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WheelSimAnalyser: A MATLAB tool for multimodal data analysis of WheelSimPhysio-2023 dataset WheelSimPhysio-2023数据集的多模态数据分析的MATLAB工具
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2024-12-13 DOI: 10.1016/j.simpa.2024.100731
Debora P. Salgado , Niall Murray , Ronan Flynn , Eduardo L.M. Naves , Yuansong Qiao , Sheila Fallon
{"title":"WheelSimAnalyser: A MATLAB tool for multimodal data analysis of WheelSimPhysio-2023 dataset","authors":"Debora P. Salgado ,&nbsp;Niall Murray ,&nbsp;Ronan Flynn ,&nbsp;Eduardo L.M. Naves ,&nbsp;Yuansong Qiao ,&nbsp;Sheila Fallon","doi":"10.1016/j.simpa.2024.100731","DOIUrl":"10.1016/j.simpa.2024.100731","url":null,"abstract":"<div><div>WheelSimAnalyser is a MATLAB-based tool designed to process and analyze the WheelSimPhysio-2023 dataset, which includes physiological, questionnaire, and system data from wheelchair simulator studies. The tool streamlines data preprocessing, feature extraction, and visualization, providing researchers with detailed descriptive metrics. By automating key steps, WheelSimAnalyser enables efficient and effective analysis, allowing researchers to derive meaningful insights from complex datasets. The tool supports research on power wheelchair mobility and user experience, enhancing the ability to interpret multimodal data.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100731"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139883","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}
引用次数: 0
A multi-agent system simulation framework with optimized spatial neighborhood search 一种优化空间邻域搜索的多智能体系统仿真框架
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2024-12-18 DOI: 10.1016/j.simpa.2024.100725
Candelaria E. Sansores , Joel A. Trejo-Sánchez , Mirbella Gallareta Negrón
{"title":"A multi-agent system simulation framework with optimized spatial neighborhood search","authors":"Candelaria E. Sansores ,&nbsp;Joel A. Trejo-Sánchez ,&nbsp;Mirbella Gallareta Negrón","doi":"10.1016/j.simpa.2024.100725","DOIUrl":"10.1016/j.simpa.2024.100725","url":null,"abstract":"<div><div>BioMASS is an innovative multi-agent spatial model designed to enhance computational efficiency in simulations involving complex sensory and locomotion functions. Traditional agent-based modeling (ABM) platforms suffer from performance degradation as the number of agents and their perception ranges increase, resulting in a quadratic growth in computational cost. BioMASS addresses this issue employing a quadruply linked list structure, which allows constant-time neighborhood search and movement. This feature allows BioMASS to simulate large populations in dynamic environments efficiently. The model has been successfully applied to marine ecosystem simulations, demonstrating its ability to track species interactions across multiple trophic levels in real-time, outperforming existing platforms.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100725"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139885","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}
引用次数: 0
KNNOR-Reg: A python package for oversampling in imbalanced regression 一个python包，用于不平衡回归中的过采样
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2025-01-03 DOI: 10.1016/j.simpa.2024.100740
Samir Brahim Belhaouari , Ashhadul Islam , Khelil Kassoul , Ala Al-Fuqaha , Abdesselam Bouzerdoum
{"title":"KNNOR-Reg: A python package for oversampling in imbalanced regression","authors":"Samir Brahim Belhaouari ,&nbsp;Ashhadul Islam ,&nbsp;Khelil Kassoul ,&nbsp;Ala Al-Fuqaha ,&nbsp;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":"2025-03-01","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}
引用次数: 0
AbNumPro: A comprehensive offline toolkit for antibody numbering and antigen-binding region prediction AbNumPro：一个全面的离线工具包，用于抗体编号和抗原结合区预测
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2024-12-27 DOI: 10.1016/j.simpa.2024.100738
Wenzhen Li , Hongyan Lin , Lvxin Peng , Qianhu Jiang , Yushu Gou , Lu Xie , Jian Huang
{"title":"AbNumPro: A comprehensive offline toolkit for antibody numbering and antigen-binding region prediction","authors":"Wenzhen Li ,&nbsp;Hongyan Lin ,&nbsp;Lvxin Peng ,&nbsp;Qianhu Jiang ,&nbsp;Yushu Gou ,&nbsp;Lu Xie ,&nbsp;Jian Huang","doi":"10.1016/j.simpa.2024.100738","DOIUrl":"10.1016/j.simpa.2024.100738","url":null,"abstract":"<div><div>Identifying complementary-determining regions (CDRs) and antigen-binding regions (ABRs) requires accurate antibody numbering, which is essential for therapeutic antibody development. AbNumPro is a comprehensive offline toolkit developed for antibody numbering and ABRs prediction, addressing the limitations of existing tools, which often lack comprehensiveness and rely solely on online services. By integrating five established numbering schemes—Kabat, Chothia, IMGT, Aho, and Martin—AbNumPro provides precise delineation of CDRs and ABRs, offering both compatibility with diverse research applications and the assurance of data security.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100738"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139900","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}
引用次数: 0
Synthetic dataset generation system for vehicle detection 车辆检测合成数据集生成系统
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2024-12-28 DOI: 10.1016/j.simpa.2024.100735
Mihaela Orić , Vlatko Galić , Filip Novoselnik
