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TomoSAM: A 3D Slicer extension using SAM for tomography segmentation TomoSAM：使用SAM进行断层扫描分割的3D切片器扩展
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-24 DOI: 10.1016/j.softx.2025.102218
Federico Semeraro , Alexandre M. Quintart , Sergio Fraile Izquierdo , Joseph C. Ferguson
{"title":"TomoSAM: A 3D Slicer extension using SAM for tomography segmentation","authors":"Federico Semeraro ,&nbsp;Alexandre M. Quintart ,&nbsp;Sergio Fraile Izquierdo ,&nbsp;Joseph C. Ferguson","doi":"10.1016/j.softx.2025.102218","DOIUrl":"10.1016/j.softx.2025.102218","url":null,"abstract":"<div><div>TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer, a highly capable software platform used for 3D image processing and visualization. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process. The source code associated with this article can be found at <span><span>https://github.com/fsemerar/SlicerTomoSAM</span><svg><path></path></svg></span> (see detailed code metadata).</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102218"},"PeriodicalIF":2.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365651","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
GeneralizIT: A Python Solution for Generalizability Theory Computations 泛化理论计算的Python解决方案
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-24 DOI: 10.1016/j.softx.2025.102235
Tyler J. Smith , Theresa J.B. Kline , Adrienne Kline
{"title":"GeneralizIT: A Python Solution for Generalizability Theory Computations","authors":"Tyler J. Smith ,&nbsp;Theresa J.B. Kline ,&nbsp;Adrienne Kline","doi":"10.1016/j.softx.2025.102235","DOIUrl":"10.1016/j.softx.2025.102235","url":null,"abstract":"<div><div>GeneralizIT is a Python package designed to streamline the application of Generalizability Theory (G-Theory) in research and practice. G-Theory extends classical test theory by estimating multiple sources of error variance, providing a more flexible and detailed approach to reliability assessment. Despite its advantages, G-Theory’s complexity can present a significant barrier to researchers. GeneralizIT addresses this challenge by offering an intuitive, user-friendly mechanism to calculate variance components, relative and absolute generalizability coefficients, and to perform decision (D) studies. D-Studies allow users to make decisions about potential study designs and target improvements in the reliability of certain facets. The package supports all univariate design types, including unbalanced designs, and allows for missing data, enabling users to perform in-depth reliability analysis with minimal coding effort. With built-in visualization tools and detailed reporting functions, GeneralizIT empowers researchers across disciplines, such as education, psychology, healthcare, and the social sciences, to harness the power of G-Theory for robust evidence-based insights. Whether applied to small or large datasets, GeneralizIT offers an accessible and computationally efficient solution to improve measurement reliability in complex data environments.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102235"},"PeriodicalIF":2.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365656","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
TensorFlores: An enhanced Python-based TinyML framework TensorFlores：一个增强的基于python的TinyML框架
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-23 DOI: 10.1016/j.softx.2025.102224
Thommas K.S. Flores , Daniel G. Costa , Ivanovitch Silva
{"title":"TensorFlores: An enhanced Python-based TinyML framework","authors":"Thommas K.S. Flores ,&nbsp;Daniel G. Costa ,&nbsp;Ivanovitch Silva","doi":"10.1016/j.softx.2025.102224","DOIUrl":"10.1016/j.softx.2025.102224","url":null,"abstract":"<div><div>The TensorFlores framework is a Python-based tool designed to optimize machine learning deployment in resource-constrained environments. It introduces evolving clustering-based quantization, supporting both quantization-aware training and post-training quantization while maintaining model accuracy. TensorFlores converts TensorFlow MLP models into optimized formats and generates platform-agnostic C++ code for embedded systems. Its modular architecture minimizes memory usage and computational overhead, enabling efficient real-time inference. By combining clustering-based quantization and automated code generation, TensorFlores enhances TinyML applications, making it a robust solution for low-power and edge AI scenarios in embedded and IoT systems.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102224"},"PeriodicalIF":2.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365041","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
GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI GenAI4UQ：一个使用条件生成人工智能进行正向和反向不确定性量化的软件
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-23 DOI: 10.1016/j.softx.2025.102232
Ming Fan , Zezhong Zhang , Dan Lu , Guannan Zhang
