Journal of Bionic Engineering最新文献

{"title":"Decoding Hand Gestures Under Arm Movements Based on Electromyography and Acceleration Signals","authors":"Xiaoru Niu,&nbsp;Dingguo Zhang,&nbsp;Chenxi Qu,&nbsp;Lei Ren,&nbsp;Zhihui Qian,&nbsp;Jianan Wu,&nbsp;Kunyang Wang,&nbsp;Luquan Ren","doi":"10.1007/s42235-026-00858-1","DOIUrl":"10.1007/s42235-026-00858-1","url":null,"abstract":"<div><p>Gesture classification based on surface electromyography (sEMG) have been extensively investigated towards the control of smart prosthetic hands. However, most studies did not consider the effects of arm position and movements that frequently occur in daily activities. This study aims to address the gesture classification challenge under arm movements. We collected sEMG and acceleration (ACC) data from fourteen participants, including two individuals with radial artery amputations, while performing gestures in both static and dynamic arm states. Using the collected data, the performance of three machine learning methods was evaluated for gesture classification under both arm movements and static arm conditions. The results revealed a 17.48% decrease in average classification accuracy in the dynamic state compared to the static state when using sEMG signals. Subsequently, the improvement in classification accuracy under arm movements was validated using both sEMG and ACC, with deep learning achieving the highest average accuracy of 84.35% across healthy subjects. Additionally, the study assessed the impact of gesture similarity on classification performance and evaluated the practical efficacy of classifiers for amputees. To further enhance gesture classification accuracy under arm movements, a two-stage gesture classification model training method based on ResNet18 was proposed. This method first learns a generalized motion prototype from population data and then adapts it to individual subjects via fine-tuning, resulting in a 2.49% improvement in average recognition accuracy across all 14 subjects.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"880 - 897"},"PeriodicalIF":5.8,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42235-026-00858-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digitizing Health Monitoring in Engineering Structures Using Deep Learning: A Novel Block Architecture for Concrete Crack Prediction in Surface and Sub-surface Dataset","authors":"Muhammad Ishfaque,&nbsp;Saif Ur Rehman Khan,&nbsp;Yulong Lou","doi":"10.1007/s42235-026-00846-5","DOIUrl":"10.1007/s42235-026-00846-5","url":null,"abstract":"<div>\u0000 \u0000 <p>Monitoring concrete cracks for structural health in civil engineering presents a significant challenge. This is primarily due to the reliance on manual investigation methods, impacts of global climatic shifts stress, and geohazard threats to engineering structures. To cope with this challenge, state-of-the-art Deep Learning (DL) models are utilized to predict concrete cracks and accurately identify subtle variations in crack patterns and sizes, which lighting conditions and surface textures can influence. Previous studies indicate that model accuracy may decrease when faced with obscured concrete cracks, irregular shapes, or limited datasets for real-world problem scenarios. Feature fusion enhances model performance by combining complementary information, resulting in more accurate predictions, but may increase complexity and potential information redundancy. The study presents the Fractur Encoder to Decoder (FractED) block, a novel architecture consisting of three sub-blocks: the inner block (Encoder), intermediate block (Intermediate block), and outer block (Decoder). This approach integrates fused features into the model without additional fine-tuning steps, allowing for comprehensive feature refinement and enhancement, ultimately optimizing model performance. The study investigates a DL methodology on three datasets, demonstrating its effectiveness in handling complex classification scenarios in civil engineering. The model achieved high accuracy rates, with 88.41% for multiclass (Deck, Pavement, and Walls) classification tasks, 91.94% on the Pillow Dam Borehole image binary dataset, and 99.77% on the Surface Crack binary dataset. The FractED block integration ensures adaptability and scalability, making it valuable for various Artificial Intelligence (AI) applications in civil engineering. The research also provides a scientific foundation for automatizing civil engineering inspection instruments for the future.</p>\u0000 </div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"992 - 1014"},"PeriodicalIF":5.