Table_3_Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.DO... حسب Kuo-Yang Huang (4470730)

Table_1_Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.DO... حسب Kuo-Yang Huang (4470730)

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems حسب Xuhui Lin (19505503)

"…</p><p dir="ltr"><b>Quality Assurance</b>: Comprehensive technical validation demonstrates the dataset's integrity, sensitivity to rainfall impacts, and capability to reveal complex traffic-weather interaction patterns.</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…"

Extraction and expression of architectural color. حسب Xin Han (1329648)

"…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …"

Basic color value distribution map of the street. حسب Xin Han (1329648)

"…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …"

SegNet architecture. حسب Xin Han (1329648)

"…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …"

Overview of workflow. حسب Xin Han (1329648)

"…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …"

Descriptive statistics for the volunteers. حسب Xin Han (1329648)

"…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …"

Jiefang North Road Street. حسب Xin Han (1329648)

"…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …"

Colors with different number of clusters. حسب Xin Han (1329648)

"…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …"

Table_1_Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images.DOCX حسب Yeo Kyoung Won (15599561)

Table_1_Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.DOCX حسب Nguyen Thanh Nhu (16400859)

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …"

Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …"

Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx حسب Hui Tang (226667)

"…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …"

Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS حسب Hui Tang (226667)

"…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …"

Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX حسب Hui Tang (226667)

"…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …"

DataSheet_1_MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study.docx حسب Fan Yang (1413)

Data Sheet 1_Triglyceride-glucose index and mortality in congestive heart failure with diabetes: a machine learning predictive model.doc حسب Lin Yu (221619)

"…Subgroup analyses were conducted based on age, gender, chronic pulmonary disease, atrial fibrillation, hypertension, and mechanical ventilation to assess the robustness of our findings. Feature selection was performed using LASSO regression, and predictive modeling was carried out using machine learning algorithms.…"

Supplementary file 1_A study on a real-world data-based VTE risk prediction model for lymphoma patients.docx حسب Changli He (22424818)

"…Model development incorporated three imputation methods, three sampling strategies, three feature selection approaches, and nine machine learning algorithms. …"