2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)最新文献_第10页

Development of a flexible sensor based on fiber Bragg grating technology for simultaneous respiratory and heartbeat measurements 基于光纤布拉格光栅技术的柔性传感器的研制，用于同时测量呼吸和心跳

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171903

D. Presti, C. Massaroni, D. Bianchi, M. Caponero, A. Gizzi, E. Schena

{"title":"Development of a flexible sensor based on fiber Bragg grating technology for simultaneous respiratory and heartbeat measurements","authors":"D. Presti, C. Massaroni, D. Bianchi, M. Caponero, A. Gizzi, E. Schena","doi":"10.1109/MeMeA57477.2023.10171903","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171903","url":null,"abstract":"In the last decades, the growing interest in the use of wearables has fostered the exploitation of innovative technologies in the development of highly performant sensing solutions for monitoring human health. Several sensing elements have been proposed to instrument wearable items and textiles, making them able to detect physiological parameters, including respiratory and cardiac parameters.This work presents a wearable sensor based on fiber Bragg grating (FBG) technology with a novel design to simultaneously measure respiration and heartbeat activities with improvements in optical fiber robustness. The proposed sensor prototype consists of a flexible matrix with an optical fiber inscribed with a 5 mm-length FBG sensing element. Only a small portion of the optical fiber (i.e., 10 mm) is integrated into the polymer matrix. In this way, the FBG is protected by the matrix and can be stretched further without any damage. A metrological characterization of the developed sensor was proposed to better investigate any changes in response to strain and temperature of the encapsulated sensing element compared to a bare FBG. Then, a preliminary assessment of the performance of the flexible sensor in monitoring respiration and heartbeat was carried out on a healthy subject. The flexible sensor was equipped with an elastic band to be fastened around the thorax. The preliminary results of this test foster future investigations to assess the capability of the proposed wearable system in monitoring cardiorespiratory activity considering different measuring sites (e.g., thorax and neck) and under various body positions (e.g., supine and standing).","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132547403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Potential Application of PVDF/CoFe2O4 Nanocomposites as Self-powered Piezoelectric Plantar Pressure Sensors PVDF/CoFe2O4纳米复合材料作为自供电压电式足底压力传感器的潜在应用

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171921

M. Fortunato, H. C. Bidsorkhi, A. D’Aloia, A. Tamburrano, M. S. Sarto

{"title":"Potential Application of PVDF/CoFe2O4 Nanocomposites as Self-powered Piezoelectric Plantar Pressure Sensors","authors":"M. Fortunato, H. C. Bidsorkhi, A. D’Aloia, A. Tamburrano, M. S. Sarto","doi":"10.1109/MeMeA57477.2023.10171921","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171921","url":null,"abstract":"This paper discusses the use of flexible nanogenerators as plantar pressure sensors, specifically focusing on the development of a sensor based on PVDF-TrFE/CoFe2O3 nanocomposites with a multilayer-graphene/gold top electrode (MGGE). The study highlights the importance of biomechanical sensors in healthcare, particularly in detecting changes in gait patterns that may arise from non-musculoskeletal irregularities in the body. The paper provides an overview of the different types of foot sensor technologies used for pressure monitoring, with a focus on piezoelectric force sensors. The use of PVDF-TrFE, a type of piezoelectric polymer, is discussed, along with techniques for enhancing its piezoelectric response. The results of the study demonstrate improved piezoelectric capabilities of the proposed nanogenerator, with a final piezoelectric coefficient d33 of 34 pm/V, which is in excellent agreement with the average value obtained by Piezoresponse Force Microscopy (PFM) d33 = (33.99 ± 5.12) pm/V. The proposed sensor has the potential to be used in the diagnosis and treatment of motor-related issues in diabetes, podiatry, and rehabilitation.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130230973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the Response to Chemoradiotherapy in Rectal Cancer Patients Using Bayesian Evolutionary Random Forest and Three-Dimensional Discrete Fourier Transform 利用贝叶斯进化随机森林和三维离散傅立叶变换预测直肠癌患者放化疗的疗效

