alexandria engineering journal最新文献_第2页

Pendulum attached to a vibrating point: Semi-analytical solution by optimal and modified homotopy perturbation method 连接到振动点的摆：最优同调扰动法和修正同调扰动法的半解析解

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.086

{"title":"Pendulum attached to a vibrating point: Semi-analytical solution by optimal and modified homotopy perturbation method","authors":"","doi":"10.1016/j.aej.2024.10.086","DOIUrl":"10.1016/j.aej.2024.10.086","url":null,"abstract":"<div><div>A simple pendulum of length <span><math><mrow><mo>(</mo><mi>b</mi><mo>)</mo></mrow></math></span> and bob mass <span><math><mrow><mo>(</mo><mi>m</mi><mo>)</mo></mrow></math></span> attached to point <span><math><mrow><mo>(</mo><mi>O</mi><mo>)</mo></mrow></math></span> is considered and investigated. The point <span><math><mrow><mo>(</mo><mi>O</mi><mo>)</mo></mrow></math></span> is oscillating vertically according to the relation <span><math><mrow><mo>(</mo><msub><mrow><mi>q</mi></mrow><mrow><mi>o</mi></mrow></msub><mi>cos</mi><mi>Ω</mi><mi>t</mi><mo>)</mo></mrow></math></span>, where <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>o</mi></mrow></msub></math></span> and <span><math><mrow><mi>Ω</mi><mspace></mspace></mrow></math></span>are amplitude and angular frequency of the external agent, respectively. The presence of time dependent oscillating term makes the governing equation is not solvable analytically. An attempt was to explore the application of optimal and modified homotopy perturbation method (OM-HPM) as a powerful semi-analytical tool for solving the oscillatory problem which exhibiting regular and irregular oscillation for some parameter set. Furthermore, the analytical expressions in series form, which is very close to the numerical solution of Runge-Kutta method is obtained. In addition, the analytical expression for the amplitude and the frequency of the oscillations for two cases: simple regular oscillation and the irregular oscillation is computed. Finally, the simplicity of the obtained solutions facilities a clear understanding, and the OM-HPM offer a robust and efficient analytical tool to obtain series based analytical solution for such kind of problems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new immunofluorescence determination of Parkinson's disease biomarkers using silver nanoparticles 利用银纳米粒子对帕金森病生物标记物进行免疫荧光测定的新方法

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.069

{"title":"A new immunofluorescence determination of Parkinson's disease biomarkers using silver nanoparticles","authors":"","doi":"10.1016/j.aej.2024.10.069","DOIUrl":"10.1016/j.aej.2024.10.069","url":null,"abstract":"<div><div>Parkinson's disease (PD) is a neurodegenerative condition marked by a steady loss of dopaminergic neurons in the brain's substantia nigra. Prompt identification and tracking of PD progression are essential for prompt intervention and efficient PD care. In this study, we developed an immunofluorescence detection approach for α-synuclein (α-syn), a critical biomarker associated with PD, that is both extremely sensitive and specific. Using polyethylene glycol (PEG)-functionalized magnetic beads (MBs) and an Ag<sup>+</sup> fluorescence probe (Ag<sup>+</sup>-FP) based on Rhodamine 6 G, the suggested method makes use of an immunofluorescence detection system. The system's workings are based on antigen-antibody complexes. Identified as Ab1-MBs@α-syn@Ab2-Ag NPs, the immuno-complexes encapsulate α-synuclein between anti-α-synuclein antibodies (Ab1) fixed on amino-MBs and Ag Nanoparticles functionalized with matching Ab2. α-synuclein detection was accomplished at a limit of less than 8 pg/mL through optimization of pH, reaction duration, and antibody concentration. The method showed very little cross-reactivity with other widely used biomarkers and a high specificity. The system showed a linear range of 524.8 ng/mL to 0.2 ng/mL. The results, which showed recovery values ranging from 97.00 % to 99.57 % and were consistent with those obtained using a commercial ELISA kit, indicated the system's potential for clinical applications in the diagnosis and monitoring of PD.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental investigation on large-aspect-ratio zirconia ceramic microchannels by waterjet-assisted laser processing 利用水刀辅助激光加工大宽比氧化锆陶瓷微通道的实验研究