{"title":"GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI","authors":"Ming Fan ,&nbsp;Zezhong Zhang ,&nbsp;Dan Lu ,&nbsp;Guannan Zhang","doi":"10.1016/j.softx.2025.102232","DOIUrl":"10.1016/j.softx.2025.102232","url":null,"abstract":"<div><div>We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102232"},"PeriodicalIF":2.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365655","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
topoEEG: An Python-framework for analyzing EEG data in neurodegeneratives disease through Topological Deep Learning topoEEG：通过拓扑深度学习分析神经退行性疾病脑电图数据的python框架
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-21 DOI: 10.1016/j.softx.2025.102222
Miriam Esteve , Alejandro Martinez-Gracia , Jesus J. Rodríguez-Sala , Antonio Falcó
{"title":"topoEEG: An Python-framework for analyzing EEG data in neurodegeneratives disease through Topological Deep Learning","authors":"Miriam Esteve ,&nbsp;Alejandro Martinez-Gracia ,&nbsp;Jesus J. Rodríguez-Sala ,&nbsp;Antonio Falcó","doi":"10.1016/j.softx.2025.102222","DOIUrl":"10.1016/j.softx.2025.102222","url":null,"abstract":"<div><div><strong>topoEEG</strong> is a Python framework designed for advanced EEG analysis, combining the MNE library with Topological Deep Learning (TDL) to enhance insights into neuroimaging, particularly for neurodegenerative diseases such as Alzheimer’s. The framework preprocesses EEG data by removing artifacts using Independent Component Analysis (ICA) and performs Power Spectral Density (PSD) analysis to identify critical frequency bands. By incorporating TDL, <strong>topoEEG</strong> uncovers topological features that traditional methods often overlook, offering deeper insights into neural activity. Unlike other standalone tools, it provides a unified solution, enhancing the accessibility of sophisticated analytics and supporting research in the diagnosis and understanding of neurodegenerative diseases.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102222"},"PeriodicalIF":2.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329623","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
IQM-Vis: A user-centric python toolbox for visualising and evaluating image quality metrics IQM-Vis：一个以用户为中心的python工具箱，用于可视化和评估图像质量指标
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-21 DOI: 10.1016/j.softx.2025.102225
M. Clifford , A. Hepburn , R. Kleinlein , J. Vila-Tomás , P. Hernández-Cámara , P. Dauden , N. Lepora , V. Laparra , R. Santos-Rodríguez
{"title":"IQM-Vis: A user-centric python toolbox for visualising and evaluating image quality metrics","authors":"M. Clifford ,&nbsp;A. Hepburn ,&nbsp;R. Kleinlein ,&nbsp;J. Vila-Tomás ,&nbsp;P. Hernández-Cámara ,&nbsp;P. Dauden ,&nbsp;N. Lepora ,&nbsp;V. Laparra ,&nbsp;R. Santos-Rodríguez","doi":"10.1016/j.softx.2025.102225","DOIUrl":"10.1016/j.softx.2025.102225","url":null,"abstract":"<div><div>Image Quality Metrics (IQMs) assess differences between images where human judgements are too expensive or infeasible due to the number of evaluations needed. Although a multitude of IQMs have been proposed in the literature, none are considered flawless. Therefore, it is crucial to understand their limitations by evaluating their suitability for different application domains. This type of evaluation is both qualitative and quantitative, lending itself best to interactive graphical tools. To this end, we created IQM-Vis, the first open source toolbox dedicated to analysing IQMs, visualising image distortions and conducting human image perception experiments, all through a simple Python interface.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102225"},"PeriodicalIF":2.4,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329622","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
OSP-SAP: A MATLAB graphical user interface for optimal sensor placement using SAP2000 sap：一个使用SAP2000的MATLAB图形用户界面，用于优化传感器位置
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-20 DOI: 10.1016/j.softx.2025.102216
Antonio S. López-Cuervo , Enrique García-Macías , Rafael Castro-Triguero , Juan Chiachío
{"title":"OSP-SAP: A MATLAB graphical user interface for optimal sensor placement using SAP2000","authors":"Antonio S. López-Cuervo ,&nbsp;Enrique García-Macías ,&nbsp;Rafael Castro-Triguero ,&nbsp;Juan Chiachío","doi":"10.1016/j.softx.2025.102216","DOIUrl":"10.1016/j.softx.2025.102216","url":null,"abstract":"<div><div>An optimal placement of sensors is key for structural modal identification since it critically determines the quality of the estimated modal properties. To facilitate this task, this paper presents OSP-SAP, a MATLAB-based graphical user interface designed for Optimal Sensor Placement (OSP) using SAP2000 finite element models. The software integrates four different optimization algorithms to determine optimal sensor configurations while providing intuitive visualization tools. Additionally, OSP-SAP streamlines practical implementation by allowing users to export the generated optimal configurations to AutoCAD and generate Word reports, making OSP more accessible to researchers and practitioners.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102216"},"PeriodicalIF":2.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321254","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
