Core data by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Video 1_Unstable occult scaphoid fracture diagnosed by dynamic point-of-care ultrasound: a case report and review.mp4 by Yong-Hyun Yoon (22562681)

“…</p>Conclusion<p>This case report highlights the pivotal role of dynamic musculoskeletal ultrasound as an adjunct in the diagnostic algorithm for acute wrist trauma. It demonstrates its ability not only to identify a radiographically occult scaphoid fracture but, more critically, to provide immediate functional information about mechanical stability. …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><h2>Project Structure</h2><pre><pre>Perception_based_neighbourhoods/<br>├── raw_data/<br>│ ├── ET_cells_glasgow/ # Glasgow grid cells for analysis<br>│ └── glasgow_open_built/ # Built area boundaries<br>├── svi_module/ # Street View Image processing<br>│ ├── svi_data/<br>│ │ ├── svi_info.csv # Image metadata (output)<br>│ │ └── images/ # Downloaded images (output)<br>│ ├── get_svi_data.py # Download street view images<br>│ └── trueskill_score.py # Generate TrueSkill scores<br>├── perception_module/ # Perception prediction<br>│ ├── output_data/<br>│ │ └── glasgow_perception.nc # Perception scores (demo data)<br>│ ├── trained_models/ # Pre-trained models<br>│ ├── pred.py # Predict perceptions from images<br>│ └── readme.md # Training instructions<br>└── cluster_module/ # Neighbourhood clustering<br> ├── output_data/<br> │ └── clusters.shp # Final neighbourhood boundaries<br> └── cluster_perceptions.py # Clustering algorithm<br></pre></pre><h2>Prerequisites</h2><ul><li>Python 3.8 or higher</li><li>GDAL/OGR libraries (for geospatial processing)</li></ul><h2>Installation</h2><ol><li>Clone this repository:</li></ol><p dir="ltr">Download the zip file</p><pre><pre>cd perception_based_neighbourhoods<br></pre></pre><ol><li>Install required dependencies:</li></ol><pre><pre>pip install -r requirements.txt<br></pre></pre><p dir="ltr">Required libraries include:</p><ul><li>geopandas</li><li>pandas</li><li>numpy</li><li>xarray</li><li>scikit-learn</li><li>matplotlib</li><li>torch (PyTorch)</li><li>efficientnet-pytorch</li></ul><h2>Usage Guide</h2><h3>Step 1: Download Street View Images</h3><p dir="ltr">Download street view images based on the Glasgow grid sampling locations.…”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…”

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf by Guangzong Li (16696443)

“…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …”

O-RAN-Based Cyberinfrastructure Training for FutureG Wireless Comm. and Sensing by Yao Zheng (21752159)

“…Key components include a modular O-RAN architecture deployed on a cloudnative infrastructure, virtualized RAN functions (vRAN), and programmable RIS panels interfaced through standardized control protocols. …”

Table 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.xlsx by Liu Haoming (22524473)

“…Background<p>Lung cancer diagnosis requires cost-effective biomarkers. Mitochondrial non-coding RNAs (mtRNAs) represent unexplored diagnostic targets.…”

Data Sheet 2_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv by Liu Haoming (22524473)

“…Background<p>Lung cancer diagnosis requires cost-effective biomarkers. Mitochondrial non-coding RNAs (mtRNAs) represent unexplored diagnostic targets.…”

Data Sheet 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv by Liu Haoming (22524473)

“…Background<p>Lung cancer diagnosis requires cost-effective biomarkers. Mitochondrial non-coding RNAs (mtRNAs) represent unexplored diagnostic targets.…”

<b>NanoNeuroBot: Beyond Healing, Toward Human Connection</b> by ahmed hossam (21420446)

“…<p dir="ltr">NanoNeuroBot & NeuroStimAI: Dual Non-Invasive Neuroregenerative Therapies for Spinal Cord Injury</p><p><br></p><p dir="ltr">⸻</p><p><br></p><p dir="ltr">Abstract / Description:</p><p><br></p><p dir="ltr">This dual-initiative research introduces two complementary, non-invasive technologies designed to restore neural connectivity and functionality in patients with Spinal Cord Injury (SCI).…”

<b>Drug Release Nanoparticle Systems Design:</b><b>Dataset Compilation and Machine Learning Modeling</b> by Shan He (14524901)

“…Herein 11 different AI/ML algorithms were used to develop the predictive AI/ML models. …”

Supplementary file 1_Plasma FGF2 and YAP1 as novel biomarkers for MCI in the elderly: analysis via bioinformatics and clinical study.docx by Yejing Zhao (22123588)

“…However, there is still a notable absence of novel biomarkers that are both efficient, minimally invasive, and cost-effective in real-world clinical settings. To address this gap, datasets GSE29378 and GSE12685 were selected to screen differentially expressed genes (DEGs), and hub genes were identified by different algorithms. …”

<b>Leveraging protected areas for dual goals of biodiversity conservation and zoonotic</b> <b>risk reduction</b> by Li Yang (13558573)

“…Each approach was run using both the Additive Benefit Function (ABF) and Core-Area Zonation (CAZ) algorithms.…”

FCP dataset for forecasting temperature, PV, price, and load by Hanwen Zhang (18259666)

“…</p><p dir="ltr">• To design and develop data-driven algorithms for accurate and reliable charging supplydemand forecasting and cost-optimal scheduling with large-volume and high-resolution data.…”

Optimizing agarase production from <i>Microbulbifer</i> sp. using response surface methodology and machine learning models by Lubhan Cherwoo (21811529)

“…The study also explores various machine learning algorithms where radial basis function neural network performed best with R-squared values of 0.989 and low mean squared error of 0.44, indicating the reliability and robustness of predicting agarase activity with high accuracy and generalization. …”

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). …”

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 (18859198)

“…While 7 Tesla (7T) MRI yields images with superior anatomical detail compared to the more prevalent 3 Tesla (3T) MRI utilized in clinical practice, its widespread implementation is limited due to prohibitive costs. To address the limited access to high-resolution imaging in the absence of 7T MRI, the ongoing development of algorithms aims to synthesize 7T-like MRI from standard 3T scans.…”

Table 4_SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.xlsx by Jidong Lang (802830)

“…</p>Methods<p>We developed a comprehensive web-based computing platform, SZBC-AI4TCM, a comprehensive web-based computing platform for traditional Chinese medicine that embodies the “ShuZhiBenCao” (Digital Herbal) concept through artificial intelligence, designed to accelerate TCM research and reduce costs by integrating advanced AI algorithms and bioinformatics tools.…”

Table 5_SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.xlsx by Jidong Lang (802830)

“…</p>Methods<p>We developed a comprehensive web-based computing platform, SZBC-AI4TCM, a comprehensive web-based computing platform for traditional Chinese medicine that embodies the “ShuZhiBenCao” (Digital Herbal) concept through artificial intelligence, designed to accelerate TCM research and reduce costs by integrating advanced AI algorithms and bioinformatics tools.…”

Table 2_SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.xlsx by Jidong Lang (802830)

“…</p>Methods<p>We developed a comprehensive web-based computing platform, SZBC-AI4TCM, a comprehensive web-based computing platform for traditional Chinese medicine that embodies the “ShuZhiBenCao” (Digital Herbal) concept through artificial intelligence, designed to accelerate TCM research and reduce costs by integrating advanced AI algorithms and bioinformatics tools.…”