Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement by Antoine Falisse (6061601)

“…The primary aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based trajectory optimization of human movement. …”

S1 Code - by Gaosha Li (20570760)

Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx by Juli Chen (12187358)

“…Background<p>Pathogenic testing for tuberculosis (TB) is not yet sufficient for early and differential clinical diagnosis; thus, we investigated the potential of screening long non-coding RNAs (lncRNAs) from human hosts and using machine learning (ML) algorithms combined with electronic health record (EHR) metrics to construct a diagnostic model.…”

Primary training parameters for the model. by Junjie Lu (160350)

Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf by Jordan D. Chambers (6732224)

Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

NEXMD Software Package for Nonadiabatic Excited State Molecular Dynamics Simulations by Walter Malone (3834031)

“…While the primary intent behind the NEXMD was to simulate nonadiabatic molecular dynamics, the code can also perform geometry optimizations, adiabatic excited state dynamics, and single-point calculations all in vacuum or in a simulated solvent. …”

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

ECE6379_PSOM.zip by Xingpeng Li (11825663)

“…Optimization algorithms that are commonly used to solve these problems will also be covered including linear programming, mixed-integer linear programming, Lagrange relaxation, dynamic programming, branch and bound, and duality theory.…”

Malware detection workflow in compressed archives by Paul A. Gagniuc (1818325)

Identifying cases of spinal cord injury or disease in a primary care electronic medical record database by John Shepherd (3459908)

“…Of these, 57 were cases of traumatic spinal cord injury (TSCI), and 67 were cases of non-traumatic spinal cord injury (NTSCI). The optimal case identification algorithm used free-text keyword searches and a physician billing code, and had 70.6% sensitivity (61.9–78.4), 98.5% specificity (97.3–99.3), 89.9% PPV (82.2–95.0), 94.7% NPV (92.8–96.3), and an F-score of 79.1.…”

Table_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.pdf by Michael Moor (10887888)

Data_Sheet_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.PDF by Michael Moor (10887888)

Supplementary file 1_A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab.docx by Linlin Yan (4480570)

“…Using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms, we conducted a comprehensive analysis of lecanemab-related AEs, restricting the analysis to AEs with the role code of primary suspect (PS).…”

LCLU location-allocation with spatial contiguity and compactness by Sitarani Safitri (18129556)

CSPP instance by peixiang wang (19499344)

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

“…</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>…”