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MI-NiDIA: A scalable framework for modeling flocculation kinetics and floc evolution in water treatment MI-NiDIA：水处理中絮凝动力学和絮凝体演变建模的可扩展框架
IF 2.1
Software Impacts Pub Date : 2024-05-01 DOI: 10.1016/j.simpa.2024.100662
Abayomi O. Bankole , Rodrigo Moruzzi , Rogério G. Negri , Cassio M. Oishi , Afolashade R. Bankole , Abraham O. James
{"title":"MI-NiDIA: A scalable framework for modeling flocculation kinetics and floc evolution in water treatment","authors":"Abayomi O. Bankole ,&nbsp;Rodrigo Moruzzi ,&nbsp;Rogério G. Negri ,&nbsp;Cassio M. Oishi ,&nbsp;Afolashade R. Bankole ,&nbsp;Abraham O. James","doi":"10.1016/j.simpa.2024.100662","DOIUrl":"10.1016/j.simpa.2024.100662","url":null,"abstract":"<div><p>This paper presents a scalable framework for modeling floc evolution and flocculation kinetics in water treatment. Unlike the existing methods that subjects Non-intrusive Dynamic Image Analysis (NiDIA) data to complex mathematical concepts, the proposed software devised a scaling concept for NiDIA data and designed an effective algorithm with the capability to predict varying floc lengths and the underlying kinetics under a broad flocculation conditions (<span><math><mrow><mtext>G</mtext><mi>f</mi></mrow></math></span> and <span><math><mrow><mtext>T</mtext><mi>f</mi></mrow></math></span>). Technically, the designed machine-intelligence framework (MI-NiDIA) involves data preprocessing, automatic parameter selection, validation and prediction of floc length evolution with metrics. For instance, MI-NiDIA-MLP recorded <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.95–1.0 for varying floc length at <span><math><mrow><mtext>G</mtext><mi>f</mi><mspace></mspace><mn>60</mn><mspace></mspace><msup><mrow><mi>s</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000502/pdfft?md5=6a51bd0a25608cc2c5543ea48ccd7c45&pid=1-s2.0-S2665963824000502-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141057190","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
Raster Forge: Interactive raster manipulation library and GUI for Python Raster Forge：适用于 Python 的交互式光栅操作库和图形用户界面
IF 2.1
Software Impacts Pub Date : 2024-05-01 DOI: 10.1016/j.simpa.2024.100657
Afonso Oliveira , Nuno Fachada , João P. Matos-Carvalho
{"title":"Raster Forge: Interactive raster manipulation library and GUI for Python","authors":"Afonso Oliveira ,&nbsp;Nuno Fachada ,&nbsp;João P. Matos-Carvalho","doi":"10.1016/j.simpa.2024.100657","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100657","url":null,"abstract":"<div><p>Raster Forge is a Python library and graphical user interface for raster data manipulation and analysis. The tool is focused on remote sensing applications, particularly in wildfire management. It allows users to import, visualize, and process raster layers for tasks such as image compositing or topographical analysis. For wildfire management, it generates fuel maps using predefined models. Its impact extends from disaster management to hydrological modeling, agriculture, and environmental monitoring. Raster Forge can be a valuable asset for geoscientists and researchers who rely on raster data analysis, enhancing geospatial data processing and visualization across various disciplines.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000459/pdfft?md5=19ec98729016d05ea702f9bf456c6771&pid=1-s2.0-S2665963824000459-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078593","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 two-tiered framework for anomaly classification in IoT networks utilizing CNN-BiLSTM model 利用 CNN-BiLSTM 模型进行物联网网络异常分类的双层框架
IF 2.1
Software Impacts Pub Date : 2024-05-01 DOI: 10.1016/j.simpa.2024.100646
Yue Guan, Morteza Noferesti, Naser Ezzati-Jivan
{"title":"A two-tiered framework for anomaly classification in IoT networks utilizing CNN-BiLSTM model","authors":"Yue Guan,&nbsp;Morteza Noferesti,&nbsp;Naser Ezzati-Jivan","doi":"10.1016/j.simpa.2024.100646","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100646","url":null,"abstract":"<div><p>The paper introduces ACS-IoT, an Anomaly Classification System for IoT networks, structured as a two-tiered framework. In the first, it employs a decision tree classifier for anomaly detection. In the second, a CNN-BiLSTM model is utilized for more profound analysis and classification of anomaly types. To address data imbalance, SMOTE is used, and feature selection is enhanced with PSO. The approach showcases strong practical applicability in real-world industrial settings, achieving an accuracy of 88%, precision of 89%, recall of 88%, and F1-score of 88% for multi-class classification, surpassing other machine learning approaches by at least 6% in all metrics.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000344/pdfft?md5=15788ce74802898e90065f9e6dee2a0b&pid=1-s2.0-S2665963824000344-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825075","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
CUDA code to generate computational models and predict mechanical properties for metallic surface nanocoatings 生成计算模型并预测金属表面纳米涂层机械性能的 CUDA 代码
IF 2.1
