Pub Date : 2025-12-15DOI: 10.1016/j.simpa.2025.100804
Thuan Van Tran, Triet Minh Nguyen, Quy Thanh Lu
Skin is an important part of the guardian system, which helps to protect us from harmful factors such as physical impact, bacteria, viruses, and especially daily ultraviolet (UV) radiation. However, the changing of the environment in the present era creates prolonged exposure to UV, which can damage the skin and increase the risk of skin cancer. Thus, a skin cancer classification and detection framework called SMCS (Sampling in MobileNet for Skin Classification) was published by taking the power of artificial intelligence and deep learning. In this pipeline, skin illnesses can be discovered early, which aids doctors and patients in diagnosis and treatment while reducing both time and cost.
皮肤是保护系统的重要组成部分，它有助于保护我们免受有害因素的影响，如物理冲击，细菌，病毒，尤其是日常紫外线（UV）辐射。然而，当今时代环境的变化使人长时间暴露在紫外线下，这会损害皮肤，增加患皮肤癌的风险。因此，利用人工智能和深度学习的力量，发表了一个名为SMCS （Sampling in MobileNet for skin classification）的皮肤癌分类检测框架。在这个管道中，皮肤疾病可以早期发现，这有助于医生和患者的诊断和治疗，同时减少时间和成本。
{"title":"SMCS: A lightweight MobileNet-based framework for skin cancer classification, segmentation, and explanation","authors":"Thuan Van Tran, Triet Minh Nguyen, Quy Thanh Lu","doi":"10.1016/j.simpa.2025.100804","DOIUrl":"10.1016/j.simpa.2025.100804","url":null,"abstract":"<div><div>Skin is an important part of the guardian system, which helps to protect us from harmful factors such as physical impact, bacteria, viruses, and especially daily ultraviolet (UV) radiation. However, the changing of the environment in the present era creates prolonged exposure to UV, which can damage the skin and increase the risk of skin cancer. Thus, a skin cancer classification and detection framework called SMCS (Sampling in MobileNet for Skin Classification) was published by taking the power of artificial intelligence and deep learning. In this pipeline, skin illnesses can be discovered early, which aids doctors and patients in diagnosis and treatment while reducing both time and cost.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100804"},"PeriodicalIF":1.2,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925679","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 : 2025-12-05DOI: 10.1016/j.simpa.2025.100805
Spiros Gkousis, Evina Katsou
Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) are becoming key methods for sustainability analysis. Current software solutions usually focus on one method, omitting synergies and the provision of a holistic picture of system sustainability. Integrating LCA and LCC software with complex system models, uncertainty, and optimization tools remains a barrier for integrated techno-sustainability assessments. Lcpy is an open-source python package that enables using parametric or simulation process models, in-time projections, multiple scenarios, and flexible modelling for simple and dynamic LCA and LCC, uncertainty analysis and optimization. Visualization and storage functions are provided allowing end-to-end LCA and LCC analyses.
{"title":"Lcpy: An open-source python package for parametric and dynamic life cycle assessment and life cycle costing analysis","authors":"Spiros Gkousis, Evina Katsou","doi":"10.1016/j.simpa.2025.100805","DOIUrl":"10.1016/j.simpa.2025.100805","url":null,"abstract":"<div><div>Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) are becoming key methods for sustainability analysis. Current software solutions usually focus on one method, omitting synergies and the provision of a holistic picture of system sustainability. Integrating LCA and LCC software with complex system models, uncertainty, and optimization tools remains a barrier for integrated techno-sustainability assessments. Lcpy is an open-source python package that enables using parametric or simulation process models, in-time projections, multiple scenarios, and flexible modelling for simple and dynamic LCA and LCC, uncertainty analysis and optimization. Visualization and storage functions are provided allowing end-to-end LCA and LCC analyses.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100805"},"PeriodicalIF":1.2,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737668","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 : 2025-12-01DOI: 10.1016/j.simpa.2025.100803
Qinghan Meng , Zhitao Mao , Hao Chen , Yuanyuan Huang , Hongwu Ma
GO-HKP is a Gene Ontology hierarchy-driven framework for predicting enzyme turnover numbers () with improved coverage, generalizability, and interpretability. It integrates curated UniProt data, ontology-based propagation, and sequence-driven GO annotation (DeepGO-SE) to infer for both annotated and novel enzymes. Benchmarking across four genome-scale metabolic models demonstrated substantial improvements in reaction coverage — by 56.67%, 25.1%, 16.0%, and 14.5% — compared with existing methods, highlighting its strong gap-filling capability. GO-HKP offers a biologically grounded, scalable, and transparent approach, supporting applications in metabolic engineering, drug discovery, and systems biology. The framework and Python package are available via GitHub for broad usability and reproducibility.
