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An open-source reference framework for the implementation of type 3 Asset Administration Shells 一个用于实现类型3资产管理shell的开源参考框架
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-09 DOI: 10.1016/j.simpa.2025.100807
Ekaitz Hurtado , Isabel Sarachaga , Aintzane Armentia , Oskar Casquero
{"title":"An open-source reference framework for the implementation of type 3 Asset Administration Shells","authors":"Ekaitz Hurtado ,&nbsp;Isabel Sarachaga ,&nbsp;Aintzane Armentia ,&nbsp;Oskar Casquero","doi":"10.1016/j.simpa.2025.100807","DOIUrl":"10.1016/j.simpa.2025.100807","url":null,"abstract":"<div><div>Seamlessly integrating assets into distributed digital ecosystems based on Industry 4.0/5.0 demands measurable impact: lower engineering cost, interoperability and adaptability. SMIA (Self-configurable Manufacturing Industrial Agents) addresses this as an reference framework for implementing autonomous digital counterparts of assets, unifying industrial and software standards. Its dual-layer architecture combines machine-interpretable semantic modeling with distributed functional software, enhancing interoperability, flexibility, and autonomy. SMIA represents assets by executing domain-specific tasks and performing peer-to-peer communication through standardized interfaces. Following open scientific software principles, it integrates mature technologies and provides reproducible deployment artifacts (e.g., Docker), ensuring traceability and extensibility while reducing engineering effort and technological fragmentation.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100807"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
E-STRANGE: A programming support platform in academia for code ethics, quality, and efficiency E-STRANGE：学术界关于代码伦理、质量和效率的编程支持平台
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-02-11 DOI: 10.1016/j.simpa.2026.100814
Oscar Karnalim, Yehezkiel David Setiawan
{"title":"E-STRANGE: A programming support platform in academia for code ethics, quality, and efficiency","authors":"Oscar Karnalim,&nbsp;Yehezkiel David Setiawan","doi":"10.1016/j.simpa.2026.100814","DOIUrl":"10.1016/j.simpa.2026.100814","url":null,"abstract":"<div><div>In learning programming, students are often focused on the correctness of the programs. However, other important aspects should be considered, including code ethics, quality, and efficiency. This platform supports students in learning about these aspects through their own submissions. For each submission, a comprehensive report will be provided, showing instructors’ expectations, simulated similarities, obvious similarities, the likelihood of AI-generated code, code quality issues, and the likelihood of inefficiency. Gamification is applied to promote engagement. More game points will be obtained by responding to relevant quizzes and submitting original, high-quality, and efficient programs.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100814"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ICMP-Flood-SDN: A Python based machine learning application for ICMP flood DDoS attack detection in software defined networks ICMP- flood - sdn：一个基于Python的机器学习应用程序，用于在软件定义网络中检测ICMP flood DDoS攻击
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.simpa.2026.100819
Sudesh Kumar, Sunanda Gupta
{"title":"ICMP-Flood-SDN: A Python based machine learning application for ICMP flood DDoS attack detection in software defined networks","authors":"Sudesh Kumar,&nbsp;Sunanda Gupta","doi":"10.1016/j.simpa.2026.100819","DOIUrl":"10.1016/j.simpa.2026.100819","url":null,"abstract":"<div><div>ICMP-flood-SDN is an artificial intelligence based DDoS detection application that uses support vector machines (SVMs) as a machine learning model for the classification of ICMP flood DDoS traffic in software defined networks. The ICMP-flood-SDN was built using the ICMP-Flood DDoS dataset and Python-based machine learning libraries on Jupiter Notebook. The application utilizes the Mininet emulator, RYU controller, and hping3 tool to create a normal and ICMP flood traffic dataset in software defined network.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100819"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ASReview Dory: Bringing new and exciting models to ASReview LAB ASReview多利：为ASReview LAB带来新的和令人兴奋的模型
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-06 DOI: 10.1016/j.simpa.2025.100809
Timo van der Kuil , Jelle Jasper Teijema , Jonathan de Bruin , Rens van de Schoot
