SoftwareX最新文献

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A scalable web system for multi-index 3D point cloud visualization and real-time sensor monitoring in precision agriculture 面向精准农业多指标三维点云可视化和实时传感器监测的可扩展web系统

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-10 DOI: 10.1016/j.softx.2025.102443

Andoni Salcedo-Navarro, Guillem Montalban-Faet, Jaume Segura-Garcia, Miguel Garcia-Pineda

New technologies are transforming precision agriculture by enabling real-time monitoring, data-driven decision-making, and resource optimization. We present a web-based visor system that integrates 3D point cloud visualization of multi-vegetative indices with live sensor streams to form a digital replica of agricultural fields. The Potree-based viewer overlays geolocated point clouds onto an OpenStreetMap layer. A Node.js/Express REST API ingests heterogeneous sensor data (XML, JSON, CSV) into MongoDB, with Redis caching for low-latency retrieval. A Three.js first-person module enables immersive field walkthroughs, while a lazy-load mechanism lets users toggle vegetative indices on demand. Historical data are rendered via Chart.js. Deployed on Kubernetes, the system scales dynamically and remains resilient. Future work includes advanced data normalization, WebSockets-based push updates, and AR overlays. This open-source platform demonstrates how monitoring systems can drive sustainable, high-yield agriculture.

新技术通过实现实时监测、数据驱动决策和资源优化，正在改变精准农业。我们提出了一个基于网络的遮阳板系统，该系统将多植物指数的三维点云可视化与实时传感器流集成在一起，形成农业领域的数字复制品。基于potree的查看器将地理位置的点云叠加到OpenStreetMap层上。Node.js/Express REST API将异构传感器数据（XML， JSON， CSV）摄取到MongoDB中，并使用Redis缓存进行低延迟检索。Three.js的第一人称模块支持沉浸式现场漫游，而惰性加载机制允许用户根据需要切换植物索引。历史数据通过Chart.js呈现。部署在Kubernetes上，系统可以动态扩展并保持弹性。未来的工作包括高级数据规范化、基于websockets的推送更新和AR覆盖。这个开源平台展示了监控系统如何推动可持续的高产农业。

引用次数: 0

CSTrack dashboard: A social network data visualization platform for interactive and integrative analysis of discourse CSTrack仪表盘：一个社交网络数据可视化平台，用于交互和整合话语分析

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-07 DOI: 10.1016/j.softx.2025.102438

Fernando Martinez-Martinez , David Roldán-Álvarez , Estefanía Martín-Barroso

The discussion in social networks is of general interest, but the extraction, curation and visualization of this information turns difficult for those without programming knowledge. In the framework of the project CSTrack, which studies the activities in Citizen Science, we present an easily accessible dashboard aimed to provide a platform for people of different levels of expertise and professionals. They can retrieve valuable information about the trends and topics inside Twitter with a standardized pipeline for analysis that provides a complete understanding of the state of the conversation in social networks. With this platform, we present an alternative to the lack of standardization in social networking analysis and also, we aim to palliate the insufficiency of replication of social network research.

社交网络中的讨论引起了普遍的兴趣，但是对于那些没有编程知识的人来说，这些信息的提取、管理和可视化变得很困难。在研究公民科学活动的CSTrack项目框架内，我们提供了一个易于访问的仪表板，旨在为不同水平的专业知识和专业人员提供一个平台。他们可以检索有关Twitter内部趋势和主题的有价值的信息，通过标准化的管道进行分析，从而全面了解社交网络中的对话状态。有了这个平台，我们提出了一种替代缺乏标准化的社交网络分析，同时，我们的目标是缓解社交网络研究的复制不足。

引用次数: 0

Open sensing: An interactive online tool for environmental data collection, monitoring, analysis, and modelling for custom environmental sensor devices 开放传感：一个交互式在线工具，用于环境数据收集、监测、分析和定制环境传感器设备建模

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-07 DOI: 10.1016/j.softx.2025.102439

Carlos Sandoval Olascoaga, Khoi Ngo, Dezeng Kong, Leonard Schrage

Open Sensing (OS) is an open-source environmental monitoring and analysis platform addressing the critical need for accessible, granular environmental data in vulnerable communities. As urbanization accelerates globally, existing monitoring systems remain prohibitively expensive and technically complex. OS combines cost-effective, self-powered sensor networks with intuitive web-based visualization and spatial analysis tools. The platform enables non-technical users to collect, analyse, and model environmental data through customizable interfaces. The software was developed through a single-page architecture, which allows users to deploy the application without a dedicated backend server, while still providing API server functionality for data upload, data download, and data analysis. Deployed across multiple cities, OS has supported urban farm impact assessment, air quality advocacy, and environmental education. By democratizing environmental monitoring, OS empowers communities to understand and communicate environmental health impacts for evidence-based policy decisions.

