Pub Date : 2026-02-01Epub Date: 2026-02-03DOI: 10.1016/j.softx.2026.102538
Dedalo Marchetti , Daniele Bailo , Giuseppe Falcone , Jan Michalek , Rossana Paciello , Alessandro Piscini
The study of earthquake preparation phases often relies on fragmented approaches, limiting reproducibility and comparison between methods. To address this, we developed a Virtual Research Environment (VRE) for multiparametric and multidisciplinary earthquake investigations. Built as a Jupyter Notebook with MATLAB and Python kernels, the VRE integrates seismic, geodetic, atmospheric, and ionospheric data into a unified and automated workflow. Users can define spatial, temporal and other parameters to retrieve and process data across layers. Its effectiveness is demonstrated through the analysis of the 2016 Central Italy and 2025 Marmara earthquakes, where the tool proved capability to easy reproduce cross-domain results.
{"title":"SEISMO-VRE: A tool for a multiparametric and multidisciplinary study of an earthquake","authors":"Dedalo Marchetti , Daniele Bailo , Giuseppe Falcone , Jan Michalek , Rossana Paciello , Alessandro Piscini","doi":"10.1016/j.softx.2026.102538","DOIUrl":"10.1016/j.softx.2026.102538","url":null,"abstract":"<div><div>The study of earthquake preparation phases often relies on fragmented approaches, limiting reproducibility and comparison between methods. To address this, we developed a Virtual Research Environment (VRE) for multiparametric and multidisciplinary earthquake investigations. Built as a Jupyter Notebook with MATLAB and Python kernels, the VRE integrates seismic, geodetic, atmospheric, and ionospheric data into a unified and automated workflow. Users can define spatial, temporal and other parameters to retrieve and process data across layers. Its effectiveness is demonstrated through the analysis of the 2016 Central Italy and 2025 Marmara earthquakes, where the tool proved capability to easy reproduce cross-domain results.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102538"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-29DOI: 10.1016/j.softx.2026.102529
Debabrata Adhikari, Jesper John Lisegaard, Jesper Henri Hattel, Sankhya Mohanty
PermXCT is an open-source computational framework designed to predict virtual permeability in fiber-reinforced polymer composites based on data extracted from X-ray computed tomography (XCT). It provides an automated and reproducible workflow that connects imaging based geometry extraction, mesh generation, and numerical flow simulation for permeability estimation. The framework integrates both mesoscale and microscale morphological characteristics, such as intra and inter-yarn porosity and fiber orientation, to capture realistic flow pathways within complex composite geometries. PermXCT utilises a combination of established open-source tools, including DREAM3D for mesh creation, OpenFOAM for fluid flow simulation, and Python and MATLAB for data processing and automation. Computational efficiency is achieved through optimized meshing strategies and domain scaling, enabling large XCT datasets to be analyzed with reduced computational cost. Validation against experimental permeability measurements demonstrates strong agreement, confirming the reliability and physical accuracy of the imaging based predictions. By minimizing uncertainties and repeatability issues associated with experimental permeability testing, PermXCT provides a robust foundation for XCT-informed virtual permeability characterization.
