Pub Date : 2026-04-01Epub Date: 2026-01-06DOI: 10.1016/j.simpa.2025.100809
Timo van der Kuil , Jelle Jasper Teijema , Jonathan de Bruin , Rens van de Schoot
Systematic reviewing is a time-consuming process which can be accelerated through screening prioritisation via active learning. ASReview Dory enables researchers to test, validate, and apply a wide range of embedders and classifiers in systematic literature screening. It extends ASReview LAB, an open source, lightweight, and user-friendly environment with proven default models and extensibility through Python entry points. ASReview Dory adds ready-to-use transformer-based embedders, neural classifiers, and a framework for integrating custom models. Once installed, these models are directly available in ASReview LAB without additional configuration and can be systematically evaluated using the API or ASReview Makita.
{"title":"ASReview Dory: Bringing new and exciting models to ASReview LAB","authors":"Timo van der Kuil , Jelle Jasper Teijema , Jonathan de Bruin , Rens van de Schoot","doi":"10.1016/j.simpa.2025.100809","DOIUrl":"10.1016/j.simpa.2025.100809","url":null,"abstract":"<div><div>Systematic reviewing is a time-consuming process which can be accelerated through screening prioritisation via active learning. ASReview Dory enables researchers to test, validate, and apply a wide range of embedders and classifiers in systematic literature screening. It extends ASReview LAB, an open source, lightweight, and user-friendly environment with proven default models and extensibility through Python entry points. ASReview Dory adds ready-to-use transformer-based embedders, neural classifiers, and a framework for integrating custom models. Once installed, these models are directly available in ASReview LAB without additional configuration and can be systematically evaluated using the API or ASReview Makita.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100809"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925680","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}
Pub Date : 2026-04-01Epub Date: 2026-01-07DOI: 10.1016/j.simpa.2025.100808
S. Malekpour , S.M. Dehghan , M.A. Najafgholipour , S. Behravesh
Probabilistic and reliability analyses utilizing finite element software are frequently constrained by manual model creation and result extraction. This study presents an open-source, MATLAB-based framework integrated with Abaqus that automates randomized model generation through Monte Carlo simulation, performs analyses, and retrieves target results via a lightweight Python script in noGUI mode. The modular tool reduces user intervention and facilitates automated variations in geometry, material properties, and loading conditions. This framework enables rapid model generation and result extraction for hundreds of analyses in seconds, significantly reducing manual effort and potential human error.
{"title":"MatAbaAutoRel: A MATLAB–Abaqus framework for automated reliability analysis","authors":"S. Malekpour , S.M. Dehghan , M.A. Najafgholipour , S. Behravesh","doi":"10.1016/j.simpa.2025.100808","DOIUrl":"10.1016/j.simpa.2025.100808","url":null,"abstract":"<div><div>Probabilistic and reliability analyses utilizing finite element software are frequently constrained by manual model creation and result extraction. This study presents an open-source, MATLAB-based framework integrated with Abaqus that automates randomized model generation through Monte Carlo simulation, performs analyses, and retrieves target results via a lightweight Python script in noGUI mode. The modular tool reduces user intervention and facilitates automated variations in geometry, material properties, and loading conditions. This framework enables rapid model generation and result extraction for hundreds of analyses in seconds, significantly reducing manual effort and potential human error.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100808"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925682","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}
Pub Date : 2026-04-01Epub Date: 2026-02-12DOI: 10.1016/j.simpa.2026.100819
Sudesh Kumar, Sunanda Gupta
ICMP-flood-SDN is an artificial intelligence based DDoS detection application that uses support vector machines (SVMs) as a machine learning model for the classification of ICMP flood DDoS traffic in software defined networks. The ICMP-flood-SDN was built using the ICMP-Flood DDoS dataset and Python-based machine learning libraries on Jupiter Notebook. The application utilizes the Mininet emulator, RYU controller, and hping3 tool to create a normal and ICMP flood traffic dataset in software defined network.
