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pyNusinov: A Python3 software package for Solar Extreme and Far Ultraviolet radiation modeling pyNusinov：一个用于太阳极端和远紫外线辐射建模的Python3软件包
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-10-16 DOI: 10.1016/j.simpa.2025.100792
Boris Prokhorov , Oleg Zolotov , Yulia Romanovskaya , Anton Tatarnikov , Yulia Shapovalova
{"title":"pyNusinov: A Python3 software package for Solar Extreme and Far Ultraviolet radiation modeling","authors":"Boris Prokhorov ,&nbsp;Oleg Zolotov ,&nbsp;Yulia Romanovskaya ,&nbsp;Anton Tatarnikov ,&nbsp;Yulia Shapovalova","doi":"10.1016/j.simpa.2025.100792","DOIUrl":"10.1016/j.simpa.2025.100792","url":null,"abstract":"<div><div>The pyNusinov package implements Nusinov’s extreme ultraviolet (EUVT) and far ultraviolet (FUVT) solar radiation models. Jointly, these models cover the 5–242 [nm] solar irradiance range but with different wavelength steps and a small gap between 105–115 [nm]. To third-party users, EUVT and FUVT models were published as analytical formulas and tables of corresponding coefficients. The release of the pyNusinov package provides a robust way to use, disseminate, install, and update these models. It significantly improves the models’ usage workflow, benefits from Python3 infrastructure, and facilitates early career researchers’ engagement.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100792"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Version 2.0 — FLECO, enhancements for cyber situational awareness training and research 版本2.0 - FLECO，增强网络态势感知训练和研究
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-11-28 DOI: 10.1016/j.simpa.2025.100800
Manuel Domínguez-Dorado , David Cortés-Polo , Francisco J. Rodríguez-Pérez , Jesús Galeano-Brajones , Jesús Calle-Cancho
{"title":"Version 2.0 — FLECO, enhancements for cyber situational awareness training and research","authors":"Manuel Domínguez-Dorado ,&nbsp;David Cortés-Polo ,&nbsp;Francisco J. Rodríguez-Pérez ,&nbsp;Jesús Galeano-Brajones ,&nbsp;Jesús Calle-Cancho","doi":"10.1016/j.simpa.2025.100800","DOIUrl":"10.1016/j.simpa.2025.100800","url":null,"abstract":"<div><div>This article presents FLECO Studio 2.0, which enhances cybersecurity situational awareness training through realistic scenarios. Organizations face increasing cyber threats but struggle with a global shortage of cybersecurity professionals. To address this, they must upskill existing personnel across all functional areas. Situational awareness is crucial for identifying, understanding, and responding to threats dynamically. FLECO Studio 2.0 includes new features designed to improve this skill, enabling multidisciplinary teams to assess risks, anticipate attacks, and coordinate effective responses. These enhancements strengthen an organization’s cybersecurity posture, fostering a unified and proactive defense against evolving threats.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100800"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SkinProNet: An attention-based deep learning system for skin disease classification and segmentation SkinProNet：一个基于注意力的深度学习系统，用于皮肤病分类和分割
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-10-31 DOI: 10.1016/j.simpa.2025.100798
N Annalakshmi , S Umarani
{"title":"SkinProNet: An attention-based deep learning system for skin disease classification and segmentation","authors":"N Annalakshmi ,&nbsp;S Umarani","doi":"10.1016/j.simpa.2025.100798","DOIUrl":"10.1016/j.simpa.2025.100798","url":null,"abstract":"<div><div>SkinProNet is an AI-powered software tool designed for the classification and segmentation of skin lesions, including potentially life-threatening conditions like melanoma. It employs a novel hybrid deep learning architecture that combines advanced preprocessing methods with state-of-the-art models: EfficientNetV2Small for feature extraction, an optimized ACRNN for accurate classification, and U<sup>2</sup>-Net++ for precise lesion segmentation. This integrated approach enhances early detection and diagnosis of skin diseases. The model classifies six types of skin diseases with a high accuracy of 94.04% using both benchmark datasets and real-world clinical images. The results highlight the model’s potential as a reliable computer-aided diagnostic tool in dermatology. By leveraging attention-based mechanisms and efficient neural architectures, the software supports healthcare practitioners in diagnosing skin conditions quickly, accurately, and non-invasively.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100798"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
IRJT-Secure: Open-source image steganography with Quadristego embedding and Huffman compression IRJT-Secure：开源图像隐写与Quadristego嵌入和霍夫曼压缩
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-11-14 DOI: 10.1016/j.simpa.2025.100801
Irsyad Fikriansyah Ramadhan , Ntivuguruzwa Jean De La Croix , Tohari Ahmad
