Processes of Acquisition a Second Language and Learning a Foreign Language: Terminology Standardisation and Harmonization by Oksana Turkevych

“…The semantics of this group is analyzed and some aspects of normalization (compliance with linguistic and terminological norms) and harmonization (compliance with variants that function in English) are proposed. The difference between the terms of language acquisition and language learning is specified. …”
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The effect of educational intervention based on mobile learning on the knowledge and self-efficacy of healthcare providers in vaccination by Leila Shamsi, Mojgan Janighorban, Soheila Mohamadirizi

“…BACKGROUND: The gap between the knowledge and self-efficacy of healthcare providers and their actual function in doing vaccination calls for the implementation of a program that can empower them. …”
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Multi-Link Fragmentation-Aware Deep Reinforcement Learning RSA Algorithm in Elastic Optical Network by Jing Jiang, Yushu Su, Jingchi Cheng, Tao Shang

“…Deep reinforcement learning has been extensively applied for resource allocation in elastic optical networks. …”
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Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning by Bruno Matos Porto, Flavio Sanson Fogliatto

“…Methods Six machine learning (ML) algorithms were tested on forecasting horizons of 7 and 45 days. …”
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EEG-Based Inverse Reinforcement Learning for Safety-Oriented Global Path Planning in Dynamic Environments by Hao Zhu, Jialin Wang, Rui Gao

“…The method integrates a pruned WNFG-SSCCNet-ADMM classifier for EEG signal mapping, apprenticeship learning for reward function extraction, and Q-learning for policy optimization. …”
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Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

“…Identifying patients at highest risk for renal damage is essential for implementing early interventions, improving bladder management strategies, and preserving renal function. This study aims to develop an effective machine learning model to predict renal damage in children with CSD. …”
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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

“…Correct classification of these sites provides crucial insight for understanding structure and function as well as guiding downstream design tasks, including structure-based drug discovery and de novo biomolecule design. …”
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Target Inference Three-Dimensional Intercept-Angle Guidance With Deep Latent Reinforcement Meta-Learning by Weihong Wang, Chen Liang, Chaoran Yan, Zhenghua Liu, Na Wang, Zhengjie Sun, Yongming Wen, Xuemei Huang, Yue Yu

“…The proposed guidance law is derived using deep latent reinforcement meta-learning. Different from existing online gradient descent-based reinforcement meta-learning method, the proposed approach is aware of environment variations through latent context vector inferred with amortized variational inference. …”
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A Multifeature Fusion Short-Term Traffic Flow Prediction Model Based on Deep Learnings by Chunxu Chai, Chuanxiang Ren, Changchang Yin, Hui Xu, Qiu Meng, Juan Teng, Ge Gao

“…And then, the number of neurons in the model, the loss function, and other parameters such as the optimization algorithm are discussed and set up through simulation experiments. …”
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The Use of Problem-Based Learning to Improve Higher Order Thinking Skills of fifth-Grade Students by Monika Handayani, Rif'at Shafwatul Anam

“…This study aims to determine the influence of the Problem-Based Learning (PBL) model on HOTS in science learning for grade V elementary school students. …”
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Detection of failures in HV surge arrester using chaos pattern with deep learning neural network by Chun‐Chun Hung, Meng‐Hui Wang, Shiue‐Der Lu, Cheng‐Chien Kuo

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DEANE: Context-Aware Dual-Craft Graph Contrastive Learning for Enhanced Extractive Question Answering by Dongfen Ye, Jianqiang Zhou, Gang Huang

“…Finally, a multi-view contrastive loss is leveraged to learn latent representations from augmented graphs. …”
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Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms by Abdullah Alharbi, Wael Alosaimi, Radhya Sahal, Hager Saleh

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Accurate and Robust Stellar Rotation Periods Catalog for 82771 Kepler Stars Using Deep Learning by Ilay Kamai, Hagai B. Perets

“…Our model, LightPred, is a novel deep-learning model designed to extract stellar rotation periods from light curves. …”
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SGD-TripleQNet: An Integrated Deep Reinforcement Learning Model for Vehicle Lane-Change Decision by Yang Liu, Tianxing Yang, Liwei Tian, Jianbiao Pei

“…Traditional deep reinforcement learning (DRL) methods face challenges such as slow convergence, unstable decisions, and low accuracy when dealing with complex traffic environments. …”
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The Use of Machine Learning Algorithms for Water Quality Index Prediction in the Sai Gon River, Vietnam by Thuy Nguyen Thi Diem, Mai Nguyen Thi Huynh, Tra Tran Quang

“…Recent and accelerated advances in machine learning have led to various promising applications in water quality assessment. …”
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Generative Adversarial Network for Real‐Time Flash Drought Monitoring: A Deep Learning Study by Ehsan Foroumandi, Keyhan Gavahi, Hamid Moradkhani

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APPLICATION AND PERFORMANCE COMPARISON OF MULTI-OUTPUT MACHINE LEARNING FOR NUMERICAL-NUMERICAL AND NUMERICAL-CATEGORICAL OUTPUTS by Karin Joan, Robyn Irawan, Benny Yong

“…Multi-Output Machine Learning is an advancement of traditional machine learning, designed to predict multiple output variables simultaneously while considering the relationships between these output variables. …”
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Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer by Zihao Bai, Hao Wang, Jingru Han, Jia An, Zhaocong Yang, Xuming Mo

“…A machine learning model was developed to create the Gastric Cancer Multi-Omics Programmed Cell Death Signature (GMPS), targeting PCD-related genes. …”
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A Deep Reinforcement Learning–Based Urban Traffic Control Model for Vehicle-to-Everything Ecosystem by Lingyu Zheng, Han Chen, Yajie Zou

“…This paper proposes a novel deep reinforcement learning (DRL) framework for urban traffic signal control within the vehicle-to-everything (V2X) ecosystem. …”
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