahdi Rashvand Avaei, Attilio Matera, Francesco Genovese, Vincenzo Verrastro, Naouel Admane, Orkhan Mammadov, Sabina Laveglia, Giovanni Carlo Di Renzo

“…To model the data fit/prediction, principal components regression and partial least squares regression algorithms were considered. …”
Get full text

Quantifying training response in cycling based on cardiovascular drift using machine learning by Artur Barsumyan, Artur Barsumyan, Raman Shyla, Anton Saukkonen, Christian Soost, Jan Adriaan Graw, Rene Burchard, Rene Burchard, Rene Burchard

“…Based on aerobic decoupling (power-to-heart rate ratio) and cardiovascular drift of each test ride, a prediction model was created using ML (Logistic regression, Variational Gaussian Process models and k-nearest neighbors algorithm) that indicated whether or not an athlete was responding to the training. …”
Get full text

Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO₂: A Data-Driven Approach by Man Fang, Yutong Yao, Chao Pang, Xiehang Chen, Yutao Wei, Fan Zhou, Xiaokun Zhang, Yong Xiang

“…Specifically, the RF and XGBoost models have the highest fitting performance for IC and EC prediction, with R² values of 0.8882 and 0.8318, respectively. …”
Get full text

Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon by Yibo Luo, Chunlin Li, Jinhong Huang, Chengcheng Dong, Junjie Wang

“…This study presents a novel framework integrating fractional-order derivative (FOD) techniques with machine learning algorithms for SOC estimation in mangrove wetlands. …”
Get full text