开放传感（OS）是一个开源的环境监测和分析平台，解决了脆弱社区对可访问的颗粒环境数据的迫切需求。随着全球城市化的加速，现有的监测系统仍然非常昂贵，技术上也非常复杂。OS结合了成本效益高、自供电的传感器网络与直观的基于web的可视化和空间分析工具。该平台允许非技术用户通过可定制的接口收集、分析和建模环境数据。该软件是通过单页面架构开发的，允许用户在没有专用后端服务器的情况下部署应用程序，同时仍然为数据上传、数据下载和数据分析提供API服务器功能。OS在多个城市部署，支持城市农场影响评估、空气质量倡导和环境教育。通过环境监测的民主化，生态系统使社区能够了解和交流环境健康影响，从而制定基于证据的政策决策。

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引用次数: 0

RelRe: A command-line tool to predict the trajectory of variant replacement in an epidemic using relative instantaneous reproduction numbers RelRe：一个命令行工具，用于使用相对瞬时繁殖数来预测流行病中变体替换的轨迹

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-07 DOI: 10.1016/j.softx.2025.102440

Kimihito Ito , Michael A. Zeller , Chayada Piantham , Richard Musonda

During the SARS-CoV-2 pandemic, we have witnessed the emergence and disappearance of variants. When random mutations generate a new variant capable of infecting more individuals than existing variants, such a variant poses a public health threat due to the virus’s increased transmissibility. Therefore, it is essential to know how transmissible a new variant is compared to existing ones. In this paper, we introduce a computer program called RelRe, which allows users to estimate the relative instantaneous reproduction numbers among variants as well as the relative generation time using time series data of variant counts. Based on the estimated parameters, one can predict future variant replacements. The program was implemented with the Julia language, and its source code is available on our GitHub page (https://github.com/KimihitoIto/RelRe).

在SARS-CoV-2大流行期间，我们目睹了变体的出现和消失。当随机突变产生比现有变体能够感染更多个体的新变体时，由于病毒的传播性增加，这种变体构成公共卫生威胁。因此，了解新变种与现有变种相比的传染性是至关重要的。在本文中，我们介绍了一个名为RelRe的计算机程序，该程序允许用户使用变异计数的时间序列数据来估计变异之间的相对瞬时复制数以及相对生成时间。根据估计的参数，可以预测未来的变型替换。该程序是用Julia语言实现的，其源代码可在我们的GitHub页面（https://github.com/KimihitoIto/RelRe）上获得。

引用次数: 0

NeuroSpikeX: Comprehensive detection and characterization of neuronal calcium dynamics NeuroSpikeX：神经元钙动力学的综合检测和表征

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-06 DOI: 10.1016/j.softx.2025.102435

J.A. Sergay , A. Hai , C. Franck

NeuroSpikeX is a user-friendly tool for the quantitative analysis of neuronal calcium dynamics. It provides robust calcium spike detection, comprehensive network metrics, and intuitive graphical interfaces. NeuroSpikeX seamlessly integrates into existing workflows using outputs from the established algorithm NeuroCa, enhancing accuracy and reproducibility. The code effectively analyzes calcium dynamics across numerous in vitro datasets containing multiple experimental time points. NeuroSpikeX facilitates detailed cell and network analyses in large datasets, making rigorous calcium transient characterization accessible to researchers with minimal coding expertise.

NeuroSpikeX是一个用户友好的工具，用于神经元钙动力学的定量分析。它提供了强大的钙峰值检测，全面的网络指标和直观的图形界面。NeuroSpikeX使用已建立的算法NeuroCa的输出无缝集成到现有的工作流程中，提高了准确性和可重复性。该代码有效地分析了钙动力学跨越许多体外数据集包含多个实验时间点。NeuroSpikeX有助于在大型数据集中进行详细的细胞和网络分析，使研究人员可以用最少的编码专业知识进行严格的钙瞬态表征。

引用次数: 0

EUTROPY: A Python-based software optimized with Just-In-Time compilation for simulating eutrophication dynamics in aquatic systems EUTROPY：一个基于python的软件优化与即时编译模拟富营养化动态在水生系统

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-06 DOI: 10.1016/j.softx.2025.102430

Burak Kaynaroglu , Arturas Razinkovas-Baziukas , Rasa Idzelytė , Edvinas Tiškus , Mindaugas Zilius , Jovita Mėžinė , Georg Umgiesser

This study presents EUTROPY, an efficient, open-source modeling tool developed in Python for simulating primary production and investigating eutrophication dynamics. Although compiled languages such as Fortran and C++ are preferred in numerical modeling, Python was selected for its usability. Performance was enhanced using Numba for just-in-time (JIT) compilation, achieving speedups up to 40 times and outperforming a Fortran-based model. In addition, a Shiny interface supports interactive post-processing and visualization. This approach eliminates the two-language problem, enabling both simulation and analysis in one environment, making EUTROPY practical for academic use and a foundation for future applications in environmental management.

