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Retuve: Automated multi-modality analysis of hip dysplasia with open source AI 回报：使用开源AI对髋关节发育不良进行自动化多模态分析

IF 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-10-01 DOI: 10.1016/j.simpa.2025.100791

Adam McArthur , Stephanie Wichuk , Stephen Burnside , Andrew Kirby , Alexander Scammon , Damian Sol , Abhilash Hareendranathan , Jacob L. Jaremko

Developmental dysplasia of the hip (DDH) poses significant diagnostic challenges, hindering timely intervention. Current screening methodologies lack standardization, and AI-driven studies suffer from reproducibility issues due to limited data and code availability. To address these limitations, we introduce Retuve, an open-source framework for multi-modality DDH analysis, encompassing both ultrasound (US) and X-ray imaging. Retuve provides a complete and reproducible workflow, offering open datasets comprising expert-annotated US and X-ray images, pre-trained models with training code and weights, and a user-friendly Python Application Programming Interface (API). The framework integrates segmentation and landmark detection models, enabling automated measurement of key diagnostic parameters such as the alpha angle and acetabular index. By adhering to open-source principles, Retuve promotes transparency, collaboration, and accessibility in DDH research. This framework can democratize DDH screening, facilitate early diagnosis, and improve patient outcomes by enabling widespread screening and early intervention. The GitHub repository/code can be found here: https://github.com/radoss-org/retuve

发育性髋关节发育不良（DDH）提出了重大的诊断挑战，阻碍了及时干预。目前的筛选方法缺乏标准化，并且由于数据和代码可用性有限，人工智能驱动的研究存在可重复性问题。为了解决这些限制，我们介绍了Retuve，一个多模态DDH分析的开源框架，包括超声（US）和x射线成像。Retuve提供了一个完整且可重复的工作流程，提供开放数据集，包括专家注释的美国和x射线图像，带有训练代码和权重的预训练模型，以及用户友好的Python应用程序编程接口（API）。该框架集成了分割和地标检测模型，能够自动测量关键诊断参数，如α角和髋臼指数。通过坚持开源原则，Retuve促进了DDH研究的透明度、协作性和可访问性。该框架可以使DDH筛查大众化，促进早期诊断，并通过广泛筛查和早期干预改善患者预后。GitHub存储库/代码可以在这里找到：https://github.com/radoss-org/retuve

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Py2ONTO-Edit: A python-based tool for ontology term extraction and translation Py2ONTO-Edit：基于python的本体术语提取和翻译工具

IF 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-10-01 DOI: 10.1016/j.simpa.2025.100785

Zhe Wang , Zunfan Chen , Zhigang Wang , Sheng Yang , Xiaolin Yang , Heinrich Herre , Yan Zhu

This paper presents Py2ONTO-Edit, an ontology editing tool that integrates the low-level functionality of Owlready2 to simplify the extraction and translation of ontology terms. It offers two extraction methods: 1. Global extraction method. 2. Selective-depth extraction method. Another key feature is the translation of ontology terms using multiple translation packages to add non-English labels (e.g., Chinese, French, German) to the ontology. This paper presents two main contributions: 1. Implementation of flexible features for term extraction. 2. Enabling of multilingual translation of ontology terms. Py2ONTO-Edit is an easy-to-use Python tool for developers focused on ontology term reuse and translation.

本文介绍了一个本体编辑工具Py2ONTO-Edit，它集成了Owlready2的底层功能，以简化本体术语的提取和翻译。它提供了两种提取方法：1。全局提取方法。2. 选择性深度提取法。另一个关键特性是本体术语的翻译，使用多个翻译包向本体添加非英语标签（例如中文、法语、德语）。本文提出了两个主要贡献：1。实现灵活的术语提取功能。2. 支持本体术语的多语言翻译。Py2ONTO-Edit是一个易于使用的Python工具，面向专注于本体术语重用和翻译的开发人员。

引用次数: 0

Deep learning framework with Hadamard-based feature fusion for node influence power prediction 基于hadamard特征融合的深度学习框架用于节点影响功率预测

