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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 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.

太阳耀斑预测是空间天气预报的核心挑战，直接影响卫星运行、航空安全和电网可靠性。机器学习在这项任务中取得了最先进的性能，特别是在应用于光球磁场参数时。FlaPLeT是一个开源的全栈web平台，支持端到端的机器学习工作流程，用于基于多变量时间序列的太阳耀斑预测，而无需任何编码专业知识。该系统使用React、Django、芹菜和PostgreSQL构建，将数据集预处理、数据增强、功能网络（图）构建和机器学习模型训练集成到模块化异步任务中，生成可下载的数据集、训练模型和结构化JSON报告。该平台部署在专用的Windows服务器上，使用NGINX， Waitress, Redis， TLS加密和reCAPTCHA来确保安全和可扩展的操作。flelet降低了太阳物理学家将机器学习应用于光球磁场数据的障碍，并系统地评估预处理策略和超参数选择如何影响耀斑预测精度。它基于云的部署消除了本地硬件的限制，使全球的研究人员可以通过标准的web浏览器访问该平台。

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引用次数: 0

A toolbox for real orthogonal polynomials 实正交多项式的工具箱

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 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.

本文提出了离散正交多项式（DOPs）的开源跨平台工具箱，使其在科学计算和信号/图像处理工作流程中的实际应用成为可能。提出的工具箱包括六个DOP族：Hahn、Meixner、Charlier、Krawtchouk、chebichef和Racah多项式，使用c++、Python和MATLAB实现，使用跨平台的一致接口。工具箱提供了构造正交多项式基的例程，并使用它们进行一维、二维和三维信号的正多项式域变换和逆多项式域变换。由于可实现的多项式阶数受到数值条件和有限精度算法的影响，因此该工具箱旨在为基于矩和变换应用的实际阶数提供可靠的性能。总的来说，工具箱促进了可重复的实验，并支持研究人员和新用户使用基于dop的变换和矩。

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引用次数: 0

IoT-Sim: An interactive platform for designing and securing smart device networks IoT-Sim：用于设计和保护智能设备网络的交互平台

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102527

Alejandro Diez Bermejo, Branly Martinez Gonzalez, Beatriz Gil-Arroyo, Jaime Rincón Arango, Daniel Urda Muñoz

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.

IoT- sim是一款轻量级模块化工具，用于创建、配置和测试检测物联网（IoT）网络攻击的模型。它为模拟连接设备之间的通信和评估入侵检测模型提供了一个交互式环境。该框架允许研究人员在受控条件下设计网络拓扑，注入不同类型的攻击和基准检测算法。通过在开源设计中结合可用性和灵活性，模拟器是物联网安全解决方案的教育、研究和快速原型设计的宝贵资源。

引用次数: 0

RSD: An R package to calculate stochastic dominance RSD：一个计算随机优势的R包

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 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.

随机优势是利用两个随机变量的概率分布函数进行比较的一种经典方法。对于所有的随机顺序，随机优势并不总是在随机变量之间建立一个顺序，几乎是为了解决这种情况而开发的随机优势，从而将随机优势的适用性扩展到许多现实世界的问题。我们开发了一个R包，其中包含一系列用于测试离散随机变量的一阶和二阶（几乎）随机优势的方法。本文描述了该软件包，并使用涵盖一系列可能发生的场景的合成数据集和一个实际示例来说明这些方法，其中使用随机优势对离散随机变量进行比较可以应用于辅助决策。

引用次数: 0

DICLab2D: An open-source digital image correlation algorithm for Julia language DICLab2D: Julia语言的开源数字图像相关算法

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102532

Dennis Quaresma Pureza, José Luis Vital de Brito, Guilherme Santana Alencar, Luís Augusto Conte Mendes Veloso

This work presents DICLab2D, an open-source digital image correlation (DIC) algorithm developed in the Julia programming language. DICLab2D is a local subset-based 2D DIC code that employs both the inverse compositional Gauss-Newton (IC-GN) and the backward subtractive Gauss-Newton (BS-GN) methods. The algorithm is equipped with shape functions up to the fourth order, reliability-guided displacement tracking, and a dual analysis mode - area and line probes. Standardized tests from the DIC challenge were used to evaluate algorithm performance. The results show that DICLab2D achieves performance equivalent or exceeding that of existing commercial and open-source DIC codes.

