Pub Date : 2025-04-22DOI: 10.1016/j.simpa.2025.100754
Savaş Öztürk
In hazardous scenarios with life-threatening risks or in costly or time-consuming situations, autonomous systems must first be tested in a computer environment. This paper introduces MRTASim software developed for multi-robot task allocation experiments. MRTASim uses JADE (Java Agent-Based Development Environment) and allows comparing variations of functions such as path planning, bid valuation, and task scheduling. It displays the results on maps prepared in the environment editor, and automated experiments ensure the accuracy of the results. Academic studies produced with the software, which is constantly updated with new methods and parameters, have been presented at conferences and published in journals.
{"title":"MRTASim: An agent-based multi-robot task allocation simulation","authors":"Savaş Öztürk","doi":"10.1016/j.simpa.2025.100754","DOIUrl":"10.1016/j.simpa.2025.100754","url":null,"abstract":"<div><div>In hazardous scenarios with life-threatening risks or in costly or time-consuming situations, autonomous systems must first be tested in a computer environment. This paper introduces MRTASim software developed for multi-robot task allocation experiments. MRTASim uses JADE (Java Agent-Based Development Environment) and allows comparing variations of functions such as path planning, bid valuation, and task scheduling. It displays the results on maps prepared in the environment editor, and automated experiments ensure the accuracy of the results. Academic studies produced with the software, which is constantly updated with new methods and parameters, have been presented at conferences and published in journals.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100754"},"PeriodicalIF":1.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917551","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}
Rapid estimation of building loss and fatalities is essential for post-earthquake response. The Post-Earthquake Building Damage and Fatality Estimation System (Ebdfes) was developed using Python to capture real-time earthquake events and automatically trigger assessments of building damage and fatalities. Specifically, Ebdfes considers building damage as a multidimensional correlated variable and employs sampling techniques to simulate potential earthquake impacts. By integrating Kriging interpolation and multithreading techniques, the system significantly enhances computational efficiency. Furthermore, Ebdfes provides visualization of estimation results, offering valuable support for decision-making in emergency management and capital allocation for catastrophe insurance companies.
{"title":"Ebdfes: Post-earthquake building damage and fatality estimation system","authors":"Dengke Zhao, Siran Yang, Jianming Wang, Ji’an Liao, Zhaoyan Li, Zifa Wang","doi":"10.1016/j.simpa.2025.100749","DOIUrl":"10.1016/j.simpa.2025.100749","url":null,"abstract":"<div><div>Rapid estimation of building loss and fatalities is essential for post-earthquake response. The Post-Earthquake Building Damage and Fatality Estimation System (Ebdfes) was developed using Python to capture real-time earthquake events and automatically trigger assessments of building damage and fatalities. Specifically, Ebdfes considers building damage as a multidimensional correlated variable and employs sampling techniques to simulate potential earthquake impacts. By integrating Kriging interpolation and multithreading techniques, the system significantly enhances computational efficiency. Furthermore, Ebdfes provides visualization of estimation results, offering valuable support for decision-making in emergency management and capital allocation for catastrophe insurance companies.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100749"},"PeriodicalIF":1.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870754","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}
Structures based on Triply periodic minimal surfaces (TPMS) are employed in catalysis, energy storage, and tissue engineering for biomedical applications, among others. They excel in mechanical performance, surface area, and transport properties compared to traditional lattice structures. This work introduces a versatile tool designed to facilitate the creation and manipulation of TPMS-based structures. It provides total control over lattice properties, for 3D printing and Finite Element simulation purposes. By operating within a Python environment, ensuring accessibility and compatibility with both online and offline workflows, LisbonTPMS-tool aims to become a valuable resource to employ TPMS-based structures in practical applications.