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Analysis of Wear Mechanisms and Cutting Surface Performance in Bioinspired Woodpecker-Beak Rotary Blades for Wood Machining","authors":"Shaofeng Ru,&nbsp;Yangding Han,&nbsp;Hao Li","doi":"10.1007/s42235-026-00844-7","DOIUrl":"10.1007/s42235-026-00844-7","url":null,"abstract":"<div><p>Cutting tools for extracting natural latex from wood have been used for centuries, yet research on tapping knives remains scarce, particularly regarding efficient surface wood cutting tools. This study designed two rotary blades biomimicking woodpecker beak angles for wood-cutting tests. Tests examined surface quality and blade wear under varying parameters. Results demonstrated that the Beak-Edge-Blade was optimal for low-speed (5 mm/s) parallel-to-grain cutting based on peak cutting forces and energy consumption, while the Tip-Beak-Blade performed better in high-speed (15 mm/s) parallel and cross-grain cutting. Both blades showed comparable performance at medium speed (10 mm/s). TiCN coating effectively reduced blade wear but increased cutting forces and energy consumption at both low and high speeds, while reducing these parameters at medium speed. Parallel-to-grain cutting achieved better surface integrity (Sa, Sz, Str) than cross-grain. EDS/SEM revealed two wear mechanisms: fiber adhesion and edge chipping. Beak-Edge-Blade suffered chipping; Tip-Beak-Blade exhibited adhesion. Findings aid precision tool design, notably depth-sensitive tools like rubber-tapping blades.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"926 - 949"},"PeriodicalIF":5.8,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"CLeFTNet: Convolutional LeNet Forward Taylor Network for Autism Spectrum Disorder Detection Using Multimodal Data","authors":"Aswathy Wilson,&nbsp;J. Anitha","doi":"10.1007/s42235-026-00851-8","DOIUrl":"10.1007/s42235-026-00851-8","url":null,"abstract":"<div>\u0000 \u0000 <p>Autistic Spectrum Disorder (ASD) is a neurodevelopmental disorder that leads to complexities in social interactions. Based on the statistics of the WHO, more patients diagnosed with ASD is steadily increasing. Most of the recent investigations focused on medical treatment and data collection, but they did not focus on ASD diagnosis related to Deep Learning (DL). To solve this issue, this work introduces an efficient model for ASD detection with multi-modal data utilizing Convolutional LeNet Forward Taylor network (CLeFTNet). Here, the CLeFTNet is developed by combining CNN and LeNet using the Taylor series. This work involves two kinds of inputs: First, an input brain image is pre-processed by an Adaptive Wiener filter (AWF) and Region of Interest (RoI) extraction. Then, the Box neighborhood search algorithm is employed for extracting the pivotal region regarding functional connectivity, and feature extraction is performed. Simultaneously, input autism data is normalized using Min–Max normalization, and then Matusita and Chord Distance is deployed for feature selection. Thereafter, the Synthetic Minority Oversampling Technique (SMOTE) is considered for data augmentation. The results from the above two processes are employed for detecting ASD by considering CLeFTNet. The analytic metric, namely accuracy, sensitivity and specificity attained 91.723%, 91.396% and 91.573%.</p>\u0000 </div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"1200 - 1216"},"PeriodicalIF":5.8,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Theory Evolution Optimization: A Metaheuristic Algorithm BaSed on Evolution Process of Theory","authors":"Jiacong Liu,&nbsp;Jiaze Tu,&nbsp;Chunguang Bi,&nbsp;Huiling Chen,&nbsp;Ali Asghar Heidari,&nbsp;Hao Xie,&nbsp;Lei Liu,&nbsp;Yi Chen","doi":"10.1007/s42235-025-00833-2","DOIUrl":"10.1007/s42235-025-00833-2","url":null,"abstract":"<div>\u0000 \u0000 <p>Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods. To advance the field’s focus on algorithmic performance, this study introduces the Theory Evolution Optimization (TEO) – an efficient metaheuristic inspired by the evolution of scientific theory. TEO simulates the competitive, accumulative, and replacement processes among scientific hypotheses, mirroring the evolution from a hypothesis to an established scientific theory. The performance of TEO is validated through extensive experimental simulations and benchmarked against 28 popular algorithms, including highly competitive champions such as EBOwithCMAR, LSHADE_cnEpSi, and LSHADE. Pairwise comparisons between TEO and the latest algorithms are conducted using the Wilcoxon signed-rank test, with multiple comparisons managed by the Friedman test. Initially, TEO is tested on the classical IEEE CEC2017 and the latest IEEE CEC2022 benchmark functions. TEO successfully addresses four prominent engineering design problems in constrained continuous space for practical applications. Additionally, a binary TEO (BTEO) variant is introduced and applied to feature selection tasks in discrete space. Experimental results consistently demonstrate that TEO proposes highly competitive outcomes in optimization problems. The source codes for this research are accessible to the public at https://aliasgharheidari.com/TEO.html.