CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”

CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

“…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”

Multidomain, Automated Photopatterning of DNA-functionalized Hydrogels (MAPDH). by Moshe Rubanov (7289156)

Antibody challenge outcomes. by Andrea Blasco (7439102)

“…Also shown is the benchmark algorithm implemented in Python (A1) and C++ (A2); note that benchmark algorithms A1 and A2 have perfect accuracy (<i>ACC</i> equal to unity). …”

Bespoke software controls the sorter GUI and reports on instrument status to the user. by Diane M. Wiener (1956583)

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making by Xiaofei Zhang (16483224)

“…To achieve policy learning within limited BiTL training periods, we add two modification features to the proposed algorithm based on TD3. …”

Least Bridges Graphs by Richard Frost (14064363)

“…This toolbox was originally created for analysis of values arising in genetic distance measures. The contained algorithms correct errors in commonly used nearest-neighbor and cluster software that are not designed with distances in mind (e.g. nearest-neighbor topology functions currently found in Python, SPSS, etc).…”

SParse EXact (SPEX) LU and Cholesky Factorization Library by Erick Moreno-Centeno (19460626)

“…<p dir="ltr">Exact solutions of sparse systems of linear equations (SLEs) are of fundamental importance for some applications within mathematics (e.g., in computer-assisted proofs), computer science (e.g., in computing radial basis functions for scattered data interpolation), and engineering (e.g., in studies of anharmonic oscillations in semiconductors). …”

Presentation_1_NeuroEditor: a tool to edit and visualize neuronal morphologies.pdf by Ivan Velasco (9463019)

“…Moreover, NeuroEditor can be easily extended by users, who can program their own algorithms in Python and run them within the tool. Last, this paper includes an example showing how users can easily define a customized workflow by applying a sequence of editing operations. …”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”

VinaLigGen: a method to generate LigPlots and retrieval of hydrogen and hydrophobic interactions from protein-ligand complexes by Raghvendra Agrawal (17135479)

“…This paper describes an implementation of an automation technique on the executable programs like ligplot.exe, hbplus.exe and hbadd.exe to obtain the 2D interaction map (LigPlots) of the protein and ligand complex (*.ps) and hydrogen bonds and hydrophobic interactions in *.csv format for molecules to be considered for virtual screening by using some sorting & searching algorithms and python’s file handling functions, and it also mentions the program’s limitations and availability of the program. …”

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images by Shuyu Li (18401358)

“…</p><p dir="ltr">The first directory contains raw MRI data in <i>.ima</i> format within <i>rawdata_DICOM</i>. Additionally, the acquired MRI scans were converted from DICOM to the Neuroimaging Informatics Technology Initiative (NIfTI) format and organized in accordance with the Brain Imaging Data Structure (BIDS) format by employing the BIDScoin Python application (version 4.3.0) and stored in <i>rawdata_BIDS</i> directory.…”

Landscape17 by Vlad Carare (22092515)

“…</p><h3>Density functional theory calculations</h3><p dir="ltr">The reference potential energy landscapes were computed using density functional theory with the ωB97x hybrid-energy exchange correlation functional and a 6-31G(d) basis set within Psi4. …”

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

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”