Engineering Applications of Artificial Intelligence最新文献

{"title":"Modeling category and multi-level user intentions for session-based recommendation","authors":"Shanshan Hua,&nbsp;Mingxin Gan,&nbsp;Menghan Li","doi":"10.1016/j.engappai.2025.111248","DOIUrl":"10.1016/j.engappai.2025.111248","url":null,"abstract":"<div><div>Session-based recommendation is extensively used in online services and businesses, which predicts users’ next interest with anonymous behavior sequence. Past research on session-based recommendation capturing user preference commonly focused solely on extracting the diversity of user intentions and the identification information of interacted items. However, fine-grained user intentions, which are reflected by both individual and consecutive items in session sequences, and the side information, item categories, which can help to learn the potential user intentions in session sequences have not been explored deeply. These insights inspire us to model <u>c</u>ategory and multi-level user <u>i</u>ntentions for <u>s</u>ession-based recommendation (CIS). Specifically, session sequences are first split pairwise between levels, and connected sequentially within levels to construct multi-level user intention graphs and item category graphs and model the fine-grained information. Moreover, we refine the representation of user intentions and item categories with graph convolutional network and iterative update process, in order to extract potential user intentions based on category semantics. Finally, we generate session embeddings based on each level of user intentions, and introduce an Integration Predicting strategy to anticipate users’ next interested item. Extensive experiment results on Diginetica dataset and Tmall dataset demonstrate that CIS is superior to other state-of-the-art baselines, with improvements of 24.38% in Hit Ratio (HR), 69.56% in Mean Reciprocal Rank (MRR) and 55.94% in Normalized Discounted Cumulative Gain (NDCG).</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111248"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced process monitoring using machine learning-based control charts for poisson-distributed data","authors":"Faraz Mukhtiar ,&nbsp;Babar Zaman ,&nbsp;Naveed Razzaq Butt","doi":"10.1016/j.engappai.2025.111227","DOIUrl":"10.1016/j.engappai.2025.111227","url":null,"abstract":"<div><div>The ability to detect shifts (e.g., outliers) in process monitoring is crucial for maintaining high-quality standards and operational efficiency in industrial environments. Control Charts (CCs) provide an organized framework for recognizing and managing anomalies, generally caused by assignable factors (e.g., identifiable issues) rather than inherent process variation. Traditional CCs, such as classical Shewhart, CUSUM, and EWMA, are commonly used to monitor Poisson observations in modern industries. The classical exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) CCs are individually effective at detecting small-to-moderate shifts while Shewhart CCs identify moderate-to-large shifts in the process location and/or dispersion parameters. However, the classical CCs face limitations due to their sensitivity being constrained to specific ranges of shifts. To enhance the detection abilities of classical CCs in detecting all kinds of shifts in the process location parameter, this study proposes the integration of Machine Learning (ML) techniques into CCs to optimize the shift’s detection in process location parameter across a wider range. This study generates a dataset using the statistics of classical CCs based on Poisson-distributed data, which includes both in-control (stable process) and out-of-control (unstable process) processes. This dataset is used to train ML models, which are pre-processed through normalization and feature engineering through a heuristic approach before training. The performance of ML models is evaluated using standard regression metrics, specifically mean squared error and the coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>-score), to ensure effective generalization across varying process conditions. After training, these models are implemented within the proposed ML-based CC (<span><math><mrow><mi>M</mi><mi>L</mi><mi>C</mi><mi>C</mi></mrow></math></span>) schemes. Their process monitoring capabilities are then rigorously compared with traditional and existing CCs, utilizing key performance indicators such as average run length and standard deviation of run length. These metrics are computed through a Python-based algorithm developed using the Monte Carlo simulation method. For practical purposes, implementing the proposed <span><math><mrow><mi>M</mi><mi>L</mi><mi>C</mi><mi>C</mi></mrow></math></span> schemes with real-life data in the food processing industry, specifically the packaging of frozen orange juice concentrate. This practical example highlights the superiority of proposed <span><math><mrow><mi>M</mi><mi>L</mi><mi>C</mi><mi>C</mi></mrow></math></span> schemes in the early detection of shift(s) in process location parameter(s) against classical CCs in real-life scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"157 ","pages":"Article 111227"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flow decomposition based spatial–temporal filtering self-attention networks for traffic flow forecasting","authors":"Ying Tang,&nbsp;Dawei Wu,&nbsp;Zhetao Han","doi":"10.1016/j.engappai.2025.111243","DOIUrl":"10.1016/j.engappai.2025.111243","url":null,"abstract":"<div><div>Traffic prediction is essential for intelligent transportation systems but challenging due to complex spatial–temporal dependencies. Current methods often overlook data entanglement caused by stable flow sequences and traffic events, and attention mechanisms may introduce irrelevant spatial information. In this paper, we propose FDFSAN (<strong>F</strong>low <strong>D</strong>ecomposition based spatial–temporal <strong>F</strong>iltering <strong>S</strong>elf-<strong>A</strong>ttention <strong>N</strong>etworks), which addresses these issues by decomposing traffic flow data into stationary and sudden components modeled through a dual-channel spatial–temporal network. Our <strong>filtering self-attention mechanism</strong> captures both temporal and spatial dependencies, integrating information from nearby and distant roads while minimizing irrelevant spatial noise. Extensive experiments on four real-world datasets show that FDFSAN outperforms state-of-the-art methods, making it suitable for urban traffic networks with dynamic spatial correlations and anomalies.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111243"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long short-term memory neural network- decline curve analysis production forecast method for horizontal wells in tight reservoir based on sequence decomposition and reconstruction","authors":"Jinxin Cao ,&nbsp;Yiqiang Li ,&nbsp;Yaqian Zhang ,&nbsp;Xuechen Tang ,&nbsp;Qihang Li ,&nbsp;Yuling Zhang ,&nbsp;Zheyu Liu","doi":"10.1016/j.engappai.2025.111482","DOIUrl":"10.1016/j.engappai.2025.111482","url":null,"abstract":"<div><div>Oil production is a key parameter for evaluating geological potential. Conventional methods struggle to forecast nonlinear and non-stationary production sequences with a strong temporal trend due to the poor physical properties of tight reservoirs. This research proposes a forecast method that integrates a long short-term memory (LSTM) neural network with decline curve analysis (DCA). First, empirical mode decomposition (EMD) is applied to the production sequence, extracting multiple fluctuating intrinsic mode functions (IMF) related to manual construction and a residual (RES) representing reservoir energy depletion. Approximate entropy (ApEn) is then used to categorize each IMF into three groups based on sequence decomposition results, facilitating the reconstruction of the production sequence. LSTM forecasts the recombined IMF sequence, while DCA predicts the residual component. Results indicate that EMD effectively separates time trends, and both reconstructed components and residuals can be accurately predicted. Compared with stand-alone LSTM, back-propagation (BP) neural network, random-forest (RF) and convolutional-neural-network (CNN) models, the proposed method reduces production forecast errors by at least 25 %. This research incorporates signal-processing techniques and physical constraints into production forecasting. These enhancements provide a more accurate and reliable method for forecasting production in hydraulically fractured horizontal wells within tight-oil reservoirs.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111482"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cumulative absolute velocity prediction method based on surrounding strong motion records and deep learning","authors":"Qingle Cheng ,&nbsp;Chengshuai Niu ,&nbsp;Hongyu Zhao ,&nbsp;Jin Zhuang ,&nbsp;Yuan Tian ,&nbsp;Xinzheng Lu","doi":"10.1016/j.engappai.2025.111416","DOIUrl":"10.1016/j.engappai.2025.111416","url":null,"abstract":"<div><div>Cumulative absolute velocity (CAV) is a critical parameter for assessing seismic destructiveness. Existing post-earthquake CAV prediction methods, such as interpolation techniques and ground motion prediction equations (GMPEs), face challenges in simultaneously leveraging historical strong-motion data and real-time observations from strong-motion stations. To address this limitation, this study proposes a novel CAV prediction method based on surrounding strong-motion records and deep learning. The method introduces a station group construction approach, where each group consists of a target station (an unmonitored location) and four surrounding stations with observed data. Using the Japanese strong-motion database, a dataset of 10,463 station groups was established to train the network model. A graph-based feature representation method, designed specifically for station groups, was implemented as the network input. Based on this, a graph neural network (GNN) model, GraphStation, was developed to predict CAV at unmonitored target locations. The performance of the proposed method was compared with interpolation methods and GMPEs, yielding the following key findings: (1) the proposed model achieves a coefficient of determination (R²) of 0.91 for CAV prediction, outperforming existing methods. (2) By training on the station group database and utilizing real-time observations from surrounding stations, the method effectively integrates historical strong-motion data and real-time monitoring data, providing a robust and accurate approach for CAV prediction in regions lacking post-earthquake monitoring data.