Predicting plant uptake of potential contaminants of emerging concerns using machine learning models (2018–2025): A global review by Alexis Munyengabe, Maria Fezile Banda, Wilma Augustyn

“…The review analyzes diverse ML algorithms, including ensemble models (random forest, gradient boosted regression trees, eXtreme gradient boosting) and deep learning (deep neural networks, recurrent neural networks, long short-term memory) alongside key predictors like soil pH, organic matter, and plant traits. …”
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Research on application of artificial intelligence safety production management and control platform in Shendong mining area by Yazhong CUI, Jianrong HE, Yanyan REN

“…Training data and validation datasets are collected from monitoring points deployed at some production sites in Shendong mines as research objects. The relevant trained AI models and algorithms are deployed to the scene of coal mine safety production, further improving the intelligence level of coal mine expert systems, robots, decision management, safety management and equipment monitoring. …”
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Tracing the Evolution and Legacy of Professor A.P. Iofinov’s Scientific School by S. G. Mudarisov, R. R. Kamaletdinov

“…Iofinov’s works and his scientific school have given rise to the development of new algorithms and computer programs essential for automating the control processes of agricultural machinery and forecasting yields. …”
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Linguo-Conceptual Studies of Literary Text: Evolution of Theoretical and Methodological Approaches by I. V. Kononova, T. A. Pruczkikh

“…The main concepts that influenced the formation of cognitive poetics are identified; the relevant problems of cognitive studies of poetic and prose texts are analyzed. …”
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Empirical Evaluation and Analysis of YOLO Models in Smart Transportation by Lan Anh Nguyen, Manh Dat Tran, Yongseok Son

“…You Only Look Once (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. …”
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From Molecules to Medicines: The Role of AI-Driven Drug Discovery Against Alzheimer’s Disease and Other Neurological Disorders by Mashael A. Alghamdi

“…Artificial intelligence (AI) tools are of considerable interest in modern drug discovery processes and, by exploiting machine learning (ML) algorithms and deep learning (DL) tools, as well as data analytics, can expedite the identification of new drug targets and potential lead molecules. …”
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Informational Approaches in Modelling Social and Economic Relations: Study on Migration and Access to Services in the European Union by Florentina-Loredana Dragomir-Constantin, Camelia Madalina Beldiman, Monica Laura Zlati

“…The results highlight the superiority of the EM model in detecting relevant clusters and the usefulness of M5P trees in highlighting complex economic influences on population mobility. …”
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A systematic literature review on integrating AI-powered smart glasses into digital health management for proactive healthcare solutions by Boyuan Wang, Ying Zheng, Xihao Han, Liang Kong, Gexin Xiao, Zunxiong Xiao, Shanji Chen

“…Notably, some cutting-edge smart glasses have adopted advanced artificial intelligence algorithms, particularly large language models (LLMs) with context awareness and human-like interaction capabilities. …”
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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems by Natalya Denissova, Serik Nurakynov, Olga Petrova, Daniker Chepashev, Gulzhan Daumova, Alena Yelisseyeva

“…The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. …”
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Prevention of falls in hospitalized patients—evaluation of the effectiveness of a monitoring system (Verso Vision) developed with artificial intelligence by Corrado Gervasi, Erik Perego, Francesca Galli, Valter Torri, Massimo Castoldi, Emilio Bombardieri

“…IntroductionThe prevention of accidental falls in hospital is an important aspect of a healthcare management strategy, since they represent a relevant socio-economic problem. The Verso Vision System (VS) is an artificial intelligence-based system for accidental fall prevention and management, which uses computer vision algorithms to monitor environments and people in real time.MethodsThe efficacy of VS monitoring in terms of reduction of accidentals falls was retrospectively evaluated in a group of 362 hospitalized patients at Humanitas Gavazzeni Hospital.ResultsOf the 362 patients included in the analysis, 580 statistical units, 228 monitored with VS and 355 without VS were obtained splitting the observation of each patient based on the presence of VS monitoring and the Stratify score. …”
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Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…This study aimed to identify ToxCast bioassays relevant to DART and develop machine learning models to screen biocides in consumer products for their DART potential. …”
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FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations by Pengfei Zhou, Weiqing Min, Chaoran Fu, Ying Jin, Mingyu Huang, Xiangyang Li, Shuhuan Mei, Shuqiang Jiang

