Pub Date : 2025-06-10DOI: 10.1016/j.simpa.2025.100771
Sait Alp , Sara Akan , Taymaz Akan , Mohammad Alfrad Nobel Bhuiyan
This study introduces a reproducible pipeline for classifying Alzheimer’s Disease from structural brain MRI utilizing a joint transformer architecture that integrates Vision Transformer and Time-Series Transformer models. The proposed framework uses pre-trained ViT for feature extraction from 2D slices of MRI volumes, followed by sequential modeling with a transformer-based classifier to capture inter-slice dependencies. The method is evaluated on the ADNI dataset, involving both binary (AD vs. NC) and multiclass (AD, MCI, NC) classification tasks across axial, sagittal, and coronal planes.
本研究介绍了一种可重复的管道，利用集成视觉变压器和时间序列变压器模型的联合变压器架构，从结构脑MRI中对阿尔茨海默病进行分类。所提出的框架使用预训练的ViT从MRI体积的二维切片中提取特征，然后使用基于变压器的分类器进行顺序建模以捕获切片间的依赖关系。该方法在ADNI数据集上进行了评估，包括二元（AD vs. NC）和多类别（AD， MCI， NC）跨轴向，矢状面和冠状面分类任务。
{"title":"MRI-based Alzheimer’s disease classification using Vision Transformer and time-series transformer: A step-by-step guide","authors":"Sait Alp , Sara Akan , Taymaz Akan , Mohammad Alfrad Nobel Bhuiyan","doi":"10.1016/j.simpa.2025.100771","DOIUrl":"10.1016/j.simpa.2025.100771","url":null,"abstract":"<div><div>This study introduces a reproducible pipeline for classifying Alzheimer’s Disease from structural brain MRI utilizing a joint transformer architecture that integrates Vision Transformer and Time-Series Transformer models. The proposed framework uses pre-trained ViT for feature extraction from 2D slices of MRI volumes, followed by sequential modeling with a transformer-based classifier to capture inter-slice dependencies. The method is evaluated on the ADNI dataset, involving both binary (AD vs. NC) and multiclass (AD, MCI, NC) classification tasks across axial, sagittal, and coronal planes.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100771"},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298005","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 : 2025-06-10DOI: 10.1016/j.simpa.2025.100775
S. Herold-Garcia , H.L. Varona-Gonzalez , X. Gual-Arnau
Shape analysis within shape space provides a robust framework for examining geometric properties of objects, enabling comparisons invariant to translation, rotation, and scaling. A key task is computing geodesic distances between shapes, which quantify similarity but are computationally intensive due to the need for exhaustive parameterization searches. Recent advancements propose heuristic methods to simplify these computations, such as fixing parameterizations based on the major axis of shapes, significantly reducing computational costs while maintaining high accuracy (e.g., 96.03% in erythrocyte classification). This article introduces a software tool that leverages this heuristic to efficiently compute shape-space distances, aligning shapes considering their major axis, and using templates like circles and ellipses. The tool accelerates morphological analysis, making it ideal for high performance applications in fields like biology and medicine. By streamlining the computation of geodesic distances between shapes and enabling rapid retrieval of information, this software improves research workflows and supports the study of shape-dependent features in diverse fields from cellular morphology to diagnostic hematology.