{"title":"ASReview Dory: Bringing new and exciting models to ASReview LAB","authors":"Timo van der Kuil ,&nbsp;Jelle Jasper Teijema ,&nbsp;Jonathan de Bruin ,&nbsp;Rens van de Schoot","doi":"10.1016/j.simpa.2025.100809","DOIUrl":"10.1016/j.simpa.2025.100809","url":null,"abstract":"<div><div>Systematic reviewing is a time-consuming process which can be accelerated through screening prioritisation via active learning. ASReview Dory enables researchers to test, validate, and apply a wide range of embedders and classifiers in systematic literature screening. It extends ASReview LAB, an open source, lightweight, and user-friendly environment with proven default models and extensibility through Python entry points. ASReview Dory adds ready-to-use transformer-based embedders, neural classifiers, and a framework for integrating custom models. Once installed, these models are directly available in ASReview LAB without additional configuration and can be systematically evaluated using the API or ASReview Makita.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100809"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MatAbaAutoRel: A MATLAB–Abaqus framework for automated reliability analysis MatAbaAutoRel：用于自动化可靠性分析的MATLAB-Abaqus框架
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-07 DOI: 10.1016/j.simpa.2025.100808
S. Malekpour , S.M. Dehghan , M.A. Najafgholipour , S. Behravesh
{"title":"MatAbaAutoRel: A MATLAB–Abaqus framework for automated reliability analysis","authors":"S. Malekpour ,&nbsp;S.M. Dehghan ,&nbsp;M.A. Najafgholipour ,&nbsp;S. Behravesh","doi":"10.1016/j.simpa.2025.100808","DOIUrl":"10.1016/j.simpa.2025.100808","url":null,"abstract":"<div><div>Probabilistic and reliability analyses utilizing finite element software are frequently constrained by manual model creation and result extraction. This study presents an open-source, MATLAB-based framework integrated with Abaqus that automates randomized model generation through Monte Carlo simulation, performs analyses, and retrieves target results via a lightweight Python script in noGUI mode. The modular tool reduces user intervention and facilitates automated variations in geometry, material properties, and loading conditions. This framework enables rapid model generation and result extraction for hundreds of analyses in seconds, significantly reducing manual effort and potential human error.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100808"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
LeukoXAI-Lite: A reusable explainable AI toolkit for federated leukemia diagnosis with visual explanations and performance analysis LeukoXAI-Lite：一个可重复使用的可解释的AI工具包，用于联合白血病诊断，具有可视化解释和性能分析
IF 1.2
Software Impacts Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.simpa.2026.100813
Khadija Parwez , Syed Irfan Sohail , Ali Raza , Mohammad Abdullah Zia
{"title":"LeukoXAI-Lite: A reusable explainable AI toolkit for federated leukemia diagnosis with visual explanations and performance analysis","authors":"Khadija Parwez ,&nbsp;Syed Irfan Sohail ,&nbsp;Ali Raza ,&nbsp;Mohammad Abdullah Zia","doi":"10.1016/j.simpa.2026.100813","DOIUrl":"10.1016/j.simpa.2026.100813","url":null,"abstract":"<div><div>LeukoXAI-Lite: A flexible and modular software framework for the interpretable diagnosis of Acute Lymphoblastic Leukemia by deep learning algorithms and visual explanation tools. The system adopts EfficientNetB3-based convolutional neural network, which is embedded in a hierarchical federated learning framework. This approach facilitates distributed model training with simulated health participants cooperating yet guarantee the private of sensitive patient information. In addition to disease categorization, our framework is equipped with a profound explainable artificial intelligence module, based upon 18 distinct visualization methods that includes saliency maps, guided backpropagation, gradient_based methods, SmoothGrad, VarGrad, SquareGrad, Grad-CAM, Grad-CAM++, HiResCAM, Respond-CAM, Score-CAM, Faster Score-CAM, oclusion sensitivity, LIME, SHAP, sobol attribution, and a fusion approach. These approaches produce visual heatmaps which highlight diagnostically important regions in microscopic images of blood cells, making the model more interpretable for clinical deployment. LeukoXAI-Lite also comes with instruments for systematic evaluation of the performance of the predictive model and explanation methods. We support common classification on based metrics (accuracy, precision, recall, F1 score, Kappa score and MCC) as well the explanation specific ones like Deletion, Insertion, Fidelity and Stability. It is implemented with open-source python libraries to be lightweight, adaptable and compatible with real-world use in medical imaging. LeukoXAI-Lite facilitates such kind of trustworthy and interpretable artificial intelligence solutions for the clinical diagnostics by means promoting transparency, reproducibility and privacy friendly learning.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100813"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-09-01 DOI: 10.1016/j.simpa.2025.100783