</p>\u0000 </div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"1015 - 1060"},"PeriodicalIF":5.8,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Design of Flexible Buoyancy Modules for Deep-Sea Bionic Robots Via Structured Grid-Based Contour Adaptation","authors":"Ruilong Luo,&nbsp;Jiawei Lin,&nbsp;Biao Wang,&nbsp;Fang Wang","doi":"10.1007/s42235-026-00849-2","DOIUrl":"10.1007/s42235-026-00849-2","url":null,"abstract":"<div><p>Due to their lightweight and flexibility, soft biomimetic robots are popular in deep-sea exploration. However, existing buoyancy materials lack optimal compatibility. This study proposes a flexible, pressure-resistant, multi-medium buoyancy module comprising a flexible cavity filled with a Hollow Glass Microsphere (HGM)-water mixture and introduces structured-grid thinking, which enables contour adaptation to complex biomimetic robot morphologies. The density and pressure resistance of the buoyancy modules were experimentally tested, and the effects of varying silicone hardness, wall thickness, and volume percentage of HGM in the mixture on the performance of the buoyancy modules were compared. The results indicate that the density of the buoyancy modules ranges from 0.751 to 0.964 g/cm³. Under a pressure of 30 MPa, the volume change rate of the buoyancy modules is between 1.74% and 2.13%. The effect of air content in the flexible cavity on buoyancy modules under high pressure was examined by comparing experimental findings with simulations.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"701 - 715"},"PeriodicalIF":5.8,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147561238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Propagation Alongside Crossover: An Evolutionary Algorithm for Continuous Optimization and Feature Selection","authors":"Najibeh Farzi-Veijouyeh,&nbsp;Vahideh Sahargahi,&nbsp;Neda Matin","doi":"10.1007/s42235-026-00840-x","DOIUrl":"10.1007/s42235-026-00840-x","url":null,"abstract":"<div><p>Researchers continuously advance optimization algorithms, recognizing that no meta-heuristic can solve all problem types, as stated by the No Free Lunch theorem. This paper introduces the Propagation alongside Crossover (PAC) algorithm to address continuous optimization challenges. The primary goal of PAC is to structure the algorithmic phases in a manner that achieves a robust balance between exploration and exploitation through appropriately designed mechanisms at each stage. PAC simultaneously leverages the benefits of propagation, crossover, and mutation. Three independent operators are defined to generate new candidate solutions separately, and a novel selection strategy allows individuals produced by each operator, along with members of the current population, to independently enter the next generation. This design preserves population diversity, prevents all individuals from converging toward a single point, and enhances the algorithm’s ability to explore the solution space effectively. A key innovation of PAC is its three-mode propagation mechanism, which comprises local search, linear propagation toward the target point, and tear-drop shaped propagation toward the target point. Tear-drop propagation provides a precise and adaptive search around promising solutions, increasing diversity and preventing entrapment in local optima. The target point is typically set as the global optimum; however, when propagating the global optimum itself, a random point is used as the target to further enhance exploration and escape from local optima. The initial population is generated using chaotic mapping to ensure broad coverage of the search space. PAC was rigorously evaluated on 51 benchmark functions and three engineering problems, considering scalability, convergence, sensitivity, and computational efficiency. Comparative analyses with established optimization algorithms demonstrate PAC’s superior performance, as confirmed by Wilcoxon signed-rank and Friedman statistical tests. Furthermore, PAC was applied as a feature selection method on four diverse datasets, achieving substantial dimensionality reduction while outperforming comparative methods in classification accuracy. These results highlight PAC’s versatility, robustness, and practical effectiveness.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"1112 - 1175"},"PeriodicalIF":5.8,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147561693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"FRE-Net: A Fuzzy Richards Functions-Based Ensemble Network for Brain Tumor Detection","authors":"Arash Hekmat,&nbsp;Omair Bilal,&nbsp;Zuping Zhang,&nbsp;Saif Ur Rehman Khan,&nbsp;Sohaib Asif","doi":"10.1007/s42235-026-00850-9","DOIUrl":"10.1007/s42235-026-00850-9","url":null,"abstract":"<div><p>Accurate classification of brain tumors from medical images is essential for enabling timely diagnosis and effective treatment. This study aimed to develop an innovative method for the diagnosis of brain tumors through a Fuzzy Richards Functions-based Ensemble Network (FRE-Net). The parameters of the Richards function are optimized through Grid Search (GS) for selecting an optimal set of parameters. Our proposed method integrates three well-established pre-trained Convolutional Neural Networks (CNNs): MobileNetV1, MobileNetV2, ResNet50V2. To increase the robustness of these models, we incorporate a novel Lightweight Multiscale with Squeeze and Excitation (LiteMSSE) Block, which improves performance by enhancing multi-scale feature extraction and enabling the network to capture more detailed spatial information for focusing on the most relevant features to improve overall diagnostic performance. Additionally, probabilities from the individual models are aggregated using a Fuzzy Richards Functions approach, which reduces the error between observed and ground truth data, further enhancing detection accuracy. The key innovation of this study lies in the design of novel LiteMSSE Block and use of Fuzzy Richard Function, which together enhance multi-scale feature extraction and combines diverse model predictions intelligently. The proposed FRE-Net method achieves an impressive accuracy of 98.47% on the four-class Kaggle dataset and 99.00% on the BR35H dataset by highlighting its potential as a powerful tool for diagnosis of brain MRI more precisely. Through extensive evaluations, we determine that our proposed ensemble method outperforms individual backbone models and existing methods.