Pub Date : 2024-12-27DOI: 10.1016/j.simpa.2024.100739
Ruijin Wang , Yuchen Du , Chunchun Dai , Yang Deng , Jiantao Leng , Tienchong Chang
Researchers have long been concerned with the extrapolation capabilities of machine learning (ML) models, particularly when dealing with insufficient training data. The recently proposed solution-guided machine learning (SGML) method addresses this issue by integrating existing solutions as additional features to supplement limited training data. We have applied this method to solve the strong nonlinearity in nanoindentation and present an approximate solution to the tangential entropic force in an asymmetrical two dimensional bilayer. To make this method more accessible, we developed a user-friendly Python library called SGML, available on GitHub and PyPI. This paper introduces the architecture and functionality of the library, provides a usage example, and discusses its potential impact and applications.
{"title":"SGML: A Python library for solution-guided machine learning","authors":"Ruijin Wang , Yuchen Du , Chunchun Dai , Yang Deng , Jiantao Leng , Tienchong Chang","doi":"10.1016/j.simpa.2024.100739","DOIUrl":"10.1016/j.simpa.2024.100739","url":null,"abstract":"<div><div>Researchers have long been concerned with the extrapolation capabilities of machine learning (ML) models, particularly when dealing with insufficient training data. The recently proposed solution-guided machine learning (SGML) method addresses this issue by integrating existing solutions as additional features to supplement limited training data. We have applied this method to solve the strong nonlinearity in nanoindentation and present an approximate solution to the tangential entropic force in an asymmetrical two dimensional bilayer. To make this method more accessible, we developed a user-friendly Python library called SGML, available on GitHub and PyPI. This paper introduces the architecture and functionality of the library, provides a usage example, and discusses its potential impact and applications.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100739"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139884","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 : 2024-12-27DOI: 10.1016/j.simpa.2024.100738
Wenzhen Li , Hongyan Lin , Lvxin Peng , Qianhu Jiang , Yushu Gou , Lu Xie , Jian Huang
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.
{"title":"AbNumPro: A comprehensive offline toolkit for antibody numbering and antigen-binding region prediction","authors":"Wenzhen Li , Hongyan Lin , Lvxin Peng , Qianhu Jiang , Yushu Gou , Lu Xie , 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":"2024-12-27","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}
Pub Date : 2024-12-27DOI: 10.1016/j.simpa.2024.100733
Mochammad Hannats Hanafi Ichsan , Cecilia Sik-Lanyi , Tibor Guzsvinecz
This paper presents the development of a Multi-Browser Virtual Environment (VE) aimed at improving the user experience of internet browsing through Desktop Virtual Reality (VR) technology. By integrating multiple web browsers within the Virtual Environment (VE), users can engage in more intuitive and interactive browsing experiences. This study explores the development of Multi-Browser VE in the early stage of development, an evaluation model to assess this system by measuring usability and user feedback compared to the traditional browsing experience. Initial studies suggest that the Multi-Browser VE offers good usability and a more excellent browsing experience than traditional desktop-based interfaces.
{"title":"Multi-browser VE: Enhancing internet browsing experience through virtual reality","authors":"Mochammad Hannats Hanafi Ichsan , Cecilia Sik-Lanyi , Tibor Guzsvinecz","doi":"10.1016/j.simpa.2024.100733","DOIUrl":"10.1016/j.simpa.2024.100733","url":null,"abstract":"<div><div>This paper presents the development of a Multi-Browser Virtual Environment (VE) aimed at improving the user experience of internet browsing through Desktop Virtual Reality (VR) technology. By integrating multiple web browsers within the Virtual Environment (VE), users can engage in more intuitive and interactive browsing experiences. This study explores the development of Multi-Browser VE in the early stage of development, an evaluation model to assess this system by measuring usability and user feedback compared to the traditional browsing experience. Initial studies suggest that the Multi-Browser VE offers good usability and a more excellent browsing experience than traditional desktop-based interfaces.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100733"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139881","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 : 2024-12-18DOI: 10.1016/j.simpa.2024.100725
Candelaria E. Sansores , Joel A. Trejo-Sánchez , Mirbella Gallareta Negrón
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.