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.080

{"title":"Experimental investigation on large-aspect-ratio zirconia ceramic microchannels by waterjet-assisted laser processing","authors":"","doi":"10.1016/j.aej.2024.10.080","DOIUrl":"10.1016/j.aej.2024.10.080","url":null,"abstract":"<div><div>Zirconia (ZrO<sub>2</sub>) ceramic has excellent mechanical properties and superior chemical stability, making it widely used in aerospace, microelectronics, biomedicine, and mechanical manufacturing. However, due to its difficult-to-machine characteristics, traditional machining methods struggle with fabricating large-aspect-ratio (LAR) microchannels in zirconia ceramics. This study compares direct laser machining (DLM) and waterjet-assisted laser micromachining (WJALM) in preparing LAR zirconia microchannels, focusing on surface morphology, heat-affected zones, microhardness, chemical and phase composition. Subsequently, parameter experiments of WJALM were carried out to achieve superior machined quality LAR zirconia microchannels by assessing the geometric profile and ablation-area-ratio. Experimental results indicated that WJALM significantly surpasses DLM, achieving a 46 % decrease in areal surface roughness (Sa), WJALM reduced the heat-affected zone depth by approximately 37 % compared to DLM. The WJALM process also enhanced the ablation-area-ratio by 61 %, achieving superior machining quality under optimized conditions of 27 W laser power, 100 mm/s scanning speed, and 8 m/s waterjet velocity.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VAR-YOLOv8s: IoT-based automatic foul detection in soccer matches VAR-YOLOv8s：基于物联网的足球比赛犯规自动检测系统

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.031

{"title":"VAR-YOLOv8s: IoT-based automatic foul detection in soccer matches","authors":"","doi":"10.1016/j.aej.2024.10.031","DOIUrl":"10.1016/j.aej.2024.10.031","url":null,"abstract":"<div><div>The application of Internet of Things (IoT) technology and its ongoing evolution have drawn a lot of interest to the field of intelligent sports referee system research. In this article, we present a novel VAR-YOLOv8 model that significantly improves the accuracy and robustness of error detection in football matches by combining MPDIoU, a residual local feature network (RLFN), and a video assistant referee system “VARS” module. Experimental results show how well the model can handle dense gates and rapidly changing parameters. It also does a good job of recognizing and classifying different types of faults in difficult situations. The concept uses Internet of Things (IoT) technology to enable real-time data collection and processing, providing strong technical support for smart sports refereeing systems, significant practical application value and many advancement opportunities. Through testing utilizing the SoccerNet dataset, the VAR-YOLOv8s demonstrate accomplished an normal [email protected] of 80.5 and [email protected] of 31.0 amid the testing handle. To move forward the insights and productivity of shrewd arbitrage frameworks, future investigate will center on optimizing show execution and exploring unused information enlargement and combination procedures.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SLM-DFS: A systematic literature map of deepfake spread on social media SLM-DFS：社交媒体上深度伪造传播的系统文献图谱

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.076

{"title":"SLM-DFS: A systematic literature map of deepfake spread on social media","authors":"","doi":"10.1016/j.aej.2024.10.076","DOIUrl":"10.1016/j.aej.2024.10.076","url":null,"abstract":"<div><div>In recent years, deepfakes (DFs)-realistically manipulated media created using artificial intelligence—have raised significant concerns. As this technology evolves, the urgency for effective detection methods to counter misuse intensifies. Computer science researchers are increasingly focused on stopping the spread of deepfakes (DFs) on social media. However, there has been no comprehensive overview of research in this area. This paper presents a systematic literature map that analyzes research on DF spread on social media from 286 primary studies published between 2018 and June 2024. The studies are categorized by their research type, contribution and focus, revealing a predominant emphasis on detection solutions. Notably, there are significant gaps in evaluating these solutions, using digital interventions to curb dissemination, and managing DF propagation. This literature map will aid researchers, practitioners, and policymakers navigate the rapidly evolving field of DF detection by presenting a structured overview of the available knowledge. The findings of this literature map suggest that DF detection is a multidisciplinary field that requires collaboration between experts in computer vision, machine learning, cybersecurity, and media forensics to address its current and future challenges</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced R-GAN: Generating anomaly data for improved detection in imbalanced datasets using regularized generative adversarial networks 高级 R-GAN：利用正则化生成对抗网络生成异常数据，以改进不平衡数据集的检测工作

