Procedia Computer Science最新文献_第2页

End-to-End Conformer Neural Networks for Multilingual Automatic Speech Recognition and Understanding 多语言自动语音识别与理解的端到端自适应神经网络

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1016/j.procs.2026.01.063

Hassan AlMashhadani , Alaa A. Alsaffar , Hillal Ali AlMaqbali , Ahmed Al-Amayreh

{"title":"End-to-End Conformer Neural Networks for Multilingual Automatic Speech Recognition and Understanding","authors":"Hassan AlMashhadani , Alaa A. Alsaffar , Hillal Ali AlMaqbali , Ahmed Al-Amayreh","doi":"10.1016/j.procs.2026.01.063","DOIUrl":"10.1016/j.procs.2026.01.063","url":null,"abstract":"<div><div>Educational and e-learning environments must incorporate Spoken Language Understanding (SLU) and Automatic Speech Recognition (ASR) to ensure accessible, scalable, and inclusive digital learning. Existing algorithms struggle with low-resource languages, accents, and linguistic variety, making them unsuitable for real-world multilingual educational environments. Therefore, this study proposes EduLinguaNet, a Multilingual Conformer Network for online classes and schools, which solves these issues in accurate speech detection. The proposed EduLinguaNet employs an end-to-end Conformer Neural network architecture for precise translation and semantic comprehension across multiple languages, utilizing layer-by-layer neural networks for local feature modeling and self-attention for long-distance interactions. The proposed model is trained and assessed utilizing large-scale multilingual educational corpora, language learning platforms, interactive e-learning sessions, and lecture recordings. Experimental data show that EduLinguaNet outperforms Transformers and Recurrent Neural Networks (RNNs) in intent recognition and Word Error Rate (WER). Robustness in low-resource language settings and code-switching performance indicate the system’s adaptability for diverse learners. Finally, EduLinguaNet improves e-learning via accurate, real-time, multilingual speech detection and interpretation.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"275 ","pages":"Pages 541-549"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552986","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}

引用次数: 0

Reinforcement-Learned Ethical Transformers for Socially Responsible Text Generation in Sensitive Domains 敏感领域社会责任文本生成的强化学习伦理转换器

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1016/j.procs.2026.01.066

Afaf Abdullah Ali Alalawi , Yasmeen Anjeer AlShehhi , Bidoor Noori Ishaq , Maen Qaddoura

{"title":"Reinforcement-Learned Ethical Transformers for Socially Responsible Text Generation in Sensitive Domains","authors":"Afaf Abdullah Ali Alalawi , Yasmeen Anjeer AlShehhi , Bidoor Noori Ishaq , Maen Qaddoura","doi":"10.1016/j.procs.2026.01.066","DOIUrl":"10.1016/j.procs.2026.01.066","url":null,"abstract":"<div><div>Neural language models have revolutionized text creation, benefiting chatbots, content production, and automated support systems. However, these models frequently provide biased, incorrect, or harmful content in sensitive domains including healthcare, legal services, and mental health support. In many cases, these models are trained without clear ethical guidelines. This raises ethical and security concerns concerning automated text-generation systems. To address these difficulties, this research introduces Ethical Neural Models (ENM), a novel framework for socially acceptable text generation in sensitive domains. The ENM neural language model has an ethical framework. This technique uses dual-objective training by combining ethical alignment with traditional language modeling losses. Following domain-specific norms and human-curated ethical criteria may help the model avoid prejudice and damage while preserving linguistic fluency and relevance. ENM was compared to controlled generation baselines and fine-tuning on healthcare and legal advisory datasets. ENM reduces biased or inappropriate outputs by over 35%, according to statistical and human evaluations. ENM remains competitive in contextual relevance and fluency. For sensitive applications, ENM proves that ethical and responsible text generation is possible and efficient. Ethical principles in ENM increase the dependability and security of automated language systems in high-stakes social scenarios.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"275 ","pages":"Pages 567-574"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552989","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}

引用次数: 0

A Modular Multi-Agentic Architecture for Automating Multiple-Choice Question Generation in Language Assessment 语言评估中选择题自动生成的模块化多主体体系结构

