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HoloFarm: Enhancing agricultural learning through immersive technology HoloFarm：通过沉浸式技术增强农业学习
IF 1.3
Software Impacts Pub Date : 2025-05-15 DOI: 10.1016/j.simpa.2025.100768
Muhamad Keenan Ario , Muhammad Fikri Hasani , Khairatul Balqis , Messya Carment
{"title":"HoloFarm: Enhancing agricultural learning through immersive technology","authors":"Muhamad Keenan Ario ,&nbsp;Muhammad Fikri Hasani ,&nbsp;Khairatul Balqis ,&nbsp;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}
引用次数: 0
EduXgame: Gamified learning for secondary education EduXgame：中学教育的游戏化学习
IF 1.3
Software Impacts Pub Date : 2025-05-05 DOI: 10.1016/j.simpa.2025.100761
Achour Khaoula , Lachgar Mohamed , Elloubab Aya , Ait Ouahda Younes , Laanaoui My Driss , Ourahay Mustapha
{"title":"EduXgame: Gamified learning for secondary education","authors":"Achour Khaoula ,&nbsp;Lachgar Mohamed ,&nbsp;Elloubab Aya ,&nbsp;Ait Ouahda Younes ,&nbsp;Laanaoui My Driss ,&nbsp;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}
引用次数: 0
TextRegress: A Python package for advanced regression analysis on long-form text data texttregress：一个Python包，用于对长格式文本数据进行高级回归分析
IF 1.3
Software Impacts Pub Date : 2025-05-05 DOI: 10.1016/j.simpa.2025.100760
Jinhang Jiang , Ben Liu , Weiyao Peng , Karthik Srinivasan
{"title":"TextRegress: A Python package for advanced regression analysis on long-form text data","authors":"Jinhang Jiang ,&nbsp;Ben Liu ,&nbsp;Weiyao Peng ,&nbsp;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}
引用次数: 0
PINNs-MPF: An Efficient Physics-Informed Machine Learning-based Solver for Multi-Phase-Field Simulations using Tensorflow pass - mpf：一个高效的基于物理信息的基于机器学习的求解器，用于使用Tensorflow进行多相场模拟
IF 1.3
Software Impacts Pub Date : 2025-05-02 DOI: 10.1016/j.simpa.2025.100753
Seifallah Elfetni , Reza Darvishi Kamachali
{"title":"PINNs-MPF: An Efficient Physics-Informed Machine Learning-based Solver for Multi-Phase-Field Simulations using Tensorflow","authors":"Seifallah Elfetni ,&nbsp;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}
引用次数: 0
MedRoPax: A comprehensive software for solving heterogeneous vehicle routing problem with 3D loading constraints and cardboard box packing for medical supply distribution MedRoPax：一款综合软件，用于解决医疗用品配送中具有3D装载约束和纸箱包装的异构车辆路线问题
IF 1.3
Software Impacts Pub Date : 2025-04-30 DOI: 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
{"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 ,&nbsp;Santana Yuda Pradata ,&nbsp;Raka Satya Prasasta ,&nbsp;Gemilang Santiyuda ,&nbsp;Muhammad Alfian Amrizal ,&nbsp;Tri Kuntoro Priyambodo ,&nbsp;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}
引用次数: 0
PubMedMetaTool: Automated metadata extraction from PubMed using Python for bibliometric analysis PubMedMetaTool：使用Python从PubMed自动提取元数据，用于文献计量分析
IF 1.3
Software Impacts Pub Date : 2025-04-29 DOI: 10.1016/j.simpa.2025.100766
Leandro Rodrigues da Silva Souza , Daniel Hilário da Silva , Caio Tonus Ribeiro , Daiane Alves da Silva , Slawomir J. Nasuto , Catherine M. Sweeney-Reed , Adriano de Oliveira Andrade , Adriano Alves Pereira
{"title":"PubMedMetaTool: Automated metadata extraction from PubMed using Python for bibliometric analysis","authors":"Leandro Rodrigues da Silva Souza ,&nbsp;Daniel Hilário da Silva ,&nbsp;Caio Tonus Ribeiro ,&nbsp;Daiane Alves da Silva ,&nbsp;Slawomir J. Nasuto ,&nbsp;Catherine M. Sweeney-Reed ,&nbsp;Adriano de Oliveira Andrade ,&nbsp;Adriano Alves Pereira","doi":"10.1016/j.simpa.2025.100766","DOIUrl":"10.1016/j.simpa.2025.100766","url":null,"abstract":"<div><div>Bibliometric analyses often depend on extracting metadata from large scientific databases, a process that is still largely manual, repetitive, and error prone. This paper presents PubMedMetaTool, an open-source Python-based solution that automates the retrieval and transformation of bibliographic metadata from PubMed, using either article titles or Digital Object Identifiers as input. The tool implements a modular pipeline that extracts metadata using NCBI’s Entrez programming utilities and transforms it into formats compatible with tools such as Bibliometrix, VOSviewer, and pyBibX. Designed to be transparent and configurable, the tool improves bibliometric workflow efficiency, accuracy, and interoperability workflows.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100766"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941815","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
Nomad Analytix: Text-rich visual reasoning using vision models for insights and recommendations Nomad Analytix：使用视觉模型进行文本丰富的视觉推理，以获得见解和建议
IF 1.3
Software Impacts Pub Date : 2025-04-29 DOI: 10.1016/j.simpa.2025.100765
Sai Jeevan Puchakayala , Allen Bijo T. , Aswathy Ravikumar , Harini Sriraman
