Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.101995
S. Agostinelli , T. Hohenadl , A. Marrella , A. Martínez-Rojas
Robotic Process Automation (RPA) is a maturing technology in the field of Business Process Management (BPM) that automates intensive routine tasks previously performed by a human user on the User Interface (UI) of a computer system, by means of a software robot. To date, RPA tools available in the market strongly rely on the ability of human experts to manually implement the routines to automate. This work addresses the limitations of current manual RPA development by introducing SmartRPA, a cross-platform software tool. SmartRPA analyzes UI logs of past routine executions to generate software robots capable of handling intermediate user inputs, thereby reducing development time and error rates.
{"title":"SmartRPA: Generating software robots from user interface logs","authors":"S. Agostinelli , T. Hohenadl , A. Marrella , A. Martínez-Rojas","doi":"10.1016/j.softx.2024.101995","DOIUrl":"10.1016/j.softx.2024.101995","url":null,"abstract":"<div><div>Robotic Process Automation (RPA) is a maturing technology in the field of Business Process Management (BPM) that automates intensive routine tasks previously performed by a human user on the User Interface (UI) of a computer system, by means of a software robot. To date, RPA tools available in the market strongly rely on the ability of human experts to manually implement the routines to automate. This work addresses the limitations of current manual RPA development by introducing SmartRPA, a cross-platform software tool. SmartRPA analyzes UI logs of past routine executions to generate software robots capable of handling intermediate user inputs, thereby reducing development time and error rates.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 101995"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127870","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}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102006
Sanaullah , Axel Schneider , Joachim Waßmuth , Ulrich Rückert , Thorsten Jungeblut
This article introduces the enhanced Runtime Analyzing and Visualization Simulator (RAVSim) v2.0, a graphical tool that not only supports SNN design and analysis but also facilitates a comprehensive comparative analysis of various SNN models. The new version of RAVSim introduces a groundbreaking feature enabling users to conduct in-depth comparisons of SNN models, enhancing understanding and aiding in model selection for specific applications. Furthermore, with the updated version of RAVSim, researchers, and developers can effortlessly generate trained model weights using a custom dataset, eliminating the need to investigate or write complicated backend code. This new feature facilitates the seamless integration of diverse datasets, streamlining the process for further analysis and exploration. Therefore, the developers can now focus on high-level tasks and gain a clear understanding of SNN without worrying about the technical complexities of weight generation. This advancement represents a significant step towards making SNNs more accessible and user-friendly, unlocking their full potential in artificial intelligence and computational neuroscience applications. Furthermore, RAVSim’s code has undergone extensive optimization and debugging, leading to a substantial reduction in image classification simulation time compared to the previous RAVSim version. This improvement makes it easier and quicker to train models and generate weights.
{"title":"RAVSim v2.0: Enhanced visualization and comparative analysis for neural network models","authors":"Sanaullah , Axel Schneider , Joachim Waßmuth , Ulrich Rückert , Thorsten Jungeblut","doi":"10.1016/j.softx.2024.102006","DOIUrl":"10.1016/j.softx.2024.102006","url":null,"abstract":"<div><div>This article introduces the enhanced Runtime Analyzing and Visualization Simulator (RAVSim) v2.0, a graphical tool that not only supports SNN design and analysis but also facilitates a comprehensive comparative analysis of various SNN models. The new version of RAVSim introduces a groundbreaking feature enabling users to conduct in-depth comparisons of SNN models, enhancing understanding and aiding in model selection for specific applications. Furthermore, with the updated version of RAVSim, researchers, and developers can effortlessly generate trained model weights using a custom dataset, eliminating the need to investigate or write complicated backend code. This new feature facilitates the seamless integration of diverse datasets, streamlining the process for further analysis and exploration. Therefore, the developers can now focus on high-level tasks and gain a clear understanding of SNN without worrying about the technical complexities of weight generation. This advancement represents a significant step towards making SNNs more accessible and user-friendly, unlocking their full potential in artificial intelligence and computational neuroscience applications. Furthermore, RAVSim’s code has undergone extensive optimization and debugging, leading to a substantial <span><math><mrow><mo>∼</mo><mn>65</mn><mtext>%</mtext></mrow></math></span> reduction in image classification simulation time compared to the previous RAVSim version. This improvement makes it easier and quicker to train models and generate weights.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102006"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128408","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}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102005
Yasmin Adriane de Paula Campos , Paulo Haron da Silva Pereira , Robson Aparecido Duarte , José Manuel Gonzalez Túbio Perez , Gustavo Pessin , Thomas Vargas Barsante Pinto
We present a converter software program that automatically translates Python-based machine learning algorithms into Structured Text codes. This tool empowers engineers to efficiently generate machine learning models in a programming language widely used in industrial controllers. It supports the conversion of decision tree and multilayer perceptron models built using scikit-learn library. Moreover, the generated Structure Text code is compatible with ABB’s Industrial IT 800xA DCS syntax. A practical example demonstrates the effectiveness of this converter software program and its potential to enhance the integration of machine learning models into industrial automation systems.