本研究提出了EUTROPY，一个用Python开发的高效开源建模工具，用于模拟初级生产和研究富营养化动态。尽管Fortran和c++等编译语言在数值建模中更受青睐，但选择Python是因为它的可用性。使用Numba进行即时（JIT）编译，性能得到了增强，实现了高达40倍的加速，性能优于基于fortran的模型。此外，Shiny界面支持交互式后处理和可视化。这种方法消除了两种语言的问题，在一个环境中实现了模拟和分析，使EUTROPY在学术上具有实用性，并为未来在环境管理中的应用奠定了基础。

引用次数: 0

EmbedSLR: an open-source python framework for efficient embedding-based screening and bibliometric validation in systematic literature review EmbedSLR：一个开源的python框架，用于系统文献综述中有效的基于嵌入的筛选和文献计量学验证

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-06 DOI: 10.1016/j.softx.2025.102416

Sebastian Matysik , Joanna Wiśniewska , Paweł Karol Frankowski

Efficient screening is essential for systematic literature reviews (SLRs), but traditional methods tend to be slow, biased, and error-prone. EmbedSLR improves this process by combining conventional bibliometrics with modern AI techniques. Its deterministic pipeline first ranks articles based on cosine distance between user queries and embeddings, then evaluates them using lightweight bibliometric indices. This approach outperformed keyword searches by increasing shared reference indicators 41.02 times and shared keywords 6.95 times in the presented case. Compatible with local environments and Google Colab, EmbedSLR supports natural language queries, making it accessible for researchers without programming skills while ensuring reproducible, high-quality SLRs. Our contribution is a deterministic, reproducible software pipeline that standardizes embedding‑based screening (cosine) and automatic bibliometric audit. The contribution of this research is a deterministic, repeatable software process that standardizes embedding-based (cosine) selection and automatic bibliometric control.

有效的筛选对于系统文献综述（slr）是必不可少的，但传统的方法往往是缓慢的，有偏见的，容易出错。EmbedSLR通过将传统文献计量学与现代人工智能技术相结合，改进了这一过程。它的确定性管道首先根据用户查询和嵌入之间的余弦距离对文章进行排名，然后使用轻量级文献计量指标对它们进行评估。在本案例中，该方法的共享参考指标提升41.02倍，共享关键字提升6.95倍，优于关键词搜索。与本地环境和谷歌Colab兼容，EmbedSLR支持自然语言查询，使没有编程技能的研究人员可以访问它，同时确保可复制的高质量单反。我们的贡献是一个确定性的，可重复的软件管道，标准化基于嵌入的筛选（余弦）和自动文献计量审计。这项研究的贡献是一个确定性的，可重复的软件过程，标准化基于嵌入（余弦）选择和自动文献计量控制。

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引用次数: 0

WATCHED: A Web AI Agent Tool for Combating Hate speech by Expanding Data 观看：通过扩展数据来打击仇恨言论的网络人工智能代理工具

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-06 DOI: 10.1016/j.softx.2025.102431

Paloma Piot , Diego Sánchez , Javier Parapar

Online harms are a growing problem in digital spaces, putting user safety at risk and reducing trust in social media platforms. One of the most persistent forms of harm is hate speech. To address this, we need tools that combine the speed and scale of automated systems with the judgement and insight of human moderators. These tools should not only find harmful content but also explain their decisions clearly, helping to build trust and understanding. In this paper, we present WATCHED a chatbot designed to support content moderators in tackling hate speech. The chatbot is built as an Artificial Intelligence Agent system that uses Large Language Models along with several specialised tools. It compares new posts with real examples of hate speech and neutral content, uses a BERT-based classifier to help flag harmful messages, looks up slang and informal language using sources like Urban Dictionary, generates chain-of-thought reasoning, and checks platform guidelines to explain and support its decisions. This combination allows the chatbot not only to detect hate speech but to explain why content is considered harmful, grounded in both precedent and policy. Experimental results show that our proposed method surpasses existing state-of-the-art methods, reaching a macro F1 score of 0.91. Designed for moderators, safety teams, and researchers, the tool helps reduce online harms by supporting collaboration between AI and human oversight.

