Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

“…Two ML algorithms, decision tree (DT) and CatBoost, are trained using physical parameters, with CatBoost demonstrating superior performance in RHEA ductility classification. …”
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Intelligent Wireless Power Scheduling for Lunar Multienergy Systems: Deep Reinforcement Learning for Real-Time Adaptive Beam Steering and Vehicle-to-Grid Energy Optimization by Thomas Tongxin Li, Shuangqi Li, Cynthia Xin Ding, Zhaoyao Bao, Mohannad Alhazmi

“…Future work will explore the integration of hybrid energy storage models, quantum-inspired optimization for real-time decision-making, and predictive beamforming algorithms to further enhance the reliability and efficiency of lunar energy networks.…”
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The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1) by M. Buehner, J.-F. Caron, E. Lapalme, A. Caya, P. Du, Y. Rochon, S. Skachko, M. Bani Shahabadi, S. Heilliette, M. Deshaies-Jacques, W. Chang, M. Sitwell

“…In addition to describing the current MIDAS applications, a sample of the results from these systems is presented to demonstrate their performance in comparison with either systems from before the switch to using MIDAS software or similar systems at other numerical weather prediction (NWP) centres. The modular software design also allows the code that implements high-level components (e.g. observation operators, error covariance matrices, state vectors) to easily be used in many different ways depending on the application, such as for both variational and ensemble DA algorithms, for estimating the observation impact on short-term forecasts, and for performing various observation pre-processing procedures. …”
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Identification method of roof rock interface based on response characteristics of drilling parameters by LI Dianshang, LIU Cancan, WANG Chuanbing, REN Bo, REN Shuai, KANG Zhipeng

“…Then, the accuracy of rock interface identification was analyzed using parameters such as penetration rate, revolution per minute, sound pressure level, and torque using the application of the change point detection algorithm, the strucchange model in RStudio software, and the decision tree algorithm. …”
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Principles of physical factor selection in the early postoperative period of breast cancer treatment: a randomized controlled study by Inna S. Evstigneeva

“…Current models of onco-rehabilitation do not consider the patient’s functional impairment during combined treatment until the patient becomes incapacitated and the impairment develops into a chronic condition. AIM. To develop algorithms for selecting physical factors depending on the clinical and functional state of patients after radical surgical treatment of breast cancer (BC) in the early postoperative period. …”
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Artificial intelligence driven Mid-IR photoimaging device based on van der Waals heterojunctions of black phosphorus by Wang Ziqian, Wang Huide, Wang Chen, Bao Yushuo, Zheng Weiying, Weng Xiaoliang, Zhu Yihan, Liu Yi, Zhang Yule, Tian Xilin, Sun Shuo, Cao Rui, Shi Zhe, Chen Xing, Qiu Meng, Wang Hao, Liu Jun, Chen Shuqing, Zeng Yu-Jia, Liao Wugang, Huang Zhangcheng, Li Haiou, Gao Lingfeng, Li Jianqing, Fan Dianyuan, Zhang Han

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3D Reconstruction and Deformation Detection of Rescue Shaft Based on RGB-D Camera by Hairong Gu, Bokai Liu, Lishun Sun, Mostak Ahamed, Jia Luo

“…Experimental results demonstrate the superiority of the Speeded Up Robust Features (SURF) algorithm in feature extraction and the effectiveness of the Random Sample Consensus (RANSAC) algorithm in filtering mismatched points. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5&#x0025;, 15.2&#x0025;, and 9.3&#x0025; of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H&#x0027;s fitting performance in measured and predicted data was highly consistent with <italic>R</italic>² and root-mean-square error (RMSE) of 86.8&#x0025;, 2.06 cm in DBH and 79.2&#x0025;, 1.11 m in H, but CW&#x0027;s <italic>R</italic>² and RMSE of 72.2&#x0025;, 0.65 m caused crowding (C) inconsistency.…”
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Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwor... by Satrio Junaidi, Rani Valicia Anggela, Delsi Kariman

“…So, to deal with this problem, data mining classification is carried out to predict student graduation on time to find patterns for student on-time graduation predictions. …”
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Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model. by Gregory L Watson, Di Xiong, Lu Zhang, Joseph A Zoller, John Shamshoian, Phillip Sundin, Teresa Bufford, Anne W Rimoin, Marc A Suchard, Christina M Ramirez

“…We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. …”
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Current Signature-Based Bearing Fault Severity Classification Using a Robust Multilevel Cascaded Framework by Korawege N. C. Jayasena, Battur Batkhishig, Babak Nahid-Mobarakeh, Ali Emadi

“…These features are fed into an ANN-based level I algorithm using various fusion techniques, offering a more interpretable algorithmic framework. …”
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Deep learning for automated segmentation of radiation-induced changes in cerebral arteriovenous malformations following radiosurgery by Hsing-Hao Ho, Huai-Che Yang, Wen-Xiang Yang, Cheng-Chia Lee, Hsiu-Mei Wu, I-Chun Lai, Ching-Jen Chen, Syu-Jyun Peng

“…The algorithm could be used to predict the course of RICs and facilitate clinical management. …”
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Dual Stabilization of the Multidimensional Regression Object at the Given Level by V. S. Mukha, N. F. Kako

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Methodology for Studying Neural Network Descriptors in Solving the Problem of Finding Anatomical Layers in Computed Tomography Images of the Lungs by A. A. Kosareva

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Artificial neural networks-based multi-objective optimization of immersion cooling battery thermal management system using Hammersley sampling method by Muhammed Donmez, Mehmet Ihsan Karamangil

“…Multi-objective optimization, using ANN-based multi objective genetic algorithms, is conducted on a 16S1P configuration at 4C discharge and 0.008 kg/s. …”
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Descriptions of self-treatment for the middle-aged and elderly in Shanxi, China. by Rui Wang, Chenjin Ma, Kun Jiang, Ming Li, Shuangge Ma

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Sex determination of Amur tigers (Panthera tigris altaica) from footprints in snow by Jiayin Gu, Sky K. Alibhai, Zoe C. Jewell, Guangshun Jiang, Jianzhang Ma

“…The algorithm predicted 5 trails from females and 3 from males. …”
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Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker by Ni M, Peng W, Wang X, Li J

“…A series of machine learning algorithms was used to screen two feature genes (ETS1 and IL7R) to establish the diagnostic model, which exhibited satisfactory diagnostic efficiency. …”
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Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study by Junan Xie, Shilin Li, Zhen Song, Lin Shu, Qing Zeng, Guozhi Huang, Yihuan Lin

“…The multidimensional gait features extracted from the data and physical characteristics were used to establish the KOA functional feature database for the plantar pressure measurement system. 40mFPWT and TUGT regression prediction models were trained using a series of mature machine learning algorithms. …”
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Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks by Ihor Blinov, Virginijus Radziukynas, Pavlo Shymaniuk, Artur Dyczko, Kinga Stecuła, Viktoriia Sychova, Volodymyr Miroshnyk, Roman Dychkovskyi

“…Given the presence of anomalies and missing values in the operational data, a two-stage preprocessing algorithm incorporating DBSCAN clustering was applied for data cleansing and imputation. …”
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