MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures by Laura Shub, Wenjin Liu, Georgios Skiniotis, Michael J. Keiser, Michael J. Robertson

“…We present a representation of chemical environments using interaction fingerprints and develop a machine learning model to predict the identity of input water and ion sites. …”
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The future is inclusive: An invitation for interdisciplinary collaboration between social emotional learning and special education researchers by Kristabel Stark, Jessica B. Koslouski, Julie Vadhan, Madison Vega

“…We offer four action steps to move both fields forward in their research and promotion of meaningful participation of all students.…”
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Integrated Guidance and Control for Strap-Down Flight Vehicle: A Deep Reinforcement Learning Approach by Qinglong Zhang, Bin Zhao, Yifu Jiang, Jingyan Zhang, Jiale Zhang

“…This paper proposes a three-dimensional (3D) deep reinforcement learning-based integrated guidance and control (DRLIGC) method, which is restricted by the narrow field-of-view (FOV) constraint of the strap-down seeker. …”
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A Wind Power Forecasting Method Based on Lightweight Representation Learning and Multivariate Feature Mixing by Chudong Shan, Shuai Liu, Shuangjian Peng, Zhihong Huang, Yuanjun Zuo, Wenjing Zhang, Jian Xiao

“…This paper proposes a two-stage forecasting framework based on lightweight representation learning and multivariate feature mixing. In the representation learning stage, the efficient spatial pyramid module is introduced to reconstruct the dilated convolution part of the original TS2Vec representation learning model to fuse multi-scale features and better improve the gridding effect caused by dilated convolution while significantly reducing the number of parameters in the representation learning model. …”
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Reinforcement-Learning-Based Trajectory Design and Phase-Shift Control in UAV-Mounted-RIS Communications by Tianjiao Sun, Sixing Yin, Li Deng, F. Richard Yu

“…To cope with the practical issue of inaccessible information on the user terminals’ (UTs) location and channel state, a reinforcement learning (RL)-based solution is proposed to find the optimal policy with finite steps of “trial-and-error”. …”
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Implementation of environment-based learning model at the elementary education level: a systematic literature review by Suhartono Suhartono, Moh Salimi, Ratna Hidayah, Muna Fauziah, Jupriyanto Jupriyanto

“…Data were analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with data identification, screening, and determination steps. The results of this study indicate that the application of environmental-based learning models has been widely studied and implemented in various contexts and different subjects in various countries. …”
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Predicting intensive care need in women with preeclampsia using machine learning – a pilot study by Camilla Edvinsson, Ola Björnsson, Lena Erlandsson, Stefan R. Hansson

“…Aspartate aminotransferase reflects liver involvement, uric acid might be involved in several steps of the pathophysiologic process of preeclampsia, and obesity is a well-known risk factor for development of both severe and non-severe preeclampsia likely involving inflammatory pathways..…”
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Adaptive Multi-Agent Reinforcement Learning with Graph Neural Networks for Dynamic Optimization in Sports Buildings by Sen Chen, Xiaolong Chen, Qian Bao, Hongfeng Zhang, Cora Un In Wong

“…To address the adaptability limitations of traditional centralized approaches, this study proposes a decentralized multi-agent reinforcement learning framework based on graph neural networks (GNNs). …”
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Scale Is Not All You Need: Revisiting the Biomimetic Roots of Deep Learning to Overcome Fundamental Limitations by Malek Wahidi, Anthony Rizk, Rodrigue Imad

“…Deep Learning has historically drawn direct inspiration from nature, with Artificial Neural Networks initially grounded in neurobiological principles. …”
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An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals by Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed

“…To address the issue, we propose a noninvasive machine learning (ML) model-based method to estimate creatinine level using photoplethysmography (PPG) signal. …”
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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Abstract This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). …”
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Applying deep learning model to aerial image for landslide anomaly detection through optimizing process by Chwen-Huan Wang, Li Fang, Chiung-Yun Hu

“…The process employing the GANomaly deep learning model to enhance landslide anomaly detection using high-resolution (25 cm) aerial imagery. …”
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Evaluating Label Encoding and Preprocessing Techniques for Breast Cancer Prediction Using Machine Learning Algorithms by Mukesh Kumar, Vivek Bhardwaj

“…Data scaling and encoding techniques, including StandardScaler and MinMaxScaler, are employed to enhance the accuracy of these machine learning models. Additionally, preprocessing steps, such as Numerical Variable Correlation, Categorical Variables Analysis, Continuous Variables Analysis, Bivariate Analysis, Balancing Classes (oversampling function) are applied to enhance the model’s performance. …”
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Incidental mishaps and learning curves during free fibula reconstruction of mandible: a case report by Dharnappa Poojary, K. M. Sandeep, K. P. Shetty, Sameep Shetty, Premalatha Shetty, Joanna Baptist, Ritesh Singh

“…Despite the incidental mishaps, it was a learning experience for the betterment of the planning of future cases. …”
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Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models by Xuguang Zhang, Pan Li, Xu Han, Yongbin Yang, Yiwen Cui

“…The proposed approach not only enhances forecasting accuracy but also provides interpretable attention weights, offering insights into the relative importance of different time steps in making predictions. This research contributes to the growing body of work on deep learning for time series analysis and offers practical implications for improving demand forecasting in retail and supply chain management.…”
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Machine learning for early detection of plant viruses: Analyzing post-infection electrical signal patterns by Elham Ghasemi, Esmaeil Ebrahimie, Ali Niazi

“…A custom-designed, portable signal recording system was developed, incorporating multiple filtering stages at different steps to minimize noise in field conditions without the need for a Faraday cage, ensuring high-quality signal acquisition. …”
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Measurement of Fracture Networks in Rock Sample by X-Ray Tomography, Convolutional Filtering and Deep Learning by Alessia Caputo, Maria Teresa Calcagni, Giovanni Salerno, Elisa Mammoliti, Paolo Castellini

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Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data by Byeongcheol Kang, Kyungbook Lee

“…The input layer comprised 800 production data, i.e., oil production rates and water cuts for eight production wells over 50 time steps, and the output layer consisted of a probability vector for each TI. …”
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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The key innovation lies in integrating these non-linear regression models with robust data preprocessing steps, including dimensionality reduction via Principal Component Analysis (PCA), categorical feature encoding through Leave-One-Out (LOO), and outlier detection using Isolation Forest. …”
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Advancing Financial Forecasts: Stock Price Prediction Based on Time Series and Machine Learning Techniques by Cheng-Ying Yang, Min-Shiang Hwang, Yu-Wei Tseng, Chou-Chen Yang, Victor R. L. Shen

“…Recently, investors and researchers have adopted machine learning techniques with technical indicator analysis to make prediction. …”
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