Pub Date : 2026-02-01Epub Date: 2026-01-24DOI: 10.1016/j.softx.2026.102530
Halil Ibrahim Okur, Kadir Tohma
The CTA evaluation system is a comprehensive desktop application designed for academic research on the phonetic representation of the common turkic alphabet (CTA). This LLM-supported platform provides systematic analysis of CTA’s effectiveness across six Turkic languages through four core modules: transliteration engine, phonetic risk analyzer, cognate aligner, and PCE (Phonetic Correspondence Effectiveness) analyzer. The system evaluates the impact of five new CTA letters (q, x, ñ, ə, û) on phonetic clarity and cross-linguistic standardization. Built with Python and OpenAI integration, it offers both quantitative metrics and qualitative assessments, making it an essential tool for Turkic linguistics research, language policy development, and educational material creation. The platform generates comprehensive reports in multiple formats, supporting evidence-based decisions in writing system reforms and multilingual educational initiatives.
{"title":"CTA evaluation system: LLM-supported phonetic analysis platform for common Turkic alphabet","authors":"Halil Ibrahim Okur, Kadir Tohma","doi":"10.1016/j.softx.2026.102530","DOIUrl":"10.1016/j.softx.2026.102530","url":null,"abstract":"<div><div>The CTA evaluation system is a comprehensive desktop application designed for academic research on the phonetic representation of the common turkic alphabet (CTA). This LLM-supported platform provides systematic analysis of CTA’s effectiveness across six Turkic languages through four core modules: transliteration engine, phonetic risk analyzer, cognate aligner, and PCE (Phonetic Correspondence Effectiveness) analyzer. The system evaluates the impact of five new CTA letters (q, x, ñ, ə, û) on phonetic clarity and cross-linguistic standardization. Built with Python and OpenAI integration, it offers both quantitative metrics and qualitative assessments, making it an essential tool for Turkic linguistics research, language policy development, and educational material creation. The platform generates comprehensive reports in multiple formats, supporting evidence-based decisions in writing system reforms and multilingual educational initiatives.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102530"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077389","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: 2026-01-26DOI: 10.1016/j.softx.2026.102534
Orlando Arroyo
Reinforced concrete frame (RCF) buildings are used worldwide in seismic regions. Nonlinear pushover analysis is central to performance-based assessment of these structures but often demands specialized software and extensive scripting, limiting use in performance based earthquake engineering (PBEE) practice and education. RCF-3D Analysis is a web-based application that generates and analyzes three-dimensional RCF models using OpenSeesPy as backend. A guided, tabbed workflow leads users through building geometry and mass definition, RC material and fiber-section creation, beam–column and slab assignment, gravity loading, and modal and pushover analyses. Interactive plan-view visualizations support model checking, while structured data storage enables model reuse. Implemented in Python with Streamlit, RCF-3D Analysis serves practitioners and researchers engaged in PBEE applications.
钢筋混凝土框架（RCF）建筑在世界范围内用于地震区域。非线性推覆分析是这些结构基于性能评估的核心，但通常需要专门的软件和大量的脚本，限制了在基于性能的地震工程（PBEE）实践和教育中的应用。RCF- 3d Analysis是一个基于web的应用程序，它使用OpenSeesPy作为后端生成和分析三维RCF模型。一个有指导的、标签式的工作流程引导用户通过建筑几何形状和质量定义、RC材料和纤维截面创建、梁柱和板分配、重力载荷以及模态和推覆分析。交互式计划视图可视化支持模型检查，而结构化数据存储支持模型重用。RCF-3D Analysis使用Python和Streamlit实现，为从事PBEE应用的从业者和研究人员提供服务。
{"title":"RCF-3D Analysis: a web-based tool for pushover analysis of regular reinforced concrete frames","authors":"Orlando Arroyo","doi":"10.1016/j.softx.2026.102534","DOIUrl":"10.1016/j.softx.2026.102534","url":null,"abstract":"<div><div>Reinforced concrete frame (RCF) buildings are used worldwide in seismic regions. Nonlinear pushover analysis is central to performance-based assessment of these structures but often demands specialized software and extensive scripting, limiting use in performance based earthquake engineering (PBEE) practice and education. RCF-3D Analysis is a web-based application that generates and analyzes three-dimensional RCF models using OpenSeesPy as backend. A guided, tabbed workflow leads users through building geometry and mass definition, RC material and fiber-section creation, beam–column and slab assignment, gravity loading, and modal and pushover analyses. Interactive plan-view visualizations support model checking, while structured data storage enables model reuse. Implemented in Python with Streamlit, RCF-3D Analysis serves practitioners and researchers engaged in PBEE applications.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102534"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077391","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: 2026-01-28DOI: 10.1016/j.softx.2026.102520
Krzysztof Pancerz , Piotr Kulicki , Michał Kalisz , Andrzej Burda , Maciej Stanisławski , Zofia Matusiewicz , Ewa Szlachtowska , Jaromir Sarzyński
In the paper, we describe a path for creating an information flow model (a readable twin) for a deep learning model (an unreadable model). This path has been implemented as a Python tool called Human Readable Twin Explainer (HuReTEx). Properly aggregated artifacts generated by individual key layers of the deep learning model for training cases constitute the basis for building a model in the form of a flow graph. Then, the most important prediction paths are determined. These paths, in connection with appropriately presented artifacts (e.g., in the form of images or descriptions in natural language), constitute a clear explanation of the knowledge acquired by the model during the training process.