{"title":"Nomad Analytix: Text-rich visual reasoning using vision models for insights and recommendations","authors":"Sai Jeevan Puchakayala ,&nbsp;Allen Bijo T. ,&nbsp;Aswathy Ravikumar ,&nbsp;Harini Sriraman","doi":"10.1016/j.simpa.2025.100765","DOIUrl":"10.1016/j.simpa.2025.100765","url":null,"abstract":"<div><div>Nomad Analytix is an innovative business intelligence tool that uses state-of-the-art vision models to transform data analysis. This software automates complex tasks traditionally handled by data analysts, empowering non-technical teams such as marketing and sales to access advanced data analysis easily. By using natural language prompts, users can interact with data intuitively and gain valuable insights without needing extensive technical expertise. A prototype of the software, built on the Streamlit platform, will showcase its ability to generate visualizations from various data sources, including CSV, JSON, SQLite, Excel, and databases, with potential extensions to data warehouses. The integration of Vision Language Models GPT 4 Omni and GPT 4 Turbo- with this framework provides a seamless interface for data querying, visualization creation, and recommendation generation. Nomad Analytix serves as an inclusive, intelligent, and intuitive solution, bridging the gap between data and decision-making across diverse industries.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100765"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903526","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
An automated parameter optimizer for data transfer performance testing 用于数据传输性能测试的自动参数优化器
IF 1.3
Software Impacts Pub Date : 2025-04-29 DOI: 10.1016/j.simpa.2025.100764
Daqing Yun , Liudong Zuo , Yi Gu , Chase Wu
{"title":"An automated parameter optimizer for data transfer performance testing","authors":"Daqing Yun ,&nbsp;Liudong Zuo ,&nbsp;Yi Gu ,&nbsp;Chase Wu","doi":"10.1016/j.simpa.2025.100764","DOIUrl":"10.1016/j.simpa.2025.100764","url":null,"abstract":"<div><div>This work presents an automated tool for optimizing control parameters in performance testing of big data transfer over long-fat network connections. Supporting both TCP and UDT protocols, the tool identifies the optimal configurations to enhance the efficiency of large-scale data transfers. A stochastic approximation algorithm is employed for parameter optimization, streamlining the protocol and parameter selection. The tool has been evaluated in various network scenarios, including long-haul connections in real-world high-performance networks. Its modular design also enables straightforward integration of additional data transfer protocols and alternative optimization methods.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100764"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903527","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
An AI-powered solution for detecting and categorising sponsored ad segments in YouTube videos 一个人工智能解决方案，用于检测和分类YouTube视频中的赞助广告段
IF 1.3
Software Impacts Pub Date : 2025-04-28 DOI: 10.1016/j.simpa.2025.100759
Johnny Chan, Brice Valentin Kok-Shun
{"title":"An AI-powered solution for detecting and categorising sponsored ad segments in YouTube videos","authors":"Johnny Chan,&nbsp;Brice Valentin Kok-Shun","doi":"10.1016/j.simpa.2025.100759","DOIUrl":"10.1016/j.simpa.2025.100759","url":null,"abstract":"<div><div>This paper presents an AI-powered software solution for detecting and categorising sponsored advertisement segments in YouTube videos. By combining GPT-4 for ad identification, KeyBERT for keyword extraction, and custom prompts for grouping keywords into concise categories, the software provides a scalable and efficient alternative to traditional ad detection methods. It processes both auto-generated and manual transcripts, ensuring adaptability across varied contexts. The tool enables a deeper understanding of advertising strategies and ad-content alignment while maintaining ease of use and reproducibility. This work highlights the potential of AI in transforming digital advertisement analysis.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100759"},"PeriodicalIF":1.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913061","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
FeVAcS: A package for visualizing acoustic scattering from 1D periodic obstacles FeVAcS：一个用于显示一维周期性障碍物声散射的软件包
IF 1.3
Software Impacts Pub Date : 2025-04-28 DOI: 10.1016/j.simpa.2025.100756
Mete Öğüç , Ali Fethi Okyar , Tahsin Khajah
{"title":"FeVAcS: A package for visualizing acoustic scattering from 1D periodic obstacles","authors":"Mete Öğüç ,&nbsp;Ali Fethi Okyar ,&nbsp;Tahsin Khajah","doi":"10.1016/j.simpa.2025.100756","DOIUrl":"10.1016/j.simpa.2025.100756","url":null,"abstract":"<div><div>FeVAcS is an open-source finite element software specializing in one dimensional periodic acoustic analyses with scattering obstacles. Leveraging FEniCS Project’s computational capabilities, it solves the Helmholtz equation variational form. This tool simplifies mesh generation, enhances acoustic visualization, and enables easy parameter manipulation for obstacle and domain geometries, along with wave property adjustments. Featuring a user-friendly browser interface, FeVAcS improves accessibility and result sharing. It serves as a vital tool for understanding complexities within exterior acoustic analyses.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100756"},"PeriodicalIF":1.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888040","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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