Intra-hospital patient transfer algorithm. by Ekkarat Meephu (15307058)

Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

General overview of the total model. by Xueliang Guo (4797057)

Alignment optimization. by Thomas W. Christy (11836547)

Symmetry test result. by Jiyong Moon (16819302)

Fig 1 - by Jakub Stoklosa (3315042)

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

Data Sheet 1_An explainable machine learning model for predicting chronic coronary disease and identifying valuable text features.docx by Weipeng Gan (22287409)

“…A customized “stop words” list and “custom dictionary” for cardiovascular medicine were created to optimize the processing of text data. Then, ML algorithms were employed to establish CCD prediction models. …”

Table 1_An explainable machine learning model for predicting chronic coronary disease and identifying valuable text features.xlsx by Weipeng Gan (22287409)

“…A customized “stop words” list and “custom dictionary” for cardiovascular medicine were created to optimize the processing of text data. Then, ML algorithms were employed to establish CCD prediction models. …”

Table 1_Near-infrared prediction of total phosphorus in leaves content in korla fragrant pear with growth period specificity via spectral modeling.docx by Mingyang Yu (7456358)

“…At present, how to synergistically utilize growth period information and Spectral pre - processing methods to optimize the LTP Prediction model remains to be further studied. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…Another scenario involves cells displaying empty areas due to mechanical stress during the spreading/smearing process or complications during the scanning process. …”

The running times and the maximum memory usages of variant calling tools. by Ning Wang (108353)

Models and Dataset by M RN (9866504)

“…<p dir="ltr"><b>P3DE (Parameter-less Population Pyramid with Deep Ensemble):</b><br>P3DE is a hybrid feature selection framework that combines the Parameter-less Population Pyramid (P3) metaheuristic optimization algorithm with a deep ensemble of autoencoders. …”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”

Data_Sheet_1_Predicting high-frequency nutrient dynamics in the Danube River with surrogate models using sensors and Random Forest.docx by Yen Binh Tran (13258809)

“…This work presents a thorough description of the modeling workflow, including intermediate steps for feature engineering, feature selection, and hyperparameter optimization. In total, 12 surrogate models (2 algorithms ^* 3 constituents ^* 2 stations) are compared with R² and RMSE as error metrics. …”

Data_Sheet_1_Identifying Alzheimer’s disease and mild cognitive impairment with atlas-based multi-modal metrics.PDF by Zhuqing Long (16910748)

“…Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. …”

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>…”

A new GEE-based App called “Crop Mapper” for crop mapping by Anonymous During Peer Review (16029755)

“…</p> <p>The crop maps will be derived using the Random Forest machine learning algorithm and monthly gap-free Landsat Sentinel-2 time series data that was evaluated to be optimal and well documented in this paper. …”

<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)

“…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”

Effects of plant-based diets in endurance athletes: a scoping review and practical nutritional recommendations – Protocol by Diego A. Bonilla (9086201)

“…It will also optimize the basic methodology in the stages of problem formulation, bibliographic search, evaluation, analysis, and presentation of findings to systematize the review process and improve scientific robustness, following the latest recommendations of the Joanna Briggs Institute (JBI) for scoping reviews (Peters et al., 2020). …”