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171859

Camille Raets, C. Aisati, A. Rifi, K. Barbé, M. Ridder

{"title":"Predicting the Response to Chemoradiotherapy in Rectal Cancer Patients Using Bayesian Evolutionary Random Forest and Three-Dimensional Discrete Fourier Transform","authors":"Camille Raets, C. Aisati, A. Rifi, K. Barbé, M. Ridder","doi":"10.1109/MeMeA57477.2023.10171859","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171859","url":null,"abstract":"Rectal cancer remains a very deadly disease that often causes discomfort and decreases patients’ quality of life due to invasive surgeries. Therefore, it is crucial to develop a prediction method that can predict the tumor regression grade in advance, allowing us to tailor surgeries to the specific needs of each patient. In this study, we extracted quantitative data from planning CT images taken before the treatment and used them to predict the regression grade of rectal cancer after treatment. By making predictions in advance, a “wait-and-see” approach can be used for some patients, preserving their quality of life. We used the Discrete Fourier Transform to extract quantitative data from the images and created an Evolutionary Random Forest with this data. Additionally, we incorporated the prior distribution of the different regression grade groups obtained from our previous study into the Random Forest of this study. Our training results showed a normalized accuracy of 90.008%, with a total normalized accuracy of 74.968% for the Leave-One-Out cross-validation when accounting for the estimated priors. A Random Forest created without prior information yielded an unrealistic perfect classification of the training data and 71.483% in the Leave-One-Out cross-validation. The Random Forest with prior distribution information showed good results for both training and validation. However, without the prior distribution, the results were unrealistic as the regression grade has inherent variability.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130981206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decision Tree Machine Learning to Determine Direction of Steering Force for Hospital Bed Push Handle System 决策树机器学习确定病床推把系统转向力的方法

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171866

Bahareh Chimehi, Bruce Wallace

{"title":"Decision Tree Machine Learning to Determine Direction of Steering Force for Hospital Bed Push Handle System","authors":"Bahareh Chimehi, Bruce Wallace","doi":"10.1109/MeMeA57477.2023.10171866","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171866","url":null,"abstract":"This work demonstrates the use of Decision Tree and Random Forest machine learning to determine the direction of desired movement from the forces applied to two push-handles on a novel patient transfer system the Able Innovations ALTA™ patient transfer system provides a replacement transfer method for residents in care that cannot transfer on their own that have limited mobility. This new system is significantly heavier that a hospital gurney because of the weight of the mechatronics and healthcare professionals will need power assist to transport and position the system. In this work, two loadcell sensor-based prototypes have been used to measure the input forces and direction applied by a user to two handles. These two prototypes have been placed on a hospital bed to simulate the two handles on the right and left provided to hospital staff to push the system. Machine learning is used to analyze sensor measurements from each handle to predict the movement intent. The results are presented for test pushes in 6 directions that include forward and reverse in each of straight, left, and right turns. Two models of Machine Learning (decision tree classifier and random forest classifier) have been used for 8 and 14 features and are shown to be able to predict the direction of push with high accuracy. The accuracy for 8 features using decision tree and random forest has been measured 93.5% and 97.0% respectively.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114839484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of Trial-Adjusted Neurofeedback Training on Motor-Imagery Based Brain-Computer Interface Performance 试验调整神经反馈训练对基于运动图像的脑机接口性能的影响

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171918

Akima Connelly, Pengcheng Li, Phurin Rangpong, Theerawit Wilaiprasitporn, T. Yagi

{"title":"Effects of Trial-Adjusted Neurofeedback Training on Motor-Imagery Based Brain-Computer Interface Performance","authors":"Akima Connelly, Pengcheng Li, Phurin Rangpong, Theerawit Wilaiprasitporn, T. Yagi","doi":"10.1109/MeMeA57477.2023.10171918","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171918","url":null,"abstract":"Motor imagery (MI) classification based on electroencephalography (EEG) has been extensively studied and recently used more in brain-computer interfaces (BCI). This study uses left and right hand MI tasks for the BCI system. A common obstacle for MI-BCI is the inability of some participants to perform the BCI task, called BCI illiteracy. Various training protocols have been investigated to improve the performance of BCI but are designed with a balanced dataset. Similarly to how people show a bias towards a side (e.g. left or right) for motor execution tasks, it has been seen that participants also show a performance bias in MI tasks as well. To address this MI bias in participants, a novel neurofeedback protocol was designed to adjust the number of trials each condition has. Trials will be adjusted to increase the number of times participants have to perform their weak MI task. This study aims to investigate the overall effect that the trial-adjusted neurofeedback had on participant’s cognitive performance on the MI-BCI system. The effects were investigated through time-frequency and band power analysis. The time-frequency analysis showed improvement in key MI feature and band power analysis results had an improvement on the alpha and beta frequency bands. In the analysis results, trial-adjusted neurofeedback was seen to have an effect on participant’s cognitive performance on the MI-BCI task.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115957096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MeMeA 2023 Cover Page MeMeA 2023封面

