SoftwareX最新文献_第5页

BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp. BrRacemeCounter：一个基于人工智能的桌面工具，用于计数Urochloa的总状花序。

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102181

Darwin Alexis Arrechea-Castillo, Paula Espitia-Buitrago, Ronald David Arboleda, Ana Marcela Gallego-Muñoz, Valeria Moreno-Domínguez, Juan Manuel Gaviria-Valencia, Valeria Andrea Bravo, Andres Felipe Ruiz-Hurtado, Rosa N. Jauregui, Juan Andrés Cardoso

引用次数: 0

rOS-Instrumentation: An observer of operating system transactions rOS-Instrumentation：操作系统事务的观察者

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102192

Fengshan Shen, Mingyuan Jiu

{"title":"rOS-Instrumentation: An observer of operating system transactions","authors":"Fengshan Shen, Mingyuan Jiu","doi":"10.1016/j.softx.2025.102192","DOIUrl":"10.1016/j.softx.2025.102192","url":null,"abstract":"<div><div>Instrumentation is a crucial technique for accurately and comprehensively observing the concurrent behavior of operating systems. The omission of certain key behaviors can lead to incomplete or inaccurate understanding. This paper presents the source-level instrumentation module rOS-Instrumentation for the teaching-oriented operating system rCore. The module adopts a layered design strategy to achieve dependency separation, decomposing the instrumentation functionality into functions with varying degrees of reliance on the kernel. The inner-layer stub functions, which weakly depend on the kernel, can operate in more host functions, capturing events where critical kernel functions such as interrupt handling and output are executed. Meanwhile, the outer-layer output functions, which are more kernel-dependent, output the kernel function execution information recorded in memory by the stub functions at safe locations. The rOS-Instrumentation module features low dependency on kernel functionality, zero reliance on non-native development toolchains, no installation or configuration required, ease of compilation, and low technical barriers to use and customization. Users only need to reference the module name and call the user interface functions provided by the module to utilize its functionality seamlessly. Customization can be achieved by merely modifying the stub point information table within the module.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102192"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

pySEOBNR: A software package for the next generation of effective-one-body multipolar waveform models pySEOBNR：用于下一代有效的单体多极波形模型的软件包

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102080

Deyan P. Mihaylov , Serguei Ossokine , Alessandra Buonanno , Hector Estelles , Lorenzo Pompili , Michael Pürrer , Antoni Ramos-Buades

{"title":"pySEOBNR: A software package for the next generation of effective-one-body multipolar waveform models","authors":"Deyan P. Mihaylov , Serguei Ossokine , Alessandra Buonanno , Hector Estelles , Lorenzo Pompili , Michael Pürrer , Antoni Ramos-Buades","doi":"10.1016/j.softx.2025.102080","DOIUrl":"10.1016/j.softx.2025.102080","url":null,"abstract":"<div><div>We present <span>pySEOBNR</span>, a Python package for gravitational-wave (GW) modeling developed within the effective-one-body (EOB) formalism. The package contains an extensive framework to generate state-of-the-art inspiral-merger-ringdown waveform models for compact-object binaries composed of black holes and neutron stars. We document and demonstrate how to use the built-in quasi-circular precessing-spin model <span>SEOBNRv5PHM</span>, whose aligned-spin limit (<span>SEOBNRv5HM</span>) has been calibrated to numerical-relativity simulations and the nonspinning sector to gravitational self-force data using <span>pySEOBNR</span>. Furthermore, <span>pySEOBNR</span> contains the infrastructure necessary to construct, calibrate, test, and profile new waveform models in the EOB approach. The efficiency and flexibility of <span>pySEOBNR</span> will be crucial to overcome the data-analysis challenges posed by upcoming and next-generation GW detectors on the ground and in space, which will afford the possibility to observe all compact-object binaries in our Universe.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102080"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CtrlAer: Programmable real-time execution of scientific experiments using a domain specific language for the Raspberry Pi Pico/Pico 2 CtrlAer：用于树莓派Pico/Pico 2的领域特定语言的可编程实时执行科学实验

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102175

S.Hessam M. Mehr

引用次数: 0

landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images landmarker：用于二维/三维图像解剖地标定位的工具包

