Memory monitoring recognition test main screen. حسب Pedro C. Martínez-Suárez (21192459)

Task descriptions. حسب Pedro C. Martínez-Suárez (21192459)

Workflow of segmentation model creation and application. حسب Vincent Hickl (16947429)

SpatialKNifeY analysis landscape. حسب Shunsuke A. Sakai (13789939)

Error reduction over time by the HOFA-SMC. حسب Asra Sarwat (22453794)

Comparison of SMC techniques. حسب Asra Sarwat (22453794)

Proposed HOFA-SMC with experimental validation. حسب Asra Sarwat (22453794)

Summary of Tourism Dataset. حسب Jing Zhang (23775)

Segment-wise Spending Analysis. حسب Jing Zhang (23775)

Hyperparameter Parameter Setting. حسب Jing Zhang (23775)

Marketing Campaign Analysis. حسب Jing Zhang (23775)

Visitor Segmentation Validation Accuracy. حسب Jing Zhang (23775)

Integration of VAE and RNN Architecture. حسب Jing Zhang (23775)

Bayesian Changepoint Detection via Logistic Regression and the Topological Analysis of Image Series حسب Andrew M. Thomas (712104)

"…The method also successfully recovers the location and nature of changes in more traditional changepoint tasks. An implementation of our method is available in the Python package bclr.…"

Table 1_Analysis of distribution equilibrium and influencing factors for older adult meal service facilities in mainland China.xlsx حسب Feng Wang (44414)

"…Objective<p>Analyze the distribution equilibrium of older adult meal service facilities in mainland China and explore the factors influencing their distribution.</p>Methods<p>Use Python to obtain data on older adult meal service facilities, and analyze the equity of older adult meal services using descriptive statistics, the Lorenz curve, the Gini coefficient, and the Spatial Mismatch Index (SMI). …"

Contrast enhancement of digital images using dragonfly algorithm حسب Soumyajit Saha (19726163)

"…Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this <a href="https://github.com/somnath796/DA_contrast_enhancement" target="_blank">Github repository</a>.…"

Cathode carbon block material parameters [14]. حسب Chenglong Gong (20629836)

"…A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick’s second law. …"

Sodium concentration distribution cloud map. حسب Chenglong Gong (20629836)

"…A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick’s second law. …"

Sodium binding coefficient R. حسب Chenglong Gong (20629836)

"…A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick’s second law. …"

MCCN Case Study 3 - Select optimal survey locality حسب Donald Hobern (21435904)

"…</p><p dir="ltr">This is a simple implementation that uses four environmental attributes imported for all Australia (or a subset like NSW) at a moderate grid scale:</p><ol><li>Digital soil maps for key soil properties over New South Wales, version 2.0 - SEED - see <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html" target="_blank">https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html</a></li><li>ANUCLIM Annual Mean Rainfall raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer</a></li><li>ANUCLIM Annual Mean Temperature raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer</a></li></ol><h4><b>Dependencies</b></h4><ul><li>This notebook requires Python 3.10 or higher</li><li>Install relevant Python libraries with: <b>pip install mccn-engine rocrate</b></li><li>Installing mccn-engine will install other dependencies</li></ul><h4><b>Overview</b></h4><ol><li>Generate STAC metadata for layers from predefined configuratiion</li><li>Load data cube and exclude nodata values</li><li>Scale all variables to a 0.0-1.0 range</li><li>Select four layers for comparison (soil organic carbon 0-30 cm, soil pH 0-30 cm, mean annual rainfall, mean annual temperature)</li><li>Select 10 random points within NSW</li><li>Generate 10 new layers representing standardised environmental distance between one of the selected points and all other points in NSW</li><li>For every point in NSW, find the lowest environmental distance to any of the selected points</li><li>Select the point in NSW that has the highest value for the lowest environmental distance to any selected point - this is the most different point</li><li>Clean up and save results to RO-Crate</li></ol><p><br></p>…"