IF 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-10-01 DOI: 10.1016/j.simpa.2025.100793

Ali Seyfi , Asgarali Bouyer , Amin Golzari Oskouei , Bahman Arasteh , Leila Hassani

In this paper, an innovative architecture based on deep neural networks is presented. Initially, node and layer features are extracted as feature vectors. Each vector is then passed through a deep multilayer perceptron (MLP) network for enrichment. Using the Hadamard product, these vectors are multiplied element-wise to form a matrix. In the next step, to analyze feature interactions, this matrix is fed into a series of Transformer encoders arranged sequentially. Finally, an MLP network is used as a regression model to predict the influence power of the nodes.

本文提出了一种基于深度神经网络的创新结构。首先，提取节点特征和层特征作为特征向量。然后将每个向量通过深度多层感知器（MLP）网络进行富集。使用哈达玛乘积，这些向量按元素相乘形成一个矩阵。在下一步中，为了分析特征交互，该矩阵被馈送到顺序排列的一系列Transformer编码器中。最后，利用MLP网络作为回归模型来预测节点的影响能力。

引用次数: 0

HoneySeg: A segmentation tool for detecting honey areas in honeycombs HoneySeg：用于检测蜂巢中蜂蜜区域的分割工具

IF 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-09-25 DOI: 10.1016/j.simpa.2025.100788

Sergio R. Geninatti , Manuel Ortiz-Lopez , José Luis Ávila-Jiménez , José M. Flores , Francisco J. Rodriguez-Lozano

The task of evaluating hives is arduous and time-consuming for beekeepers. One of the tasks involves evaluating the honey in the combs to determine the available surface area, as this is directly related to the health of the hive. Currently, there are very few software tools specifically designed for beekeeping that help alleviate the work of beekeepers. Therefore, this paper presents HoneySeg, a Python-based application for calculating honey area and segmenting honey zones in images of honeycombs. It is an open-source tool designed specifically for beekeeping that does not require prior training for use by the beekeeper.

对养蜂人来说，评估蜂箱是一项艰巨而耗时的任务。其中一项任务是评估蜂箱中的蜂蜜，以确定可用的表面积，因为这直接关系到蜂箱的健康。目前，很少有专门为养蜂人设计的软件工具来帮助减轻养蜂人的工作。因此，本文提出了HoneySeg，这是一个基于python的应用程序，用于计算蜂巢图像中的蜂蜜面积和分割蜂蜜区域。这是一个专门为养蜂人设计的开源工具，养蜂人不需要事先培训就可以使用。

引用次数: 0

STFATool: A Sparse Time–Frequency Analysis Toolkit for non-stationary signals 用于非平稳信号的稀疏时频分析工具包

IF 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-09-16 DOI: 10.1016/j.simpa.2025.100784

Baijian Wu, Gang Yu

STFATool is a professional signal-processing application implemented in Python. It integrates several state-of-the-art sparse time–frequency analysis algorithms, including Synchroextracting Transform, Transient-Extracting Transform, Multisynchrosqueezing Transform, and Time-Reassigned Multisynchrosqueezing Transform. It provides a user-friendly interface, users can import signals for detailed time–frequency feature visualization and processing, enabling efficient extraction of critical signal characteristics.

STFATool是一个用Python实现的专业信号处理应用程序。它集成了几种最先进的稀疏时频分析算法，包括同步提取变换、瞬态提取变换、多同步压缩变换和时间重分配多同步压缩变换。它提供了一个用户友好的界面，用户可以对输入信号进行详细的时频特征可视化和处理，实现对关键信号特征的高效提取。

引用次数: 0

RAGCacheSim: A discrete-event simulator for evaluating caching strategies in Retrieval-Augmented Generation systems RAGCacheSim：用于评估检索增强生成系统中的缓存策略的离散事件模拟器

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Software Impacts

Pub Date : 2025-09-01 DOI: 10.1016/j.simpa.2025.100783

Hardik Ruparel, Tatsat Patel

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) with external knowledge retrieval but incur significant compute and latency costs. In distributed RAG deployments, semantically similar queries routed to different nodes — each with its own cache — can lead to redundant processing. We present RAGCacheSim, a discrete-event simulator for evaluating caching strategies such as Centralized Exact-match Cache (CEC), Independent Semantic Caches (IC), and Distributed Semantic Cache Coordination (DSC). It reports metrics like cache hit rate, average query latency, and coordination overhead. Built using SimPy, FastEmbed, and pybloom_live, it helps researchers optimize distributed RAG architectures.