{"title":"PermXCT: A novel framework for imaging-based virtual permeability prediction","authors":"Debabrata Adhikari, Jesper John Lisegaard, Jesper Henri Hattel, Sankhya Mohanty","doi":"10.1016/j.softx.2026.102529","DOIUrl":"10.1016/j.softx.2026.102529","url":null,"abstract":"<div><div>PermXCT is an open-source computational framework designed to predict virtual permeability in fiber-reinforced polymer composites based on data extracted from X-ray computed tomography (XCT). It provides an automated and reproducible workflow that connects imaging based geometry extraction, mesh generation, and numerical flow simulation for permeability estimation. The framework integrates both mesoscale and microscale morphological characteristics, such as intra and inter-yarn porosity and fiber orientation, to capture realistic flow pathways within complex composite geometries. PermXCT utilises a combination of established open-source tools, including DREAM3D for mesh creation, OpenFOAM for fluid flow simulation, and Python and MATLAB for data processing and automation. Computational efficiency is achieved through optimized meshing strategies and domain scaling, enabling large XCT datasets to be analyzed with reduced computational cost. Validation against experimental permeability measurements demonstrates strong agreement, confirming the reliability and physical accuracy of the imaging based predictions. By minimizing uncertainties and repeatability issues associated with experimental permeability testing, PermXCT provides a robust foundation for XCT-informed virtual permeability characterization.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102529"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-12-08DOI: 10.1016/j.softx.2025.102479
Jakub Śledziowski , Paweł Terefenko , Andrzej Giza , Kamran Tanwari , Dominik Paprotny
Modern climate impact and attribution science requires timely, high-resolution meteorological and hydrological data. The CLIMB workflow is an open-source framework integrating state-of-the-art datasets and methods for operational generation of high-resolution climate datasets tailored for attribution studies of floods, droughts, heatwaves, and other extremes. We show that global climate reanalysis can be efficiently bias-adjusted and downscaled, and further converted into readily-usable climate indicators. The choice of variables and formatting of the data enables direct application in hydrological models. The workflow implements a fully scripted pipeline that can be automated via cron scheduling, providing daily meteorological outputs. We show an application of the workflow for operational monitoring weather extremes in Poland.
{"title":"CLIMB: Framework for CLIMate data bias-adjustment and downscaling","authors":"Jakub Śledziowski , Paweł Terefenko , Andrzej Giza , Kamran Tanwari , Dominik Paprotny","doi":"10.1016/j.softx.2025.102479","DOIUrl":"10.1016/j.softx.2025.102479","url":null,"abstract":"<div><div>Modern climate impact and attribution science requires timely, high-resolution meteorological and hydrological data. The CLIMB workflow is an open-source framework integrating state-of-the-art datasets and methods for operational generation of high-resolution climate datasets tailored for attribution studies of floods, droughts, heatwaves, and other extremes. We show that global climate reanalysis can be efficiently bias-adjusted and downscaled, and further converted into readily-usable climate indicators. The choice of variables and formatting of the data enables direct application in hydrological models. The workflow implements a fully scripted pipeline that can be automated via cron scheduling, providing daily meteorological outputs. We show an application of the workflow for operational monitoring weather extremes in Poland.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102479"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-12-03DOI: 10.1016/j.softx.2025.102459
Grover Enrique Castro Guzman , Diogo Ricardo da Costa , Eduardo Silva Lira , Suzana de Siqueira Santos , Taiane Coelho Ramos , Daniel Yasumasa Takahashi , Andre Fujita
The analysis of complex networks has traditionally relied on descriptive measures, such as centrality and clustering coefficients, as well as algorithms for detecting partitions and components. Additionally, a range of software packages has been designed for visualization and structural analysis. Although these approaches provide valuable information, they primarily focus on observable network features rather than their underlying generative mechanisms. We introduce statGraph, a nonparametric statistical framework for inferring properties of unobserved network generation mechanisms. At its core, statGraph leverages graph spectra, which intrinsically capture structural information and provide a robust basis for nonparametric inference. The package implements a range of methods, including graph entropy estimation, random graph parameter estimation, model selection procedures, statistical tests for comparing graphs, correlation analysis between sets of graphs, and graph clustering algorithms. By bridging graph theory and statistics via spectral analysis, statGraph provides a comprehensive toolkit for advancing the statistical analysis of complex networks.
