Enhancing Maritime Safety: Estimating Collision Probabilities with Trajectory Prediction Boundaries Using Deep Learning Models by Robertas Jurkus, Julius Venskus, Jurgita Markevičiūtė, Povilas Treigys

“…The study introduces a collision risk score, which evaluates the likelihood of boundary overlaps as a metric for collision detection. These methods are applied to simulated test scenarios and a real-world case study involving the 2021 collision between the Scot Carrier and Karin Hoej cargo ships. …”
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Rapid and sensitive identification of Candida in blood based on M1 beads enrichment combined with multiple recombinase-aided PCR: a culture-independent approach by Yuxin Wang, Yuxin Wang, Yuxin Wang, Xiaoping Chen, Kenan Peng, Yanqing Tie, Yanqing Tie, Yanqing Tie, Yuan Gao, Zhiqiang Han, Xiaona Lyu, Hongyi Li, Ruiqing Zhang, Shijue Gao, Xinxin Shen, Xuejun Ma, Zhishan Feng, Zhishan Feng, Zhishan Feng

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Mitigating Cyber Risks in Smart Cyber-Physical Power Systems Through Deep Learning and Hybrid Security Models by M. A. S. P. Dayarathne, M. S. M. Jayathilaka, R. M. V. A. Bandara, V. Logeeshan, S. Kumarawadu, Chathura Wanigasekara

“…The study emphasizes the difficulties in identifying cyber risks in grids with significant renewable integration, such as frequency instability and diminished system inertia, and suggests energy storage alternatives and sophisticated forecasting models to mitigate these issues. By incorporating a novel pre-processing method that leverages feature derivatives, the proposed models achieve over 98% accuracy in detecting cyber threats, providing a robust framework for protecting smart power grids from evolving cyber risks.…”
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Transparent prevention and control system for water hazards in mine floors under empowerment based on spatiotemporal information fusion by Zaibin LIU, Junsheng YAN, Jianghong WANG, Yaoquan GAO, Hui YANG, Baojun BAI, Jingjin LU, Hongwei WANG, Gang WANG

“…This study introduced the spatiotemporal detection methods for water hazards at various stages, spatiotemporal registration and synchronization, and spatiotemporal information-based empowerment modes. …”
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Modeling the Analysis of the Financial Result of an Entity by Alexandra A. Derbicheva

“…Methodology. The methods used are system and complex approaches, dialectic method of cognition, scientific abstraction, analysis and synthesis, comparison methods and others. …”
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Video prediction based on temporal aggregation and recurrent propagation for surveillance videosThe datasets analysed during the current study are available in the weblink reposito... by Mohana Priya P, UlagaPriya K

“…Surveillance video datasets demonstrate substantial enhancements in predictive accuracy, highlighting the strength and efficacy of the suggested strategy in practical application. • The proposed method integrates bidirectional video prediction, temporal aggregation, and recurrent propagation to effectively reconstruct missing intermediate video frames with enhanced accuracy. • Comparative analysis using the UCF-Crime dataset demonstrates higher PSNR and SSIM values for the proposed method, indicating improved frame quality and temporal consistency over existing techniques. • This research provides a robust framework for future advancements in video frame prediction, contributing to applications in anomaly detection, surveillance, and video restoration.…”
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Monitoring air quality index with EWMA and individual charts using XGBoost and SVR residuals by Zulfani Alfasanah, M. Zaim Husnun Niam, Sri Wardiani, Muhammad Ahsan, Muhammad Hisyam Lee

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PhenoVision: A framework for automating and delivering research‐ready plant phenology data from field images by Russell Dinnage, Erin Grady, Nevyn Neal, Jonn Deck, Ellen Denny, Ramona Walls, Carrie Seltzer, Robert Guralnick, Daijiang Li

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A Low-Noise High-Resolution Temperature Measurement Technique Based on Inductive Voltage Divider and Alternating-Current Bridge by Shanghua Gao, Xiaoyi Zhu, Xiaofeng Zhang, Bing Xue, Jilou Xi, Jiang Li, Bing Zhang, Xiaolei Wang, Yuru Wang, Haoyue Zhang, Xu Wu

