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CoGenASD: A tool for the co-design and generation of cross-platform applications for people with Autism spectrum disorder CoGenASD：为自闭症谱系障碍患者共同设计和生成跨平台应用程序的工具

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-11 DOI: 10.1016/j.softx.2026.102512

Yoel Arroyo, Ana I. Molina, Carmen Lacave, Miguel Á. Redondo

Current ASD-focused app development faces key limitations, such as high technical barriers for non-experts, limited personalization, and scarce involvement of therapists, families and educators in the design process. This paper presents CoGenASD, a framework that integrates co-design principles with a Model-Driven Development (MDD) approach to support the semi-automatic generation of cross-platform applications for individuals with ASD. The tool enables multidisciplinary teams (therapists, families and educators) to collaboratively define and model participant profiles, activities, interaction modes and content, supporting the semi-automatic generation of cross-platform, accessible and tailored applications. CoGenASD lowers technical barriers, promotes inclusive design practices, and accelerates the development of support tools. Its potential impact includes increasing application effectiveness, fostering stakeholder engagement, and enabling new research on customizable interventions for neurodiverse populations.

目前以自闭症为中心的应用程序开发面临着一些关键的限制，比如对非专家的高技术壁垒，有限的个性化，以及在设计过程中治疗师，家庭和教育工作者的参与很少。本文介绍了CoGenASD，一个将协同设计原则与模型驱动开发（MDD）方法集成在一起的框架，以支持ASD患者跨平台应用程序的半自动生成。该工具使多学科团队（治疗师、家庭和教育工作者）能够协同定义和建模参与者的个人资料、活动、交互模式和内容，支持半自动生成跨平台、可访问和定制的应用程序。CoGenASD降低了技术壁垒，促进了包容性设计实践，并加速了支持工具的开发。它的潜在影响包括提高应用效率，促进利益相关者的参与，以及为神经多样性人群提供可定制干预措施的新研究。

引用次数: 0

FD-REST: A lightweight RESTful platform for real-time fault detection and diagnosis in industrial systems FD-REST：用于工业系统实时故障检测和诊断的轻量级RESTful平台

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-17 DOI: 10.1016/j.softx.2026.102513

Tuğberk Kocatekin, Aziz Kubilay Ovacıklı, Mert Yağcıoğlu

Real-time fault detection in industrial rotating machinery requires both accurate machine learning models and software frameworks capable of handling continuous sensor streams. This study introduces FD-REST, an open-source, Dockerized platform that enables the deployment, execution, and real-time visualization of multi-sensor fault diagnosis models. The system integrates vibration, ultrasound, and temperature features and employs a Deep Neural Network (DNN) to generate continuous fault similarity scores across eight mechanical conditions. All predictions and raw signals are streamed to the frontend via WebSockets and stored in a lightweight SQLite database for reproducibility, session replay, and report generation. The embedded DNN model was validated on a real-world multi-modal dataset and achieved strong predictive performance, including a Mean Squared Error (MSE) of 0.00253, an

R^{2}

score of 0.8436, and approximately 93% threshold-based classification accuracy. These results demonstrate both the numerical reliability of the model and the effectiveness of FD-REST as a streaming-oriented benchmarking environment. By providing a modular, reproducible, and on-premises-ready framework, FD-REST bridges the gap between offline algorithm development and real-time industrial deployment, offering a practical tool for researchers, engineers, and practitioners in predictive maintenance.

工业旋转机械的实时故障检测需要精确的机器学习模型和能够处理连续传感器流的软件框架。本研究引入FD-REST，这是一个开源的Dockerized平台，可以实现多传感器故障诊断模型的部署、执行和实时可视化。该系统集成了振动、超声波和温度特征，并采用深度神经网络（DNN）在八种机械条件下生成连续的故障相似度评分。所有的预测和原始信号都通过WebSockets流传输到前端，并存储在一个轻量级的SQLite数据库中，以实现再现性、会话回放和报告生成。嵌入式DNN模型在真实世界的多模态数据集上进行了验证，并取得了较强的预测性能，其中均方误差（MSE）为0.00253，R2评分为0.8436，基于阈值的分类准确率约为93%。这些结果证明了该模型的数值可靠性和FD-REST作为面向流的基准测试环境的有效性。通过提供模块化的、可重复的、预置的框架，FD-REST弥合了离线算法开发和实时工业部署之间的差距，为研究人员、工程师和实践者提供了预测性维护的实用工具。

