Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis by Liang Xu, Jing Li, Wei Gong

“…In recent years, machine learning (ML) technology in the medical field has gained momentum, which utilize various algorithms to analyze input data, identify potential patterns and trends, develop predictive models, and generate high-precision predictions of unknown data, demonstrating its clinical potential in disease diagnosis. …”
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Estimating Import Lead Times Using Business Intelligence and Machine Learning Within the CRISP-DM Framework: A Case Study in Oil and Gas Services Industry by Mohamed Annis Souames, Larbi Abderrahmane Mohammedi, Iskander Zouaghi, Angappa Gunasekaran, Samia Beldjoudi, Abderrazak Laghouag

“…Addressing the challenge of limited data availability, we employ data synthesis techniques alongside machine learning algorithms to model and predict lead times accurately across diverse import scenarios. …”
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Applied Artificial Intelligence in Materials Science and Material Design by Emigdio Chávez‐Angel, Martin Børstad Eriksen, Alejandro Castro‐Alvarez, Jose H. Garcia, Marc Botifoll, Oscar Avalos‐Ovando, Jordi Arbiol, Aitor Mugarza

“…By leveraging large datasets, AI algorithms can identify patterns, reduce noise, and predict material behavior with unprecedented accuracy. …”
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Coordinated Design of Power System Stabilizer and Virtual Inertia Control Using Modified Harris Hawk Optimization for Improving Power System Stability by Mohamad Almas Prakasa, Imam Robandi, Alberto Borghetti, Muhammad Ruswandi Djalal, Waseda Himawari

“…Based on the obtained results, HHO-MSS is 1.44% to 9.28% more accurate and 34.63% to 53.94% more consistent than Electric Eel Foraging Optimization (EEFO), and Puma Optimizer (PO), Evolutionary Mating Algorithm (EMA). With the optimal damping ratios of 9.94% to 9.96% achieved by HHO-MSS, the overall power system stability improvements, including both local and interarea responses across 38 simulations involving sudden load changes, varying inertia, and different RES levels, are as follows: 41.17% to 70.89% frequency nadir improvement, 25.9% to 67.38% power angle deviation improvement, 84.83% to 85.26% settling time reduction, and 51.57% to 89.73% average error reduction calculated with performance indices. …”
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Modern advancements of energy storage systems integrated with hybrid renewable energy sources for water pumping application by Marwa M. Ahmed, Haneen M. Bawayan, Mohamed A. Enany, Mahmoud M. Elymany, Ahmed A. Shaier

“…The manuscript also highlights the integration of artificial intelligence (AI) to optimize energy management, predict irrigation demands, and improve operational efficiency. …”
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A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods by Francisco Lopez-Jimenez, Heather M. Alger, Zachi I. Attia, Barbara Barry, Ranee Chatterjee, Rowena Dolor, Paul A. Friedman, Stephen J. Greene, Jason Greenwood, Vinay Gundurao, Sarah Hackett, Prerak Jain, Anja Kinaszczuk, Ketan Mehta, Jason O'Grady, Ambarish Pandey, Christopher Pullins, Arjun R. Puranik, Mohan Krishna Ranganathan, David Rushlow, Mark Stampehl, Vinayak Subramanian, Kitzner Vassor, Xuan Zhu, Samir Awasthi

“…Background: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. …”
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Digital Twin Based on CFD Modelling for Analysis of Two-Phase Flows During Pipeline Filling–Emptying Procedures by Duban A. Paternina-Verona, Oscar E. Coronado-Hernández, Vicente S. Fuertes-Miquel, Manuel Saba, Helena M. Ramos

“…These models effectively predict hazardous pressure peaks and vacuum conditions, confirming their reliability in optimising pipeline operations while significantly reducing computational time.…”
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Optimization Method for Crop Growth Characteristics Based on Improved Locality Preserving Projection by Jia Dongyao, Hu Po, Zou Shengxiong

“…An improved locality preserving projection algorithm is proposed to optimize the extraction of growth characteristics. …”
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Keypoint-based modeling reveals fine-grained body pose tuning in superior temporal sulcus neurons by Rajani Raman, Anna Bognár, Ghazaleh Ghamkhari Nejad, Albert Mukovskiy, Lucas Martini, Martin Giese, Rufin Vogels