Hardik Ruparel, Tatsat Patel
{"title":"RAGCacheSim: A discrete-event simulator for evaluating caching strategies in Retrieval-Augmented Generation systems","authors":"Hardik Ruparel,&nbsp;Tatsat Patel","doi":"10.1016/j.simpa.2025.100783","DOIUrl":"10.1016/j.simpa.2025.100783","url":null,"abstract":"<div><div>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 <em>RAGCacheSim</em>, 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 <span>SimPy</span>, <span>FastEmbed</span>, and <span>pybloom_live</span>, it helps researchers optimize distributed RAG architectures.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100783"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-09-16 DOI: 10.1016/j.simpa.2025.100784
Baijian Wu, Gang Yu
{"title":"STFATool: A Sparse Time–Frequency Analysis Toolkit for non-stationary signals","authors":"Baijian Wu,&nbsp;Gang Yu","doi":"10.1016/j.simpa.2025.100784","DOIUrl":"10.1016/j.simpa.2025.100784","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100784"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
‘Forensic-DataFusion-Tool’: A Python-based application for exploratory forensic data analysis using merged datasets from analytical sensors “法医数据融合工具”：一个基于python的应用程序，用于探索性法医数据分析，使用来自分析传感器的合并数据集
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-11-03 DOI: 10.1016/j.simpa.2025.100799
Giorgio Felizzato , Michele Verdi , Angelo Michele Gargantini , Nico Pellegrinelli , Francesco Saverio Romolo
{"title":"‘Forensic-DataFusion-Tool’: A Python-based application for exploratory forensic data analysis using merged datasets from analytical sensors","authors":"Giorgio Felizzato ,&nbsp;Michele Verdi ,&nbsp;Angelo Michele Gargantini ,&nbsp;Nico Pellegrinelli ,&nbsp;Francesco Saverio Romolo","doi":"10.1016/j.simpa.2025.100799","DOIUrl":"10.1016/j.simpa.2025.100799","url":null,"abstract":"<div><div>Portable sensors for on-site forensic analysis have advanced significantly, enabling reliable methods for crime scene investigation. Non-destructive analytical instruments are especially useful for providing chemical information from the same specimen. Combining data from these instruments through data fusion enhances analytical responses. Data fusion merges data from different sources to improve exploratory and predictive models. No current application supports multi-dataset fusion on a single platform. To address this, we developed a Python-based ‘Forensic-DataFusion-Tool’ to merge raw and preprocessed data from multiple sensors, speeding up data fusion and enabling future machine learning updates, including classification algorithms.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100799"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MDM: An R package for causal multivariate time series tasks MDM：用于因果关系多变量时间序列任务的R包
IF 1.2
Software Impacts Pub Date : 2025-10-01 Epub Date: 2025-11-05 DOI: 10.1016/j.simpa.2025.100796
Lilia Costa , Arthur Azevedo , Michel Miler Rocha dos Santos , Diego Carvalho Nascimento
{"title":"MDM: An R package for causal multivariate time series tasks","authors":"Lilia Costa ,&nbsp;Arthur Azevedo ,&nbsp;Michel Miler Rocha dos Santos ,&nbsp;Diego Carvalho Nascimento","doi":"10.1016/j.simpa.2025.100796","DOIUrl":"10.1016/j.simpa.2025.100796","url":null,"abstract":"<div><div>The Multiregression Dynamic Model (MDM) is a framework that combines graph theory with dynamic linear models, allowing a non-Gaussian multivariate structure to emerge in the context of causal time series. Since an optimal DAG structure is an NP-hard task, this package overcomes the all-combinations search (Integer Programming Algorithm) using heuristic algorithms (like Hill Climbing). Written using R S4 Object programming, it performs learning functions (estimating network structure and its dynamic arcs), as well as includes DAG (causal) visualization, time-varying coefficients visualization, and graphical performance checks. The MDM R package is distributed under the GPL license and is accessible from GitHub.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100796"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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