High-precision deformation monitoring and intelligent early warning for wellbore based on BDS/GNSS. by Jiang Li, Lei Dai, Keke Xu, Xinyu Mei, Yifu Liu, Jianlin Shi, Hebing Zhang

“…The early warning model can flexibly adapt to the deformation conditions at different sites and the various disturbances encountered, effectively capturing the complex nonlinear time-varying characteristics of the observation time series. The prediction of future results for one month based on one year of observation sequences achieves an accuracy better than mm, providing a safeguard for safe production in mines. …”
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Demand-Adapting Charging Strategy for Battery-Swapping Stations by Benjamín Pla, Pau Bares, Andre Aronis, Augusto Perin

“…An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. …”
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A 5G network based conceptual framework for real-time malaria parasite detection from thick and thin blood smear slides using modified YOLOv5 model by Swati Lipsa, Ranjan Kumar Dash, Korhan Cengiz, Nikola Ivković, Adnan Akhunzada

“…Objective This paper aims to address the need for real-time malaria disease detection that integrates a faster prediction model with a robust underlying network. The study first proposes a 5G network-based healthcare system and then develops an automated malaria detection model capable of providing an accurate diagnosis, particularly in areas with limited diagnostic resources. …”
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Implementation of Clustering and Association for Early Warning of Disasters in Bojonegoro Regency by Denny Nurdiansyah, Erna Hayati, Ika Purnamasari, Anna Apriana Hidayanti, Yuliana Fuji Rahayu

“…The goal was to enable better disaster prediction and preparedness in the future. The methods applied included mapping, clustering using the K-means algorithm, and association rule mining with the Apriori algorithm. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening by Keisuke Uchikawa, Kairi Furui, Masahito Ohue

“…Advances in protein structure prediction, notably AlphaFold2, have begun to address this gap. …”
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Deep Learning in Written Arabic Linguistic Studies: A Comprehensive Survey by Manar Almanea

“…This article presents a comprehensive survey on recent applications of deep learning (DL) algorithms to written Arabic. Despite the increasing amount of user-generated content in Arabic, linguistic studies focusing on Arabic suffer from low analytical resources. …”
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Psychometric properties of the German version of the Traumatic Grief Inventory-Self Report Plus (TGI-SR+) by Julia Treml, Viktoria Schmidt, Elmar Braehler, Matthias Morfeld, Anette Kersting

“…Despite the same name, both versions of PGD differ in symptom count, content, and diagnostic algorithm. A single instrument to screen for both PGD diagnoses is critical for bereavement research and care. …”
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Human-Manipulator Interface Using Particle Filter by Guanglong Du, Ping Zhang, Xueqian Wang

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Wormhole Detection Based on Ordinal MDS Using RTT in Wireless Sensor Network by Saswati Mukherjee, Matangini Chattopadhyay, Samiran Chattopadhyay, Pragma Kar

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Psychological and pedagogical features of distance learning at a university by F. G. Lovpache, B. K. Pafifova

“…The urgency of the problem is argued by the fact that at present distance learning has become the most demanded and promising form of education. Special attention is paid to the introduction of distance learning technologies, where online learning is considered as an integral part of a harmonious education system, characterized by a number of features. …”
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A numerical study on tread wear and fatigue damage of railway wheels subjected to anti-slip control by Yunfan Yang, Liang Ling, Jiacheng Wang, Wanming Zhai

“…This paper intends to investigate the impact of anti-slip control on wheel tread wear and fatigue damage under diverse wheel/rail friction conditions. To this end, a prediction model for wheel wear and fatigue damage evolution on account of a comprehensive vehicle-track interaction model is extended, where the wheel/rail non-Hertzian contact algorithm is used. …”
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Reinforcement Learning-Based Television White Space Database by Armie E. Pakzad, Raine Mattheus Manuel, Jerrick Spencer Uy, Xavier Francis Asuncion, Joshua Vincent Ligayo, Lawrence Materum

“…However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. …”
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The Use of BIM Models and Drone Flyover Data in Building Energy Efficiency Analysis by Agata Muchla, Małgorzata Kurcjusz, Maja Sutkowska, Raquel Burgos-Bayo, Eugeniusz Koda, Anna Stefańska

“…The paper examines methodologies for combining thermal imaging with BIM, including image analysis algorithms and artificial intelligence applications. …”
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Solution of Nonlinear 2nd Order Multi-Point BVP By Semi-Analytic Technique by Luma Tawfiq, Mariam Hilal

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Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation by Bernardo Cândido, Ushasree Mindala, Hamid Ebrahimy, Zhou Zhang, Robert Kallenbach

“…We applied the Boruta algorithm for feature selection to identify influential biophysical predictors and evaluated four machine learning models—Linear Regression, Decision Tree, Random Forest, and XGBoost—for biomass prediction. …”
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A Novel Method Based on Particle Flow Filters for Stellar Gyroscope Parameter Estimations by Erol Duymaz

“…However, “the particle flow filter structure” is used for the prediction and calibration of gyroscope error parameters for the first time in the literature in this study. …”
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Parking Backbone: Toward Efficient Overlay Routing in VANETs by Jinqi Zhu, Ming Liu, Yonggang Wen, Chunmei Ma, Bin Liu

“…Secondly, to a specific vehicle, a daily mobility model is established, to determine its location through a corresponding location prediction algorithm. Finally, a novel message delivery scheme is designed to efficiently transmit messages to destination vehicles through the proposed virtual overlay network. …”
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Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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LSTM-Based State-of-Charge Estimation and User Interface Development for Lithium-Ion Battery Management by Abdellah Benallal, Nawal Cheggaga, Amine Hebib, Adrian Ilinca

“…The proposed framework demonstrates superior prediction accuracy, achieving a Mean Square Error (MSE) of 0.0023 and a Mean Absolute Error (MAE) of 0.0043, outperforming traditional estimation methods. …”
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