Pub Date : 2025-05-02DOI: 10.1016/j.simpa.2025.100753
Seifallah Elfetni , Reza Darvishi Kamachali
This paper introduces PINNs-MPF, a novel Machine Learning-based solver designed for Multi-Phase-Field (MPF) and diffuse interface simulations, offering innovative approaches to address complex challenges in addressing microstructure evolution in polycrystalline materials using Machine Learning. The framework not only surpasses current limitations in handling multi-phase problems but also allows for potential upscaling to tackle more intricate scenarios. Developed in Python, the related code leverages optimized libraries like TensorFlow, showcasing efficiency and potential scalability in materials science and engineering simulations. This framework, integrating advanced techniques such as multi-networking and training optimization, setting a new standard in predictive capabilities and understanding complex physical phenomena.
{"title":"PINNs-MPF: An Efficient Physics-Informed Machine Learning-based Solver for Multi-Phase-Field Simulations using Tensorflow","authors":"Seifallah Elfetni , Reza Darvishi Kamachali","doi":"10.1016/j.simpa.2025.100753","DOIUrl":"10.1016/j.simpa.2025.100753","url":null,"abstract":"<div><div>This paper introduces PINNs-MPF, a novel Machine Learning-based solver designed for Multi-Phase-Field (MPF) and diffuse interface simulations, offering innovative approaches to address complex challenges in addressing microstructure evolution in polycrystalline materials using Machine Learning. The framework not only surpasses current limitations in handling multi-phase problems but also allows for potential upscaling to tackle more intricate scenarios. Developed in Python, the related code leverages optimized libraries like TensorFlow, showcasing efficiency and potential scalability in materials science and engineering simulations. This framework, integrating advanced techniques such as multi-networking and training optimization, setting a new standard in predictive capabilities and understanding complex physical phenomena.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100753"},"PeriodicalIF":1.3,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903592","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 : 2025-04-30DOI: 10.1016/j.simpa.2025.100763
Rudy Prietno , Santana Yuda Pradata , Raka Satya Prasasta , Gemilang Santiyuda , Muhammad Alfian Amrizal , Tri Kuntoro Priyambodo , Vincent F. Yu
Distributing medical supplies involves complex logistical challenges, including the need for optimized delivery routes and efficient packing. Medicines, whether ordered in small quantities or in bulk, are packed into cardboard boxes, which affect cargo dimensions, loading plans, and available delivery routes. Additionally, some medicines require refrigeration, making it necessary to coordinate both reefer and standard trucks. This study introduces MedRoPax, a comprehensive software solution designed to address these challenges. MedRoPax solves the 3D Loading Heterogeneous Vehicle Routing Problem (3LHVRP) and provides user-friendly tools for packing, loading visualization, and route planning. While tailored for medical supply distribution, MedRoPax is also well-suited for other logistics operations that demand both efficiency and safety.
{"title":"MedRoPax: A comprehensive software for solving heterogeneous vehicle routing problem with 3D loading constraints and cardboard box packing for medical supply distribution","authors":"Rudy Prietno , Santana Yuda Pradata , Raka Satya Prasasta , Gemilang Santiyuda , Muhammad Alfian Amrizal , Tri Kuntoro Priyambodo , Vincent F. Yu","doi":"10.1016/j.simpa.2025.100763","DOIUrl":"10.1016/j.simpa.2025.100763","url":null,"abstract":"<div><div>Distributing medical supplies involves complex logistical challenges, including the need for optimized delivery routes and efficient packing. Medicines, whether ordered in small quantities or in bulk, are packed into cardboard boxes, which affect cargo dimensions, loading plans, and available delivery routes. Additionally, some medicines require refrigeration, making it necessary to coordinate both reefer and standard trucks. This study introduces MedRoPax, a comprehensive software solution designed to address these challenges. MedRoPax solves the 3D Loading Heterogeneous Vehicle Routing Problem (3LHVRP) and provides user-friendly tools for packing, loading visualization, and route planning. While tailored for medical supply distribution, MedRoPax is also well-suited for other logistics operations that demand both efficiency and safety.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100763"},"PeriodicalIF":1.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139168","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 : 2025-04-29DOI: 10.1016/j.simpa.2025.100766
Leandro Rodrigues da Silva Souza , Daniel Hilário da Silva , Caio Tonus Ribeiro , Daiane Alves da Silva , Slawomir J. Nasuto , Catherine M. Sweeney-Reed , Adriano de Oliveira Andrade , Adriano Alves Pereira
Bibliometric analyses often depend on extracting metadata from large scientific databases, a process that is still largely manual, repetitive, and error prone. This paper presents PubMedMetaTool, an open-source Python-based solution that automates the retrieval and transformation of bibliographic metadata from PubMed, using either article titles or Digital Object Identifiers as input. The tool implements a modular pipeline that extracts metadata using NCBI’s Entrez programming utilities and transforms it into formats compatible with tools such as Bibliometrix, VOSviewer, and pyBibX. Designed to be transparent and configurable, the tool improves bibliometric workflow efficiency, accuracy, and interoperability workflows.