{"title":"LISBON TPMS TOOL: An open-source tool for the design of TPMS structures for engineering applications","authors":"J.E. Santos , R.B. Ruben , P.R. Fernandes , A.P.G. Castro","doi":"10.1016/j.simpa.2025.100747","DOIUrl":"10.1016/j.simpa.2025.100747","url":null,"abstract":"<div><div>Structures based on Triply periodic minimal surfaces (TPMS) are employed in catalysis, energy storage, and tissue engineering for biomedical applications, among others. They excel in mechanical performance, surface area, and transport properties compared to traditional lattice structures. This work introduces a versatile tool designed to facilitate the creation and manipulation of TPMS-based structures. It provides total control over lattice properties, for 3D printing and Finite Element simulation purposes. By operating within a Python environment, ensuring accessibility and compatibility with both online and offline workflows, LisbonTPMS-tool aims to become a valuable resource to employ TPMS-based structures in practical applications.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100747"},"PeriodicalIF":1.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807296","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 : 2025-03-27DOI: 10.1016/j.simpa.2025.100746
Lamis Ali Hussein , Ziad Saeed Mohammed
The ArSLR-ML is a real-time interactive application that uses multi-class Support Vector Machines (SVM) machine learning applied in the classification procedure and MediaPipe in the feature extraction procedure to recognize static Arabic sign language gestures, focusing on numbers and letters and translating them into text and Arabic audio output. The ArSLR-ML was built within the PyCharm IDE using Python with a graphical user interface (GUI), thereby allowing for effective recognition of gestures. The application utilizes the laptop camera and GUI to capture hand gestures to create dataset for machine learning models and implement them in real time.
{"title":"ArSLR-ML: A Python-based machine learning application for arabic sign language recognition","authors":"Lamis Ali Hussein , Ziad Saeed Mohammed","doi":"10.1016/j.simpa.2025.100746","DOIUrl":"10.1016/j.simpa.2025.100746","url":null,"abstract":"<div><div>The ArSLR-ML is a real-time interactive application that uses multi-class Support Vector Machines (SVM) machine learning applied in the classification procedure and MediaPipe in the feature extraction procedure to recognize static Arabic sign language gestures, focusing on numbers and letters and translating them into text and Arabic audio output. The ArSLR-ML was built within the PyCharm IDE using Python with a graphical user interface (GUI), thereby allowing for effective recognition of gestures. The application utilizes the laptop camera and GUI to capture hand gestures to create dataset for machine learning models and implement them in real time.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100746"},"PeriodicalIF":1.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.simpa.2025.100742
Zhehan Jiang , Shicong Feng
Driven by the trending multi-agent system (MAS) that harnesses collective intelligence from numerous large language models (LLMs), UsmleGPT is a Python-based application designed to enhance LLM-generated content tailored for the USMLE Step 1 scenario. The MAS aligns with the NBME’s framework, incorporating advanced prompt strategies to guide each LLM through their respective tasks and specialties. This enables UsmleGPT to surpass conventional practices in automating item generation. Beyond the original script, a freely accessible, user-friendly website complements the tool, facilitating its adoption. UsmleGPT represents a breakthrough in medical education, transforming medical exam preparation and setting new standards for high-stakes medical assessments.
{"title":"UsmleGPT: An AI application for developing MCQs via multi-agent system","authors":"Zhehan Jiang , Shicong Feng","doi":"10.1016/j.simpa.2025.100742","DOIUrl":"10.1016/j.simpa.2025.100742","url":null,"abstract":"<div><div>Driven by the trending multi-agent system (MAS) that harnesses collective intelligence from numerous large language models (LLMs), UsmleGPT is a Python-based application designed to enhance LLM-generated content tailored for the USMLE Step 1 scenario. The MAS aligns with the NBME’s framework, incorporating advanced prompt strategies to guide each LLM through their respective tasks and specialties. This enables UsmleGPT to surpass conventional practices in automating item generation. Beyond the original script, a freely accessible, user-friendly website complements the tool, facilitating its adoption. UsmleGPT represents a breakthrough in medical education, transforming medical exam preparation and setting new standards for high-stakes medical assessments.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100742"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.simpa.2025.100741
Alma Karen Bañuelos-Mezquitan, Carlos Said Silva-Chacon, Fernando Castro-Galán, Arturo Guzmán-Vázquez, Israel Román-Godínez, Ricardo A. Salido-Ruiz, Sulema Torres-Ramos
SignAPROS is a cost-effective hardware–software system for signal acquisition, featuring modules for database management, protocol configuration, and machine learning-based analysis. It supports up to four Electromyography bi-polar channels and various sensors to measure heart rate, temperature, inclination, and galvanic skin response.