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/memea57477.2023.10171856

引用次数: 0

Design and characterization of a flow silicone chamber for combined cell stimulation: a computational fluid dynamic analysis 结合细胞刺激的流动硅酮腔室的设计和特性:计算流体动力学分析

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171890

L. Apa, L. D’Alvia, B. Peruzzi, E. Rizzuto, Z. Prete

{"title":"Design and characterization of a flow silicone chamber for combined cell stimulation: a computational fluid dynamic analysis","authors":"L. Apa, L. D’Alvia, B. Peruzzi, E. Rizzuto, Z. Prete","doi":"10.1109/MeMeA57477.2023.10171890","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171890","url":null,"abstract":"Cells are able to sense and respond to mechanical stimuli occurring in their microenvironment via mechanotransduction, the cellular process by which the mechanical forces are converted in biological responses. The most relevant mechanical forces that the cells perceive are the substrate deformation and the fluid shear stress. A disruption in the capability to correctly respond to mechanical stimulations results in pathological conditions, including osteoporosis, developmental disorders, arthritis and cancer. Nowadays, the in vitro systems are employed to recreate the mechanical stimuli detected by the cells in a more controlled microenvironment. In this study, we propose the design and characterization of a flow silicone chamber, for shear stress and substrate deformation induction, that will be integrated in a system composed by a uniaxial stretching device and a flow pump system. The flow silicone chamber consists of a central fluidic region designed to have an inlet and an outlet, connected to the pump system and both communicating, through four lateral channels, with two cell channels with dimensions of 8x4 mm of length and width, respectively. Computational fluid dynamics (CFD) simulation tests were performed to evaluate the fluid shear stress distribution occurring on the surface of the chamber, where cells to be tested will be seeded. The analyses were performed by varying the dimension of the lateral channels height and the intensity of the volumetric flow rates. Our results revealed that the configuration with the lateral channels of 2 mm of height allowed to obtain the more homogeneous shear stress distribution and a reduced fluid turbulence.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131917381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sleep-Wake and Body Position Classification with Deep Learning using Pressure Sensor Mat Measurements 使用压力传感器垫测量的深度学习睡眠-觉醒和身体位置分类

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171884

G. Green, M. Bouchard, R. Goubran, R. Robillard, C. Higginson, E. Lee, F. Knoefel

{"title":"Sleep-Wake and Body Position Classification with Deep Learning using Pressure Sensor Mat Measurements","authors":"G. Green, M. Bouchard, R. Goubran, R. Robillard, C. Higginson, E. Lee, F. Knoefel","doi":"10.1109/MeMeA57477.2023.10171884","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171884","url":null,"abstract":"Polysomnography, the gold standard of sleep measurement, is a time-intensive, costly, and rather invasive procedure. Using under-bed pressure sensor arrays data simultaneously recorded with standard polysomnography, this study demonstrates that deep learning can be used to classify body position and differentiate sleep from wake. All measurements were performed in people with suspected sleep disorders referred for clinical assessments at a sleep laboratory. To perform the classification tasks, we used supervised learning and temporal convolution networks. Performance was assessed with leave-one-out cross validation on 84 participants for body position classification and 70 participants for sleep-wake classification. Results demonstrate that a pressure sensor array placed under the mattress with less than 100 sensors can outperform previous sleep-wake detection methods and is competitive with previous methods for body position classification. Our pressure sensor arrays differ from the mats used in previous work as they use significantly less sensors and are located under the mattress, making them less obtrusive. This tool has great potential as a cost-efficient mean of assessing sleep while reducing patient burden and the workload of specialized staff.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128785094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CheXViT: CheXNet and Vision Transformer to Multi-Label Chest X-Ray Image Classification CheXViT: CheXNet和视觉转换器的多标签胸部x线图像分类

