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PyMED-DX: A Python tool for diagnostic value evaluation of 2D medical images
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-26 DOI: 10.1016/j.softx.2025.102128
Gorana Gojić , Vladimir Vincan , Ognjen Kundačina , Saša Taloši , Dragiša Mišković
{"title":"PyMED-DX: A Python tool for diagnostic value evaluation of 2D medical images","authors":"Gorana Gojić ,&nbsp;Vladimir Vincan ,&nbsp;Ognjen Kundačina ,&nbsp;Saša Taloši ,&nbsp;Dragiša Mišković","doi":"10.1016/j.softx.2025.102128","DOIUrl":"10.1016/j.softx.2025.102128","url":null,"abstract":"<div><div>This paper presents a Python-based tool designed to simplify subjective assessment studies for diagnostic value assessment of 2D medical images. The tool facilitates rapid questionnaire generation, allowing users to integrate their data into predefined templates, and provides utilities to statistically and visually analyze collected responses. By reducing the technical overhead of study preparation, the tool allows users to focus on knowledge discovery while providing easy-to-use questionnaires for clinicians with no technical expertise required.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102128"},"PeriodicalIF":2.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
TrAQ: A novel, versatile, semi-automated, two-dimensional motor behavioural tracking software
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-26 DOI: 10.1016/j.softx.2025.102138
Davide Di Censo , Ilaria Rosa , Brigida Ranieri , Tiziana Di Lorenzo , Marcello Alecci , Tiziana M. Florio , Angelo Galante
{"title":"TrAQ: A novel, versatile, semi-automated, two-dimensional motor behavioural tracking software","authors":"Davide Di Censo ,&nbsp;Ilaria Rosa ,&nbsp;Brigida Ranieri ,&nbsp;Tiziana Di Lorenzo ,&nbsp;Marcello Alecci ,&nbsp;Tiziana M. Florio ,&nbsp;Angelo Galante","doi":"10.1016/j.softx.2025.102138","DOIUrl":"10.1016/j.softx.2025.102138","url":null,"abstract":"<div><div>We present TrAQ, a new MATLAB-based two-dimensional tracking software for Open Field video analysis of an unmarked single animal. TrAQ allows automatic recognition of the animal within a user-defined arena, providing a full range of quantitative kinematic behavioral parameters. TrAQ, free and non-species-specific application, was quantitively tested with rodents. Within free software an innovative feature of TrAQ is the automated counting of in-plane rotations, an important parameter in the 6-hydroxydopamine hemiparkinsonian rat model and in many rodent models of neurodegenerative diseases, and a very time-consuming manual task for highly trained human operators. Quantitative results were successfully validated against commercial software (for tracking) and manual annotation (for rotations in a hemiparkinsonian rat model). TrAQ allows the characterization of motor asymmetry using non-invasive tools, thus appreciating the spontaneous Open Field behaviour of unmarked single animal, with minimum user intervention.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102138"},"PeriodicalIF":2.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
H-Alpha anomalyzer: An anomaly detector for H-Alpha solar observations using a grid-based approach
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-25 DOI: 10.1016/j.softx.2025.102120
Mahsa Khazaei , Heba Mahdi , Kartik Chaurasiya , Azim Ahmadzadeh
{"title":"H-Alpha anomalyzer: An anomaly detector for H-Alpha solar observations using a grid-based approach","authors":"Mahsa Khazaei ,&nbsp;Heba Mahdi ,&nbsp;Kartik Chaurasiya ,&nbsp;Azim Ahmadzadeh","doi":"10.1016/j.softx.2025.102120","DOIUrl":"10.1016/j.softx.2025.102120","url":null,"abstract":"<div><div>This article presents a Python package named H-Alpha Anomalyzer for detecting anomalous H-Alpha observations of the Sun. Using this open-source package, users can transform the labor-intensive task of filtering anomalous observations from millions of instances, thereby enhancing the quality of data used for data-hungry algorithms, particularly Deep Neural Networks (DNNs). Our region-based probabilistic method offers explainability by assigning anomaly likelihoods to each cell of a given observation. Additionally, users can set a probability threshold to customize the degree of anomaly required for an entire image to be classified as anomalous. This paper also reports the quantitative validation of the method. On a modest laptop computer, this lightweight package processes ten 2k-by-2k-pixel images per second, which is significantly faster than its DNN-based counterparts.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102120"},"PeriodicalIF":2.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AIME-solve: Accessible interactive mathematical expressions solver for optimized learning and visual empowerment
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-24 DOI: 10.1016/j.softx.2025.102143
Amjad Ali , Shah Khusro , Fakhre Alam , Inayat Khan , Shabbab Ali Algamdi