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1016/j.procs.2026.01.070

Peng Zhao, John Blake, Evgeny Pyshkin

{"title":"A Modular Multi-Agentic Architecture for Automating Multiple-Choice Question Generation in Language Assessment","authors":"Peng Zhao, John Blake, Evgeny Pyshkin","doi":"10.1016/j.procs.2026.01.070","DOIUrl":"10.1016/j.procs.2026.01.070","url":null,"abstract":"<div><div>The creation of high-quality multiple-choice questions (MCQs) for language assessment is a labour-intensive task, often requiring careful balancing of linguistic appropriacy, proficiency level, topic coverage, and distractor plausibility. We present a modular, multi-agentic system built using LangChain to generate appropriate MCQs. Each agent in the system is responsible for a distinct task in the question generation pipeline. These tasks range from topic selection and question formation to answer validation, distractor generation, and coverage checks. The system supports flexible substitution of Large Language Models (LLMs), allowing comparative benchmarking across tasks in terms of generation accuracy and latency. Human expert assessment of item quality confirmed that the best-performing configurations yielded scores exceeding 95% in grammatical correctness with satisfactory speed. Our results demonstrate that multi-agent LLM-based architectures can effectively automate complex educational content creation workflows while offering transparency, modularity, and fine-grained controllability. The proposed system offers a reusable design pattern for intelligent educational content generation in broader domains.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"275 ","pages":"Pages 602-609"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552993","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}

引用次数: 0

Graph Convolutional Approaches to Scholarly Knowledge Discovery Across Disciplines 跨学科学术知识发现的图卷积方法

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1016/j.procs.2026.01.031

Anjali Krushna Kadao , Nidhi Mishra

{"title":"Graph Convolutional Approaches to Scholarly Knowledge Discovery Across Disciplines","authors":"Anjali Krushna Kadao , Nidhi Mishra","doi":"10.1016/j.procs.2026.01.031","DOIUrl":"10.1016/j.procs.2026.01.031","url":null,"abstract":"<div><div>The rapid growth of scholarly publications across various domains presents both opportunities and challenges for academics seeking to synthesize knowledge. Conventional bibliometric studies can illuminate keyword trends, co-authorship, and citations, but they often fail to capture the complex relationships between writers, publications, and study subjects. Finding new trends and transdisciplinary knowledge is harder due to this restriction. Graph GCNs explain varied academic graphs in Convolutional Academic Knowledge Discovery (GCSKD). It addresses this issue. Nodes represent authors, publications, and keywords, while edges denote citations, co-authorships, and topic similarities in GCSKD. GCSKD utilizes both local and global neighborhood data to generate node embeddings that capture both structural and semantic information. These embeddings are used for link prediction, node classification, and community discovery to identify new research subjects, partnerships, and cross-disciplinary connections. It evaluates GCSKD on enormous bibliographic datasets from computer science, biomedical research, and social science. Experimental results show that GCSKD outperforms co-citation and embedding-based algorithms in research theme clustering and in predicting impactful publications and author collaborations. GCSKD provides a scalable and efficient framework for academics to discover new knowledge across fields. Collecting complex relational patterns in large academic networks enables the automated identification of trends across multiple scientific fields, helping researchers and institutions make informed decisions.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"275 ","pages":"Pages 250-257"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147553123","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}

引用次数: 0

Intelligent 3D monitoring of historical structures with digital twin 基于数字孪生的历史建筑智能三维监测

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1016/j.procs.2026.02.026

Ramon Helwing, Frank Walther

{"title":"Intelligent 3D monitoring of historical structures with digital twin","authors":"Ramon Helwing, Frank Walther","doi":"10.1016/j.procs.2026.02.026","DOIUrl":"10.1016/j.procs.2026.02.026","url":null,"abstract":"<div><div>The preservation of large industrial heritage structures necessitates the implementation of efficient and accessible monitoring techniques to detect structural deterioration and surface changes over time. Conventional monitoring methodologies are predicated on the manual observation of objects, a process that is both highly specialized and time-consuming. The present study proposes a cost-effective and readily transferable framework for multi-temporal 3D change detection using unmanned aerial vehicles (UAVs) and open-source photogrammetry software, with a particular emphasis on machine learning-based difference detection. The framework utilizes structure-from-motion (SfM) and multi-view stereo (MVS) techniques to generate and accurately co-register temporally separated 3D models of technical-historical structures. In contradistinction to conventional approaches, the proposed method is entirely self-referential, relying solely on optical and geometrical features for alignment, thereby eliminating the necessity for specialized surveying equipment. The approach under discussion involves the fusion of both temporal model versions into a composite representation. This model is then used to identify and segment altered regions, thereby distinguishing structural changes such as corrosion.</div><div>This approach enables a highly automatable and scalable monitoring pipeline that minimizes the need for manual inspection while increasing the reliability of change detection. By incorporating texture-based comparisons, the framework improves the spatial precision of change localization. Built entirely on open-source tools and lightweight UAV platforms, the proposed method offers a cost-effective and adaptable solution that is especially suited to heritage conservation contexts, where budgetary and operational limitations frequently impede the deployment of conventional monitoring technologies.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"276 ","pages":"Pages 207-214"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147553159","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}