Software Impacts Pub Date : 2024-05-01 DOI: 10.1016/j.simpa.2024.100645
M. Bedolla-Hernández , F.J. Sánchez-Ruiz , G. Rosano-Ortega , J. Bedolla-Hernández , P.S. Schabes-Retchkiman , C.A. Vega-Lebrún , E. Vargas-Viveros
{"title":"CUDA code to generate computational models and predict mechanical properties for metallic surface nanocoatings","authors":"M. Bedolla-Hernández ,&nbsp;F.J. Sánchez-Ruiz ,&nbsp;G. Rosano-Ortega ,&nbsp;J. Bedolla-Hernández ,&nbsp;P.S. Schabes-Retchkiman ,&nbsp;C.A. Vega-Lebrún ,&nbsp;E. Vargas-Viveros","doi":"10.1016/j.simpa.2024.100645","DOIUrl":"10.1016/j.simpa.2024.100645","url":null,"abstract":"<div><p>The article presents an open-access code, written in CUDA® and C++ programming language, applicable for generating computational models of nanostructured surface coatings deposited by electrodeposition. The code uses the Schrödinger equation, energy potentials, and electrochemistry as a theoretical basis to determine the deposition and electrodeposition energies, allowing the prediction of the formation and growth of these coatings. Likewise, the parameter variation enabled within the code provides for determining the main electrodeposition parameters (voltage, current, concentration, and residence time) for experimental depositions. The code can be easily implemented for any metallic coating-substrate arrangement where the filler material is nanomaterials.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000332/pdfft?md5=004a6a432718618fdad0d7c64c55e6a0&pid=1-s2.0-S2665963824000332-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140792812","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
MR-LEAP: Mixed-Reality Learning Environment for Aspirational Programmers MR-LEAP：面向有抱负的程序员的混合现实学习环境
IF 2.1
Software Impacts Pub Date : 2024-04-24 DOI: 10.1016/j.simpa.2024.100648
Santiago Schez-Sobrino, Francisco M. García, Javier A. Albusac, Carlos Glez-Morcillo, Jose J. Castro-Schez, David Vallejo
{"title":"MR-LEAP: Mixed-Reality Learning Environment for Aspirational Programmers","authors":"Santiago Schez-Sobrino,&nbsp;Francisco M. García,&nbsp;Javier A. Albusac,&nbsp;Carlos Glez-Morcillo,&nbsp;Jose J. Castro-Schez,&nbsp;David Vallejo","doi":"10.1016/j.simpa.2024.100648","DOIUrl":"10.1016/j.simpa.2024.100648","url":null,"abstract":"<div><p>This paper presents MR-LEAP (Mixed-Reality Learning Environment for Aspirational Programmers), a framework developed for learning programming through Mixed Reality and gamification mechanics. MR-LEAP’s architecture is designed to facilitate the understanding of basic programming concepts while allowing the gradual incorporation of more complex concepts. The framework provides a simple visual level editor. MR-LEAP is supported by the Mixed Reality Toolkit framework to promote portability to new Mixed Reality devices. Our goal is to facilitate programming education using Mixed Reality technology. MR-LEAP has already been used in both research and educational.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000368/pdfft?md5=d27f1ed20fa0c4c08cfa35701088f9b6&pid=1-s2.0-S2665963824000368-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140780103","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
alPCA: An automatic software for the selection and combination of forecasts in monthly series alPCA：用于选择和组合月序列预测的自动软件
IF 2.1
Software Impacts Pub Date : 2024-04-09 DOI: 10.1016/j.simpa.2024.100644
Carlos García-Aroca, Ma. Asunción Martínez-Mayoral, Javier Morales-Socuéllamos, José Vicente Segura-Heras
{"title":"alPCA: An automatic software for the selection and combination of forecasts in monthly series","authors":"Carlos García-Aroca,&nbsp;Ma. Asunción Martínez-Mayoral,&nbsp;Javier Morales-Socuéllamos,&nbsp;José Vicente Segura-Heras","doi":"10.1016/j.simpa.2024.100644","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100644","url":null,"abstract":"<div><p>alPCA is a software coded in R and designed to automatically combine predictions from a collection of individual forecasting methods that integrate it. It employs three categories of weights derived from the PCA scores, and decision rules to determine the optimal combination of these methods. alPCA serves as an automated component within the artificial intelligence toolkit for monthly time series processing with the objective of obtaining the best forecast.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000320/pdfft?md5=7b465d8975048ac2c3a64c040483d585&pid=1-s2.0-S2665963824000320-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140543273","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
RAW-HF framework to monitor and allocate resources in real time for database management systems 实时监控和分配数据库管理系统资源的 RAW-HF 框架
IF 2.1
Software Impacts Pub Date : 2024-04-08 DOI: 10.1016/j.simpa.2024.100643
Mayank Patel , Minal Bhise
