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FlaPLeT: A full-stack web platform for end-to-end time series data processing and machine learning in solar flare prediction flplet：一个用于太阳耀斑预测的端到端时间序列数据处理和机器学习的全栈web平台

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-02-04 DOI: 10.1016/j.softx.2026.102540

MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

{"title":"FlaPLeT: A full-stack web platform for end-to-end time series data processing and machine learning in solar flare prediction","authors":"MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi","doi":"10.1016/j.softx.2026.102540","DOIUrl":"10.1016/j.softx.2026.102540","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102540"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187980","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}

引用次数: 0

niarules: Advancing interpretable machine learning through numerical association rule mining and 3D coral plot visualization nirules：通过数值关联规则挖掘和3D珊瑚图可视化推进可解释机器学习

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2025-12-08 DOI: 10.1016/j.softx.2025.102470

Iztok Fister Jr , Gerlinde Emsenhuber , Jan Hendrik Plümer , Iztok Fister , Andreas Holzinger

{"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}

引用次数: 0

CTA evaluation system: LLM-supported phonetic analysis platform for common Turkic alphabet CTA评价系统：支持llm的通用突厥字母语音分析平台

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-24 DOI: 10.1016/j.softx.2026.102530

Halil Ibrahim Okur, Kadir Tohma

引用次数: 0

RCF-3D Analysis: a web-based tool for pushover analysis of regular reinforced concrete frames RCF-3D分析：一个基于网络的工具，用于常规钢筋混凝土框架的推覆分析

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-26 DOI: 10.1016/j.softx.2026.102534

Orlando Arroyo

引用次数: 0

HuReTEx: From deep learning models to explainable information flow models HuReTEx：从深度学习模型到可解释的信息流模型

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-28 DOI: 10.1016/j.softx.2026.102520

Krzysztof Pancerz , Piotr Kulicki , Michał Kalisz , Andrzej Burda , Maciej Stanisławski , Zofia Matusiewicz , Ewa Szlachtowska , Jaromir Sarzyński

引用次数: 0

ENCERTIA: A dynamic R-shiny app to support business decision-making using data envelopment analysis ENCERTIA：一个动态的R-shiny应用程序，支持使用数据包络分析的业务决策

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-27 DOI: 10.1016/j.softx.2026.102525

María C. Bas, Rafael Benítez, Vicente J. Bolós

引用次数: 0

DEXiWare: a software development framework for building cooperative decision support systems DEXiWare：用于构建协作决策支持系统的软件开发框架

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-22 DOI: 10.1016/j.softx.2026.102531

Bojan Blažica , Vladimir Kuzmanovski , Marko Bohanec , Tanja Dergan , Aneta Ivanovska , Jurij Marinko , Robert Modic , Matevž Ogrinc , Marko Debeljak

{"title":"DEXiWare: a software development framework for building cooperative decision support systems","authors":"Bojan Blažica , Vladimir Kuzmanovski , Marko Bohanec , Tanja Dergan , Aneta Ivanovska , Jurij Marinko , Robert Modic , Matevž Ogrinc , Marko Debeljak","doi":"10.1016/j.softx.2026.102531","DOIUrl":"10.1016/j.softx.2026.102531","url":null,"abstract":"<div><div>The development of decision support systems (DSS) for agriculture increasingly relies on complex decision models, yet transforming such models into operational, user-friendly software remains challenging. DEXiWare is a software framework designed to support the development of web-based, cooperative DSS based on decision models built with the DEX (Decision EXpert) method. The framework provides a standardized workflow for operationalizing decision models, including automated model import, data handling, assessment, and scenario analysis, within a reusable backend–frontend architecture. DEXiWare integrates backend services, a web-based user interface, and a decision engine supporting top-down (goal-seeking) and bottom-up (what-if) scenario exploration. The framework is evaluated through its application in multiple agricultural DSS and through usability testing with stakeholders, demonstrating its applicability for translating qualitative decision models into operational decision support tools for sustainability assessment in agricultural production systems.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"33 ","pages":"Article 102531"},"PeriodicalIF":2.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037350","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}

引用次数: 0

Human DevOps: A tool for measuring and enhancing human factors in DevOps adoption 人力DevOps：衡量和增强采用DevOps过程中的人为因素的工具

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-19 DOI: 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

引用次数: 0

QOPTec: a modular platform for benchmarking quantum algorithms through combinatorial optimization problems QOPTec：通过组合优化问题对量子算法进行基准测试的模块化平台

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-09 DOI: 10.1016/j.softx.2026.102507

Pablo Miranda-Rodríguez, Eneko Osaba

引用次数: 0

VisNow: An open-source Java-based modular dataflow visualisation platform VisNow：一个基于java的开源模块化数据流可视化平台

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102503

Krzysztof Nowinski , Piotr Regulski , Piotr Wendykier , Bartosz Borucki , Jedrzej Nowosielski , Jakub Zelinski

{"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}

引用次数: 0