{"title":"A conversion tool for translating Python-based machine learning models to structured text codes","authors":"Yasmin Adriane de Paula Campos , Paulo Haron da Silva Pereira , Robson Aparecido Duarte , José Manuel Gonzalez Túbio Perez , Gustavo Pessin , Thomas Vargas Barsante Pinto","doi":"10.1016/j.softx.2024.102005","DOIUrl":"10.1016/j.softx.2024.102005","url":null,"abstract":"<div><div>We present a converter software program that automatically translates Python-based machine learning algorithms into Structured Text codes. This tool empowers engineers to efficiently generate machine learning models in a programming language widely used in industrial controllers. It supports the conversion of decision tree and multilayer perceptron models built using <em>scikit-learn</em> library. Moreover, the generated Structure Text code is compatible with ABB’s Industrial IT 800xA DCS syntax. A practical example demonstrates the effectiveness of this converter software program and its potential to enhance the integration of machine learning models into industrial automation systems.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102005"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128410","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}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102076
Ahren B. Fitzroy, Rebecca M.C. Spencer
Sleep science has seen a surge in discoveries fueled by enhanced data processing approaches to sleep physiology recordings. PSGpower is a MATLAB toolbox designed to make these processing steps more efficient and standardized. PSGpower imports sleep polysomnography data recorded using legacy and modern acquisition systems, and sleep-staged using a variety of software packages, for processing in a number of microstructure analysis workflows. Workflows include existing algorithms from EEGLAB and FieldTrip and custom algorithms. PSGpower is extensible, and new workflows can be added that take advantage of the common data importing, sleep stage marking, and preprocessing code.
{"title":"PSGpower: A MATLAB toolbox for analyzing sleep EEG data","authors":"Ahren B. Fitzroy, Rebecca M.C. Spencer","doi":"10.1016/j.softx.2025.102076","DOIUrl":"10.1016/j.softx.2025.102076","url":null,"abstract":"<div><div>Sleep science has seen a surge in discoveries fueled by enhanced data processing approaches to sleep physiology recordings. PSGpower is a MATLAB toolbox designed to make these processing steps more efficient and standardized. PSGpower imports sleep polysomnography data recorded using legacy and modern acquisition systems, and sleep-staged using a variety of software packages, for processing in a number of microstructure analysis workflows. Workflows include existing algorithms from EEGLAB and FieldTrip and custom algorithms. PSGpower is extensible, and new workflows can be added that take advantage of the common data importing, sleep stage marking, and preprocessing code<em>.</em></div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102076"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093063","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}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102003
Rodney Pino , Renier Mendoza , Rachelle Sambayan
Optical character recognition (OCR) is a state-of-the-art technology that allows automated detection and recognition of text from scanned documents and other images. While OCR has highly developed in popular writing systems like Roman, Brahmic, and Han scripts, there is presently a lack of technological integration for Baybayin scripts a precolonial Filipino writing system. This gap in recent advancements may be due from the script’s antiquity. However, ongoing efforts led by Philippine government and cultural institutions, are actively working towards promoting Baybayin as a means of cultural revival and heritage preservation. In this work, a Matlab-based Baybayin OCR (MaBaybay-OCR) package is introduced to automate the process of transliterating the Baybayin to its modern Filipino Latin form. MaBaybay-OCR takes raw Baybayin text image as an input and performs image analytic strategies to generate correct transliteration results. The software system implements essential pre-processing techniques such as binarization, segmentation, and computation of the character’s features of interest to precisely isolate each Baybayin character. With the use of support vector machine (SVM) classifiers, the platform can accurately identify every Baybayin character’s corresponding Latin equivalent and can realize the Baybayin word transliteration by concatenating each character recognition result. The discrimination of Baybayin from Latin or Roman texts is a distinctive feature of this recognition package. All Matlab source codes are available in a public repository for reproducibility, and a user-friendly graphical user interface (GUI) for convenient usage is provided. To the best of our knowledge, this is the first software program that offers direct transliteration of Baybayin texts up to block-level. It is expected that this work will promote the Baybayin script and contribute towards its positive cultural exposure in the Philippines.