“…Some anterior STS units exhibited better predictive performances with a view-dependent 3D model. …”
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Identification of Candidate Tolerogenic CD8+ T Cell Epitopes for Therapy of Type 1 Diabetes in the NOD Mouse Model by Cailin Yu, Jeremy C. Burns, William H. Robinson, Paul J. Utz, Peggy P. Ho, Lawrence Steinman, Alan B. Frey

“…Proteins that elicit autoantibodies in human type 1 diabetes were analyzed by predictive algorithms for candidate epitopes. Using several different tolerizing regimes using synthetic peptides, two new predicted tolerogenic CD8+ T cell epitopes were identified in the murine homolog of the major human islet autoantigen zinc transporter ZnT8 (aa 158–166 and 282–290) and one in a non-β cell protein, dopamine β-hydroxylase (aa 233–241). …”
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Specification and Verification Method of Parallel Hierarchical Timed Automata by Predicate Abstraction and Refinement by Satoshi Yamane

“…Based on the results of the analysis, the algorithm performs refinement corresponding to each abstraction. …”
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Synthesizing Sum and Difference Patterns with Low Complexity Feeding Network by Sharing Element Excitations by Jafar Ramadhan Mohammed

“…Unlike the standard optimization approaches such as genetic algorithm (GA), the described algorithm performs repeatedly deterministic transformations on the initial field until the prescribed requirements are satisfied. …”
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Convolutional neural network-based low light image enhancement method by J. Guo

“…However, noise and contrast reduction frequently have an impact on image quality in low-light situations. …”
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AlphaFold-based AI docking reveals AMPK/SIRT1-TFEB pathway modulation by traditional Chinese medicine in metabolic-associated fatty liver disease by Lulu Zhang, Yi Zheng, Mingyan Shao, Aiping Chen, Meiyi Liu, Wenlong Sun, Tianxing Li, Yini Fang, Yang Dong, Shipeng Zhao, Hui Luo, Juan Feng, Qi Wang, Lingru Li, Yanfei Zheng

“…This study investigated QGJZF’s effects in high-fat diet-induced zebrafish and golden hamsters, and in palmitate (PA) and oleic acid (OA) - induced HepG2 cells, using the SymMap database to identify potential targets and pathways of QGJZF in MAFLD and AlphaFold algorithms to predict protein structures. In vivo, QGJZF significantly alleviated hepatic lipid deposition. …”
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Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation by Matthew J. McCarthy, Jesus D. Gomez-Velez, David Hughes, Shannon Meade

“…To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. …”
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Differences in parameter estimates derived from various methods for the ORYZA (v3) Model by Jun-wei TAN, Qing-yun DUAN, Wei GONG, Zhen-hua DI

“…The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. …”
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A Machine Learning-Based Diagnostic Nomogram for Moyamoya Disease: The Validation of Hypoxia-Immune Gene Signatures by Cunxin Tan, Xilong Wang, Zhenyu Zhou, Yutong Liu, Shihao He, Yuanli Zhao

“…These genes were further refined through machine learning algorithms. The diagnostic value was confirmed using an external dataset, and a diagnostic nomogram was constructed. …”
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Multigoal-oriented a posteriori error control for heated material processing using a generalized Boussinesq model by Beuchler, Sven, Endtmayer, Bernhard, Lankeit, Johannes, Wick, Thomas

“…Therein, error reductions and effectivity indices are consulted to establish the robustness and efficiency of our framework.…”
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Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…This study presents a comprehensive machine learning (ML)-based framework to address this challenge by predicting HF efficiency using three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). …”
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Snow Depth Extraction From Time‐Lapse Imagery Using a Keypoint Deep Learning Model by C. M. Breen, W. R. Currier, C. Vuyovich, Z. Miao, L. R. Prugh

“…At 20 snow pole locations from Grand Mesa, CO (n = 9,722 images), our model successfully predicts the top and bottom of the pole with a mean absolute error (MAE) of 1.30 cm. …”
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