SoftwareXPub Date : 2024-07-17DOI: 10.1016/j.softx.2024.101815
{"title":"Stop@: A framework for scalable and noise-resistant stop-move segmentation of large datasets of trajectories in outdoor and indoor spaces","authors":"","doi":"10.1016/j.softx.2024.101815","DOIUrl":"10.1016/j.softx.2024.101815","url":null,"abstract":"<div><p>Capturing the mobility behavior of moving entities from their traces is a prominent theme in mobility data science. <em>Stop@</em> supports behavior analysis by providing a generic framework for the mining of stop-move patterns in spatial trajectories across animal and human mobility scenarios. The framework is built around a stop detection method, successfully used in diverse applications in animal ecology. The method has been recently validated against accurate ground truth stops collected in a museum, proving to be effective and robust, also for the study of human mobility. Stop@ provides a rich set of functionalities to facilitate the stop-move analysis, including the parallel processing of large datasets of trajectories collected outdoor and indoor.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001869/pdfft?md5=259a5ef5a4df474ed44e0188ee5d5661&pid=1-s2.0-S2352711024001869-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638477","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}
SoftwareXPub Date : 2024-07-16DOI: 10.1016/j.softx.2024.101816
{"title":"ArcNLET-Py: An ArcGIS-based nitrogen load estimation toolbox developed using python for ArcGIS pro","authors":"","doi":"10.1016/j.softx.2024.101816","DOIUrl":"10.1016/j.softx.2024.101816","url":null,"abstract":"<div><p>Onsite Sewage Treatment and Disposal Systems (OSTDS) are privately owned infrastructures that are critical for treating domestic wastewater in the USA. The ArcGIS-based Nitrogen Load Estimation Toolbox (ArcNLET) was developed to estimate nitrogen load from OSTDS to groundwater and surface waterbodies by simulating reactive transport of ammonium and nitrate nitrogen in soils and unconfined groundwater aquifers. Quantifying the load and removal of wastewater effluent requires resources, including data, computational power, and professional expertise that are not always available to state and local government agencies. Here, we discuss the advantages of utilizing a simplified model within a GIS to overcome data restraints, simulate the transport and load of nitrogen, and elaborate on the process of renovating and updating ArcNLET for integration with Python and ArcGIS Pro. ArcNLET-Py has the following modules: Module 0 for pre-processing the SSURGO database, Module 1 for groundwater flow simulation, Module 2 for particle tracking, Module 3 for a Vadose Zone MODel (VZMOD), Module 4 for nitrogen reactive transport modeling, and Module 5 for estimating nitrogen load to groundwater and surface waterbodies. The nitrogen reactions considered in Module 3 and Module 4 include ammonium sorption, ammonium nitrification, and nitrate denitrification in the flow path from OSTDS to surface waterbodies. As a newly introduced module, Module 0 streamlines the preparation of soil data from the SSURGO database, thereby enhancing the ease of use of ArcNLET-Py. An example of using ArcNLET-Py is presented for estimating nitrogen load in the Lakeshore neighborhood of Jacksonville, Florida. This work demonstrates the feasibility of developing complex pollution assessment software in the Python environment of ArcGIS Pro and holds implications for water environment protection.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001870/pdfft?md5=103d04e08eb0d6850cb1fd1e254c9bc6&pid=1-s2.0-S2352711024001870-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623636","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}
SoftwareXPub Date : 2024-07-16DOI: 10.1016/j.softx.2024.101819
{"title":"ORCh: A package to reduce and optimize chemical kinetics. Application to tetrafluoromethane oxidation","authors":"","doi":"10.1016/j.softx.2024.101819","DOIUrl":"10.1016/j.softx.2024.101819","url":null,"abstract":"<div><p>ORCh is a set of C++ routines to analyze the response of detailed chemical kinetics and determine the most influential species and elementary reactions for given operating conditions. The objective is to reduce the number of degrees of freedom to be solved in fluid mechanics simulations while still capturing most of the reactive flow physics. From a detailed chemical kinetics, ORCh returns a set of reduced chemical schemes of decreasing complexity. ORCh operates either from canonical laminar diffusion–reaction problems, such as one-dimensional premixed and diffusion flames, or turbulent/chemistry interaction models through the evolution of stochastic particles. The initial conditions of these canonical problems are representative of the inlets of the reactive flow system under study. In case of a significant reduction of the number of degrees of freedom, the control parameters of the reduced chemistry can be further optimized from a genetic algorithm to still match the reference detailed chemistry response. An illustrative application to tetrafluoromethane oxidation chemistry is discussed.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001900/pdfft?md5=54edfa187734e7590f4e4d19ef6db1e9&pid=1-s2.0-S2352711024001900-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623637","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}