{"title":"GD4Shapes: Geodesic distance with fixed parameterization for 2D Shapes","authors":"S. Herold-Garcia , H.L. Varona-Gonzalez , X. Gual-Arnau","doi":"10.1016/j.simpa.2025.100775","DOIUrl":"10.1016/j.simpa.2025.100775","url":null,"abstract":"<div><div>Shape analysis within shape space provides a robust framework for examining geometric properties of objects, enabling comparisons invariant to translation, rotation, and scaling. A key task is computing geodesic distances between shapes, which quantify similarity but are computationally intensive due to the need for exhaustive parameterization searches. Recent advancements propose heuristic methods to simplify these computations, such as fixing parameterizations based on the major axis of shapes, significantly reducing computational costs while maintaining high accuracy (e.g., 96.03% in erythrocyte classification). This article introduces a software tool that leverages this heuristic to efficiently compute shape-space distances, aligning shapes considering their major axis, and using templates like circles and ellipses. The tool accelerates morphological analysis, making it ideal for high performance applications in fields like biology and medicine. By streamlining the computation of geodesic distances between shapes and enabling rapid retrieval of information, this software improves research workflows and supports the study of shape-dependent features in diverse fields from cellular morphology to diagnostic hematology.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100775"},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313053","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}
ESCOX, also known as ESCOSkillExtractor, is an open-source, non-proprietary tool for identifying and classifying skills, skillsets, and occupations from job postings and general text. It utilizes the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy to structure extraction, addressing the need for taxonomy-aligned skill identification in unstructured labor market data. Developed within the SKILLAB EU Horizon project, ESCOX combines LLMs and text embeddings to map content to standardized categories. It offers a user-friendly graphical interface for researchers, educators, and HR professionals, supporting skills gap analysis, training, recruitment, and policy planning, and contributing to the development of a skills-based economy.
ESCOX，也被称为ESCOSkillExtractor，是一个开源、非专有的工具，用于从招聘启事和一般文本中识别和分类技能、技能集和职业。它利用欧洲技能、能力、资格和职业（ESCO）分类法进行结构提取，解决了在非结构化劳动力市场数据中对分类一致的技能识别的需求。ESCOX是在SKILLAB EU Horizon项目中开发的，它结合了法学硕士和文本嵌入，将内容映射到标准化类别。它为研究人员、教育工作者和人力资源专业人员提供了一个用户友好的图形界面，支持技能差距分析、培训、招聘和政策规划，并为技能经济的发展做出贡献。
{"title":"ESCOX: A tool for skill and occupation extraction using LLMs from unstructured text","authors":"Dimitrios Christos Kavargyris , Konstantinos Georgiou , Eleanna Papaioannou , Konstantinos Petrakis , Nikolaos Mittas , Lefteris Angelis","doi":"10.1016/j.simpa.2025.100772","DOIUrl":"10.1016/j.simpa.2025.100772","url":null,"abstract":"<div><div>ESCOX, also known as ESCOSkillExtractor, is an open-source, non-proprietary tool for identifying and classifying skills, skillsets, and occupations from job postings and general text. It utilizes the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy to structure extraction, addressing the need for taxonomy-aligned skill identification in unstructured labor market data. Developed within the SKILLAB EU Horizon project, ESCOX combines LLMs and text embeddings to map content to standardized categories. It offers a user-friendly graphical interface for researchers, educators, and HR professionals, supporting skills gap analysis, training, recruitment, and policy planning, and contributing to the development of a skills-based economy.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100772"},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263644","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 : 2025-05-22DOI: 10.1016/j.simpa.2025.100769
Saeid Bayat, James T. Allison
In a world increasingly reliant on technologies that sense and respond to their environment – from thermostats to energy grids – predictive capabilities are critical. However, uncertainties and complexity often hinder the adoption of advanced strategies like Model Predictive Control (MPC), leading many industries to rely on simpler, less effective methods. This paper presents a practical, open-source software tool based on the Legendre–Gauss–Radau pseudospectral method, designed to streamline MPC implementation. The software handles dynamics, constraints, and objectives efficiently while supporting black-box systems. A case study in this paper demonstrates its effectiveness, with additional examples in the supplementary material validating its versatility.