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引用次数: 0

BPMN graph transformation: A unified multi-format parser library for standardized graph-based business process model integration BPMN图转换：用于标准化的基于图的业务流程模型集成的统一多格式解析器库

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-02-03 DOI: 10.1016/j.softx.2026.102548

Kurnia Cahya Febryanto , Izzat Aji Androfaza , Lalu Aldo Wadagraprana , Riyanarto Sarno , Kelly Rossa Sungkono , Yeni Anistyasari , Joko Siswantoro , A Min Tjoa

Heterogeneous Business Process Model and Notation (BPMN) platforms present critical integration challenges, as approximately 75% of large enterprises employ multiple modeling tools lacking unified transformation capabilities. Existing solutions address only single-format conversions or provide limited cross-platform compatibility without comprehensive validation. This paper presents a production-ready multi-format BPMN parser library uniquely integrating intelligent format detection, dual-tier validation, and optimized graph transformation within a unified architecture. The library utilizes specialized parsers for BPMN 2.0 XML, XML Process Definition Language (XPDL) 2.2, native formats, and Microsoft Visio diagrams through a plugin-based architecture. Multi-criteria detection algorithms automatically identify source formats with 99.2% accuracy by analyzing file signatures, XML namespaces, structural patterns, and content heuristics. The dual-tier validation framework ensures structural BPMN 2.0 compliance through rule-based constraints derived from official OMG specifications and semantic consistency through metadata quality assessment based on established process modeling guidelines, surpassing existing tools that perform only syntactic validation. The transformation pipeline generates standardized Cypher queries optimized for process mining workflows. Evaluation across 127 real-world business process models demonstrates 98.7% overall parsing accuracy, with format-specific performance ranging from 97.2% (Visio) to 99.8% (BPMN XML), achieving 85% reduction in transformation time compared to manual approaches. Released as open-source software via the Python Package Index with complete documentation, the library establishes foundational infrastructure for cross-platform business process intelligence, enabling unified graph-based analytics across heterogeneous modeling ecosystems without format-specific preprocessing.

异构业务流程模型和符号（BPMN）平台提出了关键的集成挑战，因为大约75%的大型企业使用缺乏统一转换功能的多种建模工具。现有的解决方案只能处理单一格式的转换，或者在没有全面验证的情况下提供有限的跨平台兼容性。本文提出了一个生产就绪的多格式BPMN解析器库，该解析器库在统一的体系结构中独特地集成了智能格式检测、双层验证和优化的图转换。该库通过基于插件的体系结构，为BPMN 2.0 XML、XML过程定义语言（XML Process Definition Language， XPDL） 2.2、本机格式和Microsoft Visio图表使用专门的解析器。多标准检测算法通过分析文件签名、XML名称空间、结构模式和内容启发式，以99.2%的准确率自动识别源格式。双层验证框架通过派生自官方OMG规范的基于规则的约束确保结构化的BPMN 2.0遵从性，并通过基于已建立的流程建模指导方针的元数据质量评估确保语义一致性，超越了仅执行语法验证的现有工具。转换管道生成针对流程挖掘工作流优化的标准化Cypher查询。对127个实际业务流程模型的评估表明，总体解析准确率为98.7%，特定格式的性能范围从97.2% （Visio）到99.8% (BPMN XML)，与手动方法相比，转换时间减少了85%。该库通过Python Package Index作为开源软件发布，带有完整的文档，为跨平台业务流程智能建立了基础基础设施，支持跨异构建模生态系统的基于图形的统一分析，而无需特定格式的预处理。

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引用次数: 0

TaBSA – A framework for training and benchmarking algorithms for scheduling tasks for mobile robots working in dynamic environments TaBSA -一个用于训练和基准算法的框架，用于在动态环境中工作的移动机器人的任务调度

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-02 DOI: 10.1016/j.softx.2025.102489