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171855

Muhamad Faisal, J. T. Darmawan, Nabil Bachroin, Cries Avian, Jenq-Shiou Leu, Chia-Ti Tsai

{"title":"CheXViT: CheXNet and Vision Transformer to Multi-Label Chest X-Ray Image Classification","authors":"Muhamad Faisal, J. T. Darmawan, Nabil Bachroin, Cries Avian, Jenq-Shiou Leu, Chia-Ti Tsai","doi":"10.1109/MeMeA57477.2023.10171855","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171855","url":null,"abstract":"The popular technique in assisting radiologist to diagnose clinical thoracic for assessment of abnormalities is Chest X-Ray (CXR) imaging. The current automated system in CXR images relies on convolutional neural network (CNN) models, which focus on local features of images without considering global features. Most of the approaches utilize CNN which excels in generating inductive biases that specifically focus on potential regions of interest within an image. Although CNN models are able to achieve satisfactory performance, it is also a limiting factor to obtaining better performance in CXR classification. Recently, the adaptation of self-attention mechanism in transformer has been introduced to computer vision which enhances the performance of image classification by capturing short and long-range dependencies. Therefore, we propose a hybrid CNN-Transformer classifier for multi-label CXR images called CheXViT, a modification of CheXNet that integrates with the vision transformer (ViT) architecture. CheXNet and its remarkable performance is a perfect well-performing CXR classification model that could generate reliable and definitive feature maps for the ViT to widen the feature scope. The combination would propel the model performance by combining the inductive biases from CNN and long-range feature dependencies from the transformer. In the end, ChestX-Ray14 dataset is selected to evaluate the effectiveness of CheXViT. Our proposed method achieves a mean AUC of 0.838 and is superior to the existing methods.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125402457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The effect of elastic backboards on kinematic and neuromuscular parameters in Para-alpine sit skiers with severe disabilities 弹性背板对严重残疾高山滑雪运动员运动和神经肌肉参数的影响

2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Pub Date : 2023-06-14 DOI: 10.1109/MeMeA57477.2023.10171914

Kai Liu, Yijia Lu, Linhong Ji, Wei Li, Shanshan Wei

{"title":"The effect of elastic backboards on kinematic and neuromuscular parameters in Para-alpine sit skiers with severe disabilities","authors":"Kai Liu, Yijia Lu, Linhong Ji, Wei Li, Shanshan Wei","doi":"10.1109/MeMeA57477.2023.10171914","DOIUrl":"https://doi.org/10.1109/MeMeA57477.2023.10171914","url":null,"abstract":"Alpine sit skiing is a Winter Paralympic event, in which para-athletes ski sitting on a sit-ski due to physical impairments. Since assistive devices and trunk function play an important role in sit skiers, this single-subject study aims to estimate the effect of elastic backboards on the kinematic and trunk neuromuscular parameters of para-alpine sit skiers. A set of trunk function testing devices and a markerless OpenPose motion tracking system were developed. An experiment, including six different trunk exercises in a sitting position, was conducted on the test board for four para-alpine sit skiers with severe disabilities (LW10-1, LW10-2 × 2, LW11). The controlled experimental condition was whether there was an elastic backboard. The following parameters were recorded and calculated, including joint angular positions, and surface electromyography of trunk muscles (RA, rectus abdominis; EO, external oblique; ES, erector spinae). The multi-joint coordination of the subjects during sitting lateral flexion (SLF) exercises was extracted using a principal component analysis of the joint angles. The results showed that after adding the elastic backboard, the maximal test board inclination angles, the trunk forward flexion angles and the backward extension angles increased by 48.31%, 156.15% and 76.01% respectively. Mean peak activation of EO and ES showed an overall decrease of 23.06% and 28.56% across six exercises. Multi-joint coordination and a weight shift from the upper extremity compensation to hip joints (+46.56%) after adding the elastic backboard were found in the SLF exercise. The above results illustrated the positive influence of the elastic backboard on the kinematic and neuromuscular performance of sit skiers. The findings can support the optimized design of sit-skis and assistive devices in general, and facilitate the application of markerless motion-tracking systems in medical measurements and sports biomechanics.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126242917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0