{"title":"PubMedMetaTool: Automated metadata extraction from PubMed using Python for bibliometric analysis","authors":"Leandro Rodrigues da Silva Souza , Daniel Hilário da Silva , Caio Tonus Ribeiro , Daiane Alves da Silva , Slawomir J. Nasuto , Catherine M. Sweeney-Reed , Adriano de Oliveira Andrade , Adriano Alves Pereira","doi":"10.1016/j.simpa.2025.100766","DOIUrl":"10.1016/j.simpa.2025.100766","url":null,"abstract":"<div><div>Bibliometric analyses often depend on extracting metadata from large scientific databases, a process that is still largely manual, repetitive, and error prone. This paper presents PubMedMetaTool, an open-source Python-based solution that automates the retrieval and transformation of bibliographic metadata from PubMed, using either article titles or Digital Object Identifiers as input. The tool implements a modular pipeline that extracts metadata using NCBI’s Entrez programming utilities and transforms it into formats compatible with tools such as Bibliometrix, VOSviewer, and pyBibX. Designed to be transparent and configurable, the tool improves bibliometric workflow efficiency, accuracy, and interoperability workflows.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100766"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941815","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 : 2025-04-29DOI: 10.1016/j.simpa.2025.100764
Daqing Yun , Liudong Zuo , Yi Gu , Chase Wu
This work presents an automated tool for optimizing control parameters in performance testing of big data transfer over long-fat network connections. Supporting both TCP and UDT protocols, the tool identifies the optimal configurations to enhance the efficiency of large-scale data transfers. A stochastic approximation algorithm is employed for parameter optimization, streamlining the protocol and parameter selection. The tool has been evaluated in various network scenarios, including long-haul connections in real-world high-performance networks. Its modular design also enables straightforward integration of additional data transfer protocols and alternative optimization methods.
{"title":"An automated parameter optimizer for data transfer performance testing","authors":"Daqing Yun , Liudong Zuo , Yi Gu , Chase Wu","doi":"10.1016/j.simpa.2025.100764","DOIUrl":"10.1016/j.simpa.2025.100764","url":null,"abstract":"<div><div>This work presents an automated tool for optimizing control parameters in performance testing of big data transfer over long-fat network connections. Supporting both TCP and UDT protocols, the tool identifies the optimal configurations to enhance the efficiency of large-scale data transfers. A stochastic approximation algorithm is employed for parameter optimization, streamlining the protocol and parameter selection. The tool has been evaluated in various network scenarios, including long-haul connections in real-world high-performance networks. Its modular design also enables straightforward integration of additional data transfer protocols and alternative optimization methods.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100764"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903527","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 : 2025-04-29DOI: 10.1016/j.simpa.2025.100765
Sai Jeevan Puchakayala , Allen Bijo T. , Aswathy Ravikumar , Harini Sriraman
Nomad Analytix is an innovative business intelligence tool that uses state-of-the-art vision models to transform data analysis. This software automates complex tasks traditionally handled by data analysts, empowering non-technical teams such as marketing and sales to access advanced data analysis easily. By using natural language prompts, users can interact with data intuitively and gain valuable insights without needing extensive technical expertise. A prototype of the software, built on the Streamlit platform, will showcase its ability to generate visualizations from various data sources, including CSV, JSON, SQLite, Excel, and databases, with potential extensions to data warehouses. The integration of Vision Language Models GPT 4 Omni and GPT 4 Turbo- with this framework provides a seamless interface for data querying, visualization creation, and recommendation generation. Nomad Analytix serves as an inclusive, intelligent, and intuitive solution, bridging the gap between data and decision-making across diverse industries.