{"title":"A practical open-source approach to Model Predictive Control using the Legendre–Gauss–Radau pseudospectral method","authors":"Saeid Bayat, James T. Allison","doi":"10.1016/j.simpa.2025.100769","DOIUrl":"10.1016/j.simpa.2025.100769","url":null,"abstract":"<div><div>In a world increasingly reliant on technologies that sense and respond to their environment – from thermostats to energy grids – predictive capabilities are critical. However, uncertainties and complexity often hinder the adoption of advanced strategies like Model Predictive Control (MPC), leading many industries to rely on simpler, less effective methods. This paper presents a practical, open-source software tool based on the Legendre–Gauss–Radau pseudospectral method, designed to streamline MPC implementation. The software handles dynamics, constraints, and objectives efficiently while supporting black-box systems. A case study in this paper demonstrates its effectiveness, with additional examples in the supplementary material validating its versatility.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100769"},"PeriodicalIF":1.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123239","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 : 2025-05-19DOI: 10.1016/j.simpa.2025.100767
Azeem Ahmad , Muhammad Rashid Naeem , Yasir Javed , Mohammad Akour
This paper presents Eiffel-Store, an open-source tool for real-time traceability in Continuous Integration (CI) pipelines. Unlike traditional batch visualization tools, Eiffel-Store dynamically visualizes live Eiffel events from CI tools (e.g., Jenkins) using MongoDB and Meteor.js. It supports incremental updates, enabling users to trace faults back to specific commits across the pipeline. Events can be streamed from RabbitMQ or added manually, offering flexibility for diverse workflows. By connecting code changes to final product faults, Eiffel-Store improves transparency, debugging, and quality assurance. The tool has been tested with industry partners and is available publicly to promote adoption and further development.
{"title":"Reconstructing software evolution: Traceability from code commits to fault manifestation in CI","authors":"Azeem Ahmad , Muhammad Rashid Naeem , Yasir Javed , Mohammad Akour","doi":"10.1016/j.simpa.2025.100767","DOIUrl":"10.1016/j.simpa.2025.100767","url":null,"abstract":"<div><div>This paper presents <em>Eiffel-Store</em>, an open-source tool for real-time traceability in Continuous Integration (CI) pipelines. Unlike traditional batch visualization tools, Eiffel-Store dynamically visualizes live Eiffel events from CI tools (e.g., Jenkins) using MongoDB and Meteor.js. It supports incremental updates, enabling users to trace faults back to specific commits across the pipeline. Events can be streamed from RabbitMQ or added manually, offering flexibility for diverse workflows. By connecting code changes to final product faults, Eiffel-Store improves transparency, debugging, and quality assurance. The tool has been tested with industry partners and is available publicly to promote adoption and further development.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100767"},"PeriodicalIF":1.3,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123220","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 : 2025-05-15DOI: 10.1016/j.simpa.2025.100768
Muhamad Keenan Ario , Muhammad Fikri Hasani , Khairatul Balqis , Messya Carment
Extended reality in education has advanced, offering safe, immersive simulations. Agriculture, a key area, lacks urban exposure. HoloFarm, a VR-based farming simulation, addresses this gap using Unity and C#. It integrates physical movement, joystick navigation, and spatial audio for crop cultivation. Evaluated with 27 urban users via the Igroup Presence Questionnaire, it showed strong spatial (M=5.59) and general presence (M=5.81), though realism (M=4.10) and involvement (M=4.77). Future updates will enhance realism and enable collaborative learning, bridging theoretical and practical agricultural knowledge.