{"title":"AIME-solve: Accessible interactive mathematical expressions solver for optimized learning and visual empowerment","authors":"Amjad Ali ,&nbsp;Shah Khusro ,&nbsp;Fakhre Alam ,&nbsp;Inayat Khan ,&nbsp;Shabbab Ali Algamdi","doi":"10.1016/j.softx.2025.102143","DOIUrl":"10.1016/j.softx.2025.102143","url":null,"abstract":"<div><div>Today, education is transformed into digital learning, which is more interactive and accessible. However, students with visual disabilities still face significant challenges in mathematics learning, such as real-time feedback, error correction, step-by-step guidance, and interactive learning using digital devices such as smartphones and computers. These challenges contribute to the underrepresentation of these students in science, technology, Engineering and Mathematics (STEM) disciplines. To address this limitation, this study proposes a smartphone-based, interactive, accessible solution for students with visual disabilities to solve mathematical expression addition and subtraction problems. The systematic methodology was adopted, prior studies were reviewed, and insights gained from stakeholders and challenges were identified. Based on these insights, the algorithm was designed following pedagogy principles from mathematics textbooks. The design algorithm was implanted in Python language. The proposed solution was empirically evaluated with 27 students and 5 instructors with visual disabilities in educational settings. The qualitative and quantitative analysis findings show significant improvement in students learning. The results suggest that the proposed solution is suitable for supporting inclusive mathematics education and might be further used in other assistive learning environments.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102143"},"PeriodicalIF":2.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
UniDam: An Abaqus Plugin to break down verification of a damage model for composites
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-24 DOI: 10.1016/j.softx.2025.102134
Sérgio Costa , Miguel Herráez , Xiao Chen
{"title":"UniDam: An Abaqus Plugin to break down verification of a damage model for composites","authors":"Sérgio Costa ,&nbsp;Miguel Herráez ,&nbsp;Xiao Chen","doi":"10.1016/j.softx.2025.102134","DOIUrl":"10.1016/j.softx.2025.102134","url":null,"abstract":"<div><div>This paper presents a user-friendly plugin for Abaqus to facilitate damage simulation that may require a state-of-the-art damage model for composite materials. The plugin can perform interactive model calibration, set up single-elements required for model verification, and display their respective stress–strain curves. This tool breaks down the use of composite damage models, reducing the chances of user errors and making the model more accessible to end-users.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102134"},"PeriodicalIF":2.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ContDataQC: An R package and Shiny app for quality control of continuous water quality sensor data
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-22 DOI: 10.1016/j.softx.2025.102124
Michael J. Pennino , Jen Stamp , Erik W. Leppo , David A. Gibbs , Britta G. Bierwagen
{"title":"ContDataQC: An R package and Shiny app for quality control of continuous water quality sensor data","authors":"Michael J. Pennino ,&nbsp;Jen Stamp ,&nbsp;Erik W. Leppo ,&nbsp;David A. Gibbs ,&nbsp;Britta G. Bierwagen","doi":"10.1016/j.softx.2025.102124","DOIUrl":"10.1016/j.softx.2025.102124","url":null,"abstract":"<div><div>The ContDataQC R package is a free, open-source tool that was developed to help water quality monitoring programs perform quality control (QC) procedures on continuous sensor data. ContDataQC helps users speed up and standardize the QC process, minimize undetected data errors, and make full use of their sensor data. It has three main functions: generate QC reports to detect anomalies and erroneous data values, merge QC'd data files from different time periods, and generate time series plots and basic summary statistics. ContDataQC is currently configured to run on nine different parameters: air and water temperature, dissolved oxygen, conductivity, chlorophyll-a, air and water pressure, sensor depth, pH, turbidity, and salinity. Users can add new parameters and customize many of the requirements by editing a plain text configuration file. A web app version, through R Shiny, is available within the package or via a weblink. If accessed via the URL, it will not require the installation of R software. In this paper, we describe the main functions of ContDataQC and discuss how it is being applied in long-term regional monitoring networks for streams and lakes. Both the R Shiny web app and the R package are for users who have no existing workflow for sensor data and wish to adopt the approach of ContDataQC (which has a particular organizational scheme and sequential workflow). People without R coding experience can use the Shiny app, which has a more user-friendly interface, while users who are proficient in R may choose to use the code package.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102124"},"PeriodicalIF":2.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Statistical package for computing precision covariance matrices via modified Cholesky decomposition
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-21 DOI: 10.1016/j.softx.2025.102125
Elías D. Niño-Ruiz , Dylan Samuel Cantillo Arrieta , Giuliano Raffaele Frieri Quiroz , Nicolas Quintero Quintero