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SVM classifier for telecom user arrears based on boundary samples-based under-sampling approaches by Chuangchuang LI, Guangyue LU, Hanglong WANG

“…Telecom users’ arrears forecasting is a classification problem of unbalanced data set.To deal with the problem that the traditional SVM on the unbalanced date set had a low detection accuracy of minority class,a novel method was proposed.Based on the fact that the position of classification plane was determined by the boundary samples,the proposed method was implemented via removing some of samples closed to the classification plane to avoid the deficiency of the traditional SVM algorithm.Finally,the proposed method was compared with other approaches on unbalanced data sets.The simulation results show that the proposed method can not only increase the detection accuracy of minority but also improve the overall classification performance.…”
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Dye‐based recombinase‐aided amplification assay with enhanced sensitivity and specificity by Zijin Zhao, Yanbo You, Shaowei Hua, Xinxin Shen, Lingjun Li, Xuejun Ma

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Malware prediction technique based on program gene by Da XIAO, Bohan LIU, Baojiang CUI, Xiaochen WANG, Suoxing ZHANG

“…With the development of Internet technology,malicious programs have risen explosively.In the face of executable files without source,the current mainstream malware detection uses feature detection based on similarity,with lack of analysis of malicious sources.To resolve this status,the definition of program gene was raised,a generic method of extracting program gene was designed,and a malicious program prediction method was proposed based on program gene.Utilizing machine learning and deep-learning algorithms,the forecasting system has good prediction ability,with the accuracy rate of 99.3% in the deep-learning model,which validates the role of program gene theory in the field of malicious program analysis.…”
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A study of the radon seasonality with temporal dummy variables by Alessandro Pignatelli, Giulia Romoli, Veronica Vignoli

“…However, accurately forecasting radon concentrations remains challenging due to the influence of various factors, including meteorological conditions and seasonal fluctuations. …”
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Failure Mode and Effect Analysis on the Impact of Zakat on the Local Economy by Darmawan Darmawan, Siti Alfajriyani

“…The Failure Mode and Effect Analysis (FMEA) method was used to identify high-risk dominant factors. …”
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Genetic characterization of type 3 vaccine-derived poliovirus from a patient with hand, foot, and mouth disease in China by Wei Zhang, Dongmei Yan, Hanri Zeng, Lei Zhou, Huanying Zheng, Shuangli Zhu, Yong Zhang, Baisheng Li

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Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks by He Huang, Difei Deng, Liang Hu, Nan Sun

“…Tropical cyclones (TCs) frequently encompass multiple hazards, including extreme winds, intense rainfall, storm surges, flooding, lightning, and tornadoes. Accurate methods for forecasting TC tracks are essential to mitigate the loss of life and property associated with these hazards. …”
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Integrating high dimensional quadratic regression with penalties based predictive modeling for hydro power plants accurate tariff prediction by Ritesh Dash, Anupa Sinha, Abinash Mahapatro, Bhabasis Mohapatra, Binod Kumar Sahu

“…Graphical and numerical evaluations confirm the model’s accuracy and suitability for spot market forecasting within hydro-DISCOM integration. The study concludes with recommendations for real time deployment and extension into hybrid intelligent forecasting framework. …”
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Image reconstruction of Arctic sea ice using SWIM data at small incidence angles by Wenlong Bi, Hui Yu, Xi Zhang, Ran Yan, Ying Xu, Maohua Guo, Ning Wang, Yinghui Li, Yifan Zhang, Yanting Lu, Xinyi Bi, Meijie Liu

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Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network by Muyuan Du, Zhimeng Zhang, Chunning Ji

“…A comparative analysis of different Intrinsic Mode Function (IMF) selection ratios revealed that selecting a 50% IMF ratio effectively retains the intrinsic information of the raw data while minimizing noise. For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. …”
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Long-Term, Multivariate Time Series Generation With the Capture of Intervariate Correlations and Variatewise Characteristics by Kasumi Ohno, Kohei Makino, Makoto Miwa, Yutaka Sasaki

“…Recently, generative approaches to TSG have been explored for applications such as privacy protection, anomaly detection, and time series classification/forecasting. …”
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