Wojciech Dudek , Daniel Giełdowski , Kamil Młodzikowski , Dominik Belter , Tomasz Winiarski

This article introduces a software framework for benchmarking robot task scheduling algorithms in dynamic and uncertain service environments. The system provides standardised interfaces, configurable scenarios with movable objects, human agents, tools for automated test generation, and performance evaluation. It supports both classical and AI-based methods, enabling repeatable, comparable assessments across diverse tasks and configurations. The framework facilitates diagnosis of algorithm behaviour, identification of implementation flaws, and selection or tuning of strategies for specific applications. It includes a SysML-based domain-specific language for structured scenario modelling and integrates with the ROS-based system for runtime execution. Validated on patrol, fall assistance, and pick-and-place tasks, the open-source framework is suited for researchers and integrators developing and testing scheduling algorithms under real-world-inspired conditions.

本文介绍了一个在动态和不确定服务环境下对标机器人任务调度算法的软件框架。该系统提供了标准化的接口、可配置的带有可移动对象的场景、人工代理、自动化测试生成工具和性能评估。它既支持传统方法，也支持基于人工智能的方法，可以在不同的任务和配置中进行可重复、可比较的评估。该框架有助于诊断算法行为，识别实现缺陷，以及为特定应用程序选择或调整策略。它包括一种基于sysml的领域特定语言，用于结构化场景建模，并与基于ros的系统集成，用于运行时执行。该开源框架在巡逻、坠落辅助和拾取放置任务中得到了验证，适合研究人员和集成商在现实世界启发的条件下开发和测试调度算法。

引用次数: 0

FastRerandomize: Fast rerandomization using accelerated computing FastRerandomize：使用加速计算的快速再随机化

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-17 DOI: 10.1016/j.softx.2026.102508

Rebecca Goldstein , Connor T. Jerzak , Aniket Kamat , Fucheng Warren Zhu

We present fastrerandomize, an R package for fast, scalable rerandomization in experimental design. Rerandomization improves precision by discarding treatment assignments that fail a prespecified covariate-balance criterion, but existing implementations can become computationally prohibitive as the number of units or covariates grows. fastrerandomize introduces three complementary advances: (i) optional GPU/TPU acceleration to parallelize balance checks, (ii) memory-efficient key-only storage that avoids retaining full assignment matrices, and (iii) auto-vectorized, just-in-time compiled kernels for batched candidate generation and inference. This approach enables exact or Monte Carlo rerandomization at previously intractable scales, making it practical to adopt the tighter balance thresholds required in modern high-dimensional experiments while simultaneously quantifying the resulting gains in precision and power for a given covariate set. Our approach also supports randomization-based testing conditioned on acceptance. In controlled benchmarks, we observe order-of-magnitude speedups over baseline workflows, with larger gains as the sample size or dimensionality grows, translating into improved precision of causal estimates. Code: github.com/cjerzak/fastrerandomize-software. Interactive capsule: fastrerandomize.github.io/space.

我们提出了fasterandomize，这是一个R包，用于实验设计中的快速，可扩展的再随机化。再随机化通过丢弃未达到预先指定的协变量平衡标准的处理赋值来提高精度，但是随着单位或协变量数量的增加，现有的实现可能会变得难以计算。fastrerandomize引入了三个互补的进步：(i)可选的GPU/TPU加速来并行化平衡检查，（ii）内存高效的仅键存储，避免保留完整的分配矩阵，以及（iii）自动矢量化，实时编译内核，用于批量候选生成和推理。这种方法可以在以前难以处理的尺度上实现精确或蒙特卡罗再随机化，使得在现代高维实验中采用更严格的平衡阈值变得可行，同时量化给定协变量集的精度和功率的结果增益。我们的方法还支持基于接受度的随机化测试。在受控的基准测试中，我们观察到在基线工作流上的数量级加速，随着样本大小或维度的增长而获得更大的收益，转化为因果估计的精度的提高。代码:github.com/cjerzak/fastrerandomize-software。交互式胶囊：fasterandomize .github.io/space。

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引用次数: 0

Spatial-CustSat: An opensource package for customer satisfaction analysis in GIS environment Spatial-CustSat：一个用于GIS环境下客户满意度分析的开源软件包