引用次数: 0

Dual-Encoder Transformers for Image-Text Matching in Vision-Language Multimodal Applications 视觉语言多模态应用中图像-文本匹配的双编码器转换器

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1016/j.procs.2026.01.010

Moti Ranjan Tandi , Anjali Krushna Kadao

{"title":"Dual-Encoder Transformers for Image-Text Matching in Vision-Language Multimodal Applications","authors":"Moti Ranjan Tandi , Anjali Krushna Kadao","doi":"10.1016/j.procs.2026.01.010","DOIUrl":"10.1016/j.procs.2026.01.010","url":null,"abstract":"<div><div>Dual-Encoder Transformers have become a potent vision-language architectural framework that entails inserting images and texts into an overlapping semantic space. They facilitate useful multimodal dialogue, which is essential in applications like cross-modal retrieval, captioning and recommendation systems. Nevertheless, the current dual-encoder methods typically use global-level alignment and ignore fine-scale interactions between image segments and text segments. This restricts them in capturing subtle semantic links resulting in cross-modal representations that are mismatched or partial. In order to address these drawbacks, this paper suggests a new Hierarchical Cross-Modality Attention Distillation (HCAD) model. HCAD presents a multi-level distillation procedure joining both the local (object-word) and global (scene-sentence) features of the two encoders. Using hierarchical patterns of attention, the framework improves semantic correspondence and provides powerful fine-grained alignments in multimodal embedding spaces. The suggested approach can be successfully utilized to the field of multimodal medical report retrieval, where the accurate correspondence of medical images and diagnostic text is important to provide clinical decision support and share knowledge. Experimental findings indicate that HCAD is much more effective in retrieval accuracy, fine-grained matching and semantic robustness than the traditional dual-encoder models. The results indicate that it can be used to promote real-world vision-language multimodal tasks with more realistic and interpretable cross-modal alignment.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"275 ","pages":"Pages 74-81"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147553263","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}

引用次数: 0

Development and Evaluation of a Semi-Automated Pilot System for Selective PCB Disassembly in Support of the Circular Economy 支持循环经济的选择性PCB拆卸半自动先导系统的开发与评估

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-23 DOI: 10.1016/j.procs.2026.02.065

Lorenzo Gandini , Paolo Rosa , Sergio Terzi

{"title":"Development and Evaluation of a Semi-Automated Pilot System for Selective PCB Disassembly in Support of the Circular Economy","authors":"Lorenzo Gandini , Paolo Rosa , Sergio Terzi","doi":"10.1016/j.procs.2026.02.065","DOIUrl":"10.1016/j.procs.2026.02.065","url":null,"abstract":"<div><div>In this paper, some of the outcomes of the CIRC-UITS project will be presented, focused on the development of a semi-automated disassembly system for printed circuit boards (PCBs) within the context of promoting circular economy practices in the electronic sector. Building upon prior work, the report outlines the reconfiguration and technological advancement of a pilot plant integrating key enabling technologies (KETs) such as artificial intelligence (AI), machine vision, and collaborative robotics (cobots). The developed system aims to enhance the efficiency, selectivity, and safety of electronic component disassembly operations. The document details the hardware and software architecture of the pilot plant, including safety enhancements. Furthermore, training sessions with industrial partners are analyzed, offering insights into the system’s practical deployment and operator interaction. Finally, the report examines the challenges associated with selective disassembly, particularly data availability for AI training, and outlines future directions, including standardization efforts and scalability considerations.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"277 ","pages":"Pages 233-242"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147554206","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}

引用次数: 0

Exploring Themes and Determinants of Industry 4.0 Adoption in Smart Manufacturing Sector: A Mixed-Methods Approach 探索智能制造领域采用工业4.0的主题和决定因素：混合方法方法

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-23 DOI: 10.1016/j.procs.2026.02.061