{"title":"Statistical package for computing precision covariance matrices via modified Cholesky decomposition","authors":"Elías D. Niño-Ruiz ,&nbsp;Dylan Samuel Cantillo Arrieta ,&nbsp;Giuliano Raffaele Frieri Quiroz ,&nbsp;Nicolas Quintero Quintero","doi":"10.1016/j.softx.2025.102125","DOIUrl":"10.1016/j.softx.2025.102125","url":null,"abstract":"<div><div>We introduce a statistical package designed to compute precision covariance matrices using modified Cholesky decomposition, tailored for Atmospheric General Circulation Models (AGCMs). By incorporating predefined localization radius structures, the package achieves sparse precision matrix estimation, enhancing computational efficiency and scalability for large-scale atmospheric models. Leveraging ensemble modeling, the method ensures robustness and accuracy, with direct applications in data assimilation tasks. The package also features seamless integration with prominent climate datasets, such as ERA5 and MPAS, enabling streamlined workflows for climate modeling and prediction.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102125"},"PeriodicalIF":2.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PyGenAlgo: A simple and powerful toolkit for genetic algorithms
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-21 DOI: 10.1016/j.softx.2025.102127
Michail D. Vrettas, Stefano Silvestri
{"title":"PyGenAlgo: A simple and powerful toolkit for genetic algorithms","authors":"Michail D. Vrettas,&nbsp;Stefano Silvestri","doi":"10.1016/j.softx.2025.102127","DOIUrl":"10.1016/j.softx.2025.102127","url":null,"abstract":"<div><div>Genetic algorithms (GAs) are meta-heuristic algorithms that are used for solving constrained and unconstrained optimization problems, mimicking the process of natural selection in biological evolution. Due to the fact that GAs do not require the optimization function to be differentiable, they are suitable for application in cases where the derivative of the objective function is either unavailable or impractical to obtain numerically. This paper proposes a general purpose genetic algorithm toolkit, implemented in Python3 programming language, having only minimum dependencies in NumPy and Joblib, that handle some of the numerical and parallel execution details.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102127"},"PeriodicalIF":2.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
LFM: An R package for laplace factor model
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-20 DOI: 10.1016/j.softx.2025.102133
Siqi Liu, Guangbao Guo
{"title":"LFM: An R package for laplace factor model","authors":"Siqi Liu,&nbsp;Guangbao Guo","doi":"10.1016/j.softx.2025.102133","DOIUrl":"10.1016/j.softx.2025.102133","url":null,"abstract":"<div><div>The Laplace Factor Model (LFM) is a valuable mathematical tool used in statistics, machine learning, and data analysis. It uses the Laplace distribution to capture data sparsity and uncertainty, effectively handling complex, large-scale data. The proposed R package, called LFM, has the capability to construct factor models based on the Laplace distribution, and it allows for customized model building by flexibly adjusting the parameters of the Laplace distribution. Additionally, the LFM package integrates various techniques including Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Projection Principal Component (PPC), Stochastic Approximate Principal Component (SAPC), Sparse Principal Component (SPC), and other PC methods and the Farm Test method. By evaluating indicators such as the accuracy of parameter estimation, mean square error, and sparsity, this study verifies the effectiveness and practicality of these methods in the Laplace Factor Model.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102133"},"PeriodicalIF":2.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
TLIC: An R package for the LIC for T distribution regression analysis
IF 2.4 4区 计算机科学
SoftwareX Pub Date : 2025-03-20 DOI: 10.1016/j.softx.2025.102132
Guofu Jing, Guangbao Guo
{"title":"TLIC: An R package for the LIC for T distribution regression analysis","authors":"Guofu Jing,&nbsp;Guangbao Guo","doi":"10.1016/j.softx.2025.102132","DOIUrl":"10.1016/j.softx.2025.102132","url":null,"abstract":"<div><div>This paper introduces the TLIC R package, a novel framework that integrates the T-distribution with the Length and Information Criterion (LIC) to address optimal subset selection in regression models with T-distributed errors. Traditional subset selection methods, such as beta_AD, beta_cor, and LICnew, assume normality of errors, which may lead to biased results when dealing with heavy-tailed or skewed distributions. Through extensive simulation experiments, we demonstrate that TLIC outperforms these methods in terms of stability and sensitivity, especially under non-normal error distributions. An R package implementing the TLIC method is also developed, providing a practical tool for researchers to conduct subset selection with T-distributed errors. Our findings highlight TLIC's potential to improve subset selection accuracy in real-world applications where error distributions deviate from normality.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102132"},"PeriodicalIF":2.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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