Pub Date : 2025-09-16DOI: 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.
{"title":"STFATool: A Sparse Time–Frequency Analysis Toolkit for non-stationary signals","authors":"Baijian Wu, 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":"2025-09-16","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}
Pub Date : 2025-09-01DOI: 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.
{"title":"RAGCacheSim: A discrete-event simulator for evaluating caching strategies in Retrieval-Augmented Generation systems","authors":"Hardik Ruparel, 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":"2025-09-01","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}
Pub Date : 2025-07-01DOI: 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.
{"title":"FaultNet-Sim: A C++ simulator for failure-prone wireless sensor networks","authors":"Santana Yuda Pradata , Muhammad Alfian Amrizal , Ahmad Ridwan Tresna Nugraha , Reza Pulungan","doi":"10.1016/j.simpa.2025.100776","DOIUrl":"10.1016/j.simpa.2025.100776","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100776"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632605","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}
Pub Date : 2025-07-01DOI: 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.
{"title":"A tool for measuring program comprehensibility using readability-driven metrics","authors":"Md. Masudur Rahman, Zenun Chowdhury, Raqeebir Rab","doi":"10.1016/j.simpa.2025.100782","DOIUrl":"10.1016/j.simpa.2025.100782","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100782"},"PeriodicalIF":1.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917254","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}
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.
{"title":"GPS-2-GTFS: A Python package to process and transform raw GPS data of public transit to GTFS format","authors":"Shiveswarran Ratneswaran , Uthayasanker Thayasivam , Sivakumar Thillaiambalam","doi":"10.1016/j.simpa.2025.100780","DOIUrl":"10.1016/j.simpa.2025.100780","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100780"},"PeriodicalIF":1.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756883","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}
Pub Date : 2025-07-01DOI: 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.
{"title":"pELECTRE Tri: A computational framework and Python module for probabilistic ELECTRE Tri-B multiple-criteria decision-making","authors":"Christian Ghiaus","doi":"10.1016/j.simpa.2025.100781","DOIUrl":"10.1016/j.simpa.2025.100781","url":null,"abstract":"<div><div>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 <em>pELECTRE Tri</em> 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, <em>pELECTRE Tri</em> 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.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100781"},"PeriodicalIF":1.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893306","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}
Pub Date : 2025-07-01DOI: 10.1016/j.simpa.2025.100777
Nimmy S. John, ChangHwan Lee
Single-molecule RNA imaging has been made possible with the recent advances in microscopy methods. However, systematic analysis of these images has been challenging due to the highly variable background noise, even after applying sophisticated computational clearing methods. Here, we describe our custom MATLAB scripts that allow us to detect both nuclear nascent transcripts at the active transcription sites (ATS) and mature cytoplasmic mRNAs with single-molecule precision and reconstruct the tissue in 3D for further analysis. Our codes were initially optimized for the C. elegans germline but were designed to be broadly applicable to other species and tissue types.
{"title":"smFISH_batchRun: A smFISH image processing tool for single-molecule RNA Detection and 3D reconstruction","authors":"Nimmy S. John, ChangHwan Lee","doi":"10.1016/j.simpa.2025.100777","DOIUrl":"10.1016/j.simpa.2025.100777","url":null,"abstract":"<div><div>Single-molecule RNA imaging has been made possible with the recent advances in microscopy methods. However, systematic analysis of these images has been challenging due to the highly variable background noise, even after applying sophisticated computational clearing methods. Here, we describe our custom MATLAB scripts that allow us to detect both nuclear nascent transcripts at the active transcription sites (ATS) and mature cytoplasmic mRNAs with single-molecule precision and reconstruct the tissue in 3D for further analysis. Our codes were initially optimized for the <em>C. elegans</em> germline but were designed to be broadly applicable to other species and tissue types.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100777"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595977","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}
Pub Date : 2025-07-01DOI: 10.1016/j.simpa.2025.100778
Mehmet Anil Akbay , Christian Blum
This paper presents a web-based instance generator for Electric Vehicle Routing Problems (EVRP) with Road Junctions and Road Types, using OpenStreetMap data. Users define an area, specify network components (depots, customers, charging stations, junctions), and customize vehicle parameters. The React-based frontend enables configuration, visualization, and queries, while the Flask backend processes road networks, classifies road types, and assigns demand and service times. A RESTful API ensures real-time instance generation. Generated instances can be downloaded as text-based datasets and interactive visualizations. The tool is open-source and contributes to the area of sustainable transportation by enabling scenario-based EVRP experimentation.
