Data_Sheet_3_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... حسب Zheng Luo (284622)

Data_Sheet_4_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... حسب Zheng Luo (284622)

Data_Sheet_2_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... حسب Zheng Luo (284622)

Data_Sheet_1_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... حسب Zheng Luo (284622)

Dataset: Spatial Variability and Uncertainty of Soil Nitrogen across the Conterminous United States at Different Depths حسب Elizabeth Smith (12273647)

"…We used a random forest-regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0-5, 5-15, 15-30, 30-60, 60-100, 100-200 cm) at 5 km spatial grids. …"

Data_Sheet_1_Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.PDF حسب Jevitha Lobo (14368401)

Data_Sheet_1_Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.PDF حسب Jevitha Lobo (14368401)

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>…"

A data-driven machine learning approach for discovering potent LasR inhibitors حسب Christabel Ming Ming Koh (16798162)

"…Moreover, with many promising therapeutics falling short of expectations in clinical trials, targeting the <i>las</i> quorum sensing (QS) system remains an attractive therapeutic strategy to combat <i>P. aeruginosa</i> infection. Thus, our primary goal was to develop a drug prediction algorithm using machine learning to identify potent LasR inhibitors. …"

Data_Sheet_1_Predicting successful trading in the West Texas Intermediate crude oil cash market with machine learning nature-inspired swarm-based approaches.docx حسب Ehsan Zohreh Bojnourdi (18977486)

"…In this paper, a novel decompose-ensemble prediction approach is proposed by integrating various optimization algorithms, namely biography-based optimization (BBO), backtracking search algorithm (BSA), teaching-learning-based algorithm (TLBO), cuckoo optimization algorithm (COA), multi-verse optimization (MVO), and multilayer perceptron (MLP). …"

DataSheet_2_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... حسب Adam D. Irwin (11011752)

"…Background<p>Sepsis contributes significantly to morbidity and mortality globally. In Australia, 20,000 develop sepsis every year, resulting in 5,000 deaths, and more than AUD$846 million in expenditure. …"

DataSheet_1_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... حسب Adam D. Irwin (11011752)

"…Background<p>Sepsis contributes significantly to morbidity and mortality globally. In Australia, 20,000 develop sepsis every year, resulting in 5,000 deaths, and more than AUD$846 million in expenditure. …"

Table_1_A Phenotyping of Diastolic Function by Machine Learning Improves Prediction of Clinical Outcomes in Heart Failure.DOCX حسب Haruka Kameshima (11870333)

"…</p><p>Conclusion: Machine learning can identify patterns of diastolic function that better stratify the risk for decompensation than the current consensus recommendations in HF. Integrating this data-driven phenotyping may help in refining prognostication and optimizing treatment.…"

Supplementary Material for: The importance of early diagnosis and intervention in chronic kidney disease: Calls-to-action from nephrologists based mainly in Central/Eastern Europe حسب Covic A. (4148122)

"…Key Messages Our key calls-to-action to address these unmet needs, thus improving the standard of care for patients with CKD, are: increase disease awareness, such as through education; encourage provision of financial support for patients; develop screening algorithms; revisit primary care physician referral practices; and create epidemiological databases that rectify the paucity of data on early-stage disease. …"

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب Pierre-Alexis DELAROCHE (22092572)

"…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …"