Pub Date : 2025-02-01DOI: 10.1016/j.softx.2024.102031
Vittorio Fra , Simon F. Müller-Cleve , Gianvito Urgese , Chiara Bartolozzi
The WiN-GUI enables real-time exploration of neuron model behaviors by adjusting internal parameters and accounting for input properties such as scale and temporal resolution. The spiking responses are however often difficult to interpret and categorize. The update to version 2 introduces a systematic labeling of spike-patterns to facilitate clearer communication across researchers and disciplines, enabling a common framework to describe neuron responses without sharing data. Labels also allow researchers to intentionally target specific neuronal behaviors, fostering biologically plausible simulations or specific tuning goals. To this end, our WiN-GUI incorporates a spike-pattern classifier for automated identification and analysis of neuron activity, streamlining research and collaboration.
{"title":"WiN-GUI Version 2: A graphical tool for neuron-based encoding","authors":"Vittorio Fra , Simon F. Müller-Cleve , Gianvito Urgese , Chiara Bartolozzi","doi":"10.1016/j.softx.2024.102031","DOIUrl":"10.1016/j.softx.2024.102031","url":null,"abstract":"<div><div>The WiN-GUI enables real-time exploration of neuron model behaviors by adjusting internal parameters and accounting for input properties such as scale and temporal resolution. The spiking responses are however often difficult to interpret and categorize. The update to version 2 introduces a systematic labeling of spike-patterns to facilitate clearer communication across researchers and disciplines, enabling a common framework to describe neuron responses without sharing data. Labels also allow researchers to intentionally target specific neuronal behaviors, fostering biologically plausible simulations or specific tuning goals. To this end, our WiN-GUI incorporates a spike-pattern classifier for automated identification and analysis of neuron activity, streamlining research and collaboration.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102031"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128003","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.2025.102064
Jan Benad, Frank Röder, Manfred Eppe
One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments. Many open source implementations of current RL algorithms exist, but there is a lack of a modular suite of tools combining different robotic simulators and platforms, data visualization, hyperparameter optimization, and baseline experiments. To address this problem, we present Scilab-RL, a software framework for efficient research in cognitive modeling and reinforcement learning for robotic agents. The framework focuses on goal-conditioned reinforcement learning using Stable Baselines 3, CleanRL and the Gymnasium interface. It enables native possibilities for experiment visualizations and hyperparameter optimization. We describe how these features enable researchers to conduct experiments with minimal time effort, thus maximizing research output.
{"title":"Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research","authors":"Jan Benad, Frank Röder, Manfred Eppe","doi":"10.1016/j.softx.2025.102064","DOIUrl":"10.1016/j.softx.2025.102064","url":null,"abstract":"<div><div>One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments. Many open source implementations of current RL algorithms exist, but there is a lack of a modular suite of tools combining different robotic simulators and platforms, data visualization, hyperparameter optimization, and baseline experiments. To address this problem, we present Scilab-RL, a software framework for efficient research in cognitive modeling and reinforcement learning for robotic agents. The framework focuses on goal-conditioned reinforcement learning using Stable Baselines 3, CleanRL and the Gymnasium interface. It enables native possibilities for experiment visualizations and hyperparameter optimization. We describe how these features enable researchers to conduct experiments with minimal time effort, thus maximizing research output.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102064"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128004","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.2024.102011
Romain Guibert, Pierre Horgue, Gérald Debenest
Biofilm growth is a complex phenomenon that couples species transport and reactions with fluid flow dynamics. Simulating these processes in complex geometries remains challenging due to the different time and space scales, as well as the associated computational cost. biofilmFoam is a toolbox based on OpenFOAM that couples advection–diffusion equations with source terms (related to the reactions involved) with the dynamics of the surrounding fluid. This framework provides an efficient basis for performing reliable simulations on any domain or for building more complex models.
