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)

ACSA pseudo code for proposed control process. by Faizan e Mustafa (18004325)

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

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. …”

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)

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

Data_Sheet_1_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)

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.…”

Input features. by Nicola Lazzarini (3528197)

Baseline characteristics of patients in the study cohort. by Nicola Lazzarini (3528197)

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)

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)

CSPP instance by peixiang wang (19499344)

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

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>…”