“…We also developed the topic-based selective state space model and hierarchical topic retrieval augmented generation algorithms to improve FoodSky’s ability to capture fine-grained food semantics and generate context-aware food-relevant text. …”
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Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment f... by Sujin Kim, Ah Young Kim, Yu-Bin Shin, Seonmin Kim, Min-Sup Shin, Jinhwa Choi, Kyung Lyun Lee, Jisu Lee, Sangwon Byun, Heon-Jeong Lee, Chul-Hyun Cho

“…The primary outcome will be the development of machine-learning algorithms to predict depression and anxiety based on these digital biomarkers. …”
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Climate-Based Prediction of Rice Blast Disease Using Count Time Series and Machine Learning Approaches by Meena Arumugam Gopalakrishnan, Gopalakrishnan Chellappan, Santhosh Ganapati Patil, Santosha Rathod, Kamalakannan Ayyanar, Jagadeeswaran Ramasamy, Sathyamoorthy Nagaranai Karuppasamy, Manonmani Swaminathan

“…This work shows how machine learning algorithms can improve the prediction of rice blast, offering vital information for early disease management. …”
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Unveiling the power of high-dimensional cytometry data with cyCONDOR by Charlotte Kröger, Sophie Müller, Jacqueline Leidner, Theresa Kröber, Stefanie Warnat-Herresthal, Jannis Bastian Spintge, Timo Zajac, Anna Neubauer, Aleksej Frolov, Caterina Carraro, DELCODE Study Group, Frank Jessen, Simone Puccio, Anna C. Aschenbrenner, Joachim L. Schultze, Tal Pecht, Marc D. Beyer, Lorenzo Bonaguro

“…The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. …”
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Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separatorMendeley Data by Marko Tesanovic, Torben Bardel, Robin Karl, Sonja Berensmeier

“…Furthermore, process efficiency is often not fully realized due to the reliance on fixed operational recipes.This study presents a digital twin framework for a pilot-scale HGMS system, integrating real-time monitoring, automated control, advanced mechanistic models, and multi-objective optimization using Bayesian algorithms. The framework was validated for robustness, scalable data handling, and predictive control. …”
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Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future Directions by Norizal Abdullah, Masri Ayob, Meng Chun Lam, Nasser R. Sabar, Graham Kendall, Mohamad Khairulamirin Md Razali

“…The Physician Scheduling Problem (PSP) has emerged as a critical challenge in healthcare management, directly relevant to Sustainable Development Goal 3 (SDG 3) - Good Health and Well-being. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

“…The system captures glucose-related optical signals, which are analyzed using various machine learning algorithms, including a novel Convolutional Neural Network–Attention Hybrid Model (CNN-AHM). …”
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Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model by Merjulah Roby, Juan C. Restrepo, Haehwan Park, Satish C. Muluk, Mark K. Eskandari, Seungik Baek, Ender A. Finol

“…Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. …”
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New Advances of the P-SBAS Approach for an Efficient Parallel Processing of Large Volumes of Full-Resolution Multitemporal DInSAR Interferograms by Manuela Bonano, Pasquale Striano, Muhammad Yasir, Sabatino Buonanno, Francesco Casu, Claudio De Luca, Adele Fusco, Yenni Lorena Belen Roa, Ivana Zinno, Maria Virelli, Michele Manunta, Riccardo Lanari

“…The starting point of the developed full-resolution parallel-SBAS (FR P-SBAS) technique involves some algorithmic extensions for improving the quality of the DInSAR time series to effectively analyze extended deformations and localized displacement phenomena, such as those affecting single buildings and infrastructures. …”
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