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Comparison of performance between our next reaction implementation and the Python library from Ref. [3].
Published 2025“…For each network we repeat the simulations 100 times. Dots represent average times and bars represent standard deviations.…”
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System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool
Published 2024“…</li></ul><h2>Integration with Other Tools</h2><p dir="ltr">The System Hardware ID Generator Script is part of the broader suite of tools offered by the <a href="https://xn--mxac.net/" target="_blank">Alpha Beta Network</a>, dedicated to enhancing security and performance in <a href="https://xn--mxac.net/" target="_blank">Python programming</a>.</p><ul><li>For advanced <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">Python code protection tools</a>, consider using the <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">Local Python Code Protector Script</a>. …”
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Map of the Iberian Peninsula, including the Canary and Balearic Islands, showing the location of ICENET stations.
Published 2024“…<p>Red dots represent the ceilometers operating in near-real-time conditions while blue dots correspond to legacy stations providing data in the past or intermittently. …”
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Bayesian Changepoint Detection via Logistic Regression and the Topological Analysis of Image Series
Published 2025“…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.…”
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Table 1_Analysis of distribution equilibrium and influencing factors for older adult meal service facilities in mainland China.xlsx
Published 2025“…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). …”
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MYCroplanters can quantify the interaction between pathogenic and non-pathogenic bacteria and their effects on plant health.
Published 2025“…Data shows that N2C3 is pathogenic, whereas strains WCS365, CHA0, CH267, and Pf5 are largely non-pathogenic. Each dot represents the health score of a single plant. …”
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Contrast enhancement of digital images using dragonfly algorithm
Published 2024“…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>.…”
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Cathode carbon block material parameters [14].
Published 2025“…A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick’s second law. …”
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Sodium concentration distribution cloud map.
Published 2025“…A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick’s second law. …”
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Sodium binding coefficient R.
Published 2025“…A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick’s second law. …”
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MCCN Case Study 3 - Select optimal survey locality
Published 2025“…</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>…”
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Scope of our collection of pathogen models of metabolism.
Published 2024“…Dotted line in the background represents average Reaction, Gene, and Metabolite numbers across species. …”
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Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event
Published 2025“…<br><br><b>Missing-Data Handling & Imputation:</b></p><p dir="ltr">The following sequential steps were applied to create a complete and consistent daily time series suitable for analysis (presented in the Imputed_AP_Data_Zurich_2010-25 sheet), particularly addressing the absence of routine PM₂.₅ measurements prior to January 2016. The full implementation is detailed in the accompanying Python script (Imputation_Air_Pollutants_NABEL.py). …”