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102165

Jef Jonkers , Luc Duchateau , Glenn Van Wallendael , Sofie Van Hoecke

{"title":"landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images","authors":"Jef Jonkers , Luc Duchateau , Glenn Van Wallendael , Sofie Van Hoecke","doi":"10.1016/j.softx.2025.102165","DOIUrl":"10.1016/j.softx.2025.102165","url":null,"abstract":"<div><div>Anatomical landmark localization in 2D/3D images is a critical task in medical imaging. Although many general-purpose tools exist for landmark localization in classical computer vision tasks, such as pose estimation, they lack the specialized features and modularity necessary for anatomical landmark localization applications in the medical domain. Therefore, we introduce <span>landmarker</span>, a Python package built on PyTorch. The package provides a comprehensive, flexible toolkit for developing and evaluating landmark localization algorithms, supporting a range of methodologies, including static and adaptive heatmap regression. <span>landmarker</span> enhances the accuracy of landmark identification, streamlines research and development processes, and supports various image formats and preprocessing pipelines. Its modular design allows users to customize and extend the toolkit for specific datasets and applications, accelerating innovation in medical imaging. <span>landmarker</span> addresses a critical need for precision and customization in landmark localization tasks not adequately met by existing general-purpose pose estimation tools.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102165"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ASSUME: An agent-based simulation framework for exploring electricity market dynamics with reinforcement learning 假设：一个基于智能体的模拟框架，用于通过强化学习探索电力市场动态

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102176

Nick Harder , Kim K. Miskiw , Manish Khanra , Florian Maurer , Parag Patil , Ramiz Qussous , Christof Weinhardt , Marian Klobasa , Mario Ragwitz , Anke Weidlich

{"title":"ASSUME: An agent-based simulation framework for exploring electricity market dynamics with reinforcement learning","authors":"Nick Harder , Kim K. Miskiw , Manish Khanra , Florian Maurer , Parag Patil , Ramiz Qussous , Christof Weinhardt , Marian Klobasa , Mario Ragwitz , Anke Weidlich","doi":"10.1016/j.softx.2025.102176","DOIUrl":"10.1016/j.softx.2025.102176","url":null,"abstract":"<div><div>Electricity markets are undergoing transformative changes driven by integrating renewable energy and emerging technologies, and evolving market conditions such as shifting demand patterns, regulatory reforms, and increased price volatility. To address the complexity of electricity markets and their interactions, we present ASSUME, an open-source agent-based simulation framework that incorporates multi-agent deep reinforcement learning for modeling adaptive market participants. ASSUME offers a modular architecture for representing generator and demand-side agents, bidding strategies, and diverse market configurations. ASSUME has been proven effective in multiple research studies, demonstrating its ability to analyze complex bids, demand-side flexibility, and other market scenarios. By incorporating adaptive strategies through deep reinforcement learning, ASSUME supports dynamic strategy exploration, enabling a deeper understanding of electricity market behaviors. With its flexible architecture, documentation, tutorials, and broad accessibility, ASSUME ensures usability across different user groups, minimizing technical overhead and freeing up human resources for deeper insights into operational, economic, and policy-related challenges in this critical sector.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102176"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MLFoMpy: A post-processing tool for semiconductor TCAD data with machine-learning capabilities 具有机器学习功能的半导体TCAD数据后处理工具

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102166

Enrique Comesaña , Julian G. Fernández , Natalia Seoane , Antonio García-Loureiro

引用次数: 0

DataMetProcess: An open-source package and Shiny application for the acquisition and processing of meteorological data from INMET DataMetProcess：用于从INMET获取和处理气象数据的开源软件包和Shiny应用程序

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-05-01 DOI: 10.1016/j.softx.2025.102185

Wagner Martins dos Santos , Hoi Leong Lee , Edimir Xavier Leal Ferraz , Abelardo Antônio de Assunção Montenegro , Lady Daiane Costa de Sousa Martins , Alan Cézar Bezerra , Ênio Farias de França e Silva , Thieres George Freire da Silva , João L.M.P. de Lima , Xuguang Tang , Alexandre Maniçoba da Rosa Ferraz Jardim