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.084

{"title":"Advanced R-GAN: Generating anomaly data for improved detection in imbalanced datasets using regularized generative adversarial networks","authors":"","doi":"10.1016/j.aej.2024.10.084","DOIUrl":"10.1016/j.aej.2024.10.084","url":null,"abstract":"<div><div>The high prevalence of fraud in contemporary financial transactions necessitates advanced anomaly detection systems to address the significant imbalance between legitimate and anomalous transactions in real-time datasets. To address this issue, our study introduces a novel approach, the regularized generative adversarial network (R-GAN). Diverging from conventional resampling techniques and typical generative adversarial network (GAN) architectures, R-GAN incorporates spectral normalization for the STGAN (short for spectral normalization for GAN) generator framework, which enhances it with a similarity measure loss to improve the authenticity of the generated data. The discriminator is meticulously designed, leveraging the CELU (short for continuously differentiable exponential linear unit) activation for optimal feature extraction, ensuring diverse and representative sample generation. To ensure fairness and validate the effectiveness of our data generation process, we used PyCaret's automated machine learning framework to rigorously test different machine learning models, ultimately identifying the light gradient boosting machine as the most effective. To add transparency to our system, we applied Shapley additive explanations (SHAP), providing clear insights into the decisions made by our explainable artificial intelligence-driven model. This approach ensures high-fidelity anomaly detection in real-time environments and continuously refines through SHAP insights, significantly addressing imbalanced datasets across various applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ArtDiff: Integrating IoT and AI to enhance precision in ancient mural restoration ArtDiff：整合物联网和人工智能，提高古代壁画修复的精度

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.09.120

{"title":"ArtDiff: Integrating IoT and AI to enhance precision in ancient mural restoration","authors":"","doi":"10.1016/j.aej.2024.09.120","DOIUrl":"10.1016/j.aej.2024.09.120","url":null,"abstract":"<div><div>Ancient murals, as invaluable cultural artifacts, have profound historical and cultural significance. However, these murals often face degradation phenomena such as peeling, fading, and cracking, which compromises their preservation. Conventional methodologies for protection and restoration exhibit limitations and do not adequately address multifaceted damage conditions, thus necessitating the integration of advanced technological interventions to enhance restoration effectiveness.This paper delineates a framework for the preservation and restoration of cultural heritage buildings that uses Internet of Things (IoT) technology and Artificial Intelligence (AI). Using real-time environmental and structural health surveillance, in conjunction with security mechanisms, this framework markedly improves precision and efficiency in forecasting and identifying potential risks.Furthermore, in the context of mural restoration, this paper introduces the ArtDiff model. This model amalgamates a modified U-Net for initial crack detection with an edge-guided restoration technique, employing a diffusion model for meticulous restoration. Empirical results substantiate the superiority of the ArtDiff model in crack detection and mural restoration, delivering a greater precision and efficacy relative to existing approaches. Through the implementation of multilevel supervision strategies and an avant-garde model architecture, this study offers a sophisticated mural restoration solution, furnishing novel technological support for the preservation of cultural heritage.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D-AOCL: Analytic online continual learning for imbalanced 3D point cloud classification 3D-AOCL：不平衡三维点云分类的分析在线持续学习