{"title":"ICMP-Flood-SDN: A Python based machine learning application for ICMP flood DDoS attack detection in software defined networks","authors":"Sudesh Kumar, Sunanda Gupta","doi":"10.1016/j.simpa.2026.100819","DOIUrl":"10.1016/j.simpa.2026.100819","url":null,"abstract":"<div><div>ICMP-flood-SDN is an artificial intelligence based DDoS detection application that uses support vector machines (SVMs) as a machine learning model for the classification of ICMP flood DDoS traffic in software defined networks. The ICMP-flood-SDN was built using the ICMP-Flood DDoS dataset and Python-based machine learning libraries on Jupiter Notebook. The application utilizes the Mininet emulator, RYU controller, and hping3 tool to create a normal and ICMP flood traffic dataset in software defined network.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100819"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172656","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}
Pub Date : 2026-04-01Epub Date: 2026-01-28DOI: 10.1016/j.simpa.2026.100813
Khadija Parwez , Syed Irfan Sohail , Ali Raza , Mohammad Abdullah Zia
LeukoXAI-Lite: A flexible and modular software framework for the interpretable diagnosis of Acute Lymphoblastic Leukemia by deep learning algorithms and visual explanation tools. The system adopts EfficientNetB3-based convolutional neural network, which is embedded in a hierarchical federated learning framework. This approach facilitates distributed model training with simulated health participants cooperating yet guarantee the private of sensitive patient information. In addition to disease categorization, our framework is equipped with a profound explainable artificial intelligence module, based upon 18 distinct visualization methods that includes saliency maps, guided backpropagation, gradient_based methods, SmoothGrad, VarGrad, SquareGrad, Grad-CAM, Grad-CAM++, HiResCAM, Respond-CAM, Score-CAM, Faster Score-CAM, oclusion sensitivity, LIME, SHAP, sobol attribution, and a fusion approach. These approaches produce visual heatmaps which highlight diagnostically important regions in microscopic images of blood cells, making the model more interpretable for clinical deployment. LeukoXAI-Lite also comes with instruments for systematic evaluation of the performance of the predictive model and explanation methods. We support common classification on based metrics (accuracy, precision, recall, F1 score, Kappa score and MCC) as well the explanation specific ones like Deletion, Insertion, Fidelity and Stability. It is implemented with open-source python libraries to be lightweight, adaptable and compatible with real-world use in medical imaging. LeukoXAI-Lite facilitates such kind of trustworthy and interpretable artificial intelligence solutions for the clinical diagnostics by means promoting transparency, reproducibility and privacy friendly learning.
{"title":"LeukoXAI-Lite: A reusable explainable AI toolkit for federated leukemia diagnosis with visual explanations and performance analysis","authors":"Khadija Parwez , Syed Irfan Sohail , Ali Raza , Mohammad Abdullah Zia","doi":"10.1016/j.simpa.2026.100813","DOIUrl":"10.1016/j.simpa.2026.100813","url":null,"abstract":"<div><div>LeukoXAI-Lite: A flexible and modular software framework for the interpretable diagnosis of Acute Lymphoblastic Leukemia by deep learning algorithms and visual explanation tools. The system adopts EfficientNetB3-based convolutional neural network, which is embedded in a hierarchical federated learning framework. This approach facilitates distributed model training with simulated health participants cooperating yet guarantee the private of sensitive patient information. In addition to disease categorization, our framework is equipped with a profound explainable artificial intelligence module, based upon 18 distinct visualization methods that includes saliency maps, guided backpropagation, gradient_based methods, SmoothGrad, VarGrad, SquareGrad, Grad-CAM, Grad-CAM++, HiResCAM, Respond-CAM, Score-CAM, Faster Score-CAM, oclusion sensitivity, LIME, SHAP, sobol attribution, and a fusion approach. These approaches produce visual heatmaps which highlight diagnostically important regions in microscopic images of blood cells, making the model more interpretable for clinical deployment. LeukoXAI-Lite also comes with instruments for systematic evaluation of the performance of the predictive model and explanation methods. We support common classification on based metrics (accuracy, precision, recall, F1 score, Kappa score and MCC) as well the explanation specific ones like Deletion, Insertion, Fidelity and Stability. It is implemented with open-source python libraries to be lightweight, adaptable and compatible with real-world use in medical imaging. LeukoXAI-Lite facilitates such kind of trustworthy and interpretable artificial intelligence solutions for the clinical diagnostics by means promoting transparency, reproducibility and privacy friendly learning.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100813"},"PeriodicalIF":1.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172595","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}
Pub Date : 2025-10-01Epub Date: 2025-09-01DOI: 10.1016/j.simpa.2025.100783
Hardik Ruparel, Tatsat Patel
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) with external knowledge retrieval but incur significant compute and latency costs. In distributed RAG deployments, semantically similar queries routed to different nodes — each with its own cache — can lead to redundant processing. We present RAGCacheSim, a discrete-event simulator for evaluating caching strategies such as Centralized Exact-match Cache (CEC), Independent Semantic Caches (IC), and Distributed Semantic Cache Coordination (DSC). It reports metrics like cache hit rate, average query latency, and coordination overhead. Built using SimPy, FastEmbed, and pybloom_live, it helps researchers optimize distributed RAG architectures.