{"title":"StatGraph: an R package for complex network statistical analyses based on spectrum","authors":"Grover Enrique Castro Guzman , Diogo Ricardo da Costa , Eduardo Silva Lira , Suzana de Siqueira Santos , Taiane Coelho Ramos , Daniel Yasumasa Takahashi , Andre Fujita","doi":"10.1016/j.softx.2025.102459","DOIUrl":"10.1016/j.softx.2025.102459","url":null,"abstract":"<div><div>The analysis of complex networks has traditionally relied on descriptive measures, such as centrality and clustering coefficients, as well as algorithms for detecting partitions and components. Additionally, a range of software packages has been designed for visualization and structural analysis. Although these approaches provide valuable information, they primarily focus on observable network features rather than their underlying generative mechanisms. We introduce <strong>statGraph</strong>, a nonparametric statistical framework for inferring properties of unobserved network generation mechanisms. At its core, <strong>statGraph</strong> leverages graph spectra, which intrinsically capture structural information and provide a robust basis for nonparametric inference. The package implements a range of methods, including graph entropy estimation, random graph parameter estimation, model selection procedures, statistical tests for comparing graphs, correlation analysis between sets of graphs, and graph clustering algorithms. By bridging graph theory and statistics via spectral analysis, <strong>statGraph</strong> provides a comprehensive toolkit for advancing the statistical analysis of complex networks.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102459"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2025-12-18DOI: 10.1016/j.softx.2025.102468
Kenneth Bisgaard Christensen
Psychrometric charts are essential in HVAC design and education, but manual look-ups and spreadsheet workflows are slow and error-prone. This paper presents the Mollier h-x Diagram Web App, an open-source browser-based tool that performs fast, bidirectional moist-air calculations and visualizes results directly on a live Mollier h–x diagram. Users can enter any two of dry-bulb temperature, relative humidity, or humidity ratio to obtain the full psychrometric state, including dew-point temperature, enthalpy, entropy, and specific volume. The app is pressure-aware: barometric pressure is derived from user-selected altitude using the standard-atmosphere model, ensuring accuracy away from sea level. Implemented in vanilla JavaScript with Plotly.js, it runs entirely client-side, requires no installation, and functions offline after first load (MIT Licence). Validation against ASHRAE Fundamentals and benchmark spreadsheets shows agreement within 0.5 kJ kg⁻¹ in enthalpy and 0.3 g kg⁻¹ in humidity ratio. Performance tests confirm sub-millisecond solve times, enabling responsive interaction and rapid scenario analysis. The open architecture supports reuse in teaching, research, and HVAC practice.
湿度计图表在暖通空调设计和教育中是必不可少的，但手动查找和电子表格工作流程缓慢且容易出错。本文介绍了Mollier h-x图Web App，这是一个基于浏览器的开源工具，可以执行快速、双向的湿度空气计算，并直接在实时Mollier h-x图上显示结果。用户可以输入干球温度、相对湿度或湿度比中的任意两种，以获得完整的湿度计状态，包括露点温度、焓、熵和比容。这款应用具有压力感知功能：气压是使用标准大气模型从用户选择的高度得出的，确保了与海平面无关的准确性。在Plotly.js中使用普通JavaScript实现，它完全运行在客户端，不需要安装，并且在首次加载后离线运行（MIT许可）。对ASHRAE基础和基准电子表格的验证表明，在0.5 kJ kg -⁻¹的焓和0.3 g kg -⁻的湿度比范围内是一致的。性能测试确认了亚毫秒的求解时间，实现了响应式交互和快速场景分析。开放式架构支持教学、研究和暖通空调实践中的重用。
{"title":"Mollier h-x diagram (Web App): An open-source browser-based psychrometric calculator","authors":"Kenneth Bisgaard Christensen","doi":"10.1016/j.softx.2025.102468","DOIUrl":"10.1016/j.softx.2025.102468","url":null,"abstract":"<div><div>Psychrometric charts are essential in HVAC design and education, but manual look-ups and spreadsheet workflows are slow and error-prone. This paper presents the <em>Mollier h-x Diagram Web App</em>, an open-source browser-based tool that performs fast, bidirectional moist-air calculations and visualizes results directly on a live Mollier h–x diagram. Users can enter any two of dry-bulb temperature, relative humidity, or humidity ratio to obtain the full psychrometric state, including dew-point temperature, enthalpy, entropy, and specific volume. The app is pressure-aware: barometric pressure is derived from user-selected altitude using the standard-atmosphere model, ensuring accuracy away from sea level. Implemented in vanilla JavaScript with Plotly.js, it runs entirely client-side, requires no installation, and functions offline after first load (MIT Licence). Validation against ASHRAE Fundamentals and benchmark spreadsheets shows agreement within 0.5 kJ kg⁻¹ in enthalpy and 0.3 g kg⁻¹ in humidity ratio. Performance tests confirm sub-millisecond solve times, enabling responsive interaction and rapid scenario analysis. The open architecture supports reuse in teaching, research, and HVAC practice.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102468"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-06DOI: 10.1016/j.softx.2025.102493