{"title":"IRJT-Secure: Open-source image steganography with Quadristego embedding and Huffman compression","authors":"Irsyad Fikriansyah Ramadhan ,&nbsp;Ntivuguruzwa Jean De La Croix ,&nbsp;Tohari Ahmad","doi":"10.1016/j.simpa.2025.100801","DOIUrl":"10.1016/j.simpa.2025.100801","url":null,"abstract":"<div><div>In information security, image steganography remains a crucial technique for covert data transmission. However, achieving an optimal balance between payload capacity, imperceptibility, and robustness against steganalysis attacks remains a significant challenge. This paper presents IRJT-Secure, an open-source software implementation based on the Quadristego Logic paradigm and Huffman coding for data compression. The proposed technique creates four stego images from a single cover image, in contrast to conventional dual-image steganography. This maintains the original image’s visual integrity while enabling more effective and uniform data embedding. IRJT-Secure provides a valuable resource for advancing research and development in spatial domain steganography, supporting the creation of more secure, robust, and efficient data hiding techniques for digital security</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100801"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145576323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
TMFS-MTS: Toolbox for metaheuristic feature selection in multivariate time series 多变量时间序列的元启发式特征选择工具箱
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-09-25 DOI: 10.1016/j.simpa.2025.100789
Mariusz Oszust, Marian Wysocki
{"title":"TMFS-MTS: Toolbox for metaheuristic feature selection in multivariate time series","authors":"Mariusz Oszust,&nbsp;Marian Wysocki","doi":"10.1016/j.simpa.2025.100789","DOIUrl":"10.1016/j.simpa.2025.100789","url":null,"abstract":"<div><div>Feature selection in multivariate time series is a key challenge in modern data analysis, as high-dimensional data often include temporal dependencies and irrelevant features degrading classifier performance. To address these issues, a comparison with existing approaches is essential. Therefore, this work introduces the Toolbox for Metaheuristic Feature Selection in Multivariate Time Series (TMFS-MTS), providing an environment for feature selection and metaheuristic evaluation. It supports diverse fitness measures and advanced visualizations, including convergence curves, feature count tracking, runtime analysis, Wilcoxon tests, and 2D embeddings. Implemented in MATLAB, TMFS-MTS offers a standardized framework for advancing research in multivariate time series feature selection.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100789"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A tool for textual adversarial attack via multi-objective optimization 通过多目标优化的文本对抗性攻击工具
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-09-29 DOI: 10.1016/j.simpa.2025.100787
Zhanqi Cui, Yuanxin Qiao, Ruilin Xie, Li Li, Qifan He
{"title":"A tool for textual adversarial attack via multi-objective optimization","authors":"Zhanqi Cui,&nbsp;Yuanxin Qiao,&nbsp;Ruilin Xie,&nbsp;Li Li,&nbsp;Qifan He","doi":"10.1016/j.simpa.2025.100787","DOIUrl":"10.1016/j.simpa.2025.100787","url":null,"abstract":"<div><div>Textual adversarial attacks generate adversarial examples that retain similar semantics to the original text and feed them into the target model to detect potential vulnerabilities by comparing output differences. This approach effectively addresses the scarcity of annotated test data during the testing phase. Existing methods often rely on greedy strategies for candidate word selection, which may result in contextually inappropriate or unnatural perturbations, thereby compromising the overall quality of the adversarial examples. To address this issue, we propose MOBTAG, a tool for generating textual adversarial examples based on multi-objective optimization. MOBTAG integrates principles from both multi-objective optimization and genetic algorithms. It improves the attack success rate while maintaining high semantic similarity and readability between adversarial examples and the original texts, thereby enabling the generation of high-quality adversarial examples</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100787"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DefectDetect: A lightweight application for manual image annotation and patch extraction DefectDetect：用于手动图像注释和补丁提取的轻量级应用程序
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-10-31 DOI: 10.1016/j.simpa.2025.100795
Lejla Arapovic, Emir Sokic
{"title":"DefectDetect: A lightweight application for manual image annotation and patch extraction","authors":"Lejla Arapovic,&nbsp;Emir Sokic","doi":"10.1016/j.simpa.2025.100795","DOIUrl":"10.1016/j.simpa.2025.100795","url":null,"abstract":"<div><div>This paper presents DefectDetect, a lightweight desktop tool for manual image annotation and automatic patch extraction, developed to assist in creating annotated datasets for machine learning problems. Although it is initially designed for annotating leather defects, the application supports broader use cases. Users can annotate freely, automatically extract smaller image patches and corresponding binary masks with adjustable stride, selected defect types and rating (0–2), and export data in PNG, JSON, YOLO, or Pascal VOC formats. The tool is fully GUI-based, requires no coding knowledge for usage, and supports session saving and batch image loading. The application has proven effective in academic contexts through its use in research activities. Future plans include the addition of shape annotation functionality and support for batch processing.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100795"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Screw Process Anomaly Visualization (SPAV): A Python module for local and global machine learning visualizations for screw tightening anomaly detection 螺钉过程异常可视化（SPAV）：用于螺钉拧紧异常检测的局部和全局机器学习可视化的Python模块
