Drilling dynamics measurement of drilling motors and its application in recognition of motor operation states through machine learning by Fei Li, Haolan Song, Yifan Wang

“…Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors, predictive maintenance for drilling motors is necessary to optimize asset utilization. …”
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Application of photon-counting CT in cardiovascular diseases by WANG Mengzhen, BAO Shouyu, LIU Peng, YAN Fuhua, YANG Wenjie

“…Future studies should focus on large-sample, multicenter prospective studies to optimize imaging parameters, standardize post-processing workflows, and integrate artificial intelligence tools to enhance the clinical application of PCCT in cardiovascular disease diagnosis.…”
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RFID-embedded mattress for sleep disorder detection for athletes in sports psychology by Metin Pekgor, Aydolu Algin, Turhan Toros

“…This approach shows significant potential for sports psychology applications, enabling personalized recovery strategies and performance optimization. Future work will focus on expanding the dataset, integrating additional biometric sensors, and refining algorithms to improve diagnostic accuracy and real-time usability in clinical and home settings.…”
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A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM by Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li

“…Finally, the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) is used to optimize the parameters of the combinative model to improve the forecasting accuracy. …”
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Convolutional neural networks and vision transformers for Plankton Classification by Loris Nanni, Alessandra Lumini, Leonardo Barcellona, Stefano Ghidoni

“…The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. …”
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Utility-Based Joint Routing, Network Coding, and Power Control for Wireless Ad Hoc Networks by Kaiqian Ou, Yinlong Xu, Xiumin Wang, Wang Liu

“…Based on the expected utility, we explore the optimality in both unicast and multicast routing. For unicast routing, we propose an optimal algorithm. …”
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Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting by Ubaid Ahmed, Anzar Mahmood, Ahsan Raza Khan, Levin Kuhlmann, Khurram Saleem Alimgeer, Sohail Razzaq, Imran Aziz, Amin Hammad

“…The mutual information (MI) algorithm is implemented as a feature selection technique to identify the most important features for forecasting. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…To tackle the challenge of designing an improved diabetes classification algorithm that is more accurate, random oversampling and hyper‐tuning parameter techniques have been used in this study. …”
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Deep reinforcement learning based online lifting path planning for tower cranes in unknown dynamic environments by Kai Wang, Jing Li, Zhiyuan Yin, Jiankang Zhang, Xin Ma

“…Moreover, a novel reward function is introduced to optimize the smoothness of the lifting path, which improves the success rate and optimizes the energy and time cost. …”
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Integrating personalized shape prediction, biomechanical modeling, and wearables for bone stress prediction in runners by Liangliang Xiang, Yaodong Gu, Kaili Deng, Zixiang Gao, Vickie Shim, Alan Wang, Justin Fernandez

“…The digital twin leverages a domain adaptation-based Long Short-Term Memory (LSTM) algorithm, informed by wearable sensor data, to dynamically simulate the structural behavior of foot bones under running conditions. …”
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Deep reinforcement learning applications and prospects in industrial scenarios by JING TAN, Ligang YANG, Xiaorui LI, Zhaolin YUAN, Yunduan CUI, Chao YAO, Zongjie WANG, Xiaojuan BAN

“…Central to these systems are control algorithms, which enable the automation of operations, optimization of process parameters, and reduction of operational costs. …”
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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

“…Petrophysical constraints were derived from triple combo well logs (gamma ray, bulk density, neutron porosity), while geological constraints included stratigraphic data or spatial distance between training and target wells—petrophysical constraints most improved predictions, while stratigraphic and spatial constraints had progressively less impact. …”
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A Systematic Review and Comparative Analysis Approach to Boom Gate Access Using Plate Number Recognition by Asaju Christine Bukola, Pius Adewale Owolawi, Chuling Du, Etienne Van Wyk

“…By optimizing the capabilities of advanced YOLO algorithms, the proposed method seeks to improve the reliability and effectiveness of access control through precise and rapid plate number recognition. …”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…Using S-G smoothing filtering (S-G), multivariate scattering correction (MSC), standard normal transformation (SNV), second derivative (SD), reciprocal logarithm (LR) and continuum removal (CR) to preprocess the data, the spectral characteristics and their correlation with water content were analyzed. In order to further improve the prediction ability of the model, the competitive adaptive reweighting method (CARS) was used to optimize the characteristic band, and a prediction model was established by combining random forest regression (RFR), least squares support vector regression (LSSVR) and particle swarm optimization least squares support vector regression (PSO-LSSVR). …”
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Convergence and Quantum Advantage of Trotterized MERA for Strongly-Correlated Systems by Qiang Miao, Thomas Barthel

“…Furthermore, we show how the convergence can be substantially improved by building up the MERA layer by layer in the initialization stage and by scanning through the phase diagram during optimization. …”
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Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection by Jia Xu, Han Pu, Dong Wang

“…However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. …”
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Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China by Li Xu, Shucheng Tan, Runyang Li

“…Furthermore, the Ordered Weighted Averaging (OWA) algorithm and the Partical Swarm Optimization-Support Vector Machine (PSO-SVM) model were combined to simulate future GDV scenarios for 2030–2050 under three development preferences: environment oriented, status quo, and economically oriented. …”
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Accurate Estimation Method of Customer Baseline Load for Continuous Participation of Industrial Users in Demand Response by Heng LIANG, Geng HUANG, Bin HOU, Xi YANG, Xiaohu LUO, Da ZHANG

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Corrective soft bus-bar splitting for reliable operation of hybrid AC/DC grids by Basel Morsy, Matthew Deakin, Adolfo Anta, Jochen Cremer

“…As the original problem is NP-hard, this paper utilizes a column-and-constraint generation algorithm. Case studies on IEEE 5, 24, 39, and 67 hybrid AC/DC systems show superiority of the proposed method, manifested as significant improvement in operating costs, security, and converter redispatch needs, under different loading conditions.…”
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Features of single treasury account management in the context of budget funds liquidity management by N. S. Sergienko

“…Successful examples of integration of automated financial flow management systems, use of machine learning algorithms for forecasting and optimization of balances on single treasury account, as well as the interdepartmental interaction mechanisms to improve transparency and efficiency of budget management have been considered. …”
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