Multi-site Information Synchronization Scheme Based on Wavelet Transform to Detect Signal Singularity by Baojiang TIAN, Yan LI, Xiaoyu LIAO, Xingwei DU, Wenyan DUAN

“…To address this issue, this article proposes a self synchronization scheme for recording files of different devices in substations based on the sudden variable detection algorithm. Firstly, considering the problems of high-frequency signal loss and boundary effects in the traditional Mallat wavelet algorithm, which affect the accuracy of mutation point detection, a complementary adjacent window algorithm and an optimization strategy for data window selection are proposed, effectively solving the problems of traditional wavelet transform. …”
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Detection of human activities using multi-layer convolutional neural network by Essam Abdellatef, Rasha M. Al-Makhlasawy, Wafaa A. Shalaby

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Optimization of Dust Spray Parameters for Simulated LiDAR Sensor Contamination in Autonomous Vehicles Using a Face-Centered Composite Design by Sungho Son, Hyunmi Lee, Jiwoong Yang, Jungki Lee, Jeongah Jang, Charyung Kim, Joonho Jun, Hyungwon Park, Sunyoung Park, Woongsu Lee

“…However, dust accumulation on the LiDAR window can cause detection errors and degrade performance. …”
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Deciphering city-level residential AMI data: An unsupervised data mining framework and case study by Han Li, Miguel Heleno, Kaiyu Sun, Wanni Zhang, Luis Rodriguez Garcia, Tianzhen Hong

“…Utilizing hourly electricity consumption data for the east region of Portland, Oregon, the study systematically extracts key statistics such as start hour, duration, and peak hour of load periods across daily, weekly, and annual evaluation windows. …”
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Sugarcane Phenology Retrieval in Heterogeneous Agricultural Landscapes Based on Spatiotemporal Fusion Remote Sensing Data by Yingpin Yang, Zhifeng Wu, Dakang Wang, Cong Wang, Xiankun Yang, Yibo Wang, Jinnian Wang, Qiting Huang, Lu Hou, Zongbin Wang, Xu Chang

“…This study addresses the challenge of accurately monitoring the sugarcane phenology in complex terrains by proposing an optimized strategy integrating spatiotemporal fusion data. …”
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Application of time series database technology in coal mine safety monitoring system by Hongliang ZHANG

“…The system integrates real-time anomaly detection and trend prediction modules, dynamically identifying gas concentration mutation events through a sliding window mechanism, with a warning accuracy rate of 92.6%. …”
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Spatiotemporal Risk-Aware Patrol Planning Using Value-Based Policy Optimization and Sensor-Integrated Graph Navigation in Urban Environments by Swarnamouli Majumdar, Anjali Awasthi, Lorant Andras Szolga

“…We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). …”
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Measuring Retiming Responses of Passengers to a Prepeak Discount Fare by Tracing Smart Card Data: A Practical Experiment in the Beijing Subway by Qingru Zou, Xiangming Yao, Peng Zhao, Zijia Wang, Taoyuan Yang

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Multi-Source, Fault-Tolerant, and Robust Navigation Method for Tightly Coupled GNSS/5G/IMU System by Zhongliang Deng, Zhichao Zhang, Zhenke Ding, Bingxun Liu

“…Moreover, we derived the intrinsic relationships of filtering innovations within wireless measurement models and proposed a time-sequential, observation-driven full-source FDE and sensor recovery validation strategy. This approach employs a sliding window which expands innovation vectors temporally based on source encoding, enabling real-time validation of isolated faulty sensors and adaptive adjustment of observational data in integrated navigation solutions. …”
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Research on Visual–Inertial Measurement Unit Fusion Simultaneous Localization and Mapping Algorithm for Complex Terrain in Open-Pit Mines by Yuanbin Xiao, Wubin Xu, Bing Li, Hanwen Zhang, Bo Xu, Weixin Zhou

“…The combination of IMU pre-integration and visual feature restrictions is executed inside a tightly coupled visual–inertial framework utilizing a sliding window approach for back-end optimization, enhancing system robustness and precision. …”
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A Study on Small-Scale Snake Image Classification Based on Improved SimCLR by Lingyan Li, Ruiqing Kang, Wenjie Huang, Wenhui Feng

“…In the loss function, a supervised contrastive mechanism is introduced to exclude false negative samples using label information, which helps reduce representation noise and enhance training stability. The training strategy incorporates random erasing and random grayscale data augmentation techniques to improve performance further. …”
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CSSA-YOLO: Cross-Scale Spatiotemporal Attention Network for Fine-Grained Behavior Recognition in Classroom Environments by Liuchen Zhou, Xiangpeng Liu, Xiqiang Guan, Yuhua Cheng

“…To address these issues, we introduce CSSA-YOLO, a novel detection network that incorporates cross-scale feature optimization. …”
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Clinical and cost-effectiveness of detailed anomaly ultrasound screening in the first trimester: a mixed-methods study by Jehan N Karim, Helen Campbell, Pranav Pandya, Edward C F Wilson, Zarko Alfirevic, Trish Chudleigh, Elizabeth Duff, Jane Fisher, Hilary Goodman, Lisa Hinton, Christos Ioannou, Edmund Juszczak, Louise Linsell, Heather L Longworth, Kypros H Nicolaides, Anne Rhodes, Gordon Smith, Basky Thilaganathan, Jim Thornton, Gillian Yaz, Oliver Rivero-Arias, Aris T Papageorghiou

“…Objectives The objectives of this study were: To assess the diagnostic accuracy of first-trimester ultrasound for major structural anomalies through systematic reviews and meta-analyses of the literature and to understand how this screening should be optimally performed (i.e. anatomical protocol, anomalies to be targeted, gestational age window, ultrasound modality used and referral pathways). …”
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A Convolutional Neural Network for Early Supraventricular Arrhythmia Identification by Emilio J. Ochoa, Luis C. Revilla

“…The results underscore the immense potential of CNN and deep learning techniques in the early detection of supraventricular arrhythmias. This approach not only offers a valuable tool for healthcare professionals engaged in telemonitoring and early intervention strategies but also represents a significant contribution to the field of cardiac health monitoring. …”
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Digital augmentation of aftercare for patients with anorexia nervosa: the TRIANGLE RCT and economic evaluation by Janet Treasure, Katie Rowlands, Valentina Cardi, Suman Ambwani, David McDaid, Jodie Lord, Danielle Clark Bryan, Pamela Macdonald, Eva Bonin, Ulrike Schmidt, Jon Arcelus, Amy Harrison, Sabine Landau

“…As there were considerable missing values in outcome variables and non-adherence with ECHOMANTRA predicted later dropout from data collection, multiple imputation (MI) was used to adjust for missing data biases. …”
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