网络危害是数字空间中一个日益严重的问题，它危及用户安全，降低了人们对社交媒体平台的信任。最持久的伤害形式之一是仇恨言论。为了解决这个问题，我们需要将自动化系统的速度和规模与人类审核员的判断和洞察力结合起来的工具。这些工具不仅应该发现有害内容，还应该清楚地解释它们的决定，帮助建立信任和理解。在本文中，我们介绍了一个聊天机器人，旨在支持内容审核员处理仇恨言论。这个聊天机器人是一个人工智能代理系统，它使用大型语言模型和一些专门的工具。它将新帖子与仇恨言论和中立内容的真实例子进行比较，使用基于bert的分类器来帮助标记有害信息，使用Urban Dictionary等来源查找俚语和非正式语言，生成思维链推理，并检查平台指南以解释和支持其决定。这种结合使聊天机器人不仅可以检测仇恨言论，还可以根据先例和政策解释为什么内容被认为是有害的。实验结果表明，我们提出的方法优于现有的最先进的方法，达到了0.91的宏观F1分数。该工具专为版主、安全团队和研究人员设计，通过支持人工智能和人类监督之间的协作，有助于减少在线危害。

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引用次数: 0

Grain2mesh: A Python and cubit mesh generator from unprocessed mesoscale images Grain2mesh：一个来自未处理的中尺度图像的Python和cubit网格生成器

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-04 DOI: 10.1016/j.softx.2025.102427

Ryley G. Hill, Keegan S. Davis, Christopher W. Johnson

Predicting bulk behavior from microscale features constitutes a key objective in multiscale modeling research, often involving numerical models composed of finite elements that capture the diversity of constituent phases, shapes, and orientations within the material. The Grain2mesh toolbox allows the user to input unprocessed mesoscopic images for automatic segmentation, pre-processing, quality control, and numerical mesh generation. The numerical mesh generation incorporates Cubit routines to generate robust multi-phase mesh structure for use in computational mechanics solvers. The python classes developed contain detailed documentation and examples to support standard usage and case-specific alternative options.

从微尺度特征预测体行为是多尺度建模研究的一个关键目标，通常涉及由有限元素组成的数值模型，这些模型可以捕捉材料中组成相、形状和方向的多样性。Grain2mesh工具箱允许用户输入未经处理的介观图像，用于自动分割、预处理、质量控制和数值网格生成。数值网格生成结合Cubit例程生成鲁棒多相网格结构，用于计算力学求解。开发的python类包含详细的文档和示例，以支持标准用法和特定于案例的替代选项。

引用次数: 0

Observer: An open-source framework for automating spectator for Real-time Strategy game of StarCraft 观察者：为星际争霸即时战略游戏提供的一个自动化观察者的开源框架

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2025-11-04 DOI: 10.1016/j.softx.2025.102421

Cheong-mok Bae , Ho-Taek Joo , SungHa Lee , Kyung-Joong Kim

Selecting engaging scenes is a critical component of esports broadcasting, traditionally performed by human observers. While recent research has explored AI-based automation, existing approaches often lack comprehensive frameworks for data extraction, human behavior modeling, and interface integration. We present Observer, an open-source framework that collects and preprocesses raw in-game data from StarCraft along with human observer viewport data to train AI-based automatic observers. The system transforms gameplay into multi-channel (hereafter, feature channels) representations and uses a modified Intersection over Union (IoU) metric to evaluate the overlap between predicted and aggregated human viewports. As reported in prior work, a learned observer achieves 56.9% similarity to human behavior, surpassing representative rule-based methods (52.4% and 49.1%) on standard benchmarks. In this software paper, we focus on a standardized, reproducible pipeline and system-level metrics.

选择吸引人的场景是电子竞技广播的关键组成部分，传统上由人类观察员执行。虽然最近的研究已经探索了基于人工智能的自动化，但现有的方法往往缺乏数据提取、人类行为建模和接口集成的综合框架。我们介绍Observer，这是一个收集和预处理来自《星际争霸》的原始游戏数据以及人类观察者视角数据的开源框架，用于训练基于ai的自动观察者。该系统将游戏玩法转换为多通道（以下简称“功能通道”）表示，并使用修改过的IoU度量来评估预测和聚合的人类视角之间的重叠。正如之前的研究所报道的那样，一个学习过的观察者与人类行为的相似性达到56.9%，超过了标准基准上具有代表性的基于规则的方法（52.4%和49.1%）。在这篇软件论文中，我们关注标准化的、可重复的管道和系统级度量。

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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