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BACKGROUND: To determine the congenital heart defect (CHD) prevalence and identify the associated risk factors in children within the multi-ethnic Yunnan Region of China. METHODS: This is a prospective matched case-control screening study. Screening for CHD in children residing within 28 county districts of Yunnan Province during the period of January 2001 to December 2016 was conducted. A total of 2,421 and CHD cohort and 24,210 control cohort were derived from a total population of 400,855 children (under 18 years of age). RESULTS: A total of 2,421 children were diagnosed with CHD, yielding a CHD prevalence of 6.04 cases per 1,000 children. The prevalence of CHD by sex was 6.54 per 1,000 females versus 5.59 per 1,000 males. The ethnic groups displaying the highest CHD prevalence were the Lisu (15.51 per 1,000), Achang (13.18 per 1,000), Jingpo (12.32 per 1,000), Naxi (9.68 per 1,000), and Tibetan (8.57 per 1,000), respectively. The most common CHD was atrial septal defect, amounting to 1.94 instances per 1,000 children. We identified a number of child-associated parameters that significantly correlated with greater CHD risk, such as lower mass at birth, shorter duration of gestation, and younger age at the time of screening. We also identified a number of maternal and familial risk factors. CONCLUSIONS: This ultrasonic color Doppler imaging study revealed a relatively commonplace prevalence of CHD. Moreover, the prevalence of CHD in Yunnan Region significantly varied with sex and ethnic status. Certain child-associated, maternal, and familial risk factors may contribute to CHD risk.

Identifying a set of critical nodes with high propagation in complex networks to achieve maximum influence is an important task in the field of complex network research, especially in the background of the current rapid global spread of COVID-19. In view of this, some scholars believe that nodes with high importance in the network have stronger propagation, and many classical methods are proposed to evaluate node importance. However, this approach makes it difficult to ensure that the selected spreaders are dispersed in the network, which greatly affects the propagation ability. The VoteRank algorithm uses a voting-based method to identify nodes with strong propagation in the network, but there are some deficiencies. Here, we solve this problem by proposing the DILVoteRank algorithm. The VoteRank algorithm cannot properly reflect the importance of nodes in the network topology. Based on this, we redefine the initial voting ability of nodes in the VoteRank algorithm and introduce the degree and importance of the line (DIL) ranking method to calculate the voting score so that the algorithm can better reflect the importance of nodes in the network structure. In addition, the weakening mechanism of the VoteRank algorithm only weakens the information of neighboring nodes of the selected nodes, which does not guarantee that the identified initial spreaders are sufficiently dispersed in the network. On this basis, we consider all the neighbors nodes of the node’s nearest and next nearest neighbors, so that the crucial spreaders identified by our algorithm are more widely distributed in the network with the same initial node ratio. In order to test the algorithm performance, we simulate the DILVoteRank algorithm with six other benchmark algorithms in 12 real-world network datasets based on two propagation dynamics model. The experimental results show that our algorithm identifies spreaders that achieve stronger propagation ability and propagation scale and with more stability compared to other benchmark algorithms.

Fast computation of the landing footprint of a space-to- ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer trajectories. In order to address the usually slow computational time for the determination of the landing footprint of a space-to-ground vehicle under finite thrust, this work proposes a method that uses polynomial equations to describe the boundaries of the landing footprint and uses back propagation (BP) neural networks to quickly determine the landing footprint of the space-to-ground vehicle. First, given orbital parameters and a manoeuvre moment, the solution model of the landing footprint of a space-to-ground vehicle under finite thrust is established. Second, given arbitrary orbital parameters and an arbitrary manoeuvre moment, a fast computational model for the landing footprint of a space-to-ground vehicle based on BP neural networks is provided. Finally, the simulation results demonstrate that under the premise of ensuring accuracy, the proposed method can quickly determine the landing footprint of a space-to-ground vehicle with arbitrary orbital parameters and arbitrary manoeuvre moments. The proposed fast computational method for determining a landing footprint lays a foundation for the parking-orbit configuration and supports the design of real-time transfer trajectories.

Given the limited fuel capacity of an on-orbit service vehicle (OSV), proper OSV allocation to satellites during each service mission is critical for economic fuel consumption. This allocation problem can be formulated as an optimization problem with many continuous and discrete design variables of wide domains. This problem can be effectively handled through the proposed approach that combines the tabu search with the discrete particle swarm optimization algorithm (DPSO-TS). First of all, Pontryagin’s minimum principle and genetic algorithm (GA) are exploited to find the most fuel-efficient transfer trajectory. This fuel efficiency maximization can then serve as the performance index of the OSV allocation optimization model problem. In particular, the maximization of the minimum residual fuel over individual OSVs is proposed as a performance index for OSV allocation optimization. The optimization problem is numerically solved through the proposed DPSO-TS algorithm. Finally, the simulation results demonstrate that the DPSO-TS algorithm has a higher accuracy compared to the DPSO, the DPSO-PDM and the DPSO-CSA algorithms in the premise that these four algorithms have the basically same computational time. The DPSO-TS algorithm can effectively solve the OSV allocation optimization problem.