这项工作提出了DICLab2D，一个开源的数字图像相关（DIC）算法开发的Julia编程语言。DICLab2D是一种基于局部子集的二维DIC编码，它同时采用逆合成高斯-牛顿（IC-GN）和反向减去高斯-牛顿（BS-GN）方法。该算法具有四阶形状函数、可靠导向位移跟踪和双分析模式——面积探针和线探针。使用DIC挑战的标准化测试来评估算法性能。结果表明，DICLab2D实现了相当于或超过现有商业和开源DIC代码的性能。

引用次数: 0

Updated 2.0 to biointertidal mapper software: A satellite approach for NDVI-based intertidal habitat mapping 更新2.0生物潮间带制图软件：基于ndvi的潮间带生境卫星制图方法

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102539

Sara Haro , Ricardo Bermejo , Lara Veylit , Liam Morrison

BioIntertidal Mapper 2.0 is a user-friendly tool with a graphical user interface that automates Sentinel-2 processing (since 2017) for intertidal habitat mapping in Google Earth Engine (GEE) using the Normalized Difference Vegetation Index (NDVI). Unlike version 1.0, low-tide scenes are no longer selected using the WorldTides API; instead, images are screened by estimating the proportion of water pixels using the Normalized Difference Water Index. Reflectance inputs were updated to the Sentinel-2 Harmonized dataset. GEE authentication was simplified, the interface refined, and exports expanded to include RGB imagery alongside filtered NDVI products saved to Google Drive. The software enables rapid, reproducible operational mapping for scientists and coastal managers.

BioIntertidal Mapper 2.0是一个用户友好的工具，具有图形用户界面，可使用归一化植被指数（NDVI）在谷歌Earth Engine （GEE）中自动化Sentinel-2处理（自2017年以来）进行潮间带栖息地制图。与1.0版本不同，不再使用WorldTides API选择低潮场景；相反，通过使用归一化差水指数估计水像素的比例来筛选图像。反射率输入被更新到Sentinel-2协调数据集。GEE认证得到了简化，界面得到了改进，导出扩展到包括RGB图像以及保存到谷歌Drive的过滤后的NDVI产品。该软件为科学家和海岸管理人员提供了快速、可重复的操作地图。

引用次数: 0

Foruster: A cross-platform tool for live forensic triage and anomaly detection forster：一个跨平台的现场鉴定和异常检测工具

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102517

Marcos Jesús Sequera Fernández, Mohammadhossein Homaei, Óscar Mogollón Gutierrez, José Carlos Sancho Núñez

Foruster is a cross-platform desktop application, developed in Rust, for live-system forensic analysis. Unlike traditional tools that require system shutdown, Foruster is designed to identify and catalog files of interest on active storage volumes. Its user interface, built with the Slint framework, guides the analyst through the selection of devices, the configuration of search profiles, and the real-time visualization of results. The software features heuristic detection of anomalies, such as deceptive file extensions, and ensures the integrity of findings through cryptographic hashing, optimizing the digital forensic investigation process.

forster是一个跨平台桌面应用程序，用Rust开发，用于实时系统取证分析。与需要关闭系统的传统工具不同，forster旨在识别和编目活动存储卷上感兴趣的文件。它的用户界面是用Slint框架构建的，通过设备的选择、搜索配置文件的配置和结果的实时可视化来指导分析人员。该软件的特点是启发式检测异常，如欺骗性的文件扩展名，并通过加密散列确保结果的完整性，优化数字取证调查过程。

引用次数: 0

Collaborative hybrid intelligence platform CHIP: A modular architecture for developing and testing personalized lifestyle support interactions 协作混合智能平台CHIP：用于开发和测试个性化生活方式支持交互的模块化架构

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102536

Floris den Hengst , Shaad Alaka , Bart A. Kamphorst

The rise of lifestyle-related, non-communicable diseases such as Type II diabetes, cardiovascular diseases, and depression has prompted the development of various behavior change technologies to promote sustained healthy behaviors. User adherence, however, has remained low.

The Collaborative Hybrid Intelligence Platform CHIP is introduced to address adherence challenges by placing the user perspective at the center and facilitating dialogue-based interactions between users and their technical and non-technical support systems—including AI systems, clinicians and caretakers. These interactions aim to uncover barriers to adherence and collaboratively shape personalized lifestyle plans that align with a person’s preferences, values, and context.

CHIP is a microservice-based research platform written in Python with modules implemented as Docker containers. Its modularity allows researchers to replace or adapt specific components, such as natural language reasoners, for technical evaluation and domain-specific adaptation.

