CAP is a novel tool that can be used to assess the degree of steatosis.

Cyber-physical systems (CPS) are vulnerable to network attacks because communication relies on the network that links the various components in the CPS. The importance of network security is selfevident. In this study, we conduct a network security risk assessment from the perspectives of the host and the network, and we propose a new framework for a multidimensional network security risk assessment that includes two stages, i.e., risk identification and risk calculation. For the risk identification stage, we propose a multidimensional hierarchical index system for assessing cybersecurity risk; the system's security status is determined in three dimensions, i.e., basic operation, vulnerabilities, and threats, and these dimensions guide the data collection. In the risk calculation stage, we use a hidden Markov model (HMM) to assess the network security risk. We provide a new definition of the quality of alert and optimize the observation sequence of the HMM. The model uses a learning algorithm instead of setting the parameters manually. We introduce the concept of network node association to increase the reliability and accuracy of the risk assessment. The simulation results show that the proposed index system provides quantitative data that reflect the security status of the network. The proposed network security risk assessment method based on the improved HMM (I-HMM) reflects the security risk status in a timely and intuitive manner and detects the degree of risk that different hosts pose to the network. INDEX TERMS Hidden Markov model, network node correlation, network security risk, risk assessment. I. INTRODUCTION Cyber-physical systems (CPS) are complex systems that use sensors, computing and network technology for computation, communication and control to link the physical world and the network. Since the communication of the CPS relies on a network, CPS are plagued by security issues [1], [2]. Malicious activities that specifically target networks [3], [4] and security incidents are occurring with increasing frequency [5]. The types of network security incidents [6] include information system vulnerabilities, phishing, and malicious programs [7]. There are two reasons for the occurrence of network security incidents [8]. First, weak links exist in the network system [9] and second, the emergence of various automated attack tools has caused a sharp increase in intrusions into network systems [10], [11]. The associate editor coordinating the review of this manuscript and approving it for publication was Zhen Ling.

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With the wide application of infrared image acquisition technology in power inspection, a large number of infrared images of power equipment have been obtained. The traditional machine learning method has low accuracy and poor generalization. Therefore, in this paper, the deep learning technology is applied to infrared image detection of power equipment, and a defect detection method based on Faster region convolution neural network (RCNN) is proposed. In this method, the deep residual network is used to extract image features, and the regional proposal network is optimized according to the shape characteristics of power equipment, and the network is trained with the help of shared convolution layer. The experimental results show that the proposed method has high detection accuracy, good robustness and generalization ability.

Background: Blood pressure responses to dietary sodium intake vary among individuals. However, it is unknown whether sodium sensitivity and sodium resistance predict incidence of hypertension. Methods: We conducted a feeding study, including a 7-day low-sodium diet (51.3 mmol/day) and a 7-day high-sodium diet (307.8 mmol/day), among 1,718 Chinese individuals with normal blood pressure in 2003-2005 and follow-up studies in 2008-2009 and 2011-2012. Three blood-pressure measurements and 24-hour urinary sodium excretion were obtained on each of 3 days during baseline, low- and high-sodium interventions, and follow-up visits. Latent class models were used to identify subgroups that share a similar underlying trajectory in blood-pressure responses to dietary sodium intake. Results: Three trajectories of systolic blood pressure responses to dietary sodium intake were identified (Figure). Mean (standard deviation) changes in systolic blood pressure were -13.7 (5.5), -4.9 (3.0), and 2.4 (3.0) mmHg during the low-sodium intervention, and 11.2 (5.3), 4.4 (4.1) and -0.2 (4.1) mmHg during the high-sodium intervention ( P< 0.001 for group differences) in high sodium-sensitive, moderate sodium-sensitive, and sodium-resistant groups, respectively. Compared to individuals with moderate sodium sensitivity, multiple-adjusted odds ratio (95% confidence intervals) for incident hypertension were 1.44 (1.03 to 1.99) for those with high sodium sensitivity and 1.42 (1.02 to 1.97) for those with sodium resistance ( P <0.001 for quadratic trend). Furthermore, a J-shaped association between systolic blood pressure responses to high sodium intake and incident hypertension was identified ( P <0.001). Similar results were observed for diastolic blood pressure. Conclusions: Individuals with either high sodium sensitivity or sodium resistance are at an increased risk for developing hypertension.