{"title":"Micro-continuum modeling of biofilm growth coupled with hydrodynamics in OpenFOAM","authors":"Romain Guibert, Pierre Horgue, Gérald Debenest","doi":"10.1016/j.softx.2024.102011","DOIUrl":"10.1016/j.softx.2024.102011","url":null,"abstract":"<div><div>Biofilm growth is a complex phenomenon that couples species transport and reactions with fluid flow dynamics. Simulating these processes in complex geometries remains challenging due to the different time and space scales, as well as the associated computational cost. <span>biofilmFoam</span> is a toolbox based on OpenFOAM that couples advection–diffusion equations with source terms (related to the reactions involved) with the dynamics of the surrounding fluid. This framework provides an efficient basis for performing reliable simulations on any domain or for building more complex models.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102011"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093066","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.2024.102026
Abdelghani Saoud , Mohamed Lachgar , Mohamed Hanine , Roa El Dhimni , Kawtar El Azizi , Hajar Machmoum
In the evolving field of decision support systems, there is a critical need for tools that can address complex multi-criteria decision-making (MCDA) challenges with flexibility and a user-centric approach. This paper presents decideXpert, a platform that integrates both traditional and fuzzy versions of the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Unlike many current systems that rely on standalone methods or lack user-friendly interfaces, decideXpert provides a cohesive, single-page application. It offers a guided experience that is immediately usable without the need for programming skills. Built with a robust and scalable technology stack – Java and Spring Boot for the backend, Angular for the frontend, and MySQL for data management – decideXpert ensures cross-platform compatibility and an intuitive user experience. Its integration of fuzzy logic enhances decision-making by effectively managing subjective judgments and uncertainties. Additionally, the platform enables collaborative decision-making by allowing multiple stakeholders to contribute their expertise, ensuring a more comprehensive evaluation process. This paper explores the platform’s architecture and key functionalities, demonstrating its practical utility through case studies that highlight its adaptability in complex decision-making contexts.
{"title":"decideXpert: Collaborative system using AHP-TOPSIS and fuzzy techniques for multicriteria group decision-making","authors":"Abdelghani Saoud , Mohamed Lachgar , Mohamed Hanine , Roa El Dhimni , Kawtar El Azizi , Hajar Machmoum","doi":"10.1016/j.softx.2024.102026","DOIUrl":"10.1016/j.softx.2024.102026","url":null,"abstract":"<div><div>In the evolving field of decision support systems, there is a critical need for tools that can address complex multi-criteria decision-making (MCDA) challenges with flexibility and a user-centric approach. This paper presents decideXpert, a platform that integrates both traditional and fuzzy versions of the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Unlike many current systems that rely on standalone methods or lack user-friendly interfaces, decideXpert provides a cohesive, single-page application. It offers a guided experience that is immediately usable without the need for programming skills. Built with a robust and scalable technology stack – Java and Spring Boot for the backend, Angular for the frontend, and MySQL for data management – decideXpert ensures cross-platform compatibility and an intuitive user experience. Its integration of fuzzy logic enhances decision-making by effectively managing subjective judgments and uncertainties. Additionally, the platform enables collaborative decision-making by allowing multiple stakeholders to contribute their expertise, ensuring a more comprehensive evaluation process. This paper explores the platform’s architecture and key functionalities, demonstrating its practical utility through case studies that highlight its adaptability in complex decision-making contexts.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102026"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093067","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.2025.102074
Manuel Domínguez-Dorado , Javier Carmona-Murillo , David Cortés-Polo , Francisco J. Rodríguez-Pérez , Jesús Galeano-Brajones , Jesús Calle-Cancho
Open SimMPLS, an open-source simulator for Multiprotocol Label Switching (MPLS) networks, has been crucial in education, research, and industry for 20 years. It supports software engineering, communication networks, and learning model research, aiding academic projects and publications. Despite new technologies, MPLS remains vital for traffic engineering, ensuring Quality of Service (QoS) for high-demand applications like video streaming and cloud services. It also supports Software Defined Networks (SDN) and Network Functions Virtualization (NFV) by managing data paths and resources in virtualized environments. Open SimMPLS’s impact is evident in community contributions and ongoing studies in MPLS research and application.