SoftwareXPub Date : 2024-07-15DOI: 10.1016/j.softx.2024.101821
{"title":"GDPR consent management and automated compliance verification tool","authors":"","doi":"10.1016/j.softx.2024.101821","DOIUrl":"10.1016/j.softx.2024.101821","url":null,"abstract":"<div><p>This paper presents our scalable and interoperable tool for GDPR (General Data Protection Regulation) consent management and automated compliance verification. The tool enables GDPR-compliant data sharing and is beneficial to the industries that process personally identifiable data. The tool has been designed following the GDPR data protection by design principles and has been successfully validated against real-world industrial use case scenarios in smart cities and insurance.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001924/pdfft?md5=e210405cae815e763260b5d901c40e05&pid=1-s2.0-S2352711024001924-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623638","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}
SoftwareXPub Date : 2024-07-13DOI: 10.1016/j.softx.2024.101823
Jared Charles, Susan Gourvenec
{"title":"GγSANDnet: A neural network tool for prediction of shear stiffness (G) shear strain (γ) relationship for sands","authors":"Jared Charles, Susan Gourvenec","doi":"10.1016/j.softx.2024.101823","DOIUrl":"https://doi.org/10.1016/j.softx.2024.101823","url":null,"abstract":"<div><p>The relationship between shear stiffness modulus and shear strain is a key geotechnical parameter required for prediction of in-service performance of built infrastructure. This software utilises and rapidly trains an Artificial Neural Network (ANN) to generate a shear stiffness degradation curve from basic soil classification data (such as particle size and grain density) for any number and combination of input parameters. This enables geotechnical engineering practitioners or researchers to produce a stiffness degradation curve suitable for design or further modelling on sites with missing or low-quality site investigation data. The software was implemented in MATLAB using the Deep Learning toolbox and is available on Zenodo and GitHub. Also included is the required training dataset; alternatively, users can add their own data through the software's GUI.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001948/pdfft?md5=aa4bc1c077a8f95ed2b53144c6a0ec91&pid=1-s2.0-S2352711024001948-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605113","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}
SoftwareXPub Date : 2024-07-13DOI: 10.1016/j.softx.2024.101817
Joël N. Chapuis , Marc Wirth , Andreas Walker , Jonas Schwarz , Thomas S. Lumpe , Tian Chen , Tino Stanković
{"title":"EDACFEM: A linear truss and beam solver in MATLAB","authors":"Joël N. Chapuis , Marc Wirth , Andreas Walker , Jonas Schwarz , Thomas S. Lumpe , Tian Chen , Tino Stanković","doi":"10.1016/j.softx.2024.101817","DOIUrl":"https://doi.org/10.1016/j.softx.2024.101817","url":null,"abstract":"<div><p>The on-demand design of metamaterials such as lattices and bar structures is typically approached using computational methods due to their inherent complexity. An indispensable element of structural design is a reliable and easy to use FE simulation environment, which in turn not only benefits the design of underlying structures, but also, through easy customization and integration, propels the design of computational methods themselves. In response, this work provides a linear truss and beam FE simulation environment written in MATLAB. The simulation environment supports linear truss elements, Euler-Bernoulli besam elements, and Timoshenko beam elements. It further supports the introduction of self-weight and local truss buckling analysis. A variety of input methods are supported, these are specifically tailored towards simplifying the integration of the FE simulation environment in numerical optimization schemes. With this environment, researchers and design practitioners can easily simulate the mechanical response of complex bar structures without the need for interfacing with commercial FE software through cumbersome Application Programming Interfaces (APIs).</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001882/pdfft?md5=375a015c31c6d46d8a22bb76d7efdc22&pid=1-s2.0-S2352711024001882-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605112","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}
SoftwareXPub Date : 2024-07-13DOI: 10.1016/j.softx.2024.101796
Samuel Waldner, Jörg Huwyler, Maxim Puchkov
{"title":"A deep-learning-based workflow for reconstructing and segmenting challenging sets of time-resolved X-ray micro-computed tomography data","authors":"Samuel Waldner, Jörg Huwyler, Maxim Puchkov","doi":"10.1016/j.softx.2024.101796","DOIUrl":"https://doi.org/10.1016/j.softx.2024.101796","url":null,"abstract":"<div><p>We present a deep-learning-based software pipeline for reconstructing and segmenting large sets of time-resolved micro-computed tomography (µCT) image data. We construct and train a convolutional neural network (CNN) to consistently, rapidly, and autonomously segment the time-resolved tomography data. The preceding CT reconstruction steps are parametrized for optimal image quality for segmentation. We demonstrate how to discriminate materials with similar radiographic densities in the presence of different media, such as air and water. Our approach can be used out of the box for similar µCT data or adapted to any similarly challenging 3D image data by retraining the neural network.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001675/pdfft?md5=7a5bc6a95459d9f73e88f2254193e74b&pid=1-s2.0-S2352711024001675-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605111","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}