{"title":"Synthetic dataset generation system for vehicle detection","authors":"Mihaela Orić ,&nbsp;Vlatko Galić ,&nbsp;Filip Novoselnik","doi":"10.1016/j.simpa.2024.100735","DOIUrl":"10.1016/j.simpa.2024.100735","url":null,"abstract":"<div><div>The success of machine learning models for object detection highly depends on the training data size and quality. Generating synthetic data speeds up the data acquisition process by removing the need for human annotation. Moreover, since annotation is done automatically, there is no room for human error. We present a pipeline that automatically generates and annotates aerial images of vehicles on roads. The pipeline is structured to allow easy adding of various new vehicles and is not limited to cars only. The resolution of the generated images and the level of detail can be modified by changing the output settings.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100735"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140076","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}
引用次数: 0
Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS) 基于光谱特征分类分析软件（SSTAS）的植物病害分类
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2025-03-03 DOI: 10.1016/j.simpa.2025.100744
Hardik Jayswal, Hetvi Desai, Hasti Vakani, Mithil Mistry, Nilesh Dubey
{"title":"Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS)","authors":"Hardik Jayswal,&nbsp;Hetvi Desai,&nbsp;Hasti Vakani,&nbsp;Mithil Mistry,&nbsp;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}
引用次数: 0
PEM-SMC: An algorithm for optimizing model parameters PEM-SMC：一种模型参数优化算法
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2024-12-13 DOI: 10.1016/j.simpa.2024.100728
Gaofeng Zhu , Qiang Chen , Xiangyu Yu , Cong Xu , Kun Zhang , Yunquan Wang , Wei Gong , Tao Che
{"title":"PEM-SMC: An algorithm for optimizing model parameters","authors":"Gaofeng Zhu ,&nbsp;Qiang Chen ,&nbsp;Xiangyu Yu ,&nbsp;Cong Xu ,&nbsp;Kun Zhang ,&nbsp;Yunquan Wang ,&nbsp;Wei Gong ,&nbsp;Tao Che","doi":"10.1016/j.simpa.2024.100728","DOIUrl":"10.1016/j.simpa.2024.100728","url":null,"abstract":"<div><div>Bayesian inference is crucial for optimizing parameters in complex models, but often requires sampling due to high-dimensional, intractable posteriors. Beyond Markov-Chain Monte Carlo (MCMC) methods, Sequential Monte Carlo (SMC) algorithms offer an alternative. This paper introduces a Matlab toolbox for the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which combines the strengths of population-based MCMC and SMC. Two case studies – a complex multi-modal probability and a land surface model – demonstrate the toolbox’s capabilities. This tool is valuable for Bayesian inference across fields like statistics, ecology, hydrology, and land surface processes.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100728"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139887","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}
引用次数: 0
MaSchedule. A multi-agent tool for scheduling problems MaSchedule。用于调度问题的多代理工具
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2024-12-05 DOI: 10.1016/j.simpa.2024.100726
Joel Antonio Trejo-Sánchez , Candelaria E. Sansores , Francisco J. Hernandez-Lopez , Jonás Velasco , Daniel Fajardo Delgado , Jose Luis Lopez-Martinez , Julio Cesar Ramirez-Pacheco
{"title":"MaSchedule. A multi-agent tool for scheduling problems","authors":"Joel Antonio Trejo-Sánchez ,&nbsp;Candelaria E. Sansores ,&nbsp;Francisco J. Hernandez-Lopez ,&nbsp;Jonás Velasco ,&nbsp;Daniel Fajardo Delgado ,&nbsp;Jose Luis Lopez-Martinez ,&nbsp;Julio Cesar Ramirez-Pacheco","doi":"10.1016/j.simpa.2024.100726","DOIUrl":"10.1016/j.simpa.2024.100726","url":null,"abstract":"<div><div>Several scheduling optimization problems belong to the NP-complete class, including, task scheduling, job shop scheduling, and patient admission. These problems commonly require the development of heuristics approaches to find near-optimal solutions within reasonable timeframes. In this work, we present <span>MaSchedule</span> an open-source multi-agent tool for the design of heuristics for scheduling problems.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100726"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139893","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}
引用次数: 0
mGFD: CloudGenerator mGFD: CloudGenerator
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2024-11-28 DOI: 10.1016/j.simpa.2024.100721
Gabriela Pedraza-Jiménez, Gerardo Tinoco-Guerrero, Francisco Javier Domínguez-Mota, José Alberto Guzmán-Torres, José Gerardo Tinoco-Ruiz
{"title":"mGFD: CloudGenerator","authors":"Gabriela Pedraza-Jiménez,&nbsp;Gerardo Tinoco-Guerrero,&nbsp;Francisco Javier Domínguez-Mota,&nbsp;José Alberto Guzmán-Torres,&nbsp;José Gerardo Tinoco-Ruiz","doi":"10.1016/j.simpa.2024.100721","DOIUrl":"10.1016/j.simpa.2024.100721","url":null,"abstract":"<div><div>This work introduces mGFD: Cloud Generator, a web-based software for generating non-structured clouds of points that is useful in numerical analysis, particularly in applying the Meshless Generalized Finite Difference Method (mGFD). mGFD: CloudGenerator allows to manually define external and internal boundary nodes, using an image as a guide, providing precise control over boundary conditions. It supports image uploads (.png, .jpg, .jpeg) to guide node placement and automatically generates the internal cloud of points. The web-based software is open-source and accessible for research and has been used to produce results in some papers, such as the ones mentioned in this paper.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100721"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756998","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}
引用次数: 0
hvarma: Autoregressive moving average model of microtremor H/V spectral ratio hvarma：微颤H/V谱比的自回归移动平均模型
IF 1.3
Software Impacts Pub Date : 2025-03-01 Epub Date: 2025-03-04 DOI: 10.1016/j.simpa.2025.100745
Aleix Seguí , Arantza Ugalde , Juan José Egozcue
{"title":"hvarma: Autoregressive moving average model of microtremor H/V spectral ratio","authors":"Aleix Seguí ,&nbsp;Arantza Ugalde ,&nbsp;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}
引用次数: 0
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