Patryk Górka , Krzysztof Małecki , Stanisław Iwan , Wojciech Konicki , Karolina Nadolska , Michał Żuchora
City decision-makers have limited ability to assess the actual impact of transportation systems on air quality because available pollution detectors provide only general data and do not allow for determining the share of emissions generated by vehicle traffic. Tools that would enable analysis based on current and detailed road traffic data are lacking. Therefore, with the GRASS-NEXT project, implemented under the Polish-Norwegian Research Programme, we developed a web-based system that integrates data from portable TOPO detectors, which record detailed road traffic data for 10 vehicle categories, as well as weather and environmental data. These resources form the basis of a pollutant dispersion model, which uses the Gaussian Plume Model to calculate diffusion coefficients and total emissions of individual compounds, presenting the results on contour maps. The software was developed using multiple programming technologies, including TypeScript, Angular, Node.js, Java Spring Boot, and C. The solution innovatively combines data from mobile traffic detectors with a dynamic emissions model, enabling a precise presentation of the impact of real-world transportation systems. The application provides both visualisations and specific emission values in , creating a tool that addresses a gap in existing analytical systems. The system supports environmental management and transportation planning processes, enabling the assessment of the consequences of various urban logistics measures, such as vehicle access restrictions or the development of unloading infrastructure.
城市决策者评估交通系统对空气质量的实际影响的能力有限，因为现有的污染探测器只能提供一般数据，不能确定车辆交通产生的排放份额。目前缺乏能够根据当前和详细的道路交通数据进行分析的工具。因此，在波兰-挪威研究计划下实施的GRASS-NEXT项目中，我们开发了一个基于网络的系统，该系统集成了便携式TOPO探测器的数据，该探测器记录了10种车辆类别的详细道路交通数据，以及天气和环境数据。这些资源构成了污染物扩散模型的基础，该模型使用高斯羽流模型计算扩散系数和单个化合物的总排放量，并将结果显示在等高线地图上。该软件是使用多种编程技术开发的，包括TypeScript、Angular、Node.js、Java Spring Boot和c++。该解决方案创新性地将移动交通探测器的数据与动态排放模型相结合，能够精确呈现现实世界交通系统的影响。该应用程序提供了可视化和以μg/m3为单位的特定排放值，创造了一个解决现有分析系统空白的工具。该系统支持环境管理和运输规划过程，能够评估各种城市物流措施的后果，例如车辆通行限制或卸货基础设施的发展。
{"title":"Web application to model and visualise the spread of traffic pollution","authors":"Patryk Górka , Krzysztof Małecki , Stanisław Iwan , Wojciech Konicki , Karolina Nadolska , Michał Żuchora","doi":"10.1016/j.softx.2025.102493","DOIUrl":"10.1016/j.softx.2025.102493","url":null,"abstract":"<div><div>City decision-makers have limited ability to assess the actual impact of transportation systems on air quality because available pollution detectors provide only general data and do not allow for determining the share of emissions generated by vehicle traffic. Tools that would enable analysis based on current and detailed road traffic data are lacking. Therefore, with the GRASS-NEXT project, implemented under the Polish-Norwegian Research Programme, we developed a web-based system that integrates data from portable TOPO detectors, which record detailed road traffic data for 10 vehicle categories, as well as weather and environmental data. These resources form the basis of a pollutant dispersion model, which uses the Gaussian Plume Model to calculate diffusion coefficients and total emissions of individual compounds, presenting the results on contour maps. The software was developed using multiple programming technologies, including TypeScript, Angular, Node.js, Java Spring Boot, and C<span><math><mo>+</mo><mo>+</mo></math></span>. The solution innovatively combines data from mobile traffic detectors with a dynamic emissions model, enabling a precise presentation of the impact of real-world transportation systems. The application provides both visualisations and specific emission values in <span><math><mi>μ</mi><mi>g</mi><mrow><mo>/</mo></mrow><msup><mi>m</mi><mn>3</mn></msup></math></span>, creating a tool that addresses a gap in existing analytical systems. The system supports environmental management and transportation planning processes, enabling the assessment of the consequences of various urban logistics measures, such as vehicle access restrictions or the development of unloading infrastructure.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102493"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-06DOI: 10.1016/j.softx.2025.102502
