Health status monitoring of bridge cable and telescopic compensation device based on fiber grating sensing array.

Laying power cables along the bridge is a new way of laying submarine cables across the sea. Monitoring the health status of cables and their telescopic compensation devices is necessary. In this study, fiber grating sensing technology was used to monitor the strain, temperature, and vibration of the bridge cable of the Zhoushan-Daishan Bridge in Zhoushan, Zhejiang Province, and its compensation device. Two typhoons and one invasion event happened during the monitoring period. Temperature signals, strain signals, and time domain and time-frequency domain vibration signals were analyzed. The results showed that no fire hazards or risk of external damage were found with the bridge cable, and the monitoring system filled a gap in the in situ monitoring of the bridge cable in the Zhoushan-Daishan Bridge by the State Grid.

Electromyographic Discrepancy in Paravertebral Muscle Activity Predicts Early Curve Progression of Untreated Adolescent Idiopathic Scoliosis.

STUDY DESIGN: This study adopted a prospective cohort study design. PURPOSE: This study aimed to examine electromyogram (EMG) discrepancy in paravertebral muscle activity and scoliosis progression, determine how vertebral morphology and EMG discrepancy evolve during scoliosis progression, and identify differences in EMG activity between individuals with and without adolescent idiopathic scoliosis (AIS). OVERVIEW OF LITERATURE: Higher EMG activity is observed in the convex side of scoliotic curves, but not in populations without scoliosis, suggesting that higher EMG activity is a causative factor for curve progression. METHODS: In this study, 267 matched pairs of AIS and controls were recruited. The participants underwent EMG measurements at their first presentation and did not receive any treatment for 6 months at which point they underwent EMG and radiographs. Early curve progression was defined as >5° in Cobb angle at 6 months. The root mean square of the EMG (rms-EMG) signal was recorded with the participants in sitting and back extension. The rms-EMG ratio at the upper end vertebrae, apical vertebrae (AV), and lower end vertebrae (LEV) of the major curve was calculated. RESULTS: The rms-EMG ratio in the scoliosis cohort was high compared with that in the controls (sitting: 1.2±0.3 vs. 1.0±0.1, p<0.01; back extension: 1.1±0.2 vs. 1.0±0.1, p<0.01). An AV rms-EMG ratio in back extension, with a cutoff threshold of ≥1.5 in the major thoracic curve and ≥1.3 in the major lumbar curve, was a risk factor for early curve progression after 6 months without treatment (odds ratio, 4.1; 95% confidence interval, 2.8-5.9; p<0.01). Increases in side deviation (SD) (distance between the AV and the central sacral line) were related to a higher rms-EMG ratio in LEV of the major thoracic curve (baseline: rs=0.2, p=0.03; 6 months: rs=0.3, p<0.01). CONCLUSIONS: An EMG discrepancy was detected in the scoliosis cohort, which was related to increases in SD in the major thoracic curve. The AV rms-EMG ratio in back extension was correlated with curve progression after 6 months of no treatment.

A Neural Network Approach for Inertial Measurement Unit-Based Estimation of Three-Dimensional Spinal Curvature.

The spine is an important part of the human body. Thus, its curvature and shape are closely monitored, and treatment is required if abnormalities are detected. However, the current method of spinal examination mostly relies on two-dimensional static imaging, which does not provide real-time information on dynamic spinal behaviour. Therefore, this study explored an easier and more efficient method based on machine learning and sensors to determine the curvature of the spine. Fifteen participants were recruited and performed tests to generate data for training a neural network. This estimated the spinal curvature from the readings of three inertial measurement units and had an average absolute error of 0.261161 cm.

Energy-efficient workload allocation in edge-cloud fiber-wireless networks.

In order to realize the green computing of the edge-cloud fiber-wireless networks, the cooperation between the edge servers and the cloud servers is particularly important to reduce the network energy consumption. Therefore, this paper proposes an energy-efficient workload allocation (EEWA) scheme to improve the energy efficiency by using the architecture of edge-cloud fiber-wireless networks. The feasibility of the proposed EEWA scheme was verified on our SDN testbed. We also do the simulation to obtain the optimal results for a given set of task requests. Simulation results show that our proposed EEWA scheme greatly reduces the blocking probability and the average energy consumption of task requests in edge-cloud fiber-wireless networks.

Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches.

A large number of studies have used electromyography (EMG) to measure the paraspinal muscle activity of adolescents with idiopathic scoliosis. However, investigations on the features of these muscles are very limited even though the information is useful for evaluating the effectiveness of various types of interventions, such as scoliosis-specific exercises. The aim of this cross-sectional study is to investigate the characteristics of participants with imbalanced muscle activity and the relationships among 13 features (physical features and EMG signal value). A total of 106 participants (69% with scoliosis; 78% female; 9-30 years old) are involved in this study. Their basic profile information is obtained, and the surface EMG signals of the upper trapezius, latissimus dorsi, and erector spinae (thoracic and erector spinae) lumbar muscles are tested in the static (sitting) and dynamic (prone extension position) conditions. Then, two machine learning approaches and an importance analysis are used. About 30% of the participants in this study find that balancing their paraspinal muscle activity during sitting is challenging. The most interesting finding is that the dynamic asymmetry of the erector spinae (lumbar) group of muscles is an important (third in importance) predictor of scoliosis aside from the angle of trunk rotation and height of the subject.

A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks.

The measurement and prediction of breast skin deformation are key research directions in health-related research areas, such as cosmetic and reconstructive surgery and sports biomechanics. However, few studies have provided a systematic analysis on the deformations of aging breasts. Thus, this study has developed a model order reduction approach to predict the real-time strain of the breast skin of seniors during movement. Twenty-two women who are on average 62 years old participated in motion capture experiments, in which eight body variables were first extracted by using the gray relational method. Then, backpropagation artificial neural networks were built to predict the strain of the breast skin. After optimization, the R-value for the neural network model reached 0.99, which is within acceptable accuracy. The computer-aided system of this study is validated as a robust simulation approach for conducting biomechanical analyses and predicting breast deformation.