{"title":"Nomad Analytix: Text-rich visual reasoning using vision models for insights and recommendations","authors":"Sai Jeevan Puchakayala , Allen Bijo T. , Aswathy Ravikumar , Harini Sriraman","doi":"10.1016/j.simpa.2025.100765","DOIUrl":"10.1016/j.simpa.2025.100765","url":null,"abstract":"<div><div>Nomad Analytix is an innovative business intelligence tool that uses state-of-the-art vision models to transform data analysis. This software automates complex tasks traditionally handled by data analysts, empowering non-technical teams such as marketing and sales to access advanced data analysis easily. By using natural language prompts, users can interact with data intuitively and gain valuable insights without needing extensive technical expertise. A prototype of the software, built on the Streamlit platform, will showcase its ability to generate visualizations from various data sources, including CSV, JSON, SQLite, Excel, and databases, with potential extensions to data warehouses. The integration of Vision Language Models GPT 4 Omni and GPT 4 Turbo- with this framework provides a seamless interface for data querying, visualization creation, and recommendation generation. Nomad Analytix serves as an inclusive, intelligent, and intuitive solution, bridging the gap between data and decision-making across diverse industries.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100765"},"PeriodicalIF":1.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903526","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 : 2025-04-28DOI: 10.1016/j.simpa.2025.100759
Johnny Chan, Brice Valentin Kok-Shun
This paper presents an AI-powered software solution for detecting and categorising sponsored advertisement segments in YouTube videos. By combining GPT-4 for ad identification, KeyBERT for keyword extraction, and custom prompts for grouping keywords into concise categories, the software provides a scalable and efficient alternative to traditional ad detection methods. It processes both auto-generated and manual transcripts, ensuring adaptability across varied contexts. The tool enables a deeper understanding of advertising strategies and ad-content alignment while maintaining ease of use and reproducibility. This work highlights the potential of AI in transforming digital advertisement analysis.
{"title":"An AI-powered solution for detecting and categorising sponsored ad segments in YouTube videos","authors":"Johnny Chan, Brice Valentin Kok-Shun","doi":"10.1016/j.simpa.2025.100759","DOIUrl":"10.1016/j.simpa.2025.100759","url":null,"abstract":"<div><div>This paper presents an AI-powered software solution for detecting and categorising sponsored advertisement segments in YouTube videos. By combining GPT-4 for ad identification, KeyBERT for keyword extraction, and custom prompts for grouping keywords into concise categories, the software provides a scalable and efficient alternative to traditional ad detection methods. It processes both auto-generated and manual transcripts, ensuring adaptability across varied contexts. The tool enables a deeper understanding of advertising strategies and ad-content alignment while maintaining ease of use and reproducibility. This work highlights the potential of AI in transforming digital advertisement analysis.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100759"},"PeriodicalIF":1.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913061","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 : 2025-04-28DOI: 10.1016/j.simpa.2025.100756
Mete Öğüç , Ali Fethi Okyar , Tahsin Khajah
FeVAcS is an open-source finite element software specializing in one dimensional periodic acoustic analyses with scattering obstacles. Leveraging FEniCS Project’s computational capabilities, it solves the Helmholtz equation variational form. This tool simplifies mesh generation, enhances acoustic visualization, and enables easy parameter manipulation for obstacle and domain geometries, along with wave property adjustments. Featuring a user-friendly browser interface, FeVAcS improves accessibility and result sharing. It serves as a vital tool for understanding complexities within exterior acoustic analyses.
{"title":"FeVAcS: A package for visualizing acoustic scattering from 1D periodic obstacles","authors":"Mete Öğüç , Ali Fethi Okyar , Tahsin Khajah","doi":"10.1016/j.simpa.2025.100756","DOIUrl":"10.1016/j.simpa.2025.100756","url":null,"abstract":"<div><div>FeVAcS is an open-source finite element software specializing in one dimensional periodic acoustic analyses with scattering obstacles. Leveraging FEniCS Project’s computational capabilities, it solves the Helmholtz equation variational form. This tool simplifies mesh generation, enhances acoustic visualization, and enables easy parameter manipulation for obstacle and domain geometries, along with wave property adjustments. Featuring a user-friendly browser interface, FeVAcS improves accessibility and result sharing. It serves as a vital tool for understanding complexities within exterior acoustic analyses.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100756"},"PeriodicalIF":1.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888040","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 : 2025-04-28DOI: 10.1016/j.simpa.2025.100762
Annice Najafi, Shokoufeh Mirzaei
Multi-Criteria Decision Making (MCDM) is a branch of operations research used in a variety of domains from health care to engineering to facilitate decision-making among multiple options based on specific criteria. Several R packages have been developed for the application of traditional MCDM approaches. However, as the discipline has advanced, many new approaches have emerged, necessitating the development of innovative and comprehensive tools to enhance the accessibility of these methodologies. Here, we introduce RMCDA, a comprehensive and universal R package that offers access to a variety of established MCDM approaches (e.g., AHP, TOPSIS, PROMETHEE, and VIKOR), along with newer techniques such as Stratified MCDM (SMCDM) and the Stratified Best–Worst Method (SBWM). Our open source software intends to broaden the practical use of these methods through supplementary visualization tools and straightforward installation.