madmpy: A Python library for creating and validating Data Management Plans madmpy：用于创建和验证数据管理计划的Python库
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-16 DOI: 10.1016/j.softx.2025.102215
Alberto Ballesteros-Rodríguez , Miguel-Ángel Sicilia , Elena García-Barriocanal
{"title":"madmpy: A Python library for creating and validating Data Management Plans","authors":"Alberto Ballesteros-Rodríguez ,&nbsp;Miguel-Ángel Sicilia ,&nbsp;Elena García-Barriocanal","doi":"10.1016/j.softx.2025.102215","DOIUrl":"10.1016/j.softx.2025.102215","url":null,"abstract":"<div><div>Data Management Plans (DMPs) are documents that describe the data used and produced during the course of research projects. Machine-actionable DMPs (maDMPs) are plans written in computer-readable formats. They are designed to support the automation of data-generation processes in scientific research. The <span>madmpy</span> Python package validates maDMPs that follow any version of the RDA DMP Common Standard. These plans can be written in JSON format or built programmatically. It also supports institution- or domain-specific extensions and additional validations that adhere to the standard. The library serves as a building block for research data engineering workflows. It promotes data management and accountability through the use of structured DMPs.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102215"},"PeriodicalIF":2.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298774","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
DIC2Abaqus: Calculating mixed-mode stress intensity factors from 2D and 3D-stereo displacement fields DIC2Abaqus：计算二维和三维立体位移场的混合模式应力强度因子
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-14 DOI: 10.1016/j.softx.2025.102231
Abdalrhaman Koko , James Marrow
{"title":"DIC2Abaqus: Calculating mixed-mode stress intensity factors from 2D and 3D-stereo displacement fields","authors":"Abdalrhaman Koko ,&nbsp;James Marrow","doi":"10.1016/j.softx.2025.102231","DOIUrl":"10.1016/j.softx.2025.102231","url":null,"abstract":"<div><div>Evaluating the conditions for crack propagation under static and cyclic loads is critical for predicting the lifespan of engineering components, particularly in the energy and transport industries. Digital Image Correlation (DIC) provides precise displacement field measurements that can be used to calculate strain energy release rates and stress intensity factors (SIFs), but integrating DIC data into computer-aided engineering (CAE) software like Abaqus, a widely used finite element package, remains challenging. This paper introduces DIC2Abaqus, a freely available MATLAB-based tool that automates DIC data processing in Abaqus to extract material properties in isotropic and anisotropic elastic and elastoplastic materials. It employs the <em>J</em>-integral and interaction integral methods to compute mixed-mode SIFs, including mode III, without requiring a predefined specimen geometry or applied loads. It supports 2D and 3D-stereo DIC data and streamlines the process from geometry creation to job submission and post-processing. Validation against analytical and experimental results confirms its accuracy and reliability. By taking fracture mechanics analyses beyond ISO and ASTM <span><span>standards</span><svg><path></path></svg></span>, DIC2Abaqus offers a versatile, efficient, and accessible simulation tool for industry, research, and education.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102231"},"PeriodicalIF":2.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279250","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
A complete and open Simulink model of the Tennessee Eastman process (COSTEP) 田纳西州伊士曼过程（COSTEP）的完整开放Simulink模型
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-06-13 DOI: 10.1016/j.softx.2025.102217
Johandri Vosloo , Kenneth R. Uren , George van Schoor
{"title":"A complete and open Simulink model of the Tennessee Eastman process (COSTEP)","authors":"Johandri Vosloo ,&nbsp;Kenneth R. Uren ,&nbsp;George van Schoor","doi":"10.1016/j.softx.2025.102217","DOIUrl":"10.1016/j.softx.2025.102217","url":null,"abstract":"<div><div>The Tennessee Eastman process serves as a benchmark system for the evaluation of fault diagnosis techniques. Current simulator implementations are available in FORTRAN and in a C-mex S-function in MATLAB. The C-mex file is a conversion of the FORTRAN code to C for implementation in MATLAB. Both implementations have the limitation that not all the variables and parameters are directly accessible. Hence, a complete and open Tennessee Eastman process simulator was developed in Simulink to allow for total access to all parameters and variables and better Simulink integration. This implementation will give researchers more freedom towards the design of control and fault diagnosis techniques.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102217"},"PeriodicalIF":2.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270336","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
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