SoftwareXPub Date : 2024-07-11DOI: 10.1016/j.softx.2024.101814
Jakub Beránek , Ada Böhm , Gianluca Palermo , Jan Martinovič , Branislav Jansík
{"title":"HyperQueue: Efficient and ergonomic task graphs on HPC clusters","authors":"Jakub Beránek , Ada Böhm , Gianluca Palermo , Jan Martinovič , Branislav Jansík","doi":"10.1016/j.softx.2024.101814","DOIUrl":"https://doi.org/10.1016/j.softx.2024.101814","url":null,"abstract":"<div><p>Task graphs are a popular method for defining complex scientific simulations and experiments that run on distributed and HPC (High-performance computing) clusters, because they allow their authors to focus on the problem domain, instead of low-level communication between nodes, and also enable quick prototyping. However, executing task graphs on HPC clusters can be problematic in the presence of allocation managers like PBS or Slurm, which are not designed for executing a large number of potentially short-lived tasks with dependencies. To make task graph execution on HPC clusters more efficient and ergonomic, we have created <span>HyperQueue</span>, an open-source task graph execution runtime tailored for HPC use-cases. It enables the execution of large task graphs on top of an allocation manager by aggregating tasks into a smaller amount of PBS/Slurm allocations and dynamically load balances tasks amongst all available nodes. It can also automatically submit allocations on behalf of the user, it supports arbitrary task resource requirements and heterogeneous HPC clusters, it is trivial to deploy and does not require elevated privileges.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001857/pdfft?md5=4836c8d57801c1298552fd41cc44289b&pid=1-s2.0-S2352711024001857-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593294","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}
SoftwareXPub Date : 2024-07-10DOI: 10.1016/j.softx.2024.101812
Xiaofen Wang , Haodong Shi , Xiaotong Zhang , Yadong Wan , Peng Wang
{"title":"MicEMD: Open-source toolbox for electromagnetic modeling, inversion, and classification in underground metal target detection","authors":"Xiaofen Wang , Haodong Shi , Xiaotong Zhang , Yadong Wan , Peng Wang","doi":"10.1016/j.softx.2024.101812","DOIUrl":"https://doi.org/10.1016/j.softx.2024.101812","url":null,"abstract":"<div><p>The development and improvement of electromagnetic underground metal target detection methods can be implemented by a framework that is experimental supporting, modular, and extensible. In this paper, we organize the components of electromagnetic underground metal target detection in a comprehensive, modular, and extensible framework. Furthermore, we present an open-source toolbox in Python called MicEMD (Modeling, Inversion, and Classification in ElectroMagnetic Detection, <span>https://github.com/UndergroundDetection/MICEMD</span><svg><path></path></svg>). The graphical user interface (GUI) and the library with a Python application programming interface (API) are contained in MicEMD. Included in MicEMD are staggered frequency-domain and time-domain electromagnetic forward modeling, least-squares inversion, and data-based classification methods at present. MicEMD’s capabilities are presented by two synthetic case studies. The first example shows the application of frequency-domain inversion. The second example shows the application of time-domain classification. It is anticipated that MicEMD offers a flexible tool in electromagnetic underground metal target detection.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001833/pdfft?md5=329119e458c2ffd495c32eaac52a402f&pid=1-s2.0-S2352711024001833-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593292","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}
SoftwareXPub Date : 2024-07-10DOI: 10.1016/j.softx.2024.101813
Patryk Górka, Krzysztof Małecki
{"title":"ABMTrafSimCA: An agent-based modelling open-source software tool for traffic modelling and simulation based on cellular automata","authors":"Patryk Górka, Krzysztof Małecki","doi":"10.1016/j.softx.2024.101813","DOIUrl":"https://doi.org/10.1016/j.softx.2024.101813","url":null,"abstract":"<div><p>This article presents a standalone C++ application for conducting research in road traffic modelling based on cellular automata (CA) and a multi-agent approach. The software allows interaction with the user through the graphical interface implemented with the Qt library. It displays the results using the fundamental and space–time diagrams appreciated by researchers of micro-scale traffic modelling. The advantage of the provided software is the support for multi-cell CA and the road traffic model that handles the dynamics of acceleration and braking as well as a changeable distance from preceding vehicles. This article describes the architecture, main functionalities, and the ability to define agents and parameters affecting the modelling process.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001845/pdfft?md5=1d992bd657601ed4dc1b85b6854c7e70&pid=1-s2.0-S2352711024001845-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593291","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}