{"title":"GO-HKP: A Gene Ontology hierarchy-driven framework for enzyme kcat prediction","authors":"Qinghan Meng , Zhitao Mao , Hao Chen , Yuanyuan Huang , Hongwu Ma","doi":"10.1016/j.simpa.2025.100803","DOIUrl":"10.1016/j.simpa.2025.100803","url":null,"abstract":"<div><div>GO-HKP is a Gene Ontology hierarchy-driven framework for predicting enzyme turnover numbers (<span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span>) with improved coverage, generalizability, and interpretability. It integrates curated UniProt data, ontology-based <span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span> propagation, and sequence-driven GO annotation (DeepGO-SE) to infer <span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span> for both annotated and novel enzymes. Benchmarking across four genome-scale metabolic models demonstrated substantial improvements in reaction coverage — by 56.67%, 25.1%, 16.0%, and 14.5% — compared with existing methods, highlighting its strong gap-filling capability. GO-HKP offers a biologically grounded, scalable, and transparent approach, supporting applications in metabolic engineering, drug discovery, and systems biology. The framework and Python package are available via GitHub for broad usability and reproducibility.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100803"},"PeriodicalIF":1.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683321","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 : 2025-11-28DOI: 10.1016/j.simpa.2025.100802
Tomasz Górski
A software architecture description is a work product that reveals software architecture. An architecture view manifests the system architecture from a specific perspective. In publications presenting original software, it is crucial to introduce the functions implemented by the software and identify its users. The structure and operation of the software should also be depicted. However, many publications contain drawings that often combine content from several views. Therefore, the paper introduces a method for describing software architecture in Use Cases and Logical views of the 1+5 model. The method expresses the architecture of a new software package for real estate sales.
{"title":"Software architecture description in original software publications","authors":"Tomasz Górski","doi":"10.1016/j.simpa.2025.100802","DOIUrl":"10.1016/j.simpa.2025.100802","url":null,"abstract":"<div><div>A software architecture description is a work product that reveals software architecture. An architecture view manifests the system architecture from a specific perspective. In publications presenting original software, it is crucial to introduce the functions implemented by the software and identify its users. The structure and operation of the software should also be depicted. However, many publications contain drawings that often combine content from several views. Therefore, the paper introduces a method for describing software architecture in Use Cases and Logical views of the 1+5 model. The method expresses the architecture of a new software package for real estate sales.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"27 ","pages":"Article 100802"},"PeriodicalIF":1.2,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624935","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 : 2025-10-01DOI: 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 : 2025-10-01DOI: 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 : 2025-10-01DOI: 10.1016/j.simpa.2025.100792
Boris Prokhorov , Oleg Zolotov , Yulia Romanovskaya , Anton Tatarnikov , Yulia Shapovalova
The pyNusinov package implements Nusinov’s extreme ultraviolet (EUVT) and far ultraviolet (FUVT) solar radiation models. Jointly, these models cover the 5–242 [nm] solar irradiance range but with different wavelength steps and a small gap between 105–115 [nm]. To third-party users, EUVT and FUVT models were published as analytical formulas and tables of corresponding coefficients. The release of the pyNusinov package provides a robust way to use, disseminate, install, and update these models. It significantly improves the models’ usage workflow, benefits from Python3 infrastructure, and facilitates early career researchers’ engagement.
{"title":"pyNusinov: A Python3 software package for Solar Extreme and Far Ultraviolet radiation modeling","authors":"Boris Prokhorov , Oleg Zolotov , Yulia Romanovskaya , Anton Tatarnikov , Yulia Shapovalova","doi":"10.1016/j.simpa.2025.100792","DOIUrl":"10.1016/j.simpa.2025.100792","url":null,"abstract":"<div><div>The pyNusinov package implements Nusinov’s extreme ultraviolet (EUVT) and far ultraviolet (FUVT) solar radiation models. Jointly, these models cover the 5–242 [nm] solar irradiance range but with different wavelength steps and a small gap between 105–115 [nm]. To third-party users, EUVT and FUVT models were published as analytical formulas and tables of corresponding coefficients. The release of the pyNusinov package provides a robust way to use, disseminate, install, and update these models. It significantly improves the models’ usage workflow, benefits from Python3 infrastructure, and facilitates early career researchers’ engagement.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100792"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363255","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 : 2025-10-01DOI: 10.1016/j.simpa.2025.100798
N Annalakshmi , S Umarani
SkinProNet is an AI-powered software tool designed for the classification and segmentation of skin lesions, including potentially life-threatening conditions like melanoma. It employs a novel hybrid deep learning architecture that combines advanced preprocessing methods with state-of-the-art models: EfficientNetV2Small for feature extraction, an optimized ACRNN for accurate classification, and U2-Net++ for precise lesion segmentation. This integrated approach enhances early detection and diagnosis of skin diseases. The model classifies six types of skin diseases with a high accuracy of 94.04% using both benchmark datasets and real-world clinical images. The results highlight the model’s potential as a reliable computer-aided diagnostic tool in dermatology. By leveraging attention-based mechanisms and efficient neural architectures, the software supports healthcare practitioners in diagnosing skin conditions quickly, accurately, and non-invasively.
{"title":"SkinProNet: An attention-based deep learning system for skin disease classification and segmentation","authors":"N Annalakshmi , S Umarani","doi":"10.1016/j.simpa.2025.100798","DOIUrl":"10.1016/j.simpa.2025.100798","url":null,"abstract":"<div><div>SkinProNet is an AI-powered software tool designed for the classification and segmentation of skin lesions, including potentially life-threatening conditions like melanoma. It employs a novel hybrid deep learning architecture that combines advanced preprocessing methods with state-of-the-art models: EfficientNetV2Small for feature extraction, an optimized ACRNN for accurate classification, and U<sup>2</sup>-Net++ for precise lesion segmentation. This integrated approach enhances early detection and diagnosis of skin diseases. The model classifies six types of skin diseases with a high accuracy of 94.04% using both benchmark datasets and real-world clinical images. The results highlight the model’s potential as a reliable computer-aided diagnostic tool in dermatology. By leveraging attention-based mechanisms and efficient neural architectures, the software supports healthcare practitioners in diagnosing skin conditions quickly, accurately, and non-invasively.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"26 ","pages":"Article 100798"},"PeriodicalIF":1.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466179","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 : 2025-10-01DOI: 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}
Pub Date : 2025-10-01DOI: 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}
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