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2025-12-05 DOI: 10.1016/j.softx.2025.102478

Anastasia S. Saridou, Ioannis Kansizoglou, Athanasios P. Vavatsikos

“Spatial-CustSat” is a GIS-based package that includes three models aiming to extent customer satisfaction (CS) analysis to the spatial context using MUlticriteria Satisfaction Analysis (MUSA) methods. The first two models use spatial datasets to perform the

k

-means algorithm and create homogeneous customer zones (clusters). The distinction between the two lies in the method of declaring the number of clusters. Supported by the MUSA method, CS analysis allows the identification of areas where the company's strengths and weaknesses lie. The latter model supports the implementation of CS benchmarking analysis for companies with store networks. Based on Walter's theory that customers shop at the nearest store, it identifies the service area of each store and implements the MUSAplus method. This option enables comparative performance analysis of the stores under evaluation.

“spatial - custsat”是一个基于gis的软件包，包括三个模型，旨在使用多标准满意度分析（MUSA）方法将客户满意度（CS）分析扩展到空间环境。前两种模型使用空间数据集执行k-means算法并创建同质客户区域（集群）。两者之间的区别在于声明集群数量的方法。在MUSA方法的支持下，CS分析可以识别公司的优势和劣势所在的领域。后一种模型支持对具有商店网络的公司实施CS基准分析。基于Walter的顾客在最近的商店购物的理论，它确定了每个商店的服务区域，并实现了MUSAplus方法。此选项支持对正在评估的存储进行比较性能分析。

引用次数: 0

CUBE: Cubed-sphere projection for adaptive mesh generation in spherical coordinates CUBE：用于球坐标下自适应网格生成的立方球投影

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-16 DOI: 10.1016/j.softx.2026.102514

Federico Gatti

We present CUBE, an open-source Python framework for generating adaptive, non-singular meshes on the sphere using a cubed-sphere projection. The software maps spherical slices to Cartesian faces of an inscribed cube, avoiding the pole singularities inherent to latitude–longitude grids and producing quasi-uniform element sizes across the globe. A core feature of CUBE is error-driven spatial adaptation: the mesh is refined according to an estimator based on an approximation of the

H^{1}

-seminorm of the topography discretization error, which concentrates resolution where terrain gradients are large. The implementation leverages numpy and scipy for efficient array operations, integrates gmsh via its Python API for meshing, and supports standard geospatial input (e.g., GTOPO30 digital elevation models). CUBE is intended as an extensible tool to produce high-quality input meshes for atmospheric and geophysical models, improving accuracy while reducing computational costs through targeted refinement.

我们提出了CUBE，一个开源的Python框架，用于使用立方体-球体投影在球体上生成自适应的非奇异网格。该软件将球面切片映射到一个内嵌立方体的笛卡尔面，避免了经纬度网格固有的极点奇点，并在全球范围内产生准均匀的元素尺寸。CUBE的一个核心特征是误差驱动的空间自适应：根据基于地形离散误差h1半模近似的估计器对网格进行细化，从而在地形梯度较大的地方集中分辨率。该实现利用numpy和scipy进行高效的数组操作，通过其Python API集成gmsh进行网格划分，并支持标准地理空间输入（例如，GTOPO30数字高程模型）。CUBE旨在作为一种可扩展的工具，为大气和地球物理模型生成高质量的输入网格，通过有针对性的细化提高精度，同时降低计算成本。

引用次数: 0

PPSSolver: An open-source software tool for Project Portfolio Selection and Scheduling Problems PPSSolver：一个用于项目组合选择和调度问题的开源软件工具

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2025-12-02 DOI: 10.1016/j.softx.2025.102460

Jing Liu , Saber Elsayed , Daryl Essam , Kyle Harrison , Ruhul Sarker

This paper presents an open-source software tool, PPSSolver, which is developed for academic research and practical decision-making in the Project Portfolio Selection and Scheduling Problem (PPSSP). PPSSolver provides an integrated platform for generating PPSSP instances with different complexities, optimizing PPSSPs using various algorithms, handling dynamic changes, and analyzing results. The software is designed to support researchers in algorithm development by allowing them to integrate customized solvers and evaluate them across benchmark instances. Additionally, PPSSolver provides a graphical user interface for ease of use, thereby lowering the technical barrier for practitioners.

