The Implementation and Application of a Saudi Voxel-Based Anthropomorphic Phantom in OpenMC for Radiological Imaging and Dosimetry by Ali A. A. Alghamdi

“…Radiographic projections showed optimal contrast at 70 keV. The effective dose values for 29 organs were calculated and compared with MCNPX, the ICRP-116 reference phantom, and XGBoost-based machine learning (ML) predictions. …”
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Borohydride Synthesis of Silver Nanoparticles for SERS Platforms: Indirect Glucose Detection and Analysis Using Gradient Boosting by Viktoriia Bakal, Olga Gusliakova, Anastasia Kartashova, Mariia Saveleva, Polina Demina, Ilya Kozhevnikov, Evgenii Ryabov, Daniil Bratashov, Ekaterina Prikhozhdenko

“…To enable glucose sensing, the substrates were further functionalized with glucose oxidase (GOx), allowing detection in the 1–10 mM range. Machine learning classification and regression models based on gradient boosting were employed to analyze SERS spectra, enhancing the accuracy of quantitative predictions (R² = 0.971, accuracy = 0.938, limit of detection = 0.66 mM). …”
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Investigation of Dynamic Characteristics of an Automated Position Long-Stroke Pneumatic Actuator of Fabrication System by D. A. Korotych, V. S. Sidorenko, S. P. Prikhodko

“…The optimal displacement was determined using the Portnyagin’s principle (i.e., optimal performance). …”
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Processing and Quality Evaluation of Additive Manufacturing Monolayer Specimens by Christiane Wendt, Severo Raúl Fernández-Vidal, Álvaro Gómez-Parra, Moisés Batista, Mariano Marcos

“…Frequently occurring macro- and microgeometrical defects are evaluated and categorized in order to optimize the part quality. This work also studies the effect of some manufacturing parameters such as the gap between print head and machine bed, trajectory strategy, bed leveling, and temperatures on part quality. …”
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Investigating the landslide susceptibility assessment methods for multi-scale slope units based on SDGSAT-1 and Graph Neural Networks by Xiangqi Lei, Hanhu Liu, Zhe Chen, Shaoda Li, Hang Chen, Shuai Zeng, Xiao Wang, Wenqian Bai, Wei Li, Lorenzo Picco

“…Our analysis compared LANDSAT with similar resolutions from multiple perspectives and found that SDGSAT-1 has substantial advantages. (2) The landslide susceptibility assessment method proposed in this work, based on optimal-scale slope units and GNN, demonstrated superior performance, with various evaluation metrics, such as AUC, Accuracy, and Precision far exceeding those of other machine learning models.…”
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…Future work should focus on developing adaptive learning models capable of adapting to CD without explicit re-training, thereby, ensuring optimal performance.…”
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Alternative data in finance and business: emerging applications and theory analysis (review) by Yunchuan Sun, Lu Liu, Ying Xu, Xiaoping Zeng, Yufeng Shi, Haifeng Hu, Jie Jiang, Ajith Abraham

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Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods by Saiprasad Potharaju, Swapnali N. Tambe, G. Madhukar Rao, M. V. V. Prasad Kantipudi, Kalyan Devappa Bamane, Mininath Bendre

“…Further refinement was conducted using filter-based methods—Symmetrical Uncertainty (SU), Information Gain (IG), and Gain Ratio (GR)—leading to a final subset of 104 optimal features. These features were used to train classification models (Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)) with a 70:30 train-test split and tenfold cross-validation. …”
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Environmental thresholds triggering changes in above and belowground biomass carbon in China by Xin Zhang, Shihang Zhang, Jun Zhou, Jianrong Fan

