Applied Research最新文献

{"title":"Applicability of Displacement Prediction Models for Creep-Type Highway Slopes Based on Residual Information Structure","authors":"Xinying Li,&nbsp;Wen Li,&nbsp;Weiwen Quan,&nbsp;Kang Jiang,&nbsp;Kefei Liu","doi":"10.1002/appl.70139","DOIUrl":"https://doi.org/10.1002/appl.70139","url":null,"abstract":"<div>\u0000 \u0000 <p>To improve the selection of displacement prediction models for creep-type highway slopes with low signal-to-noise ratios and limited monitoring samples, this study proposes a model suitability analysis framework based on residual information structure. A GNSS monitoring point on a cutting slope along the Hengyang–Yongzhou expressway was selected as the case study. Based on 123 consecutive days of three-dimensional daily displacement data in the X, Y, and H directions, gross errors were removed using the 3σ criterion, and high-frequency noise was reduced using adaptive Kalman filtering. Six prediction models with different structural complexities, namely rolling GM(1,1), ARIMA, BP neural network, SVR, GM(1,1)-BP, and ARIMA-SVR, were established and compared. The results show that Kalman filtering reduced the noise standard deviation in the X, Y, and H directions by 66.7%, 54.8%, and 77.0%, respectively. The optimal model varied significantly among directions. ARIMA performed best in the X direction, where the displacement series showed weak trends and near-stationary fluctuations. Rolling GM(1,1) achieved the highest accuracy in the Y direction, which was dominated by monotonic slow creep. ARIMA-SVR obtained the best comprehensive performance in the H direction, where elevation solution uncertainty and environmental disturbances caused stronger residual correlation and nonlinearity. Further analysis indicates that first-order residual autocorrelation can be used as an auxiliary indicator for deciding whether model complexity should be increased. However, final model selection should also consider trend dominance, volatility, and nonlinear characteristics. The proposed framework provides a lightweight and data-adaptive reference for automated highway slope monitoring and early warning systems.</p></div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concrete Exterior Wall Crack Detection Using a Coordinate Attention-Enhanced YOLOv11 Model","authors":"Guobao Hu,&nbsp;Xianwen Kong,&nbsp;Xiao Yang,&nbsp;Hui Xiao,&nbsp;Shaoheng Jiang","doi":"10.1002/appl.70135","DOIUrl":"https://doi.org/10.1002/appl.70135","url":null,"abstract":"<div>\u0000 \u0000 <p>Concrete exterior wall cracks are important indicators of surface deterioration and potential durability problems in building structures. However, automatic detection remains challenging because cracks are often slender, low-contrast and easily confused with rough surface textures, stains, pores and shadows. In this study, an improved YOLOv11 model with Coordinate Attention, termed CA-YOLOv11, is developed for concrete exterior wall crack detection. The Coordinate Attention module is embedded after the SPPF module and before the neck to introduce horizontal and vertical positional information into high-level crack feature representations before multi-scale feature fusion. A dataset containing 4,028 concrete exterior wall crack images is used to train and evaluate YOLOv8, YOLOv11 and CA-YOLOv11 under the same experimental conditions. The results show that CA-YOLOv11 achieves mAP50, mAP50-95, Precision, Recall and Mean IoU values of 0.845, 0.746, 0.942, 0.805 and 0.588, respectively, while maintaining efficient inference with a latency of 5.810 ms and an FPS of 172.300. Visual comparisons further show that CA-YOLOv11 produces more continuous predictions for weak and slender crack regions. These findings indicate that coordinate-sensitive spatial feature enhancement improves crack recognition and localisation without substantially increasing computational cost.</p></div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence-Driven Multi-Class Road Damage Detection Using Attention-Enhanced YOLOv8 and Weighted Boxes Fusion","authors":"Yifei Zhu,&nbsp;Tongyao Jin,&nbsp;Hanzheng Sun","doi":"10.1002/appl.70136","DOIUrl":"https://doi.org/10.1002/appl.70136","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate road damage detection is essential for pavement maintenance, traffic safety, and intelligent transportation systems. However, road damage targets often exhibit large variations in shape, scale, texture, and boundary clarity, making detection difficult under complex pavement backgrounds. To address these challenges, this study proposes an Efficient Multi-Scale Attention Fusion YOLOv8 framework, termed EF-YOLOv8, for multi-class road damage detection. The proposed framework combines an Efficient Multi-Scale Attention (EMA)-enhanced YOLOv8 branch, a Faster Region-based Convolutional Neural Network (Faster R-CNN) branch, and a Weighted Boxes Fusion (WBF) module. YOLOv8 is used as the efficient baseline detector, while the EMA module is introduced to strengthen multi-scale road damage feature representation and suppress irrelevant background information. Faster R-CNN is further incorporated as a complementary two-stage detector to provide additional localization information. The final detection results are generated through WBF, in which overlapping predictions from different detectors are integrated according to confidence scores and model weights. Experiments were conducted on the Japan subset of the Road Damage Dataset 2022 (RDD2022), including four road damage categories: longitudinal cracks, transverse cracks, alligator cracks, and potholes. An additional cross-dataset validation was further conducted on the India and Czech Republic subsets of RDD2020 to examine the external generalization ability of the proposed framework under different regional pavement and imaging conditions. The results show that EF-YOLOv8 achieves the best overall performance among all compared models, with a Precision of 0.6359, a Recall of 0.5729, an mAP50 of 0.5870, and an mAP50-95 of 0.2584. Class-wise results further indicate that the proposed framework improves the detection stability of different road damage categories. Although the fusion framework increases inference time, it provides more reliable detection results and is suitable for road inspection scenarios where detection accuracy is prioritized. The main contribution of EF-YOLOv8 lies in its task-oriented asymmetric fusion design, in which EMA-YOLOv8 serves as the primary feature-enhanced detector and Faster R-CNN is used as an auxiliary detector to provide complementary candidate boxes. The WBF module then integrates these predictions with validation-optimized fusion parameters, allowing the framework to improve detection stability beyond a direct combination of existing modules.</p></div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on an Intelligent Monitoring and Assessment Method for Asphalt Pavement Compaction Based on Roadside Vibration Acceleration","authors":"Chang Li,&nbsp;Jingyi Wang,&nbsp;Tao Xu","doi":"10.1002/appl.70133","DOIUrl":"https://doi.org/10.1002/appl.70133","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the delayed feedback of asphalt pavement compaction testing, the destructive and discontinuous nature of conventional detection methods, and the susceptibility of vibration roller axle acceleration signals to mechanical-structure vibration interference, this paper proposes an intelligent monitoring and assessment method for asphalt pavement compaction based on roadside vibration acceleration. First, the propagation characteristics of vibration energy from the compaction zone to the roadside during vibratory compaction are analyzed. The mean vibration acceleration amplitude over 20 vibration cycles is selected as the effective value of roadside vibration acceleration, denoted as F, and the Grubbs criterion and a finite impulse response (FIR) filter are used for outlier removal and noise reduction. Second, a finite element model of asphalt pavement vibratory compaction is established in Abaqus. The effective roadside vibration acceleration F and detection distance D under different compaction states are extracted, and a multivariate quadratic FDK regression model relating F, D, and the compaction degree K is developed. Finally, field tests are conducted based on the Chuzhou G328 maintenance project. The results show that the coefficient of determination of the FDK regression model reaches 0.979. Within the effective detection range of 0–1.5 m, the error between the predicted compaction degree and the core-sampling measurement does not exceed 0.5%. The results indicate that the roadside vibration monitoring method is nondestructive, low-cost, easy to deploy on site, and less affected by mechanical interference from the roller, providing a reference for dynamic quality control in asphalt pavement construction and intelligent road monitoring.</p></div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “From 0–1 Goal Programming to Binary Integer Programming: An Improved Formulation for Optimizing Nurse Rostering in a Malaysian Military Maternity Ward”","authors":"","doi":"10.1002/appl.70137","DOIUrl":"https://doi.org/10.1002/appl.70137","url":null,"abstract":"<p>Nur Azmina Hamizah Ali Jafri, Sharifah Aishah Syed Ali, Ruzanna Mat Jusoh, Muhammad Fairuz Abd Rauf, Mohd Fahmi Mohamad Amran, Siti Farrah Mohd Arshad, Ahmad Shafiq Abdul Rahman, Stefan Wolfgang Pickl, “From 0–1 Goal Programming to Binary Integer Programming: An Improved Formulation for Optimizing Nurse Rostering in a Malaysian Military Maternity Ward,” Applied Research 5 (2026): 1-12. https://doi.org/10.1002/appl.70067.</p><p>We apologize for this error.