Open SimMPLS 是多协议标签交换（MPLS）网络的开源模拟器，20 年来一直在教育、研究和工业领域发挥着重要作用。它支持软件工程、通信网络和学习模型研究，为学术项目和出版物提供帮助。尽管采用了新技术，但 MPLS 对于流量工程仍至关重要，可确保视频流和云服务等高需求应用的服务质量（QoS）。它还通过管理虚拟化环境中的数据路径和资源，支持软件定义网络（SDN）和网络功能虚拟化（NFV）。开放式 SimMPLS 的影响力体现在社区贡献和 MPLS 研究与应用方面的持续研究中。
{"title":"Leveraging OpenSimMPLS: A simulation platform for GoS/MPLS networks in research and education","authors":"Manuel Domínguez-Dorado , Javier Carmona-Murillo , David Cortés-Polo , Francisco J. Rodríguez-Pérez , Jesús Galeano-Brajones , Jesús Calle-Cancho","doi":"10.1016/j.softx.2025.102074","DOIUrl":"10.1016/j.softx.2025.102074","url":null,"abstract":"<div><div>Open SimMPLS, an open-source simulator for Multiprotocol Label Switching (MPLS) networks, has been crucial in education, research, and industry for 20 years. It supports software engineering, communication networks, and learning model research, aiding academic projects and publications. Despite new technologies, MPLS remains vital for traffic engineering, ensuring Quality of Service (QoS) for high-demand applications like video streaming and cloud services. It also supports Software Defined Networks (SDN) and Network Functions Virtualization (NFV) by managing data paths and resources in virtualized environments. Open SimMPLS’s impact is evident in community contributions and ongoing studies in MPLS research and application.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102074"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143368657","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.2024.101992
Jaebum Noh , Hanlyun Cho , Cherry Park , Dohyun Kang , Yujin Park , Junsuk Rho
{"title":"Corrigendum to “MetaCraft: Database-driven metalens design and optimization software” [SoftwareX 28 (2024) 101954]","authors":"Jaebum Noh , Hanlyun Cho , Cherry Park , Dohyun Kang , Yujin Park , Junsuk Rho","doi":"10.1016/j.softx.2024.101992","DOIUrl":"10.1016/j.softx.2024.101992","url":null,"abstract":"","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 101992"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396225","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.2024.101986
Ricardo Ribeiro de Alvarenga, Luiz Alberto Vieira Dias, Adilson Marques da Cunha
This paper presents a novel Python package designed to accelerate the execution of unit tests by capitalizing on multiprocessing techniques. To circumvent the limitations imposed by Python’s Global Interpreter Lock (GIL), which precludes concurrent execution, the proposed solution leverages threads through the reuse of code from the concurrent.futures module. Performance benchmarks comparing this approach to the standard unittest framework and the Pytest testing framework demonstrated time reductions in both scenarios. The findings suggest that this methodology could assist in software development in the Python language.
{"title":"Multtestlib: A Python package for performing unit tests using multiprocessing","authors":"Ricardo Ribeiro de Alvarenga, Luiz Alberto Vieira Dias, Adilson Marques da Cunha","doi":"10.1016/j.softx.2024.101986","DOIUrl":"10.1016/j.softx.2024.101986","url":null,"abstract":"<div><div>This paper presents a novel Python package designed to accelerate the execution of unit tests by capitalizing on multiprocessing techniques. To circumvent the limitations imposed by Python’s Global Interpreter Lock (GIL), which precludes concurrent execution, the proposed solution leverages threads through the reuse of code from the concurrent.futures module. Performance benchmarks comparing this approach to the standard unittest framework and the Pytest testing framework demonstrated time reductions in both scenarios. The findings suggest that this methodology could assist in software development in the Python language.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 101986"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092976","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.2025.102055
Gabriel Dubus , Maëlle Torterotot , Julie Béesau , Mathieu Dupont , Anatole Gros-Martial , Mathilde Michel , Elodie Morin , Paul Nguyen Hong Duc , Pierre-Yves Raumer , Olivier Adam , Flore Samaran , Dorian Cazau
Emerging detection and classification algorithms based on deep learning models require manageable large-scale manual annotations of ground truth data. To date, the challenge of creating large and accurate annotated datasets of underwater sounds has been a major obstacle to the development of robust recognition algorithms. APLOSE (Annotation PLatform for Ocean Sound Explorers) is an open-source, web-based tool which facilitates collaborative annotation campaigns in underwater acoustics. The platform was used to carry out research projects on inter-annotator variability, to build training and testing data sets for detection algorithms and to perform bioacoustics analysis on noisy datasets. In the future, it will enable the creation of high-quality reference datasets to test and train the new detection and classification algorithms.