Francesc Comalada , Vicenç Acuña , Xavier Garcia
FreshCES-Net is a modular, scalable framework for mapping freshwater Cultural Ecosystem Services (CES) using geotagged social media images. It integrates automated photo retrieval, deep learning-based image classification, and spatial modelling in a fully reproducible pipeline. The classification module employs a fine-tuned ResNet-152 Convolutional Neural Network trained on 6911 Flickr images, achieving 0.92 accuracy and 0.91 recall across five CES categories. Spatial modelling is conducted using an XGBoost model trained on biophysical covariates such as population density, river order, naturalness, accessibility, protection status, and others. Model outputs include the weight of the biophysical variables over CES presence and maps that reveal areas with unexpected CES intensity not explained by demographic or environmental variables. The framework was applied across over 150 river basins in the Iberian Peninsula, enabling large-scale CES assessments with high spatial resolution. FreshCES-Net facilitates new research questions about how freshwater landscapes influence CES distribution at large scale, while also improving the reproducibility and scalability of existing methods. The software is designed for practical use by researchers, planners, and environmental managers, requiring only basic Python experience. It uses relative paths, modular notebooks, and intermediate outputs in CSV or Excel formats. Though not commercialized, the tool is actively used in applied research and is publicly available. FreshCES-Net offers a high-performance, accessible solution for integrating CES into freshwater planning, conservation strategies, and environmental decision-making at regional to continental scales.
{"title":"FreshCES-Net: A scalable deep learning approach to map freshwater cultural ecosystem services using social media data","authors":"Francesc Comalada , Vicenç Acuña , Xavier Garcia","doi":"10.1016/j.softx.2025.102502","DOIUrl":"10.1016/j.softx.2025.102502","url":null,"abstract":"<div><div>FreshCES-Net is a modular, scalable framework for mapping freshwater Cultural Ecosystem Services (CES) using geotagged social media images. It integrates automated photo retrieval, deep learning-based image classification, and spatial modelling in a fully reproducible pipeline. The classification module employs a fine-tuned ResNet-152 Convolutional Neural Network trained on 6911 Flickr images, achieving 0.92 accuracy and 0.91 recall across five CES categories. Spatial modelling is conducted using an XGBoost model trained on biophysical covariates such as population density, river order, naturalness, accessibility, protection status, and others. Model outputs include the weight of the biophysical variables over CES presence and maps that reveal areas with unexpected CES intensity not explained by demographic or environmental variables. The framework was applied across over 150 river basins in the Iberian Peninsula, enabling large-scale CES assessments with high spatial resolution. FreshCES-Net facilitates new research questions about how freshwater landscapes influence CES distribution at large scale, while also improving the reproducibility and scalability of existing methods. The software is designed for practical use by researchers, planners, and environmental managers, requiring only basic Python experience. It uses relative paths, modular notebooks, and intermediate outputs in CSV or Excel formats. Though not commercialized, the tool is actively used in applied research and is publicly available. FreshCES-Net offers a high-performance, accessible solution for integrating CES into freshwater planning, conservation strategies, and environmental decision-making at regional to continental scales.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102502"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-01-21DOI: 10.1016/j.softx.2026.102522
Gianluca Amato , Luca Di Vita , Paolo Melchiorre , Maria Chiara Meo , Francesca Scozzari , Matteo Vitali
CONNECT is an AI-powered tool designed to support the creation of research teams targeting competitive funding calls. The tool takes a short input text (for instance the scientific objectives of a specific call) and analyzes the metadata of scholarly publications (title and abstract) from a repository to suggest a list of potential collaborators, based on semantic similarity and scientific relevance. The current instance includes all the researchers from ten research institutions located across Europe.