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.037

{"title":"3D-AOCL: Analytic online continual learning for imbalanced 3D point cloud classification","authors":"","doi":"10.1016/j.aej.2024.10.037","DOIUrl":"10.1016/j.aej.2024.10.037","url":null,"abstract":"<div><div>Recent autonomous driving systems heavily rely on 3D point cloud data collected from multiple sensors for environmental awareness and decision-making. However, it is unrealistic to expect the autonomous driving system to recognize all road environments and handle every traffic situation. Models for autonomous driving need to be updated in real time in order for the system to adapt to more situations. This is where online continual learning becomes crucial. Online continual learning is an important method in the field of autonomous driving, as it enables models to update their parameters with streaming input data for adapting to new environments and conditions. Online continual learning in the field of autonomous driving faces several challenges: inefficient data fusion, catastrophic forgetting, insufficient computational resources, violation of road privacy and categories imbalance. To tackle these challenges, we propose an Analytic Online Continual Learning method for 3D Point Cloud Classification (3D-AOCL). This approach utilizes Analytic Learning to update parameters and integrates a feature fusion module along with a category balancer to address the above issues. It is capable of fusing data in feature level, balancing samples across various categories and updating parameters by calculating the analytical solution. We have validated our method on the vehicle side, the infrastructure side, and vehicle-infrastructure cooperative data on the V2X-Seq dataset. The experimental results demonstrate that our model effectively addresses key issues in online continual learning for autonomous driving systems, outperforming other models by approximately 4.00% to 6.00% in AMCA scores while only keeping 0.75% trainable parameters.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tropical cyclone track prediction model for multidimensional features and time differences series observation 多维特征和时差序列观测的热带气旋路径预测模型

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.090

{"title":"Tropical cyclone track prediction model for multidimensional features and time differences series observation","authors":"","doi":"10.1016/j.aej.2024.10.090","DOIUrl":"10.1016/j.aej.2024.10.090","url":null,"abstract":"<div><div>Tropical Cyclones (TCs) are highly destructive weather phenomena that can cause significant social and economic damage. With the development of meteorological monitoring technology and the updating of database, accurately forecasting the track of TC movement is one of the effective ways to minimize losses. However, traditional movement track forecasting methods suffer the disadvantages of low efficiency and low accuracy. To address the these problems, a novel Convolutional Neural Network-Temporal Convolutional Network (CNN-TCN) model based on Multidimensional Features and Time Difference Series (MT-CNN-TCN) is presented in this paper. First, different types of meteorological data are processed and then the feature differences between adjoining moments are extracted. Second, a two-branch structure based on Two Dimensional Convolutional Neural Network (2DCNN), 3DCNN and TCN is taken to effectively integrate different types of meteorological features to strengthen its forecasting effect. Finally, experiments are conducted using Northwest Pacific TC data from years 2000–2019. Test results show that the proposed model MT-CNN-TCN can perform well at all three forecast periods (12 h, 24 h, and 48 h), with a significant improvement in accuracy by 7 %, 13 %, and 16 % respectively, compared with current forecasting methods such as Long Short Term Memory (LSTM).</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Topic-aware neural attention network for malicious social media spam detection 用于恶意社交媒体垃圾邮件检测的主题感知神经注意网络

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2024-10-29 DOI: 10.1016/j.aej.2024.10.073

{"title":"Topic-aware neural attention network for malicious social media spam detection","authors":"","doi":"10.1016/j.aej.2024.10.073","DOIUrl":"10.1016/j.aej.2024.10.073","url":null,"abstract":"<div><div>Social media platforms, such as Facebook and X (formally known as Twitter), have become indispensable tools in today's society because they facilitate social discussion and information sharing. This feature makes social networks more attractive for spammers who intentionally spread fake messages, post malicious links and spread rumours. Recently, several machine learning methods have been introduced for social network malicious spam classification. However, most existing methods generally rely on handcrafted features and traditional embedding models, which are relatively less effective. Therefore, inspired by the success of the neural attention network, we propose an interactive neural attention-based method for malicious spam detection by integrating long short-term memory (LSTM), topic modelling, and the BERT technique. In the proposed approach, first, we employed the LSTM encoder, which was integrated with the Twitter latent Dirichlet allocation (LDA) model via an interactive attention mechanism to jointly learn local content and global topic representations. Second, to further learn the contextualized features of texts, the model was further integrated with the BERT technique. Last, the Softmax function was then applied at the output layer for the final spam classification. A series of experiments were conducted utilizing two real-world datasets to evaluate the model. Using dataset 1, the proposed model outperformed the baseline techniques, with average improvements in recall, precision, and F1 and accuracies of 17.54 %, 6.19 %, 11.91 %, and 12.27 %, respectively. In addition, the proposed model performed well for the second dataset and obtained average gains of 11.81 %, 4.38 %, 8.12, and 7.42 in terms of recall, precision, F1, and accuracy, respectively.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0