{"title":"APLOSE: A web-based annotation platform for underwater passive acoustic monitoring","authors":"Gabriel Dubus , Maëlle Torterotot , Julie Béesau , Mathieu Dupont , Anatole Gros-Martial , Mathilde Michel , Elodie Morin , Paul Nguyen Hong Duc , Pierre-Yves Raumer , Olivier Adam , Flore Samaran , Dorian Cazau","doi":"10.1016/j.softx.2025.102055","DOIUrl":"10.1016/j.softx.2025.102055","url":null,"abstract":"<div><div>Emerging detection and classification algorithms based on deep learning models require manageable large-scale manual annotations of ground truth data. To date, the challenge of creating large and accurate annotated datasets of underwater sounds has been a major obstacle to the development of robust recognition algorithms. APLOSE (Annotation PLatform for Ocean Sound Explorers) is an open-source, web-based tool which facilitates collaborative annotation campaigns in underwater acoustics. The platform was used to carry out research projects on inter-annotator variability, to build training and testing data sets for detection algorithms and to perform bioacoustics analysis on noisy datasets. In the future, it will enable the creation of high-quality reference datasets to test and train the new detection and classification algorithms.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102055"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092183","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.2024.102028
Lorenzo Longobardi , Villiam Sozinho , Hamza Altarturi , E. Fernando Cagua , Alexander Tilley
Small-scale fisheries account for almost 90 % of global fisheries employment and are responsible for landing >40 % of the world's fish catch. Yet their importance to livelihoods and food and nutrition security in Least Developed Countries are only recently emerging due to the logistical, financial, and capacity challenges of gathering and interpreting data in this diverse, dispersed and informal sector. Peskas was designed as a low-cost solution to tackle this problem, providing a template workflow for ingestion and analysis to a decision dashboard, which can be adapted to different contexts and needs.
{"title":"Peskas: Automated analytics for small-scale, data-deficient fisheries","authors":"Lorenzo Longobardi , Villiam Sozinho , Hamza Altarturi , E. Fernando Cagua , Alexander Tilley","doi":"10.1016/j.softx.2024.102028","DOIUrl":"10.1016/j.softx.2024.102028","url":null,"abstract":"<div><div>Small-scale fisheries account for almost 90 % of global fisheries employment and are responsible for landing >40 % of the world's fish catch. Yet their importance to livelihoods and food and nutrition security in Least Developed Countries are only recently emerging due to the logistical, financial, and capacity challenges of gathering and interpreting data in this diverse, dispersed and informal sector. Peskas was designed as a low-cost solution to tackle this problem, providing a template workflow for ingestion and analysis to a decision dashboard, which can be adapted to different contexts and needs.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102028"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127768","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.2025.102042
Gauthier Grimmer, Romain Wenger, Valentin Chardon
The RiverDetectWood tool automates the classification and characterization of wood in river systems using very high spatial resolution (VHSR) aerial photographs. River wood plays a dual role in river management: it contributes to habitat diversity and geomorphological changes but can also increase flood risk and infrastructure damage. Traditional field-based river wood surveys are time-consuming and geographically limited. By integrating machine learning techniques and remote sensing, RiverDetectWood offers a cost-effective, scalable solution for monitoring river wood presence and extracting key variables, such as length, diameter, area, and volume. This tool is designed for easy use by river managers and researchers, facilitating long-term monitoring and decision-making.
{"title":"RiverDetectWood: A tool for automatic classification and quantification of river wood in river systems using aerial imagery","authors":"Gauthier Grimmer, Romain Wenger, Valentin Chardon","doi":"10.1016/j.softx.2025.102042","DOIUrl":"10.1016/j.softx.2025.102042","url":null,"abstract":"<div><div>The RiverDetectWood tool automates the classification and characterization of wood in river systems using very high spatial resolution (VHSR) aerial photographs. River wood plays a dual role in river management: it contributes to habitat diversity and geomorphological changes but can also increase flood risk and infrastructure damage. Traditional field-based river wood surveys are time-consuming and geographically limited. By integrating machine learning techniques and remote sensing, RiverDetectWood offers a cost-effective, scalable solution for monitoring river wood presence and extracting key variables, such as length, diameter, area, and volume. This tool is designed for easy use by river managers and researchers, facilitating long-term monitoring and decision-making.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"29 ","pages":"Article 102042"},"PeriodicalIF":2.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127993","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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