{"title":"RAW-HF framework to monitor and allocate resources in real time for database management systems","authors":"Mayank Patel ,&nbsp;Minal Bhise","doi":"10.1016/j.simpa.2024.100643","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100643","url":null,"abstract":"<div><p>Most websites and applications are hosted on a public or private cloud. In-house deployments also require dealing with system resources. Researchers have started considering resources utilized by application workloads to estimate and reduce application running costs. RAW-HF (Resource Availability &amp; Workload aware Hybrid Framework) framework tries to analyze two types of resource utilization; (1) System Resource Utilization and (2) Resource Utilized by each Query task. The RAW-HF code tries to provide a lightweight solution to monitor &amp; analyze the system and DBMS process resource utilization. It filters the required data in real time to find available resources and allocate query-specific resources based on their complexity by utilizing less than 2% CPU resources.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000319/pdfft?md5=cf5181eea96c0c7d3f6ac66866a6077c&pid=1-s2.0-S2665963824000319-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140605813","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
OptDNN: Automatic deep neural networks optimizer for edge computing OptDNN：用于边缘计算的深度神经网络自动优化器
IF 2.1
Software Impacts Pub Date : 2024-04-05 DOI: 10.1016/j.simpa.2024.100641
Luca Giovannesi, Gabriele Proietti Mattia, Roberto Beraldi
{"title":"OptDNN: Automatic deep neural networks optimizer for edge computing","authors":"Luca Giovannesi,&nbsp;Gabriele Proietti Mattia,&nbsp;Roberto Beraldi","doi":"10.1016/j.simpa.2024.100641","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100641","url":null,"abstract":"<div><p>DNNs are widely used for complex tasks like image and signal processing, and they are in increasing demand for implementation on Internet of Things (IoT) devices. For these devices, optimizing DNN models is a necessary task. Generally, standard optimization approaches require specialists to manually fine-tune hyper-parameters to find a good trade-off between efficiency and accuracy. In this paper, we propose OptDNN, a software that employs innovative and automatic approaches to determine optimal hyper-parameters for pruning, clustering, and quantization. The models optimized by OptDNN have a smaller memory footprint, faster inference time, and a similar accuracy to the original models.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000290/pdfft?md5=9408edc33cd6715a12afa1a8f06365fc&pid=1-s2.0-S2665963824000290-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140543982","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
gtrendsAPI: An R wrapper for the Google Trends API gtrendsAPI：谷歌趋势应用程序接口的 R 封装程序
IF 2.1
Software Impacts Pub Date : 2024-04-01 DOI: 10.1016/j.simpa.2024.100634
Ricardo A. Correia
{"title":"gtrendsAPI: An R wrapper for the Google Trends API","authors":"Ricardo A. Correia","doi":"10.1016/j.simpa.2024.100634","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100634","url":null,"abstract":"<div><p>Search engine data is a prime source of insights on information-seeking behaviour and such information is instrumental for the scientific study of human culture and behaviour. The gtrendsAPI R software package aims to facilitate programmatic access to data available from the Google Trends API. Here, I introduce the functions available through this software package and provide worked examples of how to use it. I also discuss some the potential research applications and caveats of this software and the data available through it.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000228/pdfft?md5=38103b321777d6eb8d5b2d3503237b9b&pid=1-s2.0-S2665963824000228-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140350987","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
NGPCA: Clustering of high-dimensional and non-stationary data streams NGPCA：高维非稳态数据流聚类
IF 2.1
Software Impacts Pub Date : 2024-03-29 DOI: 10.1016/j.simpa.2024.100635
Nico Migenda , Ralf Möller , Wolfram Schenck
{"title":"NGPCA: Clustering of high-dimensional and non-stationary data streams","authors":"Nico Migenda ,&nbsp;Ralf Möller ,&nbsp;Wolfram Schenck","doi":"10.1016/j.simpa.2024.100635","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100635","url":null,"abstract":"<div><p>Neural Gas Principal Component Analysis (NGPCA) is an online clustering algorithm. An NGPCA model is a mixture of local PCA units and combines dimensionality reduction with vector quantization. Recently, NGPCA has been extended with an adaptive learning rate and an adaptive potential function for accurate and efficient clustering of high-dimensional and non-stationary data streams. The algorithm achieved highly competitive results on clustering benchmark datasets compared to the state of the art. Our implementation of the algorithm was developed in MATLAB and is available as open source. This code can be easily applied to the clustering of stationary and non-stationary data.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266596382400023X/pdfft?md5=6784f267af3874ee2a02d381441cd5f4&pid=1-s2.0-S266596382400023X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140344005","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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