No abstract

Atlantoaxial disorders are often correlated with hypertension in practice. In order to study the relationship between atlantoaxial disorder and hypertension, we attempted to construct an animal model. In this work, we presented an animal model where their atlantoaxial joints were misaligned. We investigated the changes of blood pressure before and after treatments of the modeled rats. We had the following results. (1) SBP and DBP of each surgery group were significantly higher than those of control and sham groups. (2) After the second operation (the fixture was removed), SBP and DBP of both surgery groups decreased and got closer to the control and sham groups after 7 days. (3) Heart rates got significantly higher in both surgery groups, compared to control and sham groups. (4) The blood Ach levels of the surgery groups were significantly lower than those of control and sham groups. With these results, we concluded that we successfully constructed cervical atlantoaxial disorder models in rats that showed hypertension symptom. However, the underlying mechanism connecting atlantoaxial disorder and hypertension still requires further study.

Online and offline blended teaching mode, the future trend of higher education, has recently been widely used in colleges around the globe. In the article, we conducted a study on students’ learning behavior analysis and student performance prediction based on the data about students’ behavior logs in three consecutive years of blended teaching in a college’s “Java Language Programming” course. Firstly, the data from diverse platforms such as MOOC, Rain Classroom, PTA, and cnBlog are integrated and preprocessed. Secondly, a novel multiclass classification framework, combining the genetic algorithm (GA) and the error correcting output codes (ECOC) method, is developed to predict the grade levels of students. In the framework, GA is designed to realize both the feature selection and binary classifier selection to fit the ECOC models. Finally, key factors affecting grades are identified in line with the optimal subset of features selected by GA, which can be analyzed for teaching significance. The results show that the multiclass classification algorithm designed in this article can effectively predict grades compared with other algorithms. In addition, the selected subset of features corresponding to learning behaviors is pedagogically instructive.

ObjectiveThe objective of this study was to assess whether the weight-adjusted-waist index(WWI) is associated with the prevalence of asthma and age when first asthma onset appears in US adults.MethodsFor analysis we selected participants from the National Health and Nutrition Examination Survey(NHANES)database between 2001 and 2018. A dose-response curve was calculated using logistic regression,subgroup analysis,and a dose-response curve.ResultsThe study included 44480 people over the age of 20,including 6061 reported with asthma, and the increase in asthma prevalence was 15% associated with each unit increase in the WWI, after adjusting for all confounders(odds ratio(OR)=1.15,95% CI:1.11,1.20). The sensitivity analysis was performed by trichotomizing the WWI, and compared to the lowest tertile, the highest tertile WWI group displayed a 29% increase in asthma prevalence(OR=1.29,95% CI:1.19,1.40). A nonlinear correlation was found between the WWI index and the risk of asthma onset, with a threshold saturation effect indicating an inflection point of 10.53 (log-likelihood ratio test, P<0.05), as well as a positive linear correlation with age at first asthma onset.ConclusionsA higher WWI index was associated with an increased prevalence of asthma and an older age of first asthma onset.

Optimization of machining parameters is an important problem in the modern manufacturing world due to production efficiency and economics. This problem is well known to be complex and is regarded as a strongly nondeterministic polynomial (NP)-hard problem. To reduce the production cost of work-pieces in computer numerical control (CNC) machining, a novel optimization algorithm based on a combination of the bat algorithm and a divide-and-conquer strategy is proposed. First, the basic bat algorithm (BA) is modified with the aim to avoid finding the local optimal solution. In addition, a Gaussian quantum bat algorithm with direction of mean best position is developed. Second, in order to reduce the complexity of the optimization problem, the whole optimization problem is divided into several subproblems by using a divide-and-conquer strategy according to the characteristic of multipass turning operations. Finally, under a large number of machining constraints, the cutting parameters of the two stages of roughing and finishing are simultaneously optimized. Simulation results show that the proposed algorithm can find better combinations of the machining parameters than other algorithms proposed previously to further reduce the production cost. In addition, the outcome of our work presents a novel way to solve the complex optimization problem of machining parameters with a combination of traditional mathematical methods and swarm intelligence algorithms.