{"title":"HoloFarm: Enhancing agricultural learning through immersive technology","authors":"Muhamad Keenan Ario , Muhammad Fikri Hasani , Khairatul Balqis , Messya Carment","doi":"10.1016/j.simpa.2025.100768","DOIUrl":"10.1016/j.simpa.2025.100768","url":null,"abstract":"<div><div>Extended reality in education has advanced, offering safe, immersive simulations. Agriculture, a key area, lacks urban exposure. HoloFarm, a VR-based farming simulation, addresses this gap using Unity and C#. It integrates physical movement, joystick navigation, and spatial audio for crop cultivation. Evaluated with 27 urban users via the Igroup Presence Questionnaire, it showed strong spatial (M=5.59) and general presence (M=5.81), though realism (M=4.10) and involvement (M=4.77). Future updates will enhance realism and enable collaborative learning, bridging theoretical and practical agricultural knowledge.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100768"},"PeriodicalIF":1.3,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144105934","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 : 2025-05-05DOI: 10.1016/j.simpa.2025.100761
Achour Khaoula , Lachgar Mohamed , Elloubab Aya , Ait Ouahda Younes , Laanaoui My Driss , Ourahay Mustapha
EduXgame is a gamified mobile application designed to enhance the learning experience of secondary education students. The application integrates AI-driven content generation, gamification features, and interactive learning tools such as quizzes, flipcards, and matching games. It provides educators with a web interface to upload chapters, which are processed by an AI model to generate learning material dynamically. eduXgame transforms traditional learning methods into engaging, competitive, and interactive experiences, making education more accessible and enjoyable for students.
{"title":"EduXgame: Gamified learning for secondary education","authors":"Achour Khaoula , Lachgar Mohamed , Elloubab Aya , Ait Ouahda Younes , Laanaoui My Driss , Ourahay Mustapha","doi":"10.1016/j.simpa.2025.100761","DOIUrl":"10.1016/j.simpa.2025.100761","url":null,"abstract":"<div><div>EduXgame is a gamified mobile application designed to enhance the learning experience of secondary education students. The application integrates AI-driven content generation, gamification features, and interactive learning tools such as quizzes, flipcards, and matching games. It provides educators with a web interface to upload chapters, which are processed by an AI model to generate learning material dynamically. eduXgame transforms traditional learning methods into engaging, competitive, and interactive experiences, making education more accessible and enjoyable for students.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100761"},"PeriodicalIF":1.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937160","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 : 2025-05-05DOI: 10.1016/j.simpa.2025.100760
Jinhang Jiang , Ben Liu , Weiyao Peng , Karthik Srinivasan
TextRegress is an open-source Python package that leverages state-of-the-art deep learning techniques to perform regression analysis on long-form text data. Departing from conventional text mining tools that are confined to classification, sentiment, or readability metrics, TextRegress provides a unified framework for conducting predictive modeling of continuous outcomes. By integrating advanced encoding methods – including transformer-based embeddings, TF-IDF, and pre-trained Hugging Face models – with a robust PyTorch Lightning backend, TextRegress efficiently processes long texts through automatic chunking and dynamic feature integration. Its flexible architecture and customizable training paradigms empower researchers and practitioners across diverse domains to deploy sophisticated regression models, fostering reproducibility and accelerating innovation in text analytics.
{"title":"TextRegress: A Python package for advanced regression analysis on long-form text data","authors":"Jinhang Jiang , Ben Liu , Weiyao Peng , Karthik Srinivasan","doi":"10.1016/j.simpa.2025.100760","DOIUrl":"10.1016/j.simpa.2025.100760","url":null,"abstract":"<div><div>TextRegress is an open-source Python package that leverages state-of-the-art deep learning techniques to perform regression analysis on long-form text data. Departing from conventional text mining tools that are confined to classification, sentiment, or readability metrics, TextRegress provides a unified framework for conducting predictive modeling of continuous outcomes. By integrating advanced encoding methods – including transformer-based embeddings, TF-IDF, and pre-trained Hugging Face models – with a robust PyTorch Lightning backend, TextRegress efficiently processes long texts through automatic chunking and dynamic feature integration. Its flexible architecture and customizable training paradigms empower researchers and practitioners across diverse domains to deploy sophisticated regression models, fostering reproducibility and accelerating innovation in text analytics.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100760"},"PeriodicalIF":1.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935810","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 : 2025-05-02DOI: 10.1016/j.simpa.2025.100753
Seifallah Elfetni , Reza Darvishi Kamachali
This paper introduces PINNs-MPF, a novel Machine Learning-based solver designed for Multi-Phase-Field (MPF) and diffuse interface simulations, offering innovative approaches to address complex challenges in addressing microstructure evolution in polycrystalline materials using Machine Learning. The framework not only surpasses current limitations in handling multi-phase problems but also allows for potential upscaling to tackle more intricate scenarios. Developed in Python, the related code leverages optimized libraries like TensorFlow, showcasing efficiency and potential scalability in materials science and engineering simulations. This framework, integrating advanced techniques such as multi-networking and training optimization, setting a new standard in predictive capabilities and understanding complex physical phenomena.