{"title":"MaBaybay-OCR: A Matlab-based Baybayin optical character recognition package","authors":"Rodney Pino , Renier Mendoza , Rachelle Sambayan","doi":"10.1016/j.softx.2024.102003","DOIUrl":"10.1016/j.softx.2024.102003","url":null,"abstract":"<div><div>Optical character recognition (OCR) is a state-of-the-art technology that allows automated detection and recognition of text from scanned documents and other images. While OCR has highly developed in popular writing systems like Roman, Brahmic, and Han scripts, there is presently a lack of technological integration for Baybayin scripts <span><math><mo>−</mo></math></span> a precolonial Filipino writing system. This gap in recent advancements may be due from the script’s antiquity. However, ongoing efforts led by Philippine government and cultural institutions, are actively working towards promoting Baybayin as a means of cultural revival and heritage preservation. In this work, a Matlab-based Baybayin OCR (MaBaybay-OCR) package is introduced to automate the process of transliterating the Baybayin to its modern Filipino Latin form. MaBaybay-OCR takes raw Baybayin text image as an input and performs image analytic strategies to generate correct transliteration results. The software system implements essential pre-processing techniques such as binarization, segmentation, and computation of the character’s features of interest to precisely isolate each Baybayin character. With the use of support vector machine (SVM) classifiers, the platform can accurately identify every Baybayin character’s corresponding Latin equivalent and can realize the Baybayin word transliteration by concatenating each character recognition result. The discrimination of Baybayin from Latin or Roman texts is a distinctive feature of this recognition package. All Matlab source codes are available in a public repository for reproducibility, and a user-friendly graphical user interface (GUI) for convenient usage is provided. To the best of our knowledge, this is the first software program that offers direct transliteration of Baybayin texts up to block-level. It is expected that this work will promote the Baybayin script and contribute towards its positive cultural exposure in the Philippines.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102003"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093373","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}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102067
Nan Xu , Behnaz Yousefi , Nmachi Anumba , Theodore J. LaGrow , Xiaodi Zhang , Shella Keilholz
Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. QPPs have been linked to neural processes such as attention, arousal fluctuations, and cognitive function. Despite their significance, existing QPP analysis tools are limited by study-specific parameters and complex workflows. To address these challenges, we present QPPLab, an open-source MATLAB-based toolbox for detecting, analyzing, and visualizing QPPs from fMRI time series. QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. Processing times vary depending on dataset size and the selected mode, with the fast detection mode completing analyses that can be 4–6 times faster than the robust detection mode. Results include spatiotemporal templates of QPPs, sliding correlation time courses, and functional connectivity maps. By reducing manual parameter adjustments and providing user-friendly tools, QPPLab enables researchers to efficiently study QPPs across diverse datasets and species, advancing our understanding of intrinsic brain dynamics.
{"title":"QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity","authors":"Nan Xu , Behnaz Yousefi , Nmachi Anumba , Theodore J. LaGrow , Xiaodi Zhang , Shella Keilholz","doi":"10.1016/j.softx.2025.102067","DOIUrl":"10.1016/j.softx.2025.102067","url":null,"abstract":"<div><div>Quasi-periodic patterns (QPPs) are prominent spatiotemporal brain dynamics observed in functional neuroimaging data, reflecting the alternation of high and low activity across brain regions and their propagation along cortical gradients. QPPs have been linked to neural processes such as attention, arousal fluctuations, and cognitive function. Despite their significance, existing QPP analysis tools are limited by study-specific parameters and complex workflows. To address these challenges, we present <strong><em>QPPLab</em></strong>, an open-source MATLAB-based toolbox for detecting, analyzing, and visualizing QPPs from fMRI time series. QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. Processing times vary depending on dataset size and the selected mode, with the fast detection mode completing analyses that can be 4–6 times faster than the robust detection mode. Results include spatiotemporal templates of QPPs, sliding correlation time courses, and functional connectivity maps. By reducing manual parameter adjustments and providing user-friendly tools, QPPLab enables researchers to efficiently study QPPs across diverse datasets and species, advancing our understanding of intrinsic brain dynamics.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102067"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093064","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}
Pub Date : 2025-02-01DOI: 10.1016/j.softx.2025.102047
Colin Caprani , Mohammad Shihabuddin Khan , Jürgen Hackl
Structural reliability methods enable probabilistic analysis and design of structures. Furthermore, these methods are essential for the calibration of structural design codes. PySTRA (Python Structural Reliability Analysis) is a free and open-source Python package for structural reliability analysis. Its flexibility and extensibility make it applicable to an extensive suite of problems. Along with core reliability analysis functionality, PySTRA includes methods for summarizing output. PySTRA is also closely integrated with the widely-used Python scientific packages such as NumPy, SciPy, and Pandas. This paper discusses the architecture, functionality, and basic usage for PySTRA. PySTRA is highly useful for reliability engineering scientists and practitioners, particularly structural engineers.