{"title":"EVRPGen: A web-based instance generator for the electric vehicle routing problem with road junctions and road types","authors":"Mehmet Anil Akbay , Christian Blum","doi":"10.1016/j.simpa.2025.100778","DOIUrl":"10.1016/j.simpa.2025.100778","url":null,"abstract":"<div><div>This paper presents a web-based instance generator for Electric Vehicle Routing Problems (EVRP) with Road Junctions and Road Types, using OpenStreetMap data. Users define an area, specify network components (depots, customers, charging stations, junctions), and customize vehicle parameters. The React-based frontend enables configuration, visualization, and queries, while the Flask backend processes road networks, classifies road types, and assigns demand and service times. A RESTful API ensures real-time instance generation. Generated instances can be downloaded as text-based datasets and interactive visualizations. The tool is open-source and contributes to the area of sustainable transportation by enabling scenario-based EVRP experimentation.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100778"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549445","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}
Effective feature extraction from medical images is important for improving disease detection and assessment. Conventional linear transforms, such as the Radon transform, may not fully capture subtle and complex nonlinear features present in medical imaging data. To address these limitations, we present RadEx, a nonlinear extension of the Radon transform. RadEx employs parameterized nonlinear projections to facilitate the extraction of additional nonlinear feature representations from imaging modalities such as chest X-rays and retinal fundus images. Initial evaluations indicate that RadEx can offer improvements over traditional Radon transforms and raw image-based approaches in disease classification tasks, including COVID-19 detection from chest X-rays and diabetic retinopathy grading from retinal images. By capturing more complex structural and nonlinear patterns, RadEx may support enhanced diagnostic performance and illustrates the potential benefit of integrating adaptive mathematical transformations into medical imaging workflows.
{"title":"RadEx: An open source python package for nonlinear radon transformation","authors":"Farida Mohsen, Ashhadul Islam, Firas Mohsen, Zubair Shah, Samir Brahim Belhaouari","doi":"10.1016/j.simpa.2025.100779","DOIUrl":"10.1016/j.simpa.2025.100779","url":null,"abstract":"<div><div>Effective feature extraction from medical images is important for improving disease detection and assessment. Conventional linear transforms, such as the Radon transform, may not fully capture subtle and complex nonlinear features present in medical imaging data. To address these limitations, we present RadEx, a nonlinear extension of the Radon transform. RadEx employs parameterized nonlinear projections to facilitate the extraction of additional nonlinear feature representations from imaging modalities such as chest X-rays and retinal fundus images. Initial evaluations indicate that RadEx can offer improvements over traditional Radon transforms and raw image-based approaches in disease classification tasks, including COVID-19 detection from chest X-rays and diabetic retinopathy grading from retinal images. By capturing more complex structural and nonlinear patterns, RadEx may support enhanced diagnostic performance and illustrates the potential benefit of integrating adaptive mathematical transformations into medical imaging workflows.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100779"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713654","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}
Pub Date : 2025-06-17DOI: 10.1016/j.simpa.2025.100774
Ahmed Alaff , Çelebi Uluyol
Conventional vocabulary assessments emphasize precision rather than hesitation and rapidity. A machine learning system was developed utilizing behavioral analysis and linguistic insights to identify vocabulary gaps in Turkish language learners. This system integrates hesitation counts, reaction times, and answer attempts with word difficulty and thematic elements. Vocabulary strength was computed using a rule-based equation derived from behavioral indications. With 89% accuracy, 86% precision, 91% recall, and an 88% F1 score, the model showed better performance than the linear and Poisson kernel alternatives. By effectively separating complex interactions, the RBF kernel minimizes unnecessary actions and ensures accurate identification of real shortages.
{"title":"TR-VABML: Enhancing Turkish vocabulary acquisition through adaptive machine learning classification","authors":"Ahmed Alaff , Çelebi Uluyol","doi":"10.1016/j.simpa.2025.100774","DOIUrl":"10.1016/j.simpa.2025.100774","url":null,"abstract":"<div><div>Conventional vocabulary assessments emphasize precision rather than hesitation and rapidity. A machine learning system was developed utilizing behavioral analysis and linguistic insights to identify vocabulary gaps in Turkish language learners. This system integrates hesitation counts, reaction times, and answer attempts with word difficulty and thematic elements. Vocabulary strength was computed using a rule-based equation derived from behavioral indications. With 89% accuracy, 86% precision, 91% recall, and an 88% F1 score, the model showed better performance than the linear and Poisson kernel alternatives. By effectively separating complex interactions, the RBF kernel minimizes unnecessary actions and ensures accurate identification of real shortages.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100774"},"PeriodicalIF":1.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313054","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}
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