Presentation of the DySCo framework. by Giuseppe de Alteriis (20846230)

“…<p>A: What is dynamic Functional Connectivity: i) We can start from any set of brain recordings, where each signal is referred to a brain location (e.g. fMRI, EEG, intracranial recordings in rodents, and more). ii) “Static” Functional Connectivity (FC) is a matrix where each entry is a time aggregated functional measure of interaction between two regions, for example, the Pearson Correlation Coefficient. iii) Dynamic Functional Connectivity (dFC) is a FC matrix (that can be calculated in different ways, see below) that changes with time, under the assumption that patterns of brain interactions are non-stationary. …”

Helicase C tree AutoPhy analysis. by Adrian N. Ortiz-Velez (17775800)

“…<div><p>Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. …”

Mumps tree AutoPhy analysis. by Adrian N. Ortiz-Velez (17775800)

“…<div><p>Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. …”

Ski oncogene tree AutoPhy analysis. by Adrian N. Ortiz-Velez (17775800)

“…<div><p>Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. …”

Covid tree AutoPhy analysis. by Adrian N. Ortiz-Velez (17775800)

“…<div><p>Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. …”

ENQUIRE’s flowchart. by Luca Musella (19986312)

Visualization of the data pipelines. by Zifan Jiang (11985551)

Comparison against the commercial scanner. by Swathi Lakshmi Balasubramanian (12078093)

Study design and deep-learning model architecture. by Sehoon Park (1466026)

“…Conv, Convolutional layer; SepConv, Separable convolutional layer; MBConv, Mobile inverted bottleneck convolutional layer (numbers after MBConv indicate layer depth); k3/k5, kernel size 3 or 5; GAP, Global average pooling; FC, Fully connected layer; Swish, Swish activation function; DBP, Diastolic blood pressure, SBP, Systolic blood pressure; HR, Heart rate; DL-IVSS, A deep-learning algorithm leveraging time-series intraoperative vital sign signals; preOp ML, A machine learning model with 103 baseline characteristics.…”

MOMA compared to MFA-derived estimates, carbon yield efficiencies and CBA co-factor profile comparison across unconstrained, manually curated and experimentally constrained solutio... by Laura de Arroyo Garcia (9226096)

“…MOMA ranges were estimated using the wild type solution as a reference and sequentially implementing the single-gene knockouts studied by Long et al. (2019) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008125#pcbi.1008125.ref046" target="_blank">46</a>], with biomass formation as the objective function. MFA ranges were extracted from a pre-existing dataset (Long et al., 2019), using a Python algorithm to select the minimal and maximal flux ranges.…”

my-home-is-my-secret.zip by Anna Brauer (11266881)

“…</p> <p><br></p> <p>The material contains two folders; each contains an implementation of the algorithm, one in Java and one in Python.<br> </p> <p><br></p> <p>The Python script includes a function (example()) demonstrating how the mechanism class ('STT') may be used. …”

RSeqFlow-OlivePollen2: RSeqFlow1_results_2023-02-09_16.03.33.zip by M. Gonzalo Claros (8543760)

“…</p> <p>•  <strong>CTFnormalisedCPMs-{DATETIME}.tsv</strong>: Normalised CPMs for each gene (rows) in each sample replicate (columns) using the CTF algorithm.</p> <p>•  <strong>DEGs_{CONTRAST}_TREAT_P-0.1_FC-1.2_{DATETIME}.tsv</strong>: LogFC, average expression, <em>t</em> statistic, <em>P</em> value, and adjusted <em>P</em> value, for all DEGs (rows) in the contrast indicated in {CONTRAST} using the <em>treat</em> method and the <em>P</em>and <em>FC</em> indicated in the filename.…”

Datasheet1_A Workflow for Rapid Unbiased Quantification of Fibrillar Feature Alignment in Biological Images.zip by Stefania Marcotti (5896853)

“…Here we present AFT − Alignment by Fourier Transform, a workflow to quantify the alignment of fibrillar features in microscopy images exploiting 2D Fast Fourier Transforms (FFT). Using pre-existing datasets of cell and ECM images, we demonstrate our approach and compare and contrast this workflow with two other well-known ImageJ algorithms to quantify image feature alignment. …”

<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.…”

NanoDB: Research Activity Data Management System by Lorenci Gjurgjaj (19702207)

“…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …”

PREDICTION OF DEM PARAMETERS OF COATED FERTILIZER PARTICLES BASED ON GA-BP NEURAL NETWORK by Xin Du (208780)

“…The predicted values matched the expected output values, indicating that the GA-BP neural network can accurately predict the nonlinear function output, and the network predicted output can be approximated as the actual output of the function. …”

Code and Data for 'Fabrication and testing of lensed fiber optic probes for distance sensing using common path low coherence interferometry' by Radu Stancu (21165068)

“…Distance Sensing</p><p dir="ltr">Code and data to demonstrate extracting distance sensing data from A-scans and to generate Fig. 8 using the algorithm described in Fig. 7. Functions to generate distance measurements are in 'distance_sensing_utilities.py' and an example of how to use this on data in the 'data' folder is in 'distance_sensing_example.py', which generates Fig 8. …”

PSO-Optimized Electronic Load Controller with Intelligent Energy Recovery for Self-Excited Induction Generator Based Micro-Hydro Systems by MRINAL KANTI RAJAK (21838169)

“…The dataset includes: (1) <b>PSO configuration parameters</b> - complete algorithm setup with population size (N=20), adaptive inertia weights (0.9→0.4), time-varying cognitive/social coefficients (c1: 2.5→0.5, c2: 0.5→2.5), search space boundaries for all 10 optimization variables, and convergence criteria specifications; (2) <b>Multi-objective fitness function data</b> - detailed weight adaptation formulas, individual objective convergence statistics (voltage: 15.3 iter, frequency: 19.2 iter, THD: 12.8 iter, energy: 23.0 iter), and composite fitness evolution from 0.537 to 0.903 over 50 iterations; (3) <b>Particle dynamics tracking</b> - complete position and velocity trajectories for all 20 particles across optimization dimensions [Kpv, Kiv, Kdv, Kpf, Kif, Kdf, ma, θphase, fc, Ppump,ref], diversity evolution (100%→8%), and exploration/exploitation transition patterns; (4) <b>Real-time implementation metrics</b> - computational requirements (2.6 kB memory, 67% CPU utilization), execution timing (0.83 ms average, 1.2 ms worst-case), and synchronization protocols for 100 Hz optimization loops; and (5) <b>Validation datasets</b> - performance verification across six different load conditions, convergence statistics, and algorithm robustness testing results demonstrating consistent ±1.8% voltage regulation and ±0.9% frequency stability achievements, all provided in structured CSV/JSON formats with comprehensive documentation under CC-BY license.…”

Data Sheet 1_Identification of key biomarkers related to fibrocartilage chondrocytes for osteoarthritis based on bulk, single-cell transcriptomic data.docx by Bailin Pan (20300112)

“…</p>Results<p>The study identified 545 marker genes associated with FC in OA. GO and KEGG analyses revealed their biological functions; microarray analysis identified 243 DEGs on which functional-enrichment analysis were conducted. …”

Data Sheet 2_Identification of key biomarkers related to fibrocartilage chondrocytes for osteoarthritis based on bulk, single-cell transcriptomic data.csv by Bailin Pan (20300112)

“…</p>Results<p>The study identified 545 marker genes associated with FC in OA. GO and KEGG analyses revealed their biological functions; microarray analysis identified 243 DEGs on which functional-enrichment analysis were conducted. …”