{"title":"PySTRA: Python structural reliability analysis","authors":"Colin Caprani , Mohammad Shihabuddin Khan , Jürgen Hackl","doi":"10.1016/j.softx.2025.102047","DOIUrl":"10.1016/j.softx.2025.102047","url":null,"abstract":"<div><div>Structural reliability methods enable probabilistic analysis and design of structures. Furthermore, these methods are essential for the calibration of structural design codes. <span>PySTRA</span> (Python Structural Reliability Analysis) is a free and open-source <span>Python</span> package for structural reliability analysis. Its flexibility and extensibility make it applicable to an extensive suite of problems. Along with core reliability analysis functionality, <span>PySTRA</span> includes methods for summarizing output. <span>PySTRA</span> is also closely integrated with the widely-used <span>Python</span> scientific packages such as <span>NumPy</span>, <span>SciPy</span>, and <span>Pandas</span>. This paper discusses the architecture, functionality, and basic usage for <span>PySTRA</span>. <span>PySTRA</span> is highly useful for reliability engineering scientists and practitioners, particularly structural engineers.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102047"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127998","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}
Pub Date : 2024-12-03DOI: 10.1016/j.softx.2024.101987
Meysam Bagheri, Sudeshna Roy, Thorsten Pöschel
We present the implementation of two advanced capillary bridge approximations within the Discrete Element Method (DEM) framework of the open-source code MercuryDPM. While MercuryDPM already includes a simplified version of the Willett approximation, our work involves implementing both the classical Willett approximation and the recently published Bagheri approximation in MercuryDPM. Through detailed descriptions and illustrative simulations using a two-particle collision model, we demonstrate the enhanced accuracy and capabilities of these approximations in capturing the complex dynamics of wet granular matter.
{"title":"Discrete Element Simulations of particles interacting via capillary forces using MercuryDPM","authors":"Meysam Bagheri, Sudeshna Roy, Thorsten Pöschel","doi":"10.1016/j.softx.2024.101987","DOIUrl":"10.1016/j.softx.2024.101987","url":null,"abstract":"<div><div>We present the implementation of two advanced capillary bridge approximations within the Discrete Element Method (DEM) framework of the open-source code <em>MercuryDPM</em>. While <em>MercuryDPM</em> already includes a simplified version of the Willett approximation, our work involves implementing both the classical Willett approximation and the recently published Bagheri approximation in <em>MercuryDPM</em>. Through detailed descriptions and illustrative simulations using a two-particle collision model, we demonstrate the enhanced accuracy and capabilities of these approximations in capturing the complex dynamics of wet granular matter.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 101987"},"PeriodicalIF":2.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759815","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}
Pub Date : 2024-12-02DOI: 10.1016/j.softx.2024.101980
Zhao Xi , Yu Baoguo , Luo Bin
LEO (Low Earth Orbit)-based PNT (Positioning, Navigation and Timing) has become a research topic of much interest and a focus of development. As LEO satellites for PNT are expected to independently provide space-time references in the future, similar to Global Navigation Satellite System (GNSS) satellites, the accurate determination of their broadcast ephemerides becomes crucial. This paper proposes the open-source software LOBEFIT (LeO satellite Broadcast Ephemeris Fitting), which utilizes the automatic differentiation technique to fit LEO satellite broadcast ephemeris parameters. The primary advantage of this software lies in its ability to allow users to focus on implementing various broadcast ephemeris models without the need to manually derive complex partial derivatives with respect to broadcast ephemeris parameters. Centimeter-level fitting accuracy can be achieved by selecting appropriate broadcast models for different LEO satellites using this software. The paper provides the description, use and impact of the LOBEFIT software in satellite navigation.