{"title":"RAGCacheSim: A discrete-event simulator for evaluating caching strategies in Retrieval-Augmented Generation systems","authors":"Hardik Ruparel, Tatsat Patel","doi":"10.1016/j.simpa.2025.100783","DOIUrl":"10.1016/j.simpa.2025.100783","url":null,"abstract":"<div><div>Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) with external knowledge retrieval but incur significant compute and latency costs. In distributed RAG deployments, semantically similar queries routed to different nodes — each with its own cache — can lead to redundant processing. We present <em>RAGCacheSim</em>, a discrete-event simulator for evaluating caching strategies such as Centralized Exact-match Cache (CEC), Independent Semantic Caches (IC), and Distributed Semantic Cache Coordination (DSC). It reports metrics like cache hit rate, average query latency, and coordination overhead. Built using <span>SimPy</span>, <span>FastEmbed</span>, and <span>pybloom_live</span>, it helps researchers optimize distributed RAG architectures.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100783"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048507","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}
Pub Date : 2025-10-01Epub Date: 2025-09-16DOI: 10.1016/j.simpa.2025.100784
Baijian Wu, Gang Yu
STFATool is a professional signal-processing application implemented in Python. It integrates several state-of-the-art sparse time–frequency analysis algorithms, including Synchroextracting Transform, Transient-Extracting Transform, Multisynchrosqueezing Transform, and Time-Reassigned Multisynchrosqueezing Transform. It provides a user-friendly interface, users can import signals for detailed time–frequency feature visualization and processing, enabling efficient extraction of critical signal characteristics.
{"title":"STFATool: A Sparse Time–Frequency Analysis Toolkit for non-stationary signals","authors":"Baijian Wu, Gang Yu","doi":"10.1016/j.simpa.2025.100784","DOIUrl":"10.1016/j.simpa.2025.100784","url":null,"abstract":"<div><div>STFATool is a professional signal-processing application implemented in Python. It integrates several state-of-the-art sparse time–frequency analysis algorithms, including Synchroextracting Transform, Transient-Extracting Transform, Multisynchrosqueezing Transform, and Time-Reassigned Multisynchrosqueezing Transform. It provides a user-friendly interface, users can import signals for detailed time–frequency feature visualization and processing, enabling efficient extraction of critical signal characteristics.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100784"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107849","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}
Pub Date : 2025-10-01Epub Date: 2025-11-03DOI: 10.1016/j.simpa.2025.100799
Giorgio Felizzato , Michele Verdi , Angelo Michele Gargantini , Nico Pellegrinelli , Francesco Saverio Romolo
Portable sensors for on-site forensic analysis have advanced significantly, enabling reliable methods for crime scene investigation. Non-destructive analytical instruments are especially useful for providing chemical information from the same specimen. Combining data from these instruments through data fusion enhances analytical responses. Data fusion merges data from different sources to improve exploratory and predictive models. No current application supports multi-dataset fusion on a single platform. To address this, we developed a Python-based ‘Forensic-DataFusion-Tool’ to merge raw and preprocessed data from multiple sensors, speeding up data fusion and enabling future machine learning updates, including classification algorithms.
{"title":"‘Forensic-DataFusion-Tool’: A Python-based application for exploratory forensic data analysis using merged datasets from analytical sensors","authors":"Giorgio Felizzato , Michele Verdi , Angelo Michele Gargantini , Nico Pellegrinelli , Francesco Saverio Romolo","doi":"10.1016/j.simpa.2025.100799","DOIUrl":"10.1016/j.simpa.2025.100799","url":null,"abstract":"<div><div>Portable sensors for on-site forensic analysis have advanced significantly, enabling reliable methods for crime scene investigation. Non-destructive analytical instruments are especially useful for providing chemical information from the same specimen. Combining data from these instruments through data fusion enhances analytical responses. Data fusion merges data from different sources to improve exploratory and predictive models. No current application supports multi-dataset fusion on a single platform. To address this, we developed a Python-based ‘Forensic-DataFusion-Tool’ to merge raw and preprocessed data from multiple sensors, speeding up data fusion and enabling future machine learning updates, including classification algorithms.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100799"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466180","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}
Pub Date : 2025-10-01Epub Date: 2025-11-05DOI: 10.1016/j.simpa.2025.100796
Lilia Costa , Arthur Azevedo , Michel Miler Rocha dos Santos , Diego Carvalho Nascimento
The Multiregression Dynamic Model (MDM) is a framework that combines graph theory with dynamic linear models, allowing a non-Gaussian multivariate structure to emerge in the context of causal time series. Since an optimal DAG structure is an NP-hard task, this package overcomes the all-combinations search (Integer Programming Algorithm) using heuristic algorithms (like Hill Climbing). Written using R S4 Object programming, it performs learning functions (estimating network structure and its dynamic arcs), as well as includes DAG (causal) visualization, time-varying coefficients visualization, and graphical performance checks. The MDM R package is distributed under the GPL license and is accessible from GitHub.