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"157 ","pages":"Article 111416"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards pedestrian head tracking: A benchmark dataset and a multi-source data fusion network","authors":"Kailai Sun ,&nbsp;Xinwei Wang ,&nbsp;Shaobo Liu ,&nbsp;Qianchuan Zhao ,&nbsp;Gao Huang ,&nbsp;Chang Liu","doi":"10.1016/j.engappai.2025.111265","DOIUrl":"10.1016/j.engappai.2025.111265","url":null,"abstract":"<div><div>Pedestrian detection and tracking in crowded video sequences have many applications, including autonomous driving, robot navigation and pedestrian flow analysis. However, detecting and tracking pedestrians in high-density crowds face many challenges, including intra-class occlusions, complex motions, and diverse poses. Although artificial intelligence (AI) models have achieved great progress in head detection, head tracking datasets and methods are extremely lacking. Existing head datasets have limited coverage of complex pedestrian flows and scenes (e.g., pedestrian interactions, occlusions, and object interference). It is of great importance to develop new head tracking datasets and methods. To address these challenges, we present a Chinese Large-scale Cross-scene Pedestrian Head Tracking dataset (Cchead) and a Multi-source Data Fusion Network (MDFN). The dataset has features that are of considerable interest, including 10 diverse scenes of 50,528 frames with about 2,366,249 heads and 2,358 tracks. Our dataset contains diverse pedestrian moving speeds, directions, and complex crowd pedestrian flows with collision avoidance behaviors. Existing state-of-the-art (SOTA) algorithms are tested and compared on the Cchead dataset. MDFN is the first end-to-end convolutional neural network (CNN)-based head detection and tracking network that jointly trains Red, Green, Blue (RGB) frames, pixel-level motion information (optical flow and frame difference maps), depth maps, and density maps in videos. Ablation experiments confirm the significance of multi-source data fusion. Compared with SOTA pedestrian detection and tracking methods, MDFN achieves superior performance across three datasets: Cchead, Restaurant and Crowd of Heads Dataset (CroHD). To promote further development, we share our source code and trained models for global researchers: <span><span>https://github.com/kailaisun/Cchead</span><svg><path></path></svg></span>. We hope our datasets to become essential resources towards developing pedestrian tracking in dense crowds.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111265"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations","authors":"Farhan Sheth ,&nbsp;Priya Mathur ,&nbsp;Amit Kumar Gupta ,&nbsp;Sandeep Chaurasia","doi":"10.1016/j.engappai.2025.111425","DOIUrl":"10.1016/j.engappai.2025.111425","url":null,"abstract":"<div><div>This study introduces an advanced Artificial Intelligence (AI) framework for soil classification and crop recommendation, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in an ensemble approach, alongside an adaptive fuzzy logic-based decision system for crop suggestions. While existing research typically addresses soil classification or crop recommendation in isolation, this work integrates cutting-edge deep learning models and fuzzy logic to enhance both tasks. The methodology is divided into two phases: Phase 1 covers data collection, preprocessing, and augmentation using Cycle Generative Adversarial Networks (CycleGAN) to expand the curated dataset of 1189 soil images to 8,413, while Phase 2 focuses on training CNN and ViT models, ensembling these models, and developing a fuzzy logic system that considers soil type, nutrients, potential of hydrogen (pH), and climatic conditions for crop recommendations. Experimental results indicate models achieve classification accuracies of up to 89.32 % on the original dataset, improving to 91.01 % with augmented data. On the CycleGAN-augmented (CyAUG) dataset, EfficientNet v2 Large and ViT-Large/16 attain accuracies of 99.60 % and 99.73 %, respectively. Furthermore, an ensemble of these architectures achieves a perfect accuracy of 100 %. The results are also validated by K-fold cross-validation. The research also presents 'Agro Companion,' an AI-powered tool that assists farmers in soil identification and crop selection based on geological and environmental data. This framework addresses key agricultural challenges in India, offering a high-accuracy, practical solution for improving both soil classification and crop recommendation. This research delivers state-of-the-art soil classification performance and a robust AI-based crop recommendation tool to support sustainable agricultural practices.