Search

Muscle stiffness provides a key insight toward clinical assessment for rehabilitation. Regarding the high-cost and technical skill requirement of ultrasound shear wave elastography (SWE) restricting extensive clinical use, MyotonPRO has been proposed as a complementary tool for muscle stiffness measurement. There is a deficiency of studies revealing the use of this tool for measuring muscle stiffness contributing to hand control. The purpose of this study was to assess the capability and effectiveness of MyotonPRO and SWE for hand muscle stiffness characterization. The stiffness of the dominant flexor digitorum superficialis (FDS) muscle of 25 healthy participants (12 males and 13 females) aged 29.60 ± 9.81 years was evaluated while they performed grip tasks. The muscle stiffness of males and females was compared. The correlation between dynamic muscle stiffness given by MyotonPRO and Young’s modulus obtained from SWE was investigated. Statistical analysis indicated a significant difference in the dynamic muscle stiffness between genders in all conditions (p < 0.05), whereas a significant difference in Young’s modulus was found only at the resting state. A moderate correlation was found between dynamic muscle stiffness and Young’s modulus (r ranged from 0.243 to 0.489). Therefore, MyotonPRO can be used to assess the muscle stiffness of the FDS muscle at rest and during muscle contraction.

The mechanical properties and structure alteration (e.g., layer morphology and thickness) of the oesophagus wall can reflect its pathological conditions. Hence, quantitative measurement of the above-mentioned properties can play a significant role in aiding the disease diagnosis in clinical application. As a fast and non-invasive imaging modality, Optical coherence tomography (OCT) and vibrational elastography can provide high resolution (

Optical coherence tomography-based angiography (OCTA) has attracted attention in clinical applications as a non-invasive and high-resolution imaging modality. Motion artifacts are the most seen artifact in OCTA. Eigen-decomposition (ED) algorithms are popular choices for OCTA reconstruction, but have limitations in the reduction of motion artifacts. The OCTA data do not meet one of the requirements of ED, which is that the data should be normally distributed. To overcome this drawback, we propose an easy-to-deploy development of ED, windowed-ED (wED). wED applies a moving window to the input data, which can contrast the blood-flow signals with significantly reduced motion artifacts. To evaluate our wED algorithm, pre-acquired dorsal wound healing data in a murine model were used. The ideal window size was optimized by fitting the data distribution with the normal distribution. Lastly, the cross-sectional and en face results were compared among several OCTA reconstruction algorithms, Speckle Variance, A-scan ED (aED), B-scan ED, and wED. wED could reduce the background noise intensity by 18% and improve PSNR by 4.6%, compared to the second best-performed algorithm, aED. This study can serve as a guide for utilizing wED to reconstruct OCTA images with an optimized window size.

The quantification of the flow distortion effect on the measurement accuracy of the ultrasonic gas flowmeter downstream of the header is important but an area that has been of less concern in the research. By experiments and computational fluid dynamics (CFD), the influence of flow field distortion was studied. Experimental results under three different installation conditions showed that when there was flow field distortion downstream of the header, the measurement results of the gas ultrasonic flowmeter were 1% higher than those when there was no distortion, while a flow conditioner could effectively eliminate flow field distortion. Based on the experimental tests, the flow field distribution was analyzed with CFD, which showed that the flow field distortion effect generated by the header had a significant influence on the parameter of nonconforming Profile factor, while the parameters of Symmetry and Cross-flow could be obviously eliminated by the double-cross-section designing.

In the IoT (Internet of Things) environment, the existing access control schemes for device resources have some problems, such as poor scalability, high latency, security, and dynamics. Combining the advantages of the permissioned blockchain and edge computing, an access control scheme for the Internet of Things based on the permissioned blockchain and edge computing is proposed. By authenticating the user’s identity at the edge, the user’s identity is reliable and the response time is improved. In the ABAC (Attribute Based Access Control) model, the blockchain is regarded as a trusted entity, and the access control policy is written into a smart contract and deployed on the blockchain for calling. Most of the existing consensus algorithms have the problems of low throughput and scalability. A Kraft (Kademlia–Raft) consensus algorithm is introduced to solve the above issues. Security analysis and experimental results show that the scheme can achieve fine-grained, dynamic access control, has high throughput and low latency, and ensures security and reliability.

A high-pass cut-off frequency in filtering is critical to processing strong-motion records. The various processing procedures available nowadays are based on their own needs and are not universal. Regardless of the methods, a visual inspection of the filtered acceleration integration to displacement is required to determine if the selected filter passband is appropriate. A better method is to use a traversal search combined with visual inspection to determine the cut-off frequency, which is the traditional method. However, this method is inefficient and unsuitable for processing massive strong-motion records. In this study, convolutional neural networks (CNNs) were used to replace visual inspection to achieve the automatic judgment of the reasonableness of the filtered displacement time series. This paper chose the pre-trained deep neural network (DNN) models VGG19, ResNet50, InceptionV3, and InceptionResNetV2 for transfer learning, which are only trained in the fully connected layer or in all network layers. The effect of adding probability constraints on the results when predicting categories was analyzed as well. The results obtained through the VGG19 model, in which all network layers are trained and probability constraints are added to the prediction, have the lowest errors compared to the other models. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are 0.82, 0.038, 0.026, and 2.99%, respectively.

The damage pattern of masonry-infilled reinforced concrete (RC) frame structures in earthquake events is complicated, and understanding the detailed failure behavior of these structures and modeling it accurately has been a challenging task. In this paper, the extended finite element method (XFEM) is introduced to reproduce arbitrary cracks initiating and propagating in concrete frame and masonry units, combined with interface elements to model various behaviors of masonry-infilled RC frames. Within the finite element analysis program FEAP, a user element subroutine is adopted for the incorporation of XFEM and two types of extended finite elements with and without crack tip enrichments are built to simulate the behavior of concrete material for frame members and masonry blocks for the infill panel, respectively. In addition, a macro command is created to check the crack-propagation criterion and update crack and enrichment information. Furthermore, numerical examples are performed with existing test data, which reveal the efficiency of the implementation procedure. A comparison of the analytical and experimental results show that the proposed modeling can be used to predict the crack and failure process and the load-bearing capacity curves of the structures and reflect accurately the interaction of masonry infill and RC frames.