{"title":"LOBEFIT: LEO satellite broadcast ephemeris fitting open-source software based on automatic differentiation technique","authors":"Zhao Xi , Yu Baoguo , Luo Bin","doi":"10.1016/j.softx.2024.101980","DOIUrl":"10.1016/j.softx.2024.101980","url":null,"abstract":"<div><div>LEO (Low Earth Orbit)-based PNT (Positioning, Navigation and Timing) has become a research topic of much interest and a focus of development. As LEO satellites for PNT are expected to independently provide space-time references in the future, similar to Global Navigation Satellite System (GNSS) satellites, the accurate determination of their broadcast ephemerides becomes crucial. This paper proposes the open-source software LOBEFIT (LeO satellite Broadcast Ephemeris Fitting), which utilizes the automatic differentiation technique to fit LEO satellite broadcast ephemeris parameters. The primary advantage of this software lies in its ability to allow users to focus on implementing various broadcast ephemeris models without the need to manually derive complex partial derivatives with respect to broadcast ephemeris parameters. Centimeter-level fitting accuracy can be achieved by selecting appropriate broadcast models for different LEO satellites using this software. The paper provides the description, use and impact of the LOBEFIT software in satellite navigation.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 101980"},"PeriodicalIF":2.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759812","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}
Pub Date : 2024-12-02DOI: 10.1016/j.softx.2024.101996
Nafisa Anjum, Md. Mehedi Hasan, Syeda Umme Salma, Liang Zhao, Maria Valero de Clemente, Nazmus Sakib
The surging incidents of infants and toddlers screen addiction in the United States are becoming a pressing concern, given its compound impact on cognitive, mental, and physical growth. To address this era's critical child health and development problem, in this paper an innovative mHealth application– ScreenSafeFuture—is proposed. ScreenSafeFuture provides practical solutions that seamlessly fit into parents' busy lifestyles, acknowledging the effectiveness and convenience of smartphones as a healthcare tool. The solution includes essential features designed to enhance the experience between parents and their children under 3 years old encompassing an alternative activity advocator for a personalized parent-offspring scenario, screen time tracking based on current standards, an educational reservoir for parents, and a rewarding system for long-term user engagement. This paper presents a functional ScreenSafeFuture iOS prototype, that will undergo parent usability testing, followed by continuous advancements based on user feedback. Future evaluation will focus on predicting the differences in average daily screen time consumption and changes in parental media management practices. The next milestone will assess app usage frequency and duration, completion of in-app activities, user satisfaction scores, retention and completion rates. Final milestone will analyse changes in parental knowledge, shifts in parental attitudes, and increased awareness of resources. These development phases will utilize the Delphi panel consensus for a more reliable and robust outcome, ensuring its effectiveness in addressing screen addiction challenges and supporting younger generations' healthy development.
{"title":"ScreenSafeFuture: A parent-empathetic and pragmatic mhealth application for toddlers' brain development addressing screen-addiction challenges","authors":"Nafisa Anjum, Md. Mehedi Hasan, Syeda Umme Salma, Liang Zhao, Maria Valero de Clemente, Nazmus Sakib","doi":"10.1016/j.softx.2024.101996","DOIUrl":"10.1016/j.softx.2024.101996","url":null,"abstract":"<div><div>The surging incidents of infants and toddlers screen addiction in the United States are becoming a pressing concern, given its compound impact on cognitive, mental, and physical growth. To address this era's critical child health and development problem, in this paper an innovative mHealth application– ScreenSafeFuture—is proposed. ScreenSafeFuture provides practical solutions that seamlessly fit into parents' busy lifestyles, acknowledging the effectiveness and convenience of smartphones as a healthcare tool. The solution includes essential features designed to enhance the experience between parents and their children under 3 years old encompassing an alternative activity advocator for a personalized parent-offspring scenario, screen time tracking based on current standards, an educational reservoir for parents, and a rewarding system for long-term user engagement. This paper presents a functional ScreenSafeFuture iOS prototype, that will undergo parent usability testing, followed by continuous advancements based on user feedback. Future evaluation will focus on predicting the differences in average daily screen time consumption and changes in parental media management practices. The next milestone will assess app usage frequency and duration, completion of in-app activities, user satisfaction scores, retention and completion rates. Final milestone will analyse changes in parental knowledge, shifts in parental attitudes, and increased awareness of resources. These development phases will utilize the Delphi panel consensus for a more reliable and robust outcome, ensuring its effectiveness in addressing screen addiction challenges and supporting younger generations' healthy development.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 101996"},"PeriodicalIF":2.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759813","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}
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