{"title":"HuReTEx: From deep learning models to explainable information flow models","authors":"Krzysztof Pancerz , Piotr Kulicki , Michał Kalisz , Andrzej Burda , Maciej Stanisławski , Zofia Matusiewicz , Ewa Szlachtowska , Jaromir Sarzyński","doi":"10.1016/j.softx.2026.102520","DOIUrl":"10.1016/j.softx.2026.102520","url":null,"abstract":"<div><div>In the paper, we describe a path for creating an information flow model (a readable twin) for a deep learning model (an unreadable model). This path has been implemented as a Python tool called Human Readable Twin Explainer (HuReTEx). Properly aggregated artifacts generated by individual key layers of the deep learning model for training cases constitute the basis for building a model in the form of a flow graph. Then, the most important prediction paths are determined. These paths, in connection with appropriately presented artifacts (e.g., in the form of images or descriptions in natural language), constitute a clear explanation of the knowledge acquired by the model during the training process.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102520"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077488","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: 2026-01-27DOI: 10.1016/j.softx.2026.102525
María C. Bas, Rafael Benítez, Vicente J. Bolós
This study presents an interactive R-Shiny application that applies Data Envelopment Analysis (DEA) to measure and compare business efficiency. The platform incorporates directional models, orientation parameters, and alternative slack-handling strategies, enabling users to upload or filter data, compute inefficiency scores, and obtain customized targets and efficient projections. Through intuitive visualizations and dynamic benchmarking, companies can evaluate performance relative to peers of similar size or sector. The tool combines methodological advances with practical usability, offering a decision-support system that enhances strategic planning, resource optimization, and resilience. Illustrative examples demonstrate its capacity to guide companies toward improved efficiency in uncertain environments.
{"title":"ENCERTIA: A dynamic R-shiny app to support business decision-making using data envelopment analysis","authors":"María C. Bas, Rafael Benítez, Vicente J. Bolós","doi":"10.1016/j.softx.2026.102525","DOIUrl":"10.1016/j.softx.2026.102525","url":null,"abstract":"<div><div>This study presents an interactive R-Shiny application that applies Data Envelopment Analysis (DEA) to measure and compare business efficiency. The platform incorporates directional models, orientation parameters, and alternative slack-handling strategies, enabling users to upload or filter data, compute inefficiency scores, and obtain customized targets and efficient projections. Through intuitive visualizations and dynamic benchmarking, companies can evaluate performance relative to peers of similar size or sector. The tool combines methodological advances with practical usability, offering a decision-support system that enhances strategic planning, resource optimization, and resilience. Illustrative examples demonstrate its capacity to guide companies toward improved efficiency in uncertain environments.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102525"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077492","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.2025.102470
Iztok Fister Jr , Gerlinde Emsenhuber , Jan Hendrik Plümer , Iztok Fister , Andreas Holzinger
Numerical association rule mining remains comparatively underexplored in interpretable machine learning, largely due to the challenges of handling continuous variables and the limited availability of effective visualization techniques. We introduce niarules, an open-source R package that provides a complete and extensible pipeline for numerical association rule mining, complemented by advanced post-processing and interactive 3D visualization. The package integrates bio-inspired optimization-based rule mining methods within a modular architecture that encompasses data preprocessing, rule mining, and visualization. A novel radial layout engine, implemented in C++, generates Coral Plots, which depict rules sharing a common consequent as radial trees. This design facilitates intuitive exploration of antecedent specificity, alongside key quality measures such as support, confidence, and lift. By combining methodological innovation with user-friendly visualization, niarules lowers the entry barrier to numerical association rule mining and supports the development of explainable AI systems for numerical datasets.