The system has already been used in the implementation of a protocol aimed at capturing electrical signals from facial and neck muscles to detect mispronunciation in a second language supporting a master’s project.
With a user-friendly interface, SignAPROS enables users to conduct bio-signal acquisition, analyze data, and visualize results efficiently, making it a versatile and accessible tool for scientific studies.
{"title":"SignAPROS: An integrated hardware and software system for acquisition, processing, and analysis of bio-signals","authors":"Alma Karen Bañuelos-Mezquitan, Carlos Said Silva-Chacon, Fernando Castro-Galán, Arturo Guzmán-Vázquez, Israel Román-Godínez, Ricardo A. Salido-Ruiz, Sulema Torres-Ramos","doi":"10.1016/j.simpa.2025.100741","DOIUrl":"10.1016/j.simpa.2025.100741","url":null,"abstract":"<div><div>SignAPROS is a cost-effective hardware–software system for signal acquisition, featuring modules for database management, protocol configuration, and machine learning-based analysis. It supports up to four Electromyography bi-polar channels and various sensors to measure heart rate, temperature, inclination, and galvanic skin response.</div><div>The system has already been used in the implementation of a protocol aimed at capturing electrical signals from facial and neck muscles to detect mispronunciation in a second language supporting a master’s project.</div><div>With a user-friendly interface, SignAPROS enables users to conduct bio-signal acquisition, analyze data, and visualize results efficiently, making it a versatile and accessible tool for scientific studies.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100741"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present VCoFWMVIFCM, an open-source Python implementation of a multi-view fuzzy clustering algorithm based on Intuitionistic Fuzzy c-Means (IFCM). The method integrates adaptive view, feature, and sample weighting to account for varying importance and reduce outlier effects. Local neighborhood information enhances noise resistance, while a density-based initialization ensures stable centroid selection. These mechanisms collectively improve clustering robustness and accuracy for multi-view data. The modular implementation allows flexible execution and reproducibility, addressing real-world applications where multiple data perspectives exist. The code is publicly accessible on GitHub under the MIT license.
{"title":"VCoFWMVIFCM: An open-source code for viewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering","authors":"Amin Golzari Oskouei , Negin Samadi , Asgarali Bouyer , Jafar Tanha","doi":"10.1016/j.simpa.2025.100743","DOIUrl":"10.1016/j.simpa.2025.100743","url":null,"abstract":"<div><div>We present VCoFWMVIFCM, an open-source Python implementation of a multi-view fuzzy clustering algorithm based on Intuitionistic Fuzzy c-Means (IFCM). The method integrates adaptive view, feature, and sample weighting to account for varying importance and reduce outlier effects. Local neighborhood information enhances noise resistance, while a density-based initialization ensures stable centroid selection. These mechanisms collectively improve clustering robustness and accuracy for multi-view data. The modular implementation allows flexible execution and reproducibility, addressing real-world applications where multiple data perspectives exist. The code is publicly accessible on GitHub under the MIT license.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100743"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates a novel approach to plant disease classification, addressing cases where symptoms are not visually apparent. Traditional machine learning methods, reliant on observable symptoms, face challenges such as limited training data, high costs, and low interpretability. To overcome these limitations, a spectroscopy-based classification technique was developed. Experimental data, collected over 15 months at Anand Agriculture University, Gujarat, and Charotar University Space Research Centre, utilized spectral signatures (400–1000 nm) to detect mango diseases. The SSTAS Software, developed with a fine-tuned deep learning model, Deep-Spectro, demonstrated superior accuracy using an 80:20 training-to-testing ratio, surpassing existing models reported in prior research.