</p>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Development and Validation of a Simple Method for the Determination of Meropenem Concentrations in Human Plasma Using High-Performance Liquid Chromatography","authors":"Asma Aboelezz,&nbsp;Maged Kharouba,&nbsp;Sherif Hanafy Mahmoud","doi":"10.1002/appl.70138","DOIUrl":"https://doi.org/10.1002/appl.70138","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 <p>Meropenem is a beta-lactam antimicrobial that is used to treat severe infections. Critically ill patients are highly prone to pharmacokinetics variability and a fluctuation in meropenem plasma concentrations. The aim of this study is to provide a simple, rapid, precise and accurate method for the analysis of meropenem in human plasma using high-performance liquid chromatography. Sample preparation involved a single step deproteinization of plasma (0.2 mL) using cold methanol in a 2:1 ratio. Chromatographic separation was achieved on a C18 column using a mobile phase of 50 mM phosphate buffer (pH 6.5) and acetonitrile (90:10, vol/vol) at a flow rate of 1 mL/min, with a total run time of 15 min. Ultraviolet detection was performed at 300 nm for meropenem and 254 nm for caffeine (internal standard). The method demonstrated excellent linearity over the range of 0.5–100 mg/L (r² &gt; 0.99) with inter- and intra-day precision and accuracy within acceptable limits. Finally, environmental impact assessment using various greenness tools confirmed the method's sustainability. The developed method is a simple, rapid and accurate method that could be utilized for research and therapeutic drug monitoring purposes to measure meropenem concentrations in human plasma.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven and Cyber-Secure Embedded Inverter Control for Energy Optimization in Electric Vehicles","authors":"Sonal Sagar Boda,&nbsp;Siddhesh Pimpale,&nbsp;Mohammad Somon Sikder,&nbsp;Hemayet Uddin Himel,&nbsp;Zeeshan Akbar,&nbsp;Avijit Roy,&nbsp;Deepak Gupta","doi":"10.1002/appl.70128","DOIUrl":"https://doi.org/10.1002/appl.70128","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid evolution of intelligent electric vehicle (EV) powertrains requires inverter-control systems capable of simultaneously optimizing energy efficiency and ensuring cyber resilience under real-time embedded constraints. This work proposes a unified AI-driven and cyber-secure inverter-control framework for permanent magnet synchronous motor (PMSM)-based EV systems. The proposed methodology integrates a Deep Q-Network (DQN)-based deep reinforcement learning (DRL) adaptive switching controller with encrypted command validation and an embedded intrusion detection system (IDS). The DRL controller minimizes switching losses, torque ripple, and total harmonic distortion (THD) while satisfying thermal and operational constraints through multi-objective optimization. The framework is implemented on an ARM Cortex-M7 (STM32H7, 400 MHz, 2 MB Flash, 1 MB SRAM) platform connected to a three-phase inverter prototype and validated using publicly available WLTP and UDDS driving-cycle datasets in MATLAB/Simulink and hardware-in-the-loop environments. Experimental results obtained across repeated simulation and hardware-in-the-loop evaluations under WLTP and UDDS conditions demonstrate an average peak efficiency of 95.4%, representing a 4.2% improvement over conventional field-oriented control (FOC), along with a 34.5% reduction in switching losses, 67% reduction in torque ripple, and THD reduction to 3.2%. The embedded IDS achieves 98.6% attack-detection accuracy with an average detection latency of 3.2 ms and only 1.6% computational overhead. The results confirm that the proposed integrated framework provides an effective balance between adaptive energy optimization, embedded feasibility, and cybersecurity for next-generation EV power-electronic systems.</p></div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Modeling of Multi-Channel Bus Ticket Demand With Price Sensitivity Analysis","authors":"Mohd Fahmi Mohamad Amran,&nbsp;Shahizam Ibrahim,&nbsp;Syahirah Ismail,&nbsp;Azlinda Abdul Aziz,&nbsp;Mazlina Abdul Majid,&nbsp;Muhammad Naim Abdullah,&nbsp;Shazarin Ahmad Zainuddin,&nbsp;Muhammad Fairuz Abd Rauf","doi":"10.1002/appl.70134","DOIUrl":"https://doi.org/10.1002/appl.70134","url":null,"abstract":"<div>\u0000 \u0000 <p>This study develops a predictive modeling framework for multi-channel bus ticket demand using counter ticket sales and online sales data from an aggregator-based e-ticketing system in Malaysia. The study integrates historical sales data with operational and external variables, including trip frequency, vehicle registration, public and school holidays, weather indicators, and ridership trends. A SARIMAX-based time series approach is applied together with comparative machine learning models to examine seasonal