检索-增强生成（RAG）系统通过外部知识检索来增强大型语言模型（llm），但会产生大量的计算和延迟成本。在分布式RAG部署中，路由到不同节点（每个节点都有自己的缓存）的语义相似的查询可能导致冗余处理。我们提出RAGCacheSim，一个离散事件模拟器，用于评估缓存策略，如集中式精确匹配缓存（CEC），独立语义缓存（IC）和分布式语义缓存协调（DSC）。它报告诸如缓存命中率、平均查询延迟和协调开销等指标。它使用SimPy、FastEmbed和pybloom_live构建，可以帮助研究人员优化分布式RAG架构。

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FaultNet-Sim: A C++ simulator for failure-prone wireless sensor networks FaultNet-Sim：一个用于故障易发无线传感器网络的c++模拟器

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-07-01 DOI: 10.1016/j.simpa.2025.100776

Santana Yuda Pradata , Muhammad Alfian Amrizal , Ahmad Ridwan Tresna Nugraha , Reza Pulungan

Wireless sensor networks (WSNs) are crucial for various real-life applications, from environmental and health monitoring systems to home and industrial automation. However, these networks face challenges in failure-prone environments, where sensor nodes must conserve energy while ensuring data reliability. We introduce FaultNet-Sim, a multithreaded simulator that facilitates the development of optimization strategies for balancing energy consumption and data reliability by tuning data transfer intervals in WSNs. The simulator can model different failure conditions and various time-division multiple access (TDMA)-based scheduling techniques, allowing users to analyze the trade-offs between data loss and energy consumption. With customizable parameters, FaultNet-Sim is a valuable tool for researchers looking to improve the resilience and efficiency of WSNs in real-world applications.

无线传感器网络（wsn）对于各种现实应用至关重要，从环境和健康监测系统到家庭和工业自动化。然而，这些网络在容易发生故障的环境中面临挑战，传感器节点必须在确保数据可靠性的同时节省能量。我们介绍了FaultNet-Sim，这是一个多线程模拟器，通过调整WSNs中的数据传输间隔来促进平衡能耗和数据可靠性的优化策略的开发。该模拟器可以模拟不同的故障条件和各种基于时分多址（TDMA）的调度技术，允许用户分析数据丢失和能耗之间的权衡。FaultNet-Sim具有可定制的参数，对于研究人员来说，在实际应用中提高wsn的弹性和效率是一个有价值的工具。

引用次数: 0

A tool for measuring program comprehensibility using readability-driven metrics 使用可读性驱动的度量来度量程序的可理解性的工具

IF 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-07-01 DOI: 10.1016/j.simpa.2025.100782

Md. Masudur Rahman, Zenun Chowdhury, Raqeebir Rab

Program comprehensibility plays a significant role in software maintenance by enhancing code readability. Although inherently subjective, various methods to assess comprehensibility have emerged in recent years. Most of these approaches focus on structural characteristics of source code, such as lines of code, number of identifiers, cyclomatic complexity, etc. However, textual elements are equally vital, as these directly influence how humans interpret and understand code. In this paper, we present an approach that evaluates program comprehensibility based on the textual readability of source code — reflecting how it is perceived by human readers. We developed a tool to implement this proposed approach and validated its effectiveness by comparing its output with manual evaluations of code comprehensibility. The results showed complete agreement, indicating that the tool produces comprehensibility scores. This tool can support developers by identifying segments of code that are harder to comprehend, enabling targeted refactoring efforts to improve overall readability.