{"title":"PINNs-MPF: An Efficient Physics-Informed Machine Learning-based Solver for Multi-Phase-Field Simulations using Tensorflow","authors":"Seifallah Elfetni , Reza Darvishi Kamachali","doi":"10.1016/j.simpa.2025.100753","DOIUrl":"10.1016/j.simpa.2025.100753","url":null,"abstract":"<div><div>This paper introduces PINNs-MPF, a novel Machine Learning-based solver designed for Multi-Phase-Field (MPF) and diffuse interface simulations, offering innovative approaches to address complex challenges in addressing microstructure evolution in polycrystalline materials using Machine Learning. The framework not only surpasses current limitations in handling multi-phase problems but also allows for potential upscaling to tackle more intricate scenarios. Developed in Python, the related code leverages optimized libraries like TensorFlow, showcasing efficiency and potential scalability in materials science and engineering simulations. This framework, integrating advanced techniques such as multi-networking and training optimization, setting a new standard in predictive capabilities and understanding complex physical phenomena.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100753"},"PeriodicalIF":1.3,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903592","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 : 2025-04-30DOI: 10.1016/j.simpa.2025.100763
Rudy Prietno , Santana Yuda Pradata , Raka Satya Prasasta , Gemilang Santiyuda , Muhammad Alfian Amrizal , Tri Kuntoro Priyambodo , Vincent F. Yu
Distributing medical supplies involves complex logistical challenges, including the need for optimized delivery routes and efficient packing. Medicines, whether ordered in small quantities or in bulk, are packed into cardboard boxes, which affect cargo dimensions, loading plans, and available delivery routes. Additionally, some medicines require refrigeration, making it necessary to coordinate both reefer and standard trucks. This study introduces MedRoPax, a comprehensive software solution designed to address these challenges. MedRoPax solves the 3D Loading Heterogeneous Vehicle Routing Problem (3LHVRP) and provides user-friendly tools for packing, loading visualization, and route planning. While tailored for medical supply distribution, MedRoPax is also well-suited for other logistics operations that demand both efficiency and safety.
{"title":"MedRoPax: A comprehensive software for solving heterogeneous vehicle routing problem with 3D loading constraints and cardboard box packing for medical supply distribution","authors":"Rudy Prietno , Santana Yuda Pradata , Raka Satya Prasasta , Gemilang Santiyuda , Muhammad Alfian Amrizal , Tri Kuntoro Priyambodo , Vincent F. Yu","doi":"10.1016/j.simpa.2025.100763","DOIUrl":"10.1016/j.simpa.2025.100763","url":null,"abstract":"<div><div>Distributing medical supplies involves complex logistical challenges, including the need for optimized delivery routes and efficient packing. Medicines, whether ordered in small quantities or in bulk, are packed into cardboard boxes, which affect cargo dimensions, loading plans, and available delivery routes. Additionally, some medicines require refrigeration, making it necessary to coordinate both reefer and standard trucks. This study introduces MedRoPax, a comprehensive software solution designed to address these challenges. MedRoPax solves the 3D Loading Heterogeneous Vehicle Routing Problem (3LHVRP) and provides user-friendly tools for packing, loading visualization, and route planning. While tailored for medical supply distribution, MedRoPax is also well-suited for other logistics operations that demand both efficiency and safety.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100763"},"PeriodicalIF":1.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139168","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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