本文介绍了一个开源软件工具PPSSolver，它是为项目组合选择与调度问题（PPSSP）的学术研究和实际决策而开发的。PPSSolver提供了一个集成的平台，可以生成不同复杂性的PPSSP实例，使用各种算法优化PPSSP，处理动态变化和分析结果。该软件旨在支持算法开发的研究人员，允许他们集成定制的求解器并跨基准实例进行评估。此外，PPSSolver提供了一个易于使用的图形用户界面，从而降低了从业者的技术障碍。

引用次数: 0

Simet: Synthetic image metrics - a synthetic image evaluation framework Simet：合成图像度量-一个合成图像评估框架

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-29 DOI: 10.1016/j.softx.2026.102526

O. Agost , F. Aran , J. Rius , P. Fraile , I. Barri , J. Vilaplana , J. Mateo

Simet provides a modular framework designed for the rigorous evaluation of synthetic image datasets. The framework integrates data provisioning, preprocessing, feature extraction, and complementary metrics, including Fréchet Inception Distance (FID), generative Precision/Recall, and classifier two-sample area under the receiver operating characteristic curve (ROC-AUC), within a single GPU-accelerated pipeline. A restraint mechanism enables declarative pass or fail gating. YAML- and command-line (CLI)-driven orchestration, shared feature caches, and structured logs facilitate reproducible, continuous-integration (CI)-ready workflows. Extensible abstractions, including providers, transforms, feature extractors, and metrics, allow practitioners to add new data sources or tests with minimal code. Templates support downstream utility evaluations, such as training on synthetic data and testing on real data (TSTR). Simet is positioned relative to existing toolkits, and protocols are outlined to demonstrate scalable, multidimensional evaluation of synthetic image data.

Simet提供了一个模块化框架，设计用于合成图像数据集的严格评估。该框架集成了数据提供、预处理、特征提取和互补指标，包括fr起始距离（FID）、生成精度/召回率（Precision/Recall）和接收器工作特征曲线（ROC-AUC）下的分类器双样本区域，在单个gpu加速管道中。约束机制支持声明性的通过或失败控制。YAML和命令行（CLI）驱动的编排、共享特性缓存和结构化日志有助于实现可重复的、可持续集成（CI）的工作流。可扩展的抽象，包括提供者、转换、特征提取器和度量，允许从业者用最少的代码添加新的数据源或测试。模板支持下游效用评估，例如对合成数据的培训和对真实数据的测试（TSTR）。Simet相对于现有工具包定位，并概述了协议，以演示合成图像数据的可扩展、多维评估。

引用次数: 0

Reducing complexity in photonic simulations: ZenScat — an efficient 2D RCWA solver 降低光子模拟的复杂性：ZenScat -一个有效的二维RCWA求解器

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

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102480

I. Lukosiunas , D. Gailevicius , K. Staliunas

We present a comprehensive solver implementation of a 2D Rigorous Coupled Wave Analysis (RCWA), tailored specifically for conformal thin multilayer devices and 2D photonic crystals with arbitrary interface profiles. Unlike traditional diffraction efficiency analysis, our approach emphasizes beam-shaping applications. Thus, our solver uniquely incorporates parameter sweeps across both wavelength and angular domains. This enables effective optimization of devices, such as low-pass spatial filters. Our software streamlines the design and analysis of complex photonic structures, broadening the practical application of RCWA methods and enabling the rapid development and optimization of novel photonic components.

我们提出了一个2D严格耦合波分析（RCWA）的综合求解器实现，专门为保形薄多层器件和具有任意界面轮廓的2D光子晶体量身定制。与传统的衍射效率分析不同，我们的方法强调光束整形应用。因此，我们的求解器独特地结合了波长和角域的参数扫描。这使得设备的有效优化，如低通空间滤波器。我们的软件简化了复杂光子结构的设计和分析，拓宽了RCWA方法的实际应用，并使新型光子元件的快速开发和优化成为可能。

引用次数: 0

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