程序可理解性通过提高代码的可读性，在软件维护中起着重要的作用。虽然固有的主观性，但近年来出现了各种评估可理解性的方法。这些方法大多关注源代码的结构特征，如代码行数、标识符的数量、圈复杂度等。然而，文本元素同样至关重要，因为它们直接影响人类如何解释和理解代码。在本文中，我们提出了一种基于源代码的文本可读性来评估程序可理解性的方法-反映了人类读者如何感知它。我们开发了一个工具来实现这个建议的方法，并通过比较它的输出和代码可理解性的手动评估来验证它的有效性。结果显示完全一致，表明该工具产生可理解性分数。该工具可以通过识别难以理解的代码片段来支持开发人员，从而使有针对性的重构工作能够提高整体可读性。

引用次数: 0

GPS-2-GTFS: A Python package to process and transform raw GPS data of public transit to GTFS format GPS-2-GTFS：一个Python包，用于处理和转换公共交通的原始GPS数据为GTFS格式

IF 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-07-01 DOI: 10.1016/j.simpa.2025.100780

Shiveswarran Ratneswaran , Uthayasanker Thayasivam , Sivakumar Thillaiambalam

The ‘gps2gtfs’ package addresses a critical need for converting raw Global Positioning System (GPS) trajectory data from public transit vehicles into the widely used GTFS (General Transit Feed Specification) format. This transformation enables various software applications to efficiently utilize real-time transit data for purposes such as tracking, scheduling, and arrival time prediction. Developed in Python, ‘gps2gtfs’ employs techniques like geo-buffer mapping, parallel processing, and data filtering to manage challenges associated with raw GPS data, including high volume, discontinuities, and localization errors. This open-source package, available on GitHub and PyPI, enhances the development of intelligent transportation solutions and fosters improved public transit systems globally.

“gps2gtfs”包解决了将公共交通车辆的原始全球定位系统（GPS）轨迹数据转换为广泛使用的GTFS（通用交通馈送规范）格式的关键需求。这种转换使各种软件应用程序能够有效地利用实时运输数据，用于跟踪、调度和到达时间预测等目的。‘ gps2gtfs ’使用Python开发，采用地理缓冲区映射，并行处理和数据过滤等技术来管理与原始GPS数据相关的挑战，包括高容量，不连续和定位错误。这个开源包可以在GitHub和PyPI上获得，它增强了智能交通解决方案的发展，并促进了全球公共交通系统的改善。

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pELECTRE Tri: A computational framework and Python module for probabilistic ELECTRE Tri-B multiple-criteria decision-making 一个计算框架和Python模块，用于概率ELECTRE Tri- b多标准决策

IF 1.2 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-07-01 DOI: 10.1016/j.simpa.2025.100781

Christian Ghiaus

ELECTRE Tri-B is a sorting and classification method for multiple-criteria decision-making (MCDM) in which alternatives are assigned to categories. The categories are completely ordered and defined by base (or reference) profiles. The pELECTRE Tri software implements a probabilistic extension of the ELECTRE Tri-B method designed to handle uncertainty in both the decision matrix values and the base profiles delimiting the categories. Its modular architecture enables step-by-step workflows from data input to results output, ensuring flexibility and transparency in the decision-making process. Implemented as a Python module, pELECTRE Tri requires no installation and can be executed locally or online. The software is supported by comprehensive documentation, including tutorials, how-to guides, theoretical explanations, and a user reference manual.

ELECTRE Tri-B是一种多标准决策（MCDM）的排序和分类方法，其中将备选方案分配到类别。这些类别完全由基本（或参考）配置文件排序和定义。peelectre Tri软件实现了对ELECTRE Tri- b方法的概率扩展，该方法旨在处理决策矩阵值和划分类别的基本轮廓中的不确定性。其模块化架构支持从数据输入到结果输出的分步工作流程，确保决策过程的灵活性和透明度。作为Python模块实现，pELECTRE Tri不需要安装，可以在本地或在线执行。该软件由全面的文档支持，包括教程、操作指南、理论解释和用户参考手册。

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