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"1217 - 1239"},"PeriodicalIF":5.8,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147561236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Improved Scaling Law for Bio-inspired Flapping Wing Air Vehicles","authors":"Liu Liu,&nbsp;Bifeng Song,&nbsp;Xiaru Liu,&nbsp;Dong Xue,&nbsp;Jianlin Xuan,&nbsp;Yugang Zhang","doi":"10.1007/s42235-026-00854-5","DOIUrl":"10.1007/s42235-026-00854-5","url":null,"abstract":"<div>\u0000 \u0000 <p>Biologists have calculated the allometric growth patterns of birds in terms of morphological and motion parameters, expressing the five parameters of wingspan, wing area, cruising speed, flapping frequency, and flight power as power functions of weight. Aviation designers utilize this formula to derive the initial values of the morphological and motion parameters of Flapping Wing Air Vehicles (FWAV), a process we refer to as the traditional scaling method. Traditional avian scaling laws establish power-exponential relationships between body weight and key flight parameters, guiding bionic aircraft design. However, limitations persist in their direct application to FWAVs. This study proposes an enhanced scaling law methodology addressing four critical aspects: (1) Introducing a weight correction coefficient to map avian flight functionality specifically to FWAVs design requirements, isolating flight-related mass from total avian mass associated with broader biological activities. (2) Rectifying the frequency formula by incorporating wing length-frequency statistics and integrating flapping amplitude via the Strouhal number definition, comprehensively representing motion parameters through periodic average angular velocity. (3) Refitting scaling law parameters using morphological data from specific bionic objects (e.g., pigeons, eagles) to reduce parameter scatter inherent in traditional laws derived from birds spanning grams to kilograms with divergent morphologies. (4) Validating the method through the design and testing of a pigeon-inspired FWAV prototype. Experimental results demonstrate that parameters estimated via the improved method align closely with the optimized aircraft’s performance, achieving an endurance of 185 min on a single charge. This approach significantly reduces intermediate optimization needs, enhances design rationality, and provides a novel pathway for efficient FWAV development.</p>\u0000 </div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"731 - 749"},"PeriodicalIF":5.8,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147561239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Anti-inflammatory and Bone Regenerative Efficacy of Diflunisal-Loaded 3D-Printed Scaffolds in Treating Osteomyelitis","authors":"Jiehui Yang,&nbsp;Li Chen,&nbsp;Mao Yang,&nbsp;Ke Zeng,&nbsp;Chenxin Wang,&nbsp;Rui Zhang,&nbsp;Mingyue Lin,&nbsp;Huanshuo Zhang,&nbsp;Denglang Hu,&nbsp;Min Huang,&nbsp;Yubao Li,&nbsp;Yijing Stehle,&nbsp;Qin Zou","doi":"10.1007/s42235-026-00843-8","DOIUrl":"10.1007/s42235-026-00843-8","url":null,"abstract":"<div><p>Osteomyelitis caused by <i>Staphylococcus aureus</i> (<i>S. aureus</i>) is a severe inflammatory bone disease that is difficult to eradicate and can be life-threatening. Traditional treatments relying on high-dose systemic antibiotics often fail due to biofilm resistance and emerging drug-resistant strains. This study proposes a diflunisal-loaded 3D-printed scaffold as a novel therapeutic strategy. A composite biomaterial ink composed of gelatin (Gel), polycaprolactone (PCL), and nanohydroxyapatite (n-HA) was synthesized and used to fabricate customized porous scaffolds via 3D printing. Diflunisal was loaded onto the scaffolds using a Gel swelling method. <i>In vitro</i> experiments showed sustained diflunisal release under different pH conditions (pH = 6.0 and 7.4) mimicking infection and protection of bone marrow stromal cells (BMSCs) from <i>S. aureus</i> toxins. <i>In vivo</i> studies revealed significant alleviation of infection and promotion of bone regeneration at the defect site. This diflunisal-loaded Gel/PCL/n-HA scaffold integrates anti-virulence therapy with bone regeneration, offering a promising solution for osteomyelitis treatment.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"23 2","pages":"977 - 991"},"PeriodicalIF":5.8,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147560848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}