demand patterns, channel-level differences, and price sensitivity. The findings show that counter ticket sales and online sales exhibit different demand behaviors. Counter sales are relatively less sensitive to price changes and are more influenced by trip availability and seasonal travel periods, while online sales are shaped more strongly by operational efficiency, service availability, and customer purchasing behavior. The main contribution of this study is the use of price elasticity impact as a predictive variable to improve the interpretability of demand forecasting in a multi-channel ticketing environment. The proposed framework provides practical insights for bus operators to support pricing decisions, trip planning, and data-driven operational strategies in public transportation ticketing systems.</p></div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Visual Sorting of Construction and Demolition Waste Using Low-Annotation-Cost Instance Segmentation","authors":"Run Zhang,&nbsp;Ruiqi Chen,&nbsp;Xi Jiang,&nbsp;Fan Gu","doi":"10.1002/appl.70131","DOIUrl":"https://doi.org/10.1002/appl.70131","url":null,"abstract":"<div>\u0000 \u0000 <p>Construction and demolition waste (CDW) instance segmentation is essential for automated sorting and high-value recycling, yet conventional supervised methods usually depend on large scale pixel-level annotation that are particularly costly to obtain. To address this challenge, this study proposes MatMatch as a training-free automatic instance-level annotation framework for CDW scenes, rather than a deployable inference model. The pseudo-labels generated by MatMatch are further distilled into a lightweight student instance segmentation model, termed MatMatch2Real, for real-time deployment. Specifically, MatMatch first guides SAM2 to generate candidate instance masks through a box-prompting strategy based on the Lab color space and connected-component analysis, then refines the candidate masks via geometric and quality-based post-processing, and finally assigns semantic labels by matching self-supervised visual features extracted by DINOv3 against few-shot category prototypes. Without any additional model training or pixel-level supervision, MatMatch achieves an mAP50:95 of 0.766 and an mAR of 0.816 on the full dataset, substantially outperforming existing automatic annotation baselines. The distilled student model, MatMatch2Real, further achieves an mAP50:95 of 0.863, an mAR of 0.890, and an inference speed of 253.8 FPS. Notably, the student model trained solely on pseudo-labels generated by MatMatch already attains 95.1% of the fully supervised performance. With additional fine-tuning using only 1.6% of the ground-truth annotated images, it reaches a performance level comparable to full-data supervised training. These results demonstrate that the proposed framework can generate high-quality automatic instance-level annotations for CDW, substantially reduce the reliance on manual annotation, and support efficient real-time deployment.</p></div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of Needs Analysis Instrument in Integrating Green Chemistry With STEM-Based Learning Module in the Exploration of Elements in Nature Theme","authors":"Wan Mazlina Wan Mehammud,&nbsp;Nur Jahan Ahmad","doi":"10.1002/appl.70130","DOIUrl":"https://doi.org/10.1002/appl.70130","url":null,"abstract":"<div>\u0000 \u0000 <p>Students' awareness of green chemistry using different instructional methods has been studied, with project-based and problem-based learning models being the most common, particularly within the framework of science, technology, engineering, and mathematics (STEM) education. This paper shows the outcomes of a needs analysis validation process conducted to construct an educational learning module based on STEM and green chemistry that could be used in the theme exploration of elements in nature, which is part of the secondary school science curriculum. Six subject-matter experts were involved in the validation process and reviewed the instrument, and several changes were made based on their feedback. The content validity index (CVI) was used for relevance assessment of the instrument items. The needs analysis questionnaire was divided into four parts: Section A – Respondents' Demographics; Section B – Elements in the Module; Section C – Content of the Module; and Section D – Suggestions, which will address 25 items in total. The validity analysis yielded a high average scale-level CVI (S-CVI/Ave) of 0.98, demonstrating strong content validity. Consequently, the validated questionnaire will be used to gather input from science teachers for the development of the module.</p></div>","PeriodicalId":100109,"journal":{"name":"Applied Research","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/appl.70130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}