Research on Trend Analysis and Early Warning Technology of Smart Substation Telemetry Data Based on ARIMA-BP Combined Model by ZHOU Ke, WANG Xiaoming, LI Xiaobo, SONG Yi

“… In order to perform fault early warning on the secondary protection devices of smart substations based on monitoring data, a trend analysis and early warning method of smart substation telemetry data based on the ARIMA-BP combined model is proposed.Based on the analysis of the relay protection failure of the intelligent substation, the remote measurement data source for the early warning of the device status trend is selected, and the ARIMA-BP combined forecasting model is established based on the analysis of the characteristics of the data source.Based on the ARIMA-BP combined forecasting model and the historical telemetry data of the equipment, the equipment operation trend prediction is carried out.Taking the remote measurement data of the line protection device in a 220kV substation as an example, the prediction error rate of the ARIMA-BP combined model is 5% and 9% lower than that of the ARIMA model and the BP model alone. …”
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Evaluation of Health Resource Allocation Efficiency Based on Data Envelopment Analysis 2014–2018 in Two Dimensions of Time-Region by Lina Liu, Wei Xu, Yixuan Su, Xv Zhou

“…Since the new health care reform, China’s health care system has become increasingly mature and perfect, and the health awareness and needs of residents have also increased, which has raised higher requirements for the scientific understanding of the current status of health care resource allocation and making it achieve the goal of efficient operation. This paper evaluates the allocation of health resources in China from 2014 to 2018 in two dimensions: time and space by reviewing the China Health and Health Statistical Yearbook from 2014 to 2020, collecting data on the number of medical institutions, number of beds, per capita medical budget expenditure, number of consultations, and bed utilization rate during the study time interval, and analyzing them using data envelopment analysis. …”
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Precise Recognition and Feature Depth Analysis of Tennis Training Actions Based on Multimodal Data Integration and Key Action Classification by Weichao Yang

“…The model optimizes multimodal data integration and complex action classification performance, enabling precise analysis of key action features in tennis training. …”
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Assessing the Spatial Efficiency of Xi’an Rail Transit Station Areas Using a Data Envelopment Analysis (DEA) Model by Haiyan Tong, Quanhua Hou, Xiao Dong, Yaqiong Duan, Weiming Gao, Kexin Lei

“…Key factors such as land use, transportation systems, social aspects, and spatial efficiency are considered in the framework. A data envelopment analysis (DEA) method was used to evaluate the spatial efficiency of these sample station areas. …”
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Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements by Nobuji Kouno, Satoshi Takahashi, Ken Takasawa, Masaaki Komatsu, Naoaki Ishiguro, Katsuji Takeda, Ayumu Matsuoka, Maiko Fujimori, Kazuki Yokoyama, Shun Yamamoto, Yoshitaka Honma, Ken Kato, Kazutaka Obama, Ryuji Hamamoto

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Efficiency Assessment of Transit-Oriented Development by Data Envelopment Analysis: Case Study on the Den-en Toshi Line in Japan by Jing Guo, Fumihiko Nakamura, Qiang Li, Yuan Zhou

“…This study aims to assess the efficiency of TOD by applying the data envelopment analysis (DEA) method. The ridership of public transportation is considered as the direct output characteristic of TOD efficiency, and nine indicators of ridership are selected as inputs on the basis of the core concepts of TOD. …”
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Investigating the Role of Wearable Devices in Facilitating Telehealth Adoption Among the Aging Population: Mediation Analysis of US National Data by Ruijing Wang, Onur Asan, Ting Liao

“…ObjectiveThis study investigates how telehealth adoption among the aging population is influenced and mediated by relevant factors, including the use of wearable devices, demographic factors, health conditions, and physical activity levels. MethodsA secondary analysis was conducted on the Health Information National Trends Survey (HINTS 6) data collected from March to November 2022. …”
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Performance Analysis of Machine Learning Techniques for Server Health Monitoring Using Time Series Data Against DDOS Attacks by Sajja Ratan Kumar, Valli Kumari Vatsavayi

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Application of Three-Phase Linearized Power Flow and Line Loss Analysis of Distribution Network Driven by Data and Physics Fusion by Huaitian MU, Hongliang LIAN, Juan LIU, Yanqiong LI

