To reduce the end-to-end average delay of algorithm in wireless network, this paper proposes the real-time routing algorithm in spectrum network. It is analyzed that the dynamic changes of the radio network model and routing algorithm in spectrum network. Through using Markov state transition and adjusting the router with scaling factor, the high-quality resources in the network can be obtained and fully utilized, and then these can reduce the transmission time latency rate and timely adjust the route. After that the tendency of spectrum network and specific real-time algorithm are given. Finally, by using the network simulation NS-2, simulation experiments are used to estimate the performance test. Experimental results show that compared with the traditional algorithm, the proposed algorithm can obtain a lower end-to-end average delay and improves network throughput and the steady and reliability of the link connection.

Varicella is a highly infectious contagious disease, and Chongqing is one of the high incidence areas in China. To understand the epidemic regularity and predict the epidemic trend of varicella is of great significance to the risk analysis and health resource allocation in the health sector. First, we used the ‘STL’ function to decompose the incidence of varicella to understand its trend and seasonality. Second, we established SARIMA model for linear fitting, and then took the residual of the SARIMA model as the sample to fit the LS-SVM model, to explain the non-linearity of the residuals. The monthly varicella incidence peaks in April to June and October to December. Mixed model was compared to SARIMA model, the prediction error of the hybrid model was smaller, and the RMSE and MAPE values of the hybrid model were 0.7525 and 0.0647, respectively, the mixed model had a better prediction effect. Based on the study, the incidence of varicella in Chongqing has an obvious seasonal trend, and a hybrid model can also predict the incidence of varicella well. Thus, hybrid model analysis is a feasible and simple method to predict varicella in Chongqing.

<b><i>Background:</i></b> Allergic diseases are a public health problem with the largest number of patients and the widest age distribution. Chronic urticaria (CU) is a common clinical allergic disease. Bilastine is effective in the treatment of CU, especially skin wind masses and erythema. The purpose of this study was to systematically evaluate the efficacy and safety of Bilastine in the treatment of CU symptoms and to provide an evidence-based reference for clinical rational drug use. <b><i>Methods:</i></b> PubMed, Scopus, the Cochrane Library, Embase, EBSCO, and other databases were searched by computer to collect the trials on the effect of bilastine on patients with CU. The retrieval time limit was established until November 2021. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias in the included study. Meta-analysis was performed using RevMan5.4 software. <b><i>Results:</i></b> A total of 7 studies were included, including 975 patients. Meta-analysis results showed that compared to the control group, bilastine significantly improved the skin quality of life index, Total Symptom Score (TSS), and weekly urticaria activity score. The skin quality of life index DLQI score (MD = −4.98, 95% CI: −8.09 to −1.86, <i>p</i> = 0.002), skin symptom score TSS (MD = −1.62, 95% CI −2.29 to −0.94, <i>p</i> &#x3c; 0.00001), the number of hives in a week UAS-7 score (MD = −25.28, 95% CI −32.36 to −18.19, <i>p</i> &#x3c; 0.00001), and the differences were statistically significant. <b><i>Conclusions:</i></b> Bilastine has a better therapeutic effect on CU and can also significantly improve the clinical symptoms and quality of life of CU.

Introduction: Chongqing is among the areas with the highest rubella incidence rates in China. This study aimed to analyze the temporal distribution characteristics of rubella and establish a forecasting model in Chongqing, which could provide a tool for decision-making in the early warning system for the health sector. Methodology: The rubella monthly incidence data from 2004 to 2019 were obtained from the Chongqing Center of Disease and Control. The incidence from 2004 to June 2019 was fitted using the seasonal autoregressive integrated moving average (SARIMA) model and the back-propagation neural network (BPNN) model, and the data from July to December 2019 was used for validation. Results: A total of 30,083 rubella cases were reported in this study, with a significantly higher average annual incidence before the nationwide introduction of rubella-containing vaccine (RCV). The peak of rubella notification was from April to June annually. Both SARIMA and BPNN models were capable of predicting the expected incidence of rubella. However, the linear SARIMA model fits and predicts better than the nonlinear BPNN model. Conclusions: Based on the results, rubella incidence in Chongqing has an obvious seasonal trend, and SARIMA (2,1,1) × (1,1,1) 12 model can predict the incidence of rubella well. The SARIMA model is a feasible tool for producing reliable rubella forecasts in Chongqing.