MW-UNet: Multi-Scale Weighted Connection UNet for Identification and Classification of Non-Meteorological Clutter over Big Radar Data by Mengmeng Cui, Chen Zeng, Xiaolong Xu, Muhammad Bilal, Xiaoyu Xia

“…The field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather changes. To improve the quality of radar data, machine learning and fuzzy logic algorithms are often used to identify and classify non-meteorological clutter in weather data. …”
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A CART-Based Model for Analyzing the Shear Behaviors of Frozen–Thawed Silty Clay and Structure Interface by Fengpan Zhu, Bo Wang, Zhiqiang Liu

“…This study employs integrated machine learning algorithms to model the shear behavior of frozen–thawed silty clay and the structure interface. …”
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Engineering Synthetic Microbial Communities: Diversity and Applications in Soil for Plant Resilience by Arneeb Tariq, Shengzhi Guo, Fozia Farhat, Xihui Shen

“…Artificial intelligence-driven models can predict complex microbial interactions, while machine learning algorithms can analyze vast datasets to identify key microbial taxa and their functions. …”
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Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street by Tianning Yao, Yao Xu, Liang Sun, Pan Liao, Jin Wang

“…The study proposes a comprehensive and applicable spatial quality evaluation model integrating spatial quantification methods, machine learning algorithms, and multidimensional perception measurements. …”
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Passive Microwave Imagers, Their Applications, and Benefits: A Review by Nazak Rouzegari, Mohammad Bolboli Zadeh, Claudia Jimenez Arellano, Vesta Afzali Gorooh, Phu Nguyen, Huan Meng, Ralph R. Ferraro, Satya Kalluri, Soroosh Sorooshian, Kuolin Hsu

“…This review examines the relevance, applications, and benefits of PMWI data, focusing on their practical use and benefits to society rather than the specific techniques or algorithms involved in data processing. Specifically, it assesses the impact of PMWI data on Tropical Cyclone (TC) intensity and structure, global precipitation and extreme events, flood prediction, the effectiveness of tropical storm and hurricane watches, fire severity and carbon emissions, weather forecasting, and drought mitigation. …”
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A Numerical Laboratory for Simulation of Flanged Reinforced Concrete Shear Walls by Alireza Khaloo, Mohammad Tabiee, Hatef Abdoos

“…Drawing on ABAQUS, FlashLab employs shell elements with longitudinal and transverse reinforcements to accurately and rapidly model the cyclic behavior of flanged RC shear walls. The FlashLab algorithms are on the basis of the Python programming language and can examine flanged RC shear walls, with a general cross-section, which make it possible to parametrically investigate various variables. …”
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Method for Knowledge Transfer via Multi-Task Semi-Supervised Self-Paced by Yao Zhao, Hongying Liu, Huaxian Pan, Zhen Song, Chunting Liu, Anni Wei, Baoshuang Zhang, Wei Lu

“…Experimental results on several benchmark datasets show that our method achieves a performance gain of 3%-15% in classification accuracy compared to baseline algorithms, along with significant advantages in convergence speed.…”
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Replicating PET Hydrolytic Activity by Positioning Active Sites with Smaller Synthetic Protein Scaffolds by Yujing Ding, Shanshan Zhang, Xian Kong, Henry Hess, Yifei Zhang

“…Recent breakthroughs in protein structure prediction and de novo design, powered by artificial intelligence, now enable to create enzymes with desired functions without solely relying on traditional genome mining. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. …”
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Reconsidering CPR protocols: Exploring hypernatremia's role in ventricular tachycardia from a case report by Arthur Gavotto, Jean-Yves Le Guennec, Cyril Amouroux, Pascal Champeroux, Angelo Polito, Jérôme Thireau, Alain Lacampagne, Gilles Cambonie

“…Conclusion: In silico modelling can help to predict arrhythmia risk and to select the best strategies. …”
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Neural Networks in Forecasting Disease Dynamics by A. G. Hasanov, D. G. Shaybakov, S. V. Zhernakov, A. M. Men’shikov, F. F. Badretdinova, I. F. Sufiyarov, J. R. Sagadatova

“…The best convergence of the network learning process is provided by the quasi-Newton and conjugated gradient algorithms. In order to assess the effectiveness of the proposed neural network in predicting the dynamics of inflammation, a comparative analysis was carried out using a number of conventional methods, such as exponential smoothing, moving average, least squares and group data handling.Conclusion.The proposed neural network based on approximation and extrapolation of variations in the patient’s medi‑ cal history over fixed time window segments (within the ‘sliding time window’) can be successfully used for forecasting the development and outcome of erysipelas.…”
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Healthcare Economics-Vital Threat Detection System Efficiency: A Business Process Analysis Methodology by Salma Elhag, Amal Alghamdi, Amal Alahmari, Abeer Alharbi

“…The data is sent to the cloud, where it is stored and analyzed using artificial intelligence (AI) algorithms to predict critical health conditions. When any abnormalities are detected, immediate alerts are sent to family members or caregivers via mobile applications, enabling a quick response. …”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…The method is tested using a North American utility feeder data, achieving an average accuracy of 95.56% for forecasting aggregate load power, 93.70% for feeder flow predictions, and 97.53% for estimating the aggregate capacity of DERs.…”
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Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis by Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta

“…Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system.…”
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A Hybrid Analytical Framework for Cracking and Some Fruit Quality Features in Sweet Cherries by Erol Aydın, Mehmet Ali Cengiz, Leyla Demirsoy, Hüsnü Demirsoy

“…Key fruit characteristics were measured, and novel machine learning algorithms were applied to identify associations between variables. …”
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Analysis of State-of-Charge Estimation Methods for Li-Ion Batteries Considering Wide Temperature Range by Yu Miao, Yang Gao, Xinyue Liu, Yuan Liang, Lin Liu

“…Accurate state-of-charge (SOC) estimation is critical for optimizing battery performance, ensuring safety, and predicting battery lifetime. However, SOC estimation faces significant challenges under extreme temperatures and complex operating conditions. …”
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Few-shot learning for novel object detection in autonomous driving by Yifan Zhuang, Pei Liu, Hao Yang, Kai Zhang, Yinhai Wang, Ziyuan Pu

“…Perception, a critical component, extracts real-time traffic information essential for various system functionalities, such as agent behavior prediction. However, the quality of information derived from perception greatly influences overall system performance. …”
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MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers by Huaxu Yu, Jun Ding, Tong Shen, Min Liu, Yuanyue Li, Oliver Fiehn

“…We present MassCube, a Python-based open-source framework for MS data processing that we systematically benchmark against other algorithms and different types of input data. From raw data, peaks are detected by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection. …”
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State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks by Jun Peng, Xuan Zhao, Jian Ma, Dean Meng, Shuhai Jia, Kai Zhang, Chenyan Gu, Wenhao Ding

“…Three commonly used machine learning methods (BP, LSTM, TCN) and two hybrid algorithms (LSTM-KAN and TCN-KAN) were used to establish the SOH estimation model. …”
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Comprehensive improvement of energy efficiency and indoor environmental quality for university library atrium—A multi-objective fast optimization framework by Shen Xu, Yongzhong Chen, Jianlin Liu, Jian Kang, JinFeng Gao, Yuchen Qin, Wenjun Tan, Gaomei Li

“…Finally, a multi-objective fast optimization framework coupled with machine learning algorithms was used to achieve the optimal design of university library atrium. …”
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