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Comparative Analysis of RAG-Based Open-Source LLMs for Indonesian Banking Customer Service Optimization Using Simulated Data by Hendra Lijaya, Patricia Ho, Handri Santoso

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lasertram: A Python library for time resolved analysis of laser ablation inductively coupled plasma mass spectrometry data by Jordan Lubbers, Adam J.R. Kent, Chris Russo

“…Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data has a wide variety of uses in the geosciences for in-situ chemical analysis of complex natural materials. …”
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Integrating analysis of multi-omics summary data identifies novel plasma protein biomarkers and drug targets for bladder cancer by Jinlong Cao, Siyu Chen, Jirong Wang, Xinpeng Fan, Shanhui Liu, Jiaqi Shan, Xiaoran Li, Li Yang

“…Then, we validated these proteins through various methods, including meta-analysis, reverse MR, Bayesian co-localization analysis and summary-data-based MR (SMR), and pathogenic plasma proteins were divided into three layers according to the validation confidence. …”
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Identification of key genes and signaling pathway in the pathogenesis of Huntington's disease via bioinformatics and next generation sequencing data analysis by Basavaraj Vastrad, Chanabasayya Vastrad

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Association between CRP-Albumin-Lymphocyte (CALLY) index and Asthma-COPD overlap: analysis of NHANES 2015–2018 data by Shasha Fu, Zongcun Chen, Hongchuan Wu

“…Generalized additive models, subgroup analyses, and receiver operating characteristic (ROC) curve analysis were also performed. …”
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The relationship between immune cell infiltration and necroptosis gene expression in sepsis: an analysis using single-cell transcriptomic data by Shouyi Wang

“…Immune cell infiltration differences between sepsis (SE) and healthy control (HC) groups were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Differential marker genes between SE and HC groups were identified by single-cell data analysis using the Seurat and SingleR packages.ResultsOur results revealed 849 necroptosis-related DEGs, with 843 upregulated and 16 downregulated in the SE group. …”
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Diagnostic efficacy of Ovarian-Adnexal Reporting and Data System combined with contrast-enhanced ultrasound: analysis of 123 adnexal masses by HUA Rong, HU Haiyun, ZHU Yunxiao, LI Qingying, YUAN Kun

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Exploring of bladder cancer immune-related genes and potential therapeutic targets based on transcriptomic data and Mendelian randomization analysis by Zhangxiao Xu, Zhangxiao Xu, Juan Yang, Juan Yang, Yira Ma, Yira Ma, Bo Tao, Yunpeng He, Jian Wu, Yuan Zhao, Yuanjian Niu, Lijun Wang

“…Therefore, this study aimed to identify immune-related biomarkers and potential therapeutic targets for bladder cancer, thereby contributing to the development of novel therapeutic interventions.MethodsBy integrating data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and genome-wide association study (GWAS) databases, combined with differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mendelian randomization analysis, key immune-related genes in bladder cancer were identified. …”
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Prediction of new-onset migraine using clinical-genotypic data from the HUNT Study: a machine learning analysis by Fahim Faisal, Antonios Danelakis, Marte-Helene Bjørk, Bendik Winsvold, Manjit Matharu, Parashkev Nachev, Knut Hagen, International Headache Genetics Consortium, Erling Tronvik, Anker Stubberud

“…The top performing decision-tree classifier achieved a test set AUC of 0.56 when using only genotypic data, 0.68 when using only clinical data and 0.72 when using both genetic and clinical data. …”
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Meta-analysis of machine learning models for the diagnosis of central precocious puberty based on clinical, hormonal (laboratory) and imaging data by Yilin Chen, Xueqin Huang, Lu Tian

“…Pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) were calculated to assess the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP. …”
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Identifying breast cancer risk factors and evaluating biennial mammography screening efficacy using big data analysis in Taiwan by Chi-Cheng Huang, Tzu-Pin Lu, Yu-Jen Wang, Bo-Fang Chen, Hui-Ting Yang, Wei-Pin Chang, Ling-Ming Tseng

“…Clinically detected breast cancer was an independent risk factor for recurrence-free and overall survival, as well as breast cancer mortality. Big data analysis identified several risk factors for breast cancer development in Taiwanese women and confirmed the efficacy of mammography screening.…”
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