{"title":"A multi-agent system simulation framework with optimized spatial neighborhood search","authors":"Candelaria E. Sansores , Joel A. Trejo-Sánchez , 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":"2024-12-18","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}
Pub Date : 2024-12-13DOI: 10.1016/j.simpa.2024.100731
Debora P. Salgado , Niall Murray , Ronan Flynn , Eduardo L.M. Naves , Yuansong Qiao , Sheila Fallon
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.
{"title":"WheelSimAnalyser: A MATLAB tool for multimodal data analysis of WheelSimPhysio-2023 dataset","authors":"Debora P. Salgado , Niall Murray , Ronan Flynn , Eduardo L.M. Naves , Yuansong Qiao , 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":"2024-12-13","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}
Pub Date : 2024-12-13DOI: 10.1016/j.simpa.2024.100728
Gaofeng Zhu , Qiang Chen , Xiangyu Yu , Cong Xu , Kun Zhang , Yunquan Wang , Wei Gong , Tao Che
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.
{"title":"PEM-SMC: An algorithm for optimizing model parameters","authors":"Gaofeng Zhu , Qiang Chen , Xiangyu Yu , Cong Xu , Kun Zhang , Yunquan Wang , Wei Gong , 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":"2024-12-13","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}
Pub Date : 2024-12-13DOI: 10.1016/j.simpa.2024.100730
Débora Pina , Liliane Kunstmann , Daniel de Oliveira , Marta Mattoso
To train a Deep Learning (DL) model, a workflow must be executed with four well-defined activities: (i) Acquiring data, (ii) Preprocessing, (iii) Splitting and balancing the dataset, and (iv) Building and training the model. After generating several DL models, they undergo a process called model selection. After being selected, the DL model is put into a production environment to make predictions on new data. One of the challenges in supporting these analyses is related to providing relationships between candidate models, their datasets for train, test, and validation, input data, and other derivations paths. These relationships are also essential for trust, reproducibility, and evolution of the selected model. While existing solutions allow monitoring and analyzing the artifacts generated throughout the DL workflow, they often fail to establish relationships for supporting data derivation within the DL workflow. DLProv is a provenance-centric service to support DL workflow analyses and reproducibility. DLProv captures provenance data and exports provenance graphs for DL model reproducibility. DLProv is W3C PROV compliant, ensuring standardized prospective and retrospective provenance, and enables provenance capture in arbitrary execution frameworks.
{"title":"Breadcrumbs for your Deep Learning Model: Following Provenance Traces with DLProv","authors":"Débora Pina , Liliane Kunstmann , Daniel de Oliveira , Marta Mattoso","doi":"10.1016/j.simpa.2024.100730","DOIUrl":"10.1016/j.simpa.2024.100730","url":null,"abstract":"<div><div>To train a Deep Learning (DL) model, a workflow must be executed with four well-defined activities: (i) Acquiring data, (ii) Preprocessing, (iii) Splitting and balancing the dataset, and (iv) Building and training the model. After generating several DL models, they undergo a process called model selection. After being selected, the DL model is put into a production environment to make predictions on new data. One of the challenges in supporting these analyses is related to providing relationships between candidate models, their datasets for train, test, and validation, input data, and other derivations paths. These relationships are also essential for trust, reproducibility, and evolution of the selected model. While existing solutions allow monitoring and analyzing the artifacts generated throughout the DL workflow, they often fail to establish relationships for supporting data derivation within the DL workflow. DLProv is a provenance-centric service to support DL workflow analyses and reproducibility. DLProv captures provenance data and exports provenance graphs for DL model reproducibility. DLProv is W3C PROV compliant, ensuring standardized prospective and retrospective provenance, and enables provenance capture in arbitrary execution frameworks.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100730"},"PeriodicalIF":1.3,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139895","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 : 2024-12-13DOI: 10.1016/j.simpa.2024.100729
Roland Allart , Aude Alaphilippe , Marta Carpani , Nicolas Cavan , Hervé Monod , Jacques-Eric Bergez
DEXi is a software for developing qualitative hierarchical models. Widely used in the French agriculture sector to analyze the sustainability of farming systems, the sensitivity analyses of the models are still missing. The dexisensitivity R package performs such sensitivity analyses. Written using R S4 Object programming, it performs basic functions (reads DEXi models, describes and draws the models, generates and simulates scenarios) and other functions to perform different types of sensitivity analyses: analysis of variance, One-At-A-Time, sensitivity indexes using the Shapiro-Shapley approach… The dexisensitivity R package is distributed under the GPL license and is accessible from CRAN and GitHub.