To investigate whether plasma concentrations of S100β protein, neuron-specific enolase (NSE), and neuroglobin (NGB) correlate with early postoperative cognitive dysfunction (POCD) in patients undergoing total arch replacement. This prospective study analyzed 40 patients who underwent total arch replacement combined with stented elephant trunk implantation at our hospital between March 2017 and January 2019. Cognitive function was assessed using the Mini-mental State Examination (MMSE) preoperatively, on the day after extubation and on day 7 after surgery. Plasma levels of S100β, NSE, and NGB POCD were assayed preoperatively and at 1, 6, and 24 hours after cardiopulmonary bypass. POCD was defined as a decrease of at least 1 unit in the MMSE score from before surgery until day 7, and patients were stratified into those who experienced POCD or not. The 2 groups were compared in clinicodemographic characteristics and plasma levels of the 3 proteins. Plasma levels of all 3 biomarkers increased significantly during and after cardiopulmonary bypass. Levels of S100β and NSE, but not NGB, were significantly higher in the 15 patients who showed POCD than in the remainder who did not. For prediction of early POCD, S100β showed an area under the receiver operating characteristic curve (AUC) of 0.71 (95% confidence interval [CI] 0.55–0.87), sensitivity of 48%, and specificity of 87%. The corresponding values for NSE were 0.77 (95%CI 0.60–0.94), 92%, and 67%. Together, S100β and NSE showed an AUC of 0.81 (95%CI 0.66–0.96), sensitivity of 73%, and specificity of 80%. NGB did not significantly predict early POCD (AUC 0.62, 95%CI 0.43–0.80). Plasma S100β protein and NSE, but not NGB, may help predict early POCD after total arch replacement.

Taking guidance and control as the object, a new method named space-grid method was applied in the missile guidance in this paper. Assumed condition and basic theory were introduced firstly. The process and characteristic of the method was showed subsequently. Based on these work, the guidance and control way of space-grid method was analyzed in detail. Through simulation, it proved that the guidance and control way was effective and feasible. It could satisfy precision demand. The space-grid method has advantages of calculation quickly on missile computer, strong ability of antijamming and control simply. For these reasons, the new method has broad application foreground and reference value in the aspects of guidance precision and efficiency.

The computation of semantic similarity of terminologies in gene ontology is an important application of gene ontology. Moreover, it is an important approach for biologists to deal with the semantic heterogeneity of biological data sets. Existing approaches in this field are based on semantic distance or information quantity. The author proposes a comprehensive approach where the similarity is first computed on the terminological level, inducing density-constraining and depth-constraining variables based on the microscopic structure (density and depth of the nodes) of the gene ontology nodes, then based on node properties and inference-based instances, the comprehensive semantic similarity is computed. From the result of comparison with the Resnik method, the ZZL method, Lin method and Combine method, the above proposed method proves to be favorable.

At present, the use of dynamic programming method to solve the problem of the single-type missile fire distribution has been more mature. With the development of the diversification of missile types, the problem of the multi-type missile fire distribution is in urgent need for solution. Based on dynamic programming, the optimization model of the fire distribution of two types of missiles, the solution method of the model, and the example are given in this paper. And the fire distribution of more than two types of missiles is discussed in the end.

The port is the hinge of sea transportation, and it is also an important target which would be attacked by the both sides in future campaign. At present, most research models on the port capacity are only about the port taken as a berth service organization and the arrival ship as a queuing customer, but few about the full logistics process of the arrival ship loading and unloading cargoes at the port. First, based on Petri nets the model of the port's full logistics process is built; Secondly, full logistics process of the port (commercial port) is taken as a whole study object to be simulated. Finally, the flowchart and simulation conclusion is given.

When evaluating the performance of a batch of Inertial Measurement Unit (IMU) and encountering abnormal samples, the current method was uneasy to retain the stabilization of evaluating results. In the case, a method based on median theory is presented in the paper. Theory analysis and example results show that the median method overcomes the shortages of the current method effectively. Additionally, the accuracy and reliability of evaluating results can also be guaranteed by using the two methods synthetically.