与生活方式相关的非传染性疾病，如II型糖尿病、心血管疾病和抑郁症的增加，促使了各种行为改变技术的发展，以促进持续的健康行为。然而，用户的依从性仍然很低。通过将用户视角置于中心位置，并促进用户与其技术和非技术支持系统（包括人工智能系统、临床医生和护理人员）之间基于对话的互动，引入协作混合智能平台CHIP来解决依从性挑战。这些互动旨在揭示坚持的障碍，并共同塑造个性化的生活方式计划，使其与个人的偏好、价值观和环境保持一致。CHIP是一个基于微服务的研究平台，用Python编写，模块实现为Docker容器。它的模块化允许研究人员替换或调整特定组件，例如自然语言推理器，用于技术评估和特定领域的调整。

引用次数: 0

Auto-PHSSCW ABAQUS: An integrated, python-based workflow for automated buckling-to-collapse analysis of H-shaped steel composite walls Auto-PHSSCW ABAQUS：一个集成的、基于python的工作流程，用于h型钢复合墙的自动屈曲到倒塌分析

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102535

Lijian Ren

Auto-PHSSCW ABAQUS is an open-source Python package that automates the parametric buckling-to-collapse modeling of H-shaped steel composite walls in Abaqus/CAE. Unlike standard macro-based scripts, this tool employs an integrated workflow combining geometric modeling, mesh generation, and multi-step solver orchestration. A key novel technical solution is the implementation of an anchor-based smart keyword injection algorithm, which dynamically manipulates the Abaqus keyword block to automate the injection of eigenmode-based geometric imperfections—a process traditionally requiring manual input files (.inp) editing to bypass standard API limitations. Furthermore, to ensure robustness during high-throughput parametric studies, the software utilizes coordinate-based bounding box algorithms for topological identification, eliminating mesh dependency errors common in index-based scripting. The workflow also features a closed-loop data transfer protocol that autonomously links linear eigenvalue results to non-linear Static-Riks collapse analysis. The tool supports continuous, separated, and bolted splice joints, and has successfully generated over 140,000 simulations to generate datasets for machine learning, significantly lowering the computational barrier for stability research.

Auto-PHSSCW ABAQUS是一个开源的Python包，它可以在ABAQUS /CAE中自动化h型钢复合墙的参数化屈曲到倒塌建模。与标准的基于宏的脚本不同，该工具采用集成工作流，将几何建模、网格生成和多步骤求解器编排结合在一起。一个关键的新技术解决方案是实现基于锚点的智能关键字注入算法，该算法动态操纵Abaqus关键字块以自动注入基于特征模式的几何缺陷-传统上需要手动编辑输入文件（.inp）以绕过标准API限制。此外，为了确保高吞吐量参数研究期间的鲁棒性，该软件利用基于坐标的边界框算法进行拓扑识别，消除了基于索引的脚本中常见的网格依赖错误。工作流还具有闭环数据传输协议，该协议将线性特征值结果自动链接到非线性静态风险崩溃分析。该工具支持连续、分离和螺栓连接，并成功生成了超过14万次模拟，生成了用于机器学习的数据集，大大降低了稳定性研究的计算障碍。

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引用次数: 0

OntoRipple: Making waves in the knowledge graph lifecycle ontoriple：在知识图谱生命周期中掀起波澜

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 DOI: 10.1016/j.softx.2026.102542

Diego Conde-Herreros , Oscar Corcho , David Chaves-Fraga

Knowledge Graphs are commonly organised according to the structure of existing ontologies, which define the concepts, relations, and restrictions of the domain of the KG. There are ontology-dependent assets that guide how data from heterogeneous sources is integrated, transformed, validated, and exploited in the KG, such as mapping rules and validation constraints. As ontologies evolve over time, these changes must be consistently reflected in the dependent assets, ensuring that the resulting KG remains aligned with the updated ontology. While ontology evolution has been widely studied, the propagation of changes to dependent artifacts remains an open challenge, requiring manual effort that makes the process slow, error-prone, and costly. In this paper, we present OntoRipple, a set of algorithms integrated into a tool that automates the propagation of ontology changes into RML mappings and SHACL shapes to construct and validate Knowledge Graphs, ensuring consistency with the evolving ontology in a fully declarative workflow.

知识图通常根据现有本体的结构组织，本体定义了KG领域的概念、关系和限制。有一些与本体相关的资产指导如何在KG中集成、转换、验证和利用来自异构源的数据，例如映射规则和验证约束。随着本体论的发展，这些变化必须一致地反映在依赖的资产中，确保最终的KG与更新的本体论保持一致。虽然本体演化已经得到了广泛的研究，但是将变更传播到依赖的工件仍然是一个开放的挑战，需要人工的努力，这使得过程缓慢、容易出错并且代价高昂。在本文中，我们提出了ontoriple，这是一组集成到一个工具中的算法，该工具可以自动将本体更改传播为RML映射和SHACL形状，以构建和验证知识图，确保在完全声明性工作流中与不断发展的本体保持一致。

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