{"title":"dexisensitivity: An R package to perform sensitivity analyses of DEXi models","authors":"Roland Allart , Aude Alaphilippe , Marta Carpani , Nicolas Cavan , Hervé Monod , Jacques-Eric Bergez","doi":"10.1016/j.simpa.2024.100729","DOIUrl":"10.1016/j.simpa.2024.100729","url":null,"abstract":"<div><div>DEXi is a software for developing qualitative hierarchical models. Widely used in the French agriculture sector to analyze the sustainability of farming systems, the sensitivity analyses of the models are still missing. The <em>dexisensitivity</em> R package performs such sensitivity analyses. Written using R S4 Object programming, it performs basic functions (reads DEXi models, describes and draws the models, generates and simulates scenarios) and other functions to perform different types of sensitivity analyses: analysis of variance, One-At-A-Time, sensitivity indexes using the Shapiro-Shapley approach… The <em>dexisensitivity</em> R package is distributed under the GPL license and is accessible from CRAN and GitHub.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100729"},"PeriodicalIF":1.3,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139886","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 : 2024-12-05DOI: 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
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 MaSchedule an open-source multi-agent tool for the design of heuristics for scheduling problems.
{"title":"MaSchedule. A multi-agent tool for scheduling problems","authors":"Joel Antonio Trejo-Sánchez , Candelaria E. Sansores , Francisco J. Hernandez-Lopez , Jonás Velasco , Daniel Fajardo Delgado , Jose Luis Lopez-Martinez , 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":"2024-12-05","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}
Pub Date : 2024-12-05DOI: 10.1016/j.simpa.2024.100724
Bladimir Toaza, Domokos Esztergár-Kiss
SpatialzOSM, a package to spatialize aggregated locations into coordinates, thereby supporting population synthesis processes. This paper addresses the need for high-resolution data while ensuring data privacy. SpatialzOSM features include the generation of coordinates using three random distribution techniques: across zones, along road networks, and within buildings for residential locations. For non-residential locations, the package extracts points of interest from open sources. By leveraging open-source data, SpatialzOSM minimizes the risks of reidentification associated with census and survey datasets, ensuring privacy protection. This package is valuable for researchers and modelers engaged in synthetic population generation for models requiring explicit geographic location data.
{"title":"SpatialzOSM: A Python package for supporting the explicit spatialization in the population synthesis process","authors":"Bladimir Toaza, Domokos Esztergár-Kiss","doi":"10.1016/j.simpa.2024.100724","DOIUrl":"10.1016/j.simpa.2024.100724","url":null,"abstract":"<div><div>SpatialzOSM, a package to spatialize aggregated locations into coordinates, thereby supporting population synthesis processes. This paper addresses the need for high-resolution data while ensuring data privacy. SpatialzOSM features include the generation of coordinates using three random distribution techniques: across zones, along road networks, and within buildings for residential locations. For non-residential locations, the package extracts points of interest from open sources. By leveraging open-source data, SpatialzOSM minimizes the risks of reidentification associated with census and survey datasets, ensuring privacy protection. This package is valuable for researchers and modelers engaged in synthetic population generation for models requiring explicit geographic location data.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100724"},"PeriodicalIF":1.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139894","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号