{"title":"DataMetProcess: An open-source package and Shiny application for the acquisition and processing of meteorological data from INMET","authors":"Wagner Martins dos Santos , Hoi Leong Lee , Edimir Xavier Leal Ferraz , Abelardo Antônio de Assunção Montenegro , Lady Daiane Costa de Sousa Martins , Alan Cézar Bezerra , Ênio Farias de França e Silva , Thieres George Freire da Silva , João L.M.P. de Lima , Xuguang Tang , Alexandre Maniçoba da Rosa Ferraz Jardim","doi":"10.1016/j.softx.2025.102185","DOIUrl":"10.1016/j.softx.2025.102185","url":null,"abstract":"<div><div>The acquisition and understanding of meteorological data are crucial for management in various sectors of society. Thus, the objective was to develop a package in the R programming environment to download and process data provided by Brazilian National Institute of Meteorology (INMET) and to create a free web-based tool using Shiny (interactive web apps) to simplify the use of the functions provided in the package. The DataMetProcess package was developed, offering four essential functions to extract information from the database, correct time zones, change the time scale, and calculate reference evapotranspiration according to the Penman-Monteith model. The Shiny web application developed is available in three versions: (1) the code; (2) portable; and (3) a web version. The DataMetProcess package and the developed web application are effective in processing the data provided by INMET, democratizing access to information and facilitating analysis through the interactive web application. Finally, being open-source, it can be easily adapted and used worldwide. We encourage the research community to adopt this tool and provide feedback to further refine its usability and efficiency.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102185"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DFAT: A web-based toolkit for estimating demand flexibility in building-to-grid integration DFAT：一个基于web的工具包，用于估算从建筑到电网集成的需求灵活性

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-04-26 DOI: 10.1016/j.softx.2025.102131

Michael Leong , Medha Mahanta , Clara Yin , Timothy Jairus Garcia , Zach Tan , Anand Krishnan Prakash , Doug Black , Rongxin Yin

{"title":"DFAT: A web-based toolkit for estimating demand flexibility in building-to-grid integration","authors":"Michael Leong , Medha Mahanta , Clara Yin , Timothy Jairus Garcia , Zach Tan , Anand Krishnan Prakash , Doug Black , Rongxin Yin","doi":"10.1016/j.softx.2025.102131","DOIUrl":"10.1016/j.softx.2025.102131","url":null,"abstract":"<div><div>Demand Flexibility Assessment Tool (DFAT) is an open source web-based tool that estimates the demand flexibility potential of common control strategies in commercial buildings. The toolkit features a demand flexibility estimation tool that contains two calculators, basic and advanced, based on the level of input of customer data. The basic version calculates demand shed metrics for the control strategy “global temperature adjustment” and “cycle on/off compressors” using customer building information, local weather data, and electrical meter data. The advanced version, which uses detailed HVAC equipment data, calculates demand flexibility metrics for control strategies such as static pressure reset, global temperature adjustment, and cycle on/off compressors. In addition to the demand flexibility estimation tool, this toolkit offers a benchmarking tool that helps facility operators, aggregators, and utility resource managers assess demand flexibility opportunities, quantify/verify performance, and compare their performance against that of their peers.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102131"},"PeriodicalIF":2.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DREAMS: A python framework for training deep learning models on EEG data with model card reporting for medical applications DREAMS：一个python框架，用于在EEG数据上训练深度学习模型，并为医疗应用提供模型卡报告

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-04-24 DOI: 10.1016/j.softx.2025.102140

Rabindra Khadka , Pedro G. Lind , Anis Yazidi , Asma Belhadi

{"title":"DREAMS: A python framework for training deep learning models on EEG data with model card reporting for medical applications","authors":"Rabindra Khadka , Pedro G. Lind , Anis Yazidi , Asma Belhadi","doi":"10.1016/j.softx.2025.102140","DOIUrl":"10.1016/j.softx.2025.102140","url":null,"abstract":"<div><div>Electroencephalography (EEG) provides a non-invasive way to observe brain activity in real time. Deep learning has enhanced EEG analysis, enabling meaningful pattern detection for clinical and research purposes. However, most existing frameworks for EEG data analysis are either focused on preprocessing techniques or deep learning model development, often overlooking the crucial need for structured documentation and model interpretability. In this paper, we introduce DREAMS (Deep REport for AI ModelS), a Python-based framework designed to generate automated model cards for deep learning models applied to EEG data. Unlike generic model reporting tools, DREAMS is specifically tailored for EEG-based deep learning applications, incorporating domain-specific metadata, preprocessing details, performance metrics, and uncertainty quantification. The framework seamlessly integrates with deep learning pipelines, providing structured YAML-based documentation. We evaluate DREAMS through two case studies: an EEG emotion classification task using the FACED dataset and a abnormal EEG classification task using the Temple University Hospital (TUH) Abnormal dataset. These evaluations demonstrate how the generated model card enhances transparency by documenting model performance, dataset biases, and interpretability limitations. Unlike existing model documentation approaches, DREAMS provides visualized performance metrics, dataset alignment details, and model uncertainty estimations, making it a valuable tool for researchers and clinicians working with EEG-based AI. The source code for DREAMS is open-source, facilitating broad adoption in healthcare AI, research, and ethical AI development.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102140"},"PeriodicalIF":2.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0