An efficient data-driven optimization framework for elastically isotropic lattice structures by Zhengtao Shu, Hao Li, Liang Gao

“…The optimization results highlight the high efficiency of the proposed method, with the time for achieving highly near-isotropic properties being just over ten seconds. Meanwhile, the prediction accuracy exceeding 99% for each microstructure with a specified volume fraction. …”
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Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects by Jingyi Liu, Jun Sun, Yasong Wang, Xin Liu, Yingjie Zhang, Haijun Fu

“…These technologies can detect a variety of chemical components of fruit, realize the assessment of maturity, damage degree, disease degree, and are suitable for orchard picking, quality grading, shelf life prediction and other fields. However, there are limitations to these techniques. …”
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A Data-Driven Feature Extraction Process of Interleaved DC/DC Converter Due to the Degradation of the Capacitor in the Aircraft Electrical System by Chenguang Zhang, Pengfei Gao, Ming Huang, Wenjie Liu, Weilin Li, Xiaobin Zhang

“…Extracting degradation features presents a fundamental and challenging task for health assessment and remaining useful life prediction. To facilitate the efficient operation of the incipient fault diagnosis model, this paper proposes a data-driven feature extraction process for converters, which consists of two main stages. …”
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Schistosomiasis Burden and Trend Analysis in Africa: Insights from the Global Burden of Disease Study 2021 by Dandan Peng, Yajing Zhu, Lu Liu, Jianfeng Zhang, Peng Huang, Shaowen Bai, Xinyao Wang, Kun Yang

“…Data from the Global Burden of Disease Study (GBD 2021) were used to calculate annual average percentage change (AAPC) and annual percentage change (APC), with spatial global autocorrelation analysis performed to examine temporal and spatial trends. Five modeling algorithms were constructed to predict disease burden in Africa from 2022 to 2041. …”
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An approach to forecasting damage due to unfavorable circumstances associated with indistinguishability of source data by V. F. Zolotukhin, A. V. Matershev, L. A. Podkolzina

“…It is required to improve methods for assessing and predicting damage and to develop new approaches and criteria for statistical forecasting of damage and evaluating the system reliability. …”
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Utilization of remote sensing and GIS for land use and land cover mapping in Wasit province, Iraq by Abdulkareem Meena K., Sabah Jaber Hussein

“…This study investigates the effectiveness of a supervised MLC classification algorithm to produce LULC maps of Wasit Governorate from multiple satellite images using remote sensing and GIS techniques, which is of great importance for earth observation applications, such as environmental monitoring and disaster prediction. …”
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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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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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A prospective, multicenter analysis of the integrated 31-gene expression profile test for sentinel lymph node biopsy (i31-GEP for SLNB) test demonstrates reduced number of unnecess... by J. Michael Guenther, Andrew Ward, Brian J. Martin, Mark Cripe, Rohit Sharma, Stanley P. Leong, Joseph I. Clark, John Hamner, Timothy Beard

“…Methods The i31-GEP SLNB risk prediction accuracy was assessed in patients with T1-T2 tumors enrolled in the prospective, multicenter DECIDE study (n = 322). …”
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Inverse Gravimetric Problem Solving via Prolate Ellipsoidal Parameterization and Particle Swarm Optimization by Ruben Escudero González, Zulima Fernández Muñiz, Antonio Bernardo Sánchez, Juan Luis Fernández Martínez

“…The subsurface is modeled as a set of prolate ellipsoids whose parameters are optimized to minimize the misfit between observed and predicted anomalies. This approach enables efficient forward modeling with closed-form solutions and allows the incorporation of geometric and physical constraints. …”
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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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