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-09-25 DOI: 10.1016/j.simpa.2025.100786
Marta Moreno , Hugo Rocha , André Pilastri , Guilherme Moreira , Luís Miguel Matos , Paulo Cortez
{"title":"Screw Process Anomaly Visualization (SPAV): A Python module for local and global machine learning visualizations for screw tightening anomaly detection","authors":"Marta Moreno ,&nbsp;Hugo Rocha ,&nbsp;André Pilastri ,&nbsp;Guilherme Moreira ,&nbsp;Luís Miguel Matos ,&nbsp;Paulo Cortez","doi":"10.1016/j.simpa.2025.100786","DOIUrl":"10.1016/j.simpa.2025.100786","url":null,"abstract":"<div><div>Modern screwdriver systems generate real-time angle-torque data that form tightening curves that are valuable for quality inspection issues (e.g., detect faulty processes). This work describes the Screw Process Anomaly Visualization (SPAV) Python module, which provides several eXplainable AI (XAI) graphs for Machine Learning (ML) screw tightening results, namely global and local errors, with identification of most probable anomaly angle-torque locations. SPAV integrates seamlessly with the scientific Python ecosystem and is compatible with several ML implementations, including H2O and Keras deep AutoEncoders (AE).</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100786"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MDFlaker: A tool for multi-factor detection and root cause analysis of flaky tests MDFlaker：用于片状试验的多因素检测和根本原因分析的工具
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-11-07 DOI: 10.1016/j.simpa.2025.100794
Azeem Ahmad
{"title":"MDFlaker: A tool for multi-factor detection and root cause analysis of flaky tests","authors":"Azeem Ahmad","doi":"10.1016/j.simpa.2025.100794","DOIUrl":"10.1016/j.simpa.2025.100794","url":null,"abstract":"<div><div>Test flakiness, characterized by inconsistent test outcomes without changes to the codebase, is a prevalent issue in continuous integration (CI) environments. This paper introduces a practical tool named <em>MDFlaker</em> that implements a multi-factor approach for detecting flaky tests, grounded in empirical research. The tool analyzes four key factors—traceback coverage, test smells, flakiness frequency, and test size—and applies a K-Nearest Neighbors (KNN) model for classification. Depending on the test case’s execution history, the tool switches between traceback-prioritized or multi-factor classification strategy. Integrated with platforms like GitHub and Travis CI, it processes real-time data from commits, builds, and test executions to support both research experimentation and industrial deployment. By improving the accuracy and interpretability of flaky test detection, the tool helps developers regain trust in CI pipelines, reduces debugging overhead, and fosters high-quality, rapid software delivery. This work not only enhances software reliability in practice but also opens new avenues for research in automated software quality assurance.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100794"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MATLAB-AMPL integration with heuristics and association mining: An optimization-driven framework for retail shelf space allocation MATLAB-AMPL集成启发式和关联挖掘：零售货架空间分配的优化驱动框架
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-10-31 DOI: 10.1016/j.simpa.2025.100797
Gihan S. Edirisinghe , Charles L. Munson
{"title":"MATLAB-AMPL integration with heuristics and association mining: An optimization-driven framework for retail shelf space allocation","authors":"Gihan S. Edirisinghe ,&nbsp;Charles L. Munson","doi":"10.1016/j.simpa.2025.100797","DOIUrl":"10.1016/j.simpa.2025.100797","url":null,"abstract":"<div><div>This paper introduces a MATLAB-based framework for shelf-space optimization in retail, integrating AMPL via an API for solving linear and nonlinear programs. It supports two strategies: (1) Guided Random Rearrangement, creating constraint-aware layouts without prior data, and (2) Data-Driven Rearrangement, using association rule mining and mixed-integer programming to boost impulse buys. Core data structures are developed in MATLAB, while CPLEX and BARON solvers are employed through AMPL when required. The system adapts to varied retail environments, enhancing profitability and customer experience. New experiments with the Microsoft FoodMart dataset show that the data-driven method consistently outperforms random strategies.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100797"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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