{"title":"MDM: An R package for causal multivariate time series tasks","authors":"Lilia Costa , Arthur Azevedo , Michel Miler Rocha dos Santos , Diego Carvalho Nascimento","doi":"10.1016/j.simpa.2025.100796","DOIUrl":"10.1016/j.simpa.2025.100796","url":null,"abstract":"<div><div>The Multiregression Dynamic Model (MDM) is a framework that combines graph theory with dynamic linear models, allowing a non-Gaussian multivariate structure to emerge in the context of causal time series. Since an optimal DAG structure is an NP-hard task, this package overcomes the all-combinations search (Integer Programming Algorithm) using heuristic algorithms (like Hill Climbing). Written using R S4 Object programming, it performs learning functions (estimating network structure and its dynamic arcs), as well as includes DAG (causal) visualization, time-varying coefficients visualization, and graphical performance checks. The MDM R package is distributed under the GPL license and is accessible from GitHub.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100796"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466182","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}
Pub Date : 2025-10-01Epub Date: 2025-11-14DOI: 10.1016/j.simpa.2025.100801
Irsyad Fikriansyah Ramadhan , Ntivuguruzwa Jean De La Croix , Tohari Ahmad
In information security, image steganography remains a crucial technique for covert data transmission. However, achieving an optimal balance between payload capacity, imperceptibility, and robustness against steganalysis attacks remains a significant challenge. This paper presents IRJT-Secure, an open-source software implementation based on the Quadristego Logic paradigm and Huffman coding for data compression. The proposed technique creates four stego images from a single cover image, in contrast to conventional dual-image steganography. This maintains the original image’s visual integrity while enabling more effective and uniform data embedding. IRJT-Secure provides a valuable resource for advancing research and development in spatial domain steganography, supporting the creation of more secure, robust, and efficient data hiding techniques for digital security
{"title":"IRJT-Secure: Open-source image steganography with Quadristego embedding and Huffman compression","authors":"Irsyad Fikriansyah Ramadhan , Ntivuguruzwa Jean De La Croix , Tohari Ahmad","doi":"10.1016/j.simpa.2025.100801","DOIUrl":"10.1016/j.simpa.2025.100801","url":null,"abstract":"<div><div>In information security, image steganography remains a crucial technique for covert data transmission. However, achieving an optimal balance between payload capacity, imperceptibility, and robustness against steganalysis attacks remains a significant challenge. This paper presents IRJT-Secure, an open-source software implementation based on the Quadristego Logic paradigm and Huffman coding for data compression. The proposed technique creates four stego images from a single cover image, in contrast to conventional dual-image steganography. This maintains the original image’s visual integrity while enabling more effective and uniform data embedding. IRJT-Secure provides a valuable resource for advancing research and development in spatial domain steganography, supporting the creation of more secure, robust, and efficient data hiding techniques for digital security</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100801"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145576323","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}
Pub Date : 2025-10-01Epub Date: 2025-09-25DOI: 10.1016/j.simpa.2025.100789
Mariusz Oszust, Marian Wysocki
Feature selection in multivariate time series is a key challenge in modern data analysis, as high-dimensional data often include temporal dependencies and irrelevant features degrading classifier performance. To address these issues, a comparison with existing approaches is essential. Therefore, this work introduces the Toolbox for Metaheuristic Feature Selection in Multivariate Time Series (TMFS-MTS), providing an environment for feature selection and metaheuristic evaluation. It supports diverse fitness measures and advanced visualizations, including convergence curves, feature count tracking, runtime analysis, Wilcoxon tests, and 2D embeddings. Implemented in MATLAB, TMFS-MTS offers a standardized framework for advancing research in multivariate time series feature selection.
{"title":"TMFS-MTS: Toolbox for metaheuristic feature selection in multivariate time series","authors":"Mariusz Oszust, Marian Wysocki","doi":"10.1016/j.simpa.2025.100789","DOIUrl":"10.1016/j.simpa.2025.100789","url":null,"abstract":"<div><div>Feature selection in multivariate time series is a key challenge in modern data analysis, as high-dimensional data often include temporal dependencies and irrelevant features degrading classifier performance. To address these issues, a comparison with existing approaches is essential. Therefore, this work introduces the Toolbox for Metaheuristic Feature Selection in Multivariate Time Series (TMFS-MTS), providing an environment for feature selection and metaheuristic evaluation. It supports diverse fitness measures and advanced visualizations, including convergence curves, feature count tracking, runtime analysis, Wilcoxon tests, and 2D embeddings. Implemented in MATLAB, TMFS-MTS offers a standardized framework for advancing research in multivariate time series feature selection.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100789"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221949","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}
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