Rupali Saini , Sunil Sharma , Vivek Soni

{"title":"Exploring Themes and Determinants of Industry 4.0 Adoption in Smart Manufacturing Sector: A Mixed-Methods Approach","authors":"Rupali Saini , Sunil Sharma , Vivek Soni","doi":"10.1016/j.procs.2026.02.061","DOIUrl":"10.1016/j.procs.2026.02.061","url":null,"abstract":"<div><div>This paper investigates Industry 4.0 adoption in the Indian manufacturing sector using a mixed-methods approach. In the first phase, natural language processing techniques such as text mining, topic modeling, and keyword filtering were applied to a corpus of business reports and editorials (2015–February 2025) to identify key trends and barriers. Five dominant themes and barriers, such as high costs, skill gaps, and cybersecurity concerns, emerged. In the second phase, a Technology–Organization–Environment (TOE) framework based model was tested through a structured questionnaire administered to 192 personnel from manufacturing firms in India. SmartPLS software validated the model and hypotheses using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that technological and organizational factors significantly influence Industry 4.0 adoption, while environmental factors have minimal impact. The findings offer valuable insights for researchers, industry leaders, and policymakers to support digital transformation and guide strategic decisions for smart manufacturing in India.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"277 ","pages":"Pages 197-206"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147554603","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}

引用次数: 0

Building Trust in AI: A Qualitative Study of Human-AI Interaction 在人工智能中建立信任：人类与人工智能互动的定性研究

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-23 DOI: 10.1016/j.procs.2026.02.048

Viktoria Leutheuser , Felix Schäfer , Julian M. Müller , Kai-Ingo Voigt

{"title":"Building Trust in AI: A Qualitative Study of Human-AI Interaction","authors":"Viktoria Leutheuser , Felix Schäfer , Julian M. Müller , Kai-Ingo Voigt","doi":"10.1016/j.procs.2026.02.048","DOIUrl":"10.1016/j.procs.2026.02.048","url":null,"abstract":"<div><div>The implementation of artificial intelligence (AI) transitions from the technical perspective to a managerial top priority. The key to successfully managing this change is to prioritize the needs of the individuals who interact with the AI applications. This study examined the importance of trust for the acceptance and use of AI in industrial companies. By conducting a qualitative analysis based on expert interviews, this research demonstrated that trust is crucial for fostering employees’ acceptance and willingness to use AI applications. The integration of AI into existing work processes and the knowledge about human-AI interaction have been identified as the two primary pillars in mitigating employees’ fears and concerns, fostering an overall positive impact on the perception and acceptance of AI. Consequently, we have formulated a series of practical recommendations for a targeted, strategic implementation of AI and active employee engagement to increase trust. We strengthened recent literature by extending existing models of technology acceptance with trust as a key dimension, thus demonstrating the necessity of organizational and cultural changes in addition to technical changes for the adoption of AI.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"277 ","pages":"Pages 71-80"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147554606","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}

引用次数: 0

Surface Roughness Prediction in Turning Stainless Steel Applying Deep Learning and LSTM Networks 应用深度学习和LSTM网络的车削不锈钢表面粗糙度预测

Procedia Computer Science Pub Date : 2026-01-01 Epub Date: 2026-03-23 DOI: 10.1016/j.procs.2026.02.057

Christoph Lerez , Richard Petermann , Csaba Felhö , Tanuj Namboodri , Alexander Neu , Norbert Szűcs , Mortda Mohammed Sahib , Philipp Plänitz , Matthias Hackert-Oschätzchen

{"title":"Surface Roughness Prediction in Turning Stainless Steel Applying Deep Learning and LSTM Networks","authors":"Christoph Lerez , Richard Petermann , Csaba Felhö , Tanuj Namboodri , Alexander Neu , Norbert Szűcs , Mortda Mohammed Sahib , Philipp Plänitz , Matthias Hackert-Oschätzchen","doi":"10.1016/j.procs.2026.02.057","DOIUrl":"10.1016/j.procs.2026.02.057","url":null,"abstract":"<div><div>In turning, surface integrity depends on numerous interrelated factors, including cutting parameters, tool wear, and process dynamics. Monitoring these manufacturing processes using sensors and intervening promptly in unstable conditions can help maintain process stability. However, conventional process monitoring quickly reaches its limits, especially when there are few or no options to identify potential disturbances and detect relevant features in sensor signals. For example, in single-part and small-batch production, there is often not enough time to collect and analyze the necessary data. Artificial intelligence offers the potential to overcome the current limitations in process monitoring by enabling reliable feature detection in machining processes in a time- and cost-efficient manner. Data-driven models provide an effective approach for analyzing the acquired data in-process and predicting parameters associated with surface integrity and process stability. In this research, a deep learning model is developed to organize the temporal data, like time step and sensor signals, along with the non-temporal data, like cutting depth, cutting speed, and feed rate, to predict the surface roughness of the final workpiece. Based on the results, the developed model with Long Short-Term Memory network (LSTMs) represents a promising approach to provide practical guidance for the surface roughness prediction in real industrial settings.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"277 ","pages":"Pages 159-168"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147554614","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}

引用次数: 0