“…By spatially mapping these thresholds, we delineated environmentally sensitive areas—regions currently below (or above) the thresholds that are projected to exceed (or fall below) them under future climate scenarios. Using machine learning algorithms, we modeled AGBC and BGBC distributions for the years 2010 and 2100 (SSP5–8.5 scenario) and identified regions with the most significant expected changes. …”
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The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies by Tymoteusz Miller, Grzegorz Mikiciuk, Irmina Durlik, Małgorzata Mikiciuk, Adrianna Łobodzińska, Marek Śnieg

“…The findings reveal a significant increase in research output, particularly in the use of optical, acoustic, electromagnetic, and soil sensors, alongside machine learning models such as SVMs, CNNs, and random forests for optimizing irrigation, fertilization, and pest management strategies. …”
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Spatio-Temporal Travel Speed Estimation in Mixed Traffic Conditions: A Probe Vehicle-Based Approach With Autonomous Vehicle Sensor Integration by Hyungjoo Kim, Seongeun Na, Jongho Kim, Sangsoo Lee, Jiho Yeo

“…Future research directions include integrating vehicle-to-everything (V2X) communication and machine learning models to further refine estimation accuracy and predictive capabilities in dynamic urban mobility environments.…”
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Review and prospect of floating car data research in transportation by Chi Zhang, Yuming Zhou, Min Zhang, Bo Wang, Yuhan Nie

“…Future research could focus on leveraging transformer and graph neural networks to explore spatiotemporal features of data, developing lightweight real-time FCD processing algorithms, and constructing multimodal refined models tailored to specific traffic scenarios.…”
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Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery by Xiaoyu Hu, Xiuyuan Zhao, Wenhe Liu

“…Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. …”
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End-to-end neural automatic speech recognition system for low resource languages by Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú

“…The rising popularity of end-to-end (E2E) automatic speech recognition (ASR) systems can be attributed to their ability to learn complex speech patterns directly from raw data, eliminating the need for intricate feature extraction pipelines and handcrafted language models. E2E-ASR systems have consistently outperformed traditional ASRs. …”
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Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals by Chengfa Sun, Changchun Liu, Xinpei Wang, Yuanyuan Liu, Shilong Zhao

“…Final CAD classification is conducted by combining support vector machine and optimal multi-modal features. The experiment is validated on 199 simultaneously recorded ECG and PCG signals from non-CAD and CAD subjects, and achieves high performance with accuracy, sensitivity, specificity and f1-score of 98.49%, 98.57%,98.57% and 98.89%, respectively. …”
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Increment of Academic Performance Prediction of At-Risk Student by Dealing With Data Imbalance Problem by Nguyen Giap Cu, Thi Lich Nghiem, Thi Hoai Ngo, Manh Tuong Lam Nguyen, Hong Quan Phung

“…Studies on automatically predicting student learning outcomes often focus on developing and optimizing machine learning algorithms that fit the data captured from different education systems. …”
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Interpretable video-based tracking and quantification of parkinsonism clinical motor states by Daniel Deng, Jill L. Ostrem, Vy Nguyen, Daniel D. Cummins, Julia Sun, Anupam Pathak, Simon Little, Reza Abbasi-Asl

“…Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. …”
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Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study by Georgios Vontzos, Vasileios Laitsos, Dimitrios Bargiotas, Athanasios Fevgas, Aspassia Daskalopulu, Lefteri H. Tsoukalas

“…The results indicate that the proposed method exhibits satisfactory performance relative to comparison models such as Exponential smoothing, ARIMA, Light Gradient Boosting Machine and CNN-LSTM. …”
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An online monitoring method of milling cutter wear condition driven by digital twin by Xintian Zi, Shangshang Gao, Yang Xie

“…Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. …”
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A Multi Depot Multi Product Split Delivery Vehicle Routing Problem with Time Windows: A Real Cash in Transit Problem Application in Istanbul, Turkey by Ertuğrul Ayyıldız, Mehmet Can Şahin, Alev Taşkın

“…The problem is hence formulated as a mixed-integer mathematical model. The mathematical model is run for different scenarios and optimal routes are obtained. …”
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