{"title":"Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS)","authors":"Hardik Jayswal, Hetvi Desai, Hasti Vakani, Mithil Mistry, Nilesh Dubey","doi":"10.1016/j.simpa.2025.100744","DOIUrl":"10.1016/j.simpa.2025.100744","url":null,"abstract":"<div><div>This paper investigates a novel approach to plant disease classification, addressing cases where symptoms are not visually apparent. Traditional machine learning methods, reliant on observable symptoms, face challenges such as limited training data, high costs, and low interpretability. To overcome these limitations, a spectroscopy-based classification technique was developed. Experimental data, collected over 15 months at Anand Agriculture University, Gujarat, and Charotar University Space Research Centre, utilized spectral signatures (400–1000 nm) to detect mango diseases. The SSTAS Software, developed with a fine-tuned deep learning model, Deep-Spectro, demonstrated superior accuracy using an 80:20 training-to-testing ratio, surpassing existing models reported in prior research.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100744"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.simpa.2025.100745
Aleix Seguí , Arantza Ugalde , Juan José Egozcue
hvarma is a Python software for estimating the horizontal-to-vertical (H/V) spectral ratio through seismic ambient vibration measurements. It employs a parametric approach to model the H/V transfer function using an AutoRegressive Moving Average (ARMA) filter. Compared to traditional methods, this technique enhances accuracy and reliability in spectral estimates, determining the ground fundamental resonance frequency with high spectral resolution, which is important for engineering geology projects. The program inverts to find optimal filter coefficients and computes coherence between horizontal and vertical components, generating H/V transfer function visualizations across both negative and positive frequencies. Results are saved as image and text files.
{"title":"hvarma: Autoregressive moving average model of microtremor H/V spectral ratio","authors":"Aleix Seguí , Arantza Ugalde , Juan José Egozcue","doi":"10.1016/j.simpa.2025.100745","DOIUrl":"10.1016/j.simpa.2025.100745","url":null,"abstract":"<div><div>hvarma is a Python software for estimating the horizontal-to-vertical (<em>H</em>/<em>V</em>) spectral ratio through seismic ambient vibration measurements. It employs a parametric approach to model the <em>H</em>/<em>V</em> transfer function using an AutoRegressive Moving Average (ARMA) filter. Compared to traditional methods, this technique enhances accuracy and reliability in spectral estimates, determining the ground fundamental resonance frequency with high spectral resolution, which is important for engineering geology projects. The program inverts to find optimal filter coefficients and computes coherence between horizontal and vertical components, generating <em>H</em>/<em>V</em> transfer function visualizations across both negative and positive frequencies. Results are saved as image and text files.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100745"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.simpa.2024.100740
Samir Brahim Belhaouari , Ashhadul Islam , Khelil Kassoul , Ala Al-Fuqaha , Abdesselam Bouzerdoum
KNNOR-Reg is a Python package designed to address the challenge of imbalanced regression. While popular Python packages exist for tackling imbalanced classification, support for imbalanced regression remains limited. Imbalanced regression involves the underrepresentation of important ranges within a continuous target variable. KNNOR-Reg implements an oversampling technique that generates synthetic samples through interpolation between minority class samples and their nearest neighbors. The labels for synthetic samples are computed based on the inverse distance-weighted average of the nearest neighbors’ labels. KNNOR-Reg offers a user-friendly and extensible Python implementation for oversampling imbalanced regression data, aiming to reduce regressor bias and enhance model outcomes.
{"title":"KNNOR-Reg: A python package for oversampling in imbalanced regression","authors":"Samir Brahim Belhaouari , Ashhadul Islam , Khelil Kassoul , Ala Al-Fuqaha , Abdesselam Bouzerdoum","doi":"10.1016/j.simpa.2024.100740","DOIUrl":"10.1016/j.simpa.2024.100740","url":null,"abstract":"<div><div>KNNOR-Reg is a Python package designed to address the challenge of imbalanced regression. While popular Python packages exist for tackling imbalanced classification, support for imbalanced regression remains limited. Imbalanced regression involves the underrepresentation of important ranges within a continuous target variable. KNNOR-Reg implements an oversampling technique that generates synthetic samples through interpolation between minority class samples and their nearest neighbors. The labels for synthetic samples are computed based on the inverse distance-weighted average of the nearest neighbors’ labels. KNNOR-Reg offers a user-friendly and extensible Python implementation for oversampling imbalanced regression data, aiming to reduce regressor bias and enhance model outcomes.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100740"},"PeriodicalIF":1.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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