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111425"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Meta Alignment for cross-domain object detection","authors":"Yang Li ,&nbsp;Shanshan Zhang ,&nbsp;Yunan Liu ,&nbsp;Jian Yang","doi":"10.1016/j.engappai.2025.111247","DOIUrl":"10.1016/j.engappai.2025.111247","url":null,"abstract":"<div><div>Unsupervised domain adaptation (UDA) aims to adapt an object detector from a labeled source domain to an unlabeled target domain. In this task, multiple sub-tasks of different nature are involved, yet existing methods simply sum up the losses and train all the sub-tasks jointly. We, however, find that inconsistent optimization goals between different sub-tasks lead to limited adaptation performance. Specifically, from our analysis, we find notable gradient discrepancies between sub-tasks in a domain adaptive object detector, and especially significant conflicts between domain alignment and detection sub-tasks. Based on this analysis, we propose to solve UDA object detection from a multi-task learning perspective. Specifically, we divide all sub-tasks into two groups, and alleviate both inter-group and intra-group inconsistency via a novel Hierarchical Meta Alignment (HMA) method. At the first level, we construct a Meta Optimization Block (MOB) for each inter-group task pair, which is optimized via the Model-Agnostic Meta-Learning (MAML) algorithm. At the second level, all MOBs are optimized sequentially via the Reptile algorithm. Experimental results on various adaptation scenarios show that our proposed method outperforms previous methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111247"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-axis vision transformer for medical image segmentation","authors":"Abdul Rehman Khan ,&nbsp;Asifullah Khan","doi":"10.1016/j.engappai.2025.111251","DOIUrl":"10.1016/j.engappai.2025.111251","url":null,"abstract":"<div><div>Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown remarkable success in medical image segmentation, but individually, they struggle to capture both local and global contexts. To address this limitation, we propose MaxViT-UNet, a hybrid encoder–decoder architecture that integrates convolutional operations and multi-axis self-attention to capture local and global context for effective medical image segmentation. Our novel Hybrid Decoder fuses upsampled decoder features with encoder skip connections and refines them using a multi-axis attention block, repeated across decoding stages for progressive segmentation refinement. Experimental evaluation on the MoNuSeg18 and MoNuSAC20 datasets demonstrates that MaxViT-UNet outperforms traditional CNN-based U-Net by 2.36% and 14.14% Dice score, respectively. Similarly it outperforms Swin-UNet by 5.31% on MoNuSeg18 and nearly doubles the Dice score on MoNuSAC20. These results confirm the generalization and effective segmentation capabilities of our hybrid architecture across diverse histopathological datasets.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111251"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health- and behavior-aware energy management strategy for fuel cell hybrid electric vehicles based on parallel deep deterministic policy gradient learning","authors":"Haochen Sun ,&nbsp;Jing Li ,&nbsp;Chun Cheng ,&nbsp;Suzhen Shi ,&nbsp;Jing Wang ,&nbsp;Jingjing Lin ,&nbsp;Yang Liu","doi":"10.1016/j.engappai.2025.111311","DOIUrl":"10.1016/j.engappai.2025.111311","url":null,"abstract":"<div><div>To find a more optimal way to solve the energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs), the majority of existing research focuses on external driving conditions, while the driver’s behavior as a more important internal influence factor also needs to be taken into account. In this paper, a health- and behavior-aware two-layer hierarchical energy management framework using an improved adaptive parallel deep deterministic policy gradient (DDPG) learning algorithm is proposed for obtaining the optimal EMS of a multi-source FCHEV. In the upper layer, machine learning approaches are employed to recognize the real-time driver’s behavior, and Pontryagin’s minimum principle is applied to calculate the optimal equivalent factor of each driver’s behavior. In the lower layer, to protect the service life of fuel cell and battery as well as increase the learning efficiency, an adaptive fuzzy filter is used, and a health- and behavior-aware multi-objective adaptive equivalent consumption minimization strategy model is constructed and solved by an improved adaptive parallel DDPG-based algorithm. Simulation results show that, the EMS obtained by the proposed DDPG algorithm can achieve the highest fuel cell (FC) working efficiency (approximate to 56%), apparently reduce the degree of degradation of battery (BAT) from 0.42% to 0.28%, and achieve a reduction of 9.24% in terms of the total cost to use compared with deep Q network (DQN)-based EMS.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111311"},"PeriodicalIF":7.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}