{"title":"RMCDA: The comprehensive R library for applying Multi-Criteria Decision Analysis methods","authors":"Annice Najafi, Shokoufeh Mirzaei","doi":"10.1016/j.simpa.2025.100762","DOIUrl":"10.1016/j.simpa.2025.100762","url":null,"abstract":"<div><div>Multi-Criteria Decision Making (MCDM) is a branch of operations research used in a variety of domains from health care to engineering to facilitate decision-making among multiple options based on specific criteria. Several <span>R</span> packages have been developed for the application of traditional MCDM approaches. However, as the discipline has advanced, many new approaches have emerged, necessitating the development of innovative and comprehensive tools to enhance the accessibility of these methodologies. Here, we introduce <span>RMCDA</span>, a comprehensive and universal <span>R</span> package that offers access to a variety of established MCDM approaches (e.g., <span>AHP</span>, <span>TOPSIS</span>, <span>PROMETHEE</span>, and <span>VIKOR</span>), along with newer techniques such as Stratified MCDM (<span>SMCDM</span>) and the Stratified Best–Worst Method (<span>SBWM</span>). Our open source software intends to broaden the practical use of these methods through supplementary visualization tools and straightforward installation.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100762"},"PeriodicalIF":1.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888042","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 : 2025-04-28DOI: 10.1016/j.simpa.2025.100757
Irsyad Fikriansyah Ramadhan , Ntivuguruzwa Jean De La Croix , Tohari Ahmad
Ensuring secure data transmission has become crucial in modern digital communication, especially with rising risks of interception and manipulation. Steganography is vital in protecting sensitive information by embedding it within digital images without compromising their visual quality. This paper introduces IrsyadStego, an open-source using a Difference Expansion method with customized pixel difference to improve payload capacity and image fidelity. Experimental results show high PSNR and SSIM values, with a 100 dB PSNR between the cover image and the image recovered from extraction—demonstrating full reversibility. IrsyadStego supports further research, contributing to robust, secure, and efficient steganographic techniques in digital security.
{"title":"IrsyadStego: An open-source code to secure data using pixel differencing paradigm within the neighboring pixels of a digital image","authors":"Irsyad Fikriansyah Ramadhan , Ntivuguruzwa Jean De La Croix , Tohari Ahmad","doi":"10.1016/j.simpa.2025.100757","DOIUrl":"10.1016/j.simpa.2025.100757","url":null,"abstract":"<div><div>Ensuring secure data transmission has become crucial in modern digital communication, especially with rising risks of interception and manipulation. Steganography is vital in protecting sensitive information by embedding it within digital images without compromising their visual quality. This paper introduces IrsyadStego, an open-source using a Difference Expansion method with customized pixel difference to improve payload capacity and image fidelity. Experimental results show high PSNR and SSIM values, with a 100 dB PSNR between the cover image and the image recovered from extraction—demonstrating full reversibility. IrsyadStego supports further research, contributing to robust, secure, and efficient steganographic techniques in digital security.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100757"},"PeriodicalIF":1.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898493","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 : 2025-04-28DOI: 10.1016/j.simpa.2025.100758
Frederico Schmitt Kremer, João Pedro Gomes Greco, Elias Eduardo Barbosa da Rosa
Biocides play a critical role in controlling microorganisms, yet their widespread use has contributed to the emergence of bacterial resistance, often linked to antibiotic cross-resistance. Multidrug-resistant pathogens pose a growing public health concern due to their adaptability and presence in various environments, including hospitals. Previously, our group developed the Biocide Resistance Scanner (BRS), a bioinformatics pipeline designed to identify biocide resistance genes in the mobilome of ESKAPE strains isolated in Brazil. Now, we detail the implementation of BRS and extend its application to the analysis of the pathogen Campylobacter jejuni.
{"title":"BRS: A tool for detecting biocide resistance in mobile elements","authors":"Frederico Schmitt Kremer, João Pedro Gomes Greco, Elias Eduardo Barbosa da Rosa","doi":"10.1016/j.simpa.2025.100758","DOIUrl":"10.1016/j.simpa.2025.100758","url":null,"abstract":"<div><div>Biocides play a critical role in controlling microorganisms, yet their widespread use has contributed to the emergence of bacterial resistance, often linked to antibiotic cross-resistance. Multidrug-resistant pathogens pose a growing public health concern due to their adaptability and presence in various environments, including hospitals. Previously, our group developed the Biocide Resistance Scanner (BRS), a bioinformatics pipeline designed to identify biocide resistance genes in the mobilome of ESKAPE strains isolated in Brazil. Now, we detail the implementation of BRS and extend its application to the analysis of the pathogen <em>Campylobacter jejuni</em>.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"24 ","pages":"Article 100758"},"PeriodicalIF":1.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888041","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}
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