{"title":"niarules: Advancing interpretable machine learning through numerical association rule mining and 3D coral plot visualization","authors":"Iztok Fister Jr , Gerlinde Emsenhuber , Jan Hendrik Plümer , Iztok Fister , Andreas Holzinger","doi":"10.1016/j.softx.2025.102470","DOIUrl":"10.1016/j.softx.2025.102470","url":null,"abstract":"<div><div>Numerical association rule mining remains comparatively underexplored in interpretable machine learning, largely due to the challenges of handling continuous variables and the limited availability of effective visualization techniques. We introduce <span>niarules</span>, an open-source R package that provides a complete and extensible pipeline for numerical association rule mining, complemented by advanced post-processing and interactive 3D visualization. The package integrates bio-inspired optimization-based rule mining methods within a modular architecture that encompasses data preprocessing, rule mining, and visualization. A novel radial layout engine, implemented in C++, generates Coral Plots, which depict rules sharing a common consequent as radial trees. This design facilitates intuitive exploration of antecedent specificity, alongside key quality measures such as support, confidence, and lift. By combining methodological innovation with user-friendly visualization, <span>niarules</span> lowers the entry barrier to numerical association rule mining and supports the development of explainable AI systems for numerical datasets.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102470"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749405","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: 2026-01-19DOI: 10.1016/j.softx.2026.102515
Juan J. López-Jiménez, Juanjo Pérez-Sánchez, Juan M. Carrillo-de-Gea, Joaquín Nicolás Ros, José L. Fernández-Alemán
DevOps has transformed software engineering through automation, collaboration, and continuous improvement. However, human factors such as communication, psychological safety, and team dynamics have been underexplored despite their critical role in DevOps success. This article presents Human DevOps, a tool developed to assess and enhance these human-centred aspects, built upon an evidence-based human factor adoption model for DevOps. Using a Slack-based survey tool, a back-end for data analysis, and a web dashboard, Human DevOps provides practical insights to optimize DevOps culture. Human DevOps can be integrated into existing pipelines to provide real-time insights into how development teams and technologies work together during software project development.
{"title":"Human DevOps: A tool for measuring and enhancing human factors in DevOps adoption","authors":"Juan J. López-Jiménez, Juanjo Pérez-Sánchez, Juan M. Carrillo-de-Gea, Joaquín Nicolás Ros, José L. Fernández-Alemán","doi":"10.1016/j.softx.2026.102515","DOIUrl":"10.1016/j.softx.2026.102515","url":null,"abstract":"<div><div>DevOps has transformed software engineering through automation, collaboration, and continuous improvement. However, human factors such as communication, psychological safety, and team dynamics have been underexplored despite their critical role in DevOps success. This article presents Human DevOps, a tool developed to assess and enhance these human-centred aspects, built upon an evidence-based human factor adoption model for DevOps. Using a Slack-based survey tool, a back-end for data analysis, and a web dashboard, Human DevOps provides practical insights to optimize DevOps culture. Human DevOps can be integrated into existing pipelines to provide real-time insights into how development teams and technologies work together during software project development.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102515"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037345","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}
The IoT-Sim is a lightweight and modular tool designed to create, configure, and test models that detect attacks in Internet of Things (IoT) networks. It provides an interactive environment for simulating communication among connected devices and evaluating intrusion detection models. This framework allows researchers to design network topologies, inject different types of attacks, and benchmark detection algorithms under controlled conditions. By combining usability and flexibility in an open-source design, the simulator is a valuable resource for the education, research, and rapid prototyping of IoT security solutions.
{"title":"IoT-Sim: An interactive platform for designing and securing smart device networks","authors":"Alejandro Diez Bermejo, Branly Martinez Gonzalez, Beatriz Gil-Arroyo, Jaime Rincón Arango, Daniel Urda Muñoz","doi":"10.1016/j.softx.2026.102527","DOIUrl":"10.1016/j.softx.2026.102527","url":null,"abstract":"<div><div>The IoT-Sim is a lightweight and modular tool designed to create, configure, and test models that detect attacks in Internet of Things (IoT) networks. It provides an interactive environment for simulating communication among connected devices and evaluating intrusion detection models. This framework allows researchers to design network topologies, inject different types of attacks, and benchmark detection algorithms under controlled conditions. By combining usability and flexibility in an open-source design, the simulator is a valuable resource for the education, research, and rapid prototyping of IoT security solutions.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102527"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077393","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: 2026-01-30DOI: 10.1016/j.softx.2025.102461
Shayan Tohidi, Sigurdur Olafsson
Stochastic dominance is a classical method for comparing two random variables using their probability distribution functions. As for all stochastic orders, stochastic dominance does not always establish an order between the random variables, and almost stochastic dominance was developed to address such cases, thus extending the applicability of stochastic dominance to many real-world problems. We developed an R package that consists of a collection of methods for testing the first- and second-order (almost) stochastic dominance for discrete random variables. This article describes the package and illustrates these methods using both synthetic datasets covering a range of possible scenarios that can occur, and a practical example where the comparison of discrete random variables using stochastic dominance can be applied to aid decision-making.