{"title":"CONNECT: find your dream team","authors":"Gianluca Amato , Luca Di Vita , Paolo Melchiorre , Maria Chiara Meo , Francesca Scozzari , Matteo Vitali","doi":"10.1016/j.softx.2026.102522","DOIUrl":"10.1016/j.softx.2026.102522","url":null,"abstract":"<div><div><span>CONNECT</span> is an AI-powered tool designed to support the creation of research teams targeting competitive funding calls. The tool takes a short input text (for instance the scientific objectives of a specific call) and analyzes the metadata of scholarly publications (title and abstract) from a repository to suggest a list of potential collaborators, based on semantic similarity and scientific relevance. The current instance includes all the researchers from ten research institutions located across Europe.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102522"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-02-03DOI: 10.1016/j.softx.2026.102545
Alaa M. Abdul-Hadi , Aqeel Abdulazeez Mohammed , Hala Jassim Mohammed , Raafat Salih Muhammad , Almuntadher Alwhelat , Muntadher Alsabah , Basheera M. Mahmmod , Sadiq H. Abdulhussain
This paper presents an open-source, cross-platform toolbox for discrete orthogonal polynomials (DOPs), enabling their practical use in scientific computing and signal/image processing workflows. The proposed toolbox includes six DOP families: Hahn, Meixner, Charlier, Krawtchouk, Tchebichef, and Racah polynomials, implemented in C, Python, and MATLAB using consistent interfaces across platforms. The toolbox provides routines for constructing orthogonal polynomial bases and using them for forward and inverse polynomial-domain transforms of 1D, 2D, and 3D signals. Since the attainable polynomial order is influenced by numerical conditioning and finite-precision arithmetic, the toolbox is designed to provide reliable performance for practical orders relevant to moment-based and transform applications. Overall, the toolbox facilitates reproducible experimentation and supports both researchers and new users working with DOP-based transforms and moments.
{"title":"A toolbox for real orthogonal polynomials","authors":"Alaa M. Abdul-Hadi , Aqeel Abdulazeez Mohammed , Hala Jassim Mohammed , Raafat Salih Muhammad , Almuntadher Alwhelat , Muntadher Alsabah , Basheera M. Mahmmod , Sadiq H. Abdulhussain","doi":"10.1016/j.softx.2026.102545","DOIUrl":"10.1016/j.softx.2026.102545","url":null,"abstract":"<div><div>This paper presents an open-source, cross-platform toolbox for discrete orthogonal polynomials (DOPs), enabling their practical use in scientific computing and signal/image processing workflows. The proposed toolbox includes six DOP families: Hahn, Meixner, Charlier, Krawtchouk, Tchebichef, and Racah polynomials, implemented in C<span><math><mo>+</mo><mo>+</mo></math></span>, Python, and MATLAB using consistent interfaces across platforms. The toolbox provides routines for constructing orthogonal polynomial bases and using them for forward and inverse polynomial-domain transforms of 1D, 2D, and 3D signals. Since the attainable polynomial order is influenced by numerical conditioning and finite-precision arithmetic, the toolbox is designed to provide reliable performance for practical orders relevant to moment-based and transform applications. Overall, the toolbox facilitates reproducible experimentation and supports both researchers and new users working with DOP-based transforms and moments.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102545"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01Epub Date: 2026-02-04DOI: 10.1016/j.softx.2026.102540
MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi
Solar flare prediction is a central challenge in space weather forecasting, with direct implications for satellite operations, aviation safety, and power grid reliability. Machine learning has achieved state-of-the-art performance for this task, particularly when applied to photospheric magnetic field parameters. FlaPLeT is an open-source, full-stack web platform that supports end-to-end machine learning workflows for multivariate time-series–based solar flare prediction without requiring any coding expertise. Built with React, Django, Celery, and PostgreSQL, the system integrates dataset preprocessing, data augmentation, functional network (graph) construction, and machine learning model training into modular asynchronous tasks that generate downloadable datasets, trained models, and structured JSON reports. The platform is deployed on a dedicated Windows server using NGINX, Waitress, Redis, TLS encryption, and reCAPTCHA to ensure secure and scalable operation. FlaPLeT lowers the barrier for heliophysicists to apply machine learning to photospheric magnetic field data and to systematically evaluate how preprocessing strategies and hyperparameter choices affect flare-prediction accuracy. Its cloud-based deployment removes local hardware constraints and makes the platform accessible to researchers worldwide through a standard web browser.
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