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TR-VABML: Enhancing Turkish vocabulary acquisition through adaptive machine learning classification TR-VABML：通过自适应机器学习分类增强土耳其语词汇习得
IF 1.3
Software Impacts Pub Date : 2025-06-17 DOI: 10.1016/j.simpa.2025.100774
Ahmed Alaff , Çelebi Uluyol
{"title":"TR-VABML: Enhancing Turkish vocabulary acquisition through adaptive machine learning classification","authors":"Ahmed Alaff ,&nbsp;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}
引用次数: 0
TERANG: Seismic loss estimation tool for school buildings TERANG：学校建筑地震损失估算工具
IF 1.3
Software Impacts Pub Date : 2025-06-10 DOI: 10.1016/j.simpa.2025.100773
Roi Milyardi , Krishna Suryanto Pribadi , Muhamad Abduh , Irwan Meilano , Erwin Lim
{"title":"TERANG: Seismic loss estimation tool for school buildings","authors":"Roi Milyardi ,&nbsp;Krishna Suryanto Pribadi ,&nbsp;Muhamad Abduh ,&nbsp;Irwan Meilano ,&nbsp;Erwin Lim","doi":"10.1016/j.simpa.2025.100773","DOIUrl":"10.1016/j.simpa.2025.100773","url":null,"abstract":"<div><div>This article presents a MATLAB-based computational software, TERANG to estimate physical and operational losses for school building in Indonesia. The basis of the estimation model used is the HAZUS model. TERANG provides modifications to the HAZUS model on school building cost parameters and reconstruction cost, as well as adjustments to local hazard data. TERANG provides an overview of the HAZUS model adoption process for countries that do not yet have a school building database. TERANG software supports Indonesia’s seismic loss studies, estimating school damages in Bandung and Mamuju’s 2021 earthquake while raising awareness among school stakeholders.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100773"},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313009","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
MRI-based Alzheimer’s disease classification using Vision Transformer and time-series transformer: A step-by-step guide 基于mri的阿尔茨海默病分类使用视觉变压器和时间序列变压器：一步一步的指南
IF 1.3
Software Impacts Pub Date : 2025-06-10 DOI: 10.1016/j.simpa.2025.100771
Sait Alp , Sara Akan , Taymaz Akan , Mohammad Alfrad Nobel Bhuiyan
{"title":"MRI-based Alzheimer’s disease classification using Vision Transformer and time-series transformer: A step-by-step guide","authors":"Sait Alp ,&nbsp;Sara Akan ,&nbsp;Taymaz Akan ,&nbsp;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}
引用次数: 0
GD4Shapes: Geodesic distance with fixed parameterization for 2D Shapes GD4Shapes: 2D形状的固定参数化测地线距离
IF 1.3
Software Impacts Pub Date : 2025-06-10 DOI: 10.1016/j.simpa.2025.100775
S. Herold-Garcia , H.L. Varona-Gonzalez , X. Gual-Arnau
{"title":"GD4Shapes: Geodesic distance with fixed parameterization for 2D Shapes","authors":"S. Herold-Garcia ,&nbsp;H.L. Varona-Gonzalez ,&nbsp;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}
引用次数: 0
ESCOX: A tool for skill and occupation extraction using LLMs from unstructured text ESCOX：一个使用llm从非结构化文本中提取技能和职业的工具
IF 1.3
Software Impacts Pub Date : 2025-06-10 DOI: 10.1016/j.simpa.2025.100772
Dimitrios Christos Kavargyris , Konstantinos Georgiou , Eleanna Papaioannou , Konstantinos Petrakis , Nikolaos Mittas , Lefteris Angelis
{"title":"ESCOX: A tool for skill and occupation extraction using LLMs from unstructured text","authors":"Dimitrios Christos Kavargyris ,&nbsp;Konstantinos Georgiou ,&nbsp;Eleanna Papaioannou ,&nbsp;Konstantinos Petrakis ,&nbsp;Nikolaos Mittas ,&nbsp;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}
引用次数: 0
A practical open-source approach to Model Predictive Control using the Legendre–Gauss–Radau pseudospectral method 一个实用的开源方法模型预测控制使用legende - gauss - radau伪谱方法
IF 1.3
Software Impacts Pub Date : 2025-05-22 DOI: 10.1016/j.simpa.2025.100769
Saeid Bayat, James T. Allison
{"title":"A practical open-source approach to Model Predictive Control using the Legendre–Gauss–Radau pseudospectral method","authors":"Saeid Bayat,&nbsp;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}
引用次数: 0
Reconstructing software evolution: Traceability from code commits to fault manifestation in CI 重构软件演化：从代码提交到CI中的错误表现的可追溯性
IF 1.3
Software Impacts Pub Date : 2025-05-19 DOI: 10.1016/j.simpa.2025.100767
Azeem Ahmad , Muhammad Rashid Naeem , Yasir Javed , Mohammad Akour
{"title":"Reconstructing software evolution: Traceability from code commits to fault manifestation in CI","authors":"Azeem Ahmad ,&nbsp;Muhammad Rashid Naeem ,&nbsp;Yasir Javed ,&nbsp;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}
引用次数: 0
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
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