{"title":"RSD: An R package to calculate stochastic dominance","authors":"Shayan Tohidi, Sigurdur Olafsson","doi":"10.1016/j.softx.2025.102461","DOIUrl":"10.1016/j.softx.2025.102461","url":null,"abstract":"<div><div>Stochastic dominance is a classical method for comparing two random variables using their probability distribution functions. As for all stochastic orders, stochastic dominance does not always establish an order between the random variables, and almost stochastic dominance was developed to address such cases, thus extending the applicability of stochastic dominance to many real-world problems. We developed an R package that consists of a collection of methods for testing the first- and second-order (almost) stochastic dominance for discrete random variables. This article describes the package and illustrates these methods using both synthetic datasets covering a range of possible scenarios that can occur, and a practical example where the comparison of discrete random variables using stochastic dominance can be applied to aid decision-making.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102461"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077490","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: 2026-01-09DOI: 10.1016/j.softx.2026.102507
Pablo Miranda-Rodríguez, Eneko Osaba
Combinatorial optimization is a critical field in many applications that remains challenging due to its general computational complexity. Quantum computing is believed to be a promising alternative to classical methods to solve these types of problems. We introduce QOPTec, a Python library for benchmarking optimization problems using quantum or hybrid solvers. QOPTec offers a simple, extensible framework for reproducible evaluation of solver performance. By enabling integration of new problems and algorithms, the tool aims to lower the entry barrier to quantum optimization and supports systematic studies of different solver approaches, helping assess their practical potential as quantum technologies evolve.
{"title":"QOPTec: a modular platform for benchmarking quantum algorithms through combinatorial optimization problems","authors":"Pablo Miranda-Rodríguez, Eneko Osaba","doi":"10.1016/j.softx.2026.102507","DOIUrl":"10.1016/j.softx.2026.102507","url":null,"abstract":"<div><div>Combinatorial optimization is a critical field in many applications that remains challenging due to its general computational complexity. Quantum computing is believed to be a promising alternative to classical methods to solve these types of problems. We introduce QOPTec, a Python library for benchmarking optimization problems using quantum or hybrid solvers. QOPTec offers a simple, extensible framework for reproducible evaluation of solver performance. By enabling integration of new problems and algorithms, the tool aims to lower the entry barrier to quantum optimization and supports systematic studies of different solver approaches, helping assess their practical potential as quantum technologies evolve.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102507"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925762","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.2025.102503
Krzysztof Nowinski , Piotr Regulski , Piotr Wendykier , Bartosz Borucki , Jedrzej Nowosielski , Jakub Zelinski
VisNow is a dataflow-driven modular platform for scientific visualisation and visual data analysis. VisNow is written entirely in Java, released under an open-source licence, and it provides an alternative to popular visualisation systems by emphasising high-level modules and a user-friendly interface. The platform supports large and complex datasets (including time-dependent multivariate data) through specialised libraries, and it is easily extensible via a plugin architecture. VisNow’s design philosophy, embodied by features such as the Read-and-Watch principle and intelligent default parameters, enables users to rapidly create visualisation pipelines and obtain immediate visual feedback. In this article, we describe VisNow’s architecture and core functionalities and we demonstrate its capabilities in three representative use cases: (1) COVID-19 outbreak modelling and visualisation, (2) cardiology applications involving coronary artery segmentation, straightening and blood flow simulation, and (3) meteorological data visualisation and analysis. We also discuss the impact of VisNow in the context of scientific computing and compare its modularity, usability, extensibility, and large-scale data handling with those of other visualisation platforms, such as ParaView, MeVisLab, and 3D Slicer.
{"title":"VisNow: An open-source Java-based modular dataflow visualisation platform","authors":"Krzysztof Nowinski , Piotr Regulski , Piotr Wendykier , Bartosz Borucki , Jedrzej Nowosielski , Jakub Zelinski","doi":"10.1016/j.softx.2025.102503","DOIUrl":"10.1016/j.softx.2025.102503","url":null,"abstract":"<div><div>VisNow is a dataflow-driven modular platform for scientific visualisation and visual data analysis. VisNow is written entirely in Java, released under an open-source licence, and it provides an alternative to popular visualisation systems by emphasising high-level modules and a user-friendly interface. The platform supports large and complex datasets (including time-dependent multivariate data) through specialised libraries, and it is easily extensible via a plugin architecture. VisNow’s design philosophy, embodied by features such as the <em>Read-and-Watch</em> principle and intelligent default parameters, enables users to rapidly create visualisation pipelines and obtain immediate visual feedback. In this article, we describe VisNow’s architecture and core functionalities and we demonstrate its capabilities in three representative use cases: (1) COVID-19 outbreak modelling and visualisation, (2) cardiology applications involving coronary artery segmentation, straightening and blood flow simulation, and (3) meteorological data visualisation and analysis. We also discuss the impact of VisNow in the context of scientific computing and compare its modularity, usability, extensibility, and large-scale data handling with those of other visualisation platforms, such as ParaView, MeVisLab, and 3D Slicer.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102503"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925769","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}
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