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In order to analyze the operation rule of steam ejector used in sewage treatment field, this paper established the mathematical model of steam ejector, simulated the internal flow state, analyzed the influence rule of external parameters on the performance of steam ejector, and analyzed the influence rule of ejector position on the water production ratio of steam ejector multi-effect evaporation desalination system. The research results show that under the same steam ejector structure, as the pressure of the working steam increases, the mixing and disturbance of the working steam and the ejected steam become more intense, and the diamond shock wave structure increases. As the pressure of the ejected steam increases, the momentum exchange effect of the shear blending of working steam and ejected steam is accelerated, and the speed increase rate becomes larger, and the diamond shock wave structure gradually gathers toward the nozzle. As the pressure rise of the ejected steam inlet and outlet increases, the backflow phenomenon near the wall in the mixing chamber becomes more serious, and the diamond shock wave structure is reduced. With the forward movement of the ejector position of the thermal steam compressor, the pressure of the ejected steam increases, and the water production ratio of the steam ejector multi-effect evaporation seawater desalination system shows a downward trend. Therefore, choosing appropriate working condition parameters can effectively improve the working performance of steam ejector and improve its operating efficiency in the field of sewage treatment.

INTRODUCTION: The aim of this study was to document the scope of violations of the Canadian Radio-television and Telecommunications Commission (CRTC) “Code for Broadcast Advertising of Alcoholic Beverages” (CRTC Code) by drinking venues posting alcohol-related content on social media platforms, and to assess whether CRTC Code violations by drinking venues relate to their popularity among university students and to students’ drinking behaviours. METHODS: In phase 1 of the study, a probability sample of 477 students from four Canadian universities responded to a questionnaire about their drinking and preferred drinking venues. In phase 2, a probability sample of 78 students assessed the compliance of drinking venues’ social media posts with the 17 CRTC Code guidelines. We pooled both datasets and linked them by drinking venues. RESULTS: Popular drinking venues were overwhelmingly posting alcohol-related content that contravenes the CRTC Code. Adjusted effect estimates show that a decrease in the mean level of compliance with the CRTC Code was significantly associated with a 1% increase in popularity score of drinking venues (t-test, p < .001). With regard to drinking behaviours, a 1% increase in the overall mean level of compliance with the CRTC Code was associated with 0.458 fewer drinking days per week during a semester (t-test, p = .01), 0.294 fewer drinks per occasion (t-test, p = .048) and a lesser likelihood of consuming alcohol when attending a drinking venue (t-test, p = .001). CONCLUSION: The results of this study serve as a reminder to territorial and provincial regulatory agencies to review their practices to ensure that alcohol advertising guidelines are applied and enforced consistently. More importantly, these results call for the adoption of federal legislation with a public health mandate that would apply to all media, including print, television and radio, digital and social.

The development and research of an ultrasonic-based concrete structural health monitoring system encounters a variety of problems, such as demands of decreasing complexity, high accuracy, and extendable system output. Aiming at these requirements, a low-cost extendable system based on FPGA with adjustable system output has been designed, and the performance has been evaluated by different assessment parameters set in this paper. Besides the description of the designed system and the experiments in air medium, the residual similarity and Pearson correlation coefficients of experimental and theoretical data have been used to evaluate the submodules’ output. The output performance of the overall system is evaluated by the Pearson correlation coefficient, root-mean-square error (RMSE), and magnitude-squared coherence with 40 experimental data. The maximum, median, minimum, and mean values in three-parameter datasets are analyzed for discussing the working condition of the system. The experimental results show that the system works stably and reliably with tunable frequency and amplitude output.

Concrete is the main material in building. Since its poor structural integrity may cause accidents, it is significant to detect defects in concrete. However, it is a challenging topic as the unevenness of concrete would lead to the complex dynamics with uncertainties in the ultrasonic diagnosis of defects. Note that the detection results mainly depend on the direct parameters, e.g., the time of travel through the concrete. The current diagnosis accuracy and intelligence level are difficult to meet the design requirement for automatic and increasingly high-performance demands. To solve the mentioned problems, our contribution of this paper can be summarized as establishing a diagnosis model based on the GA-BPNN method and ultrasonic information extracted that helps engineers identify concrete defects. Potentially, the application of this model helps to improve the working efficiency, diagnostic accuracy and automation level of ultrasonic testing instruments. In particular, we propose a simple and effective signal recognition method for small-size concrete hole defects. This method can be divided into two parts: (1) signal effective information extraction based on wavelet packet transform (WPT), where mean value, standard deviation, kurtosis coefficient, skewness coefficient and energy ratio are utilized as features to characterize the detection signals based on the analysis of the main frequency node of the signals, and (2) defect signal recognition based on GA optimized back propagation neural network (GA-BPNN), where the cross-validation method has been used for the stochastic division of the signal dataset and it leads to the BPNN recognition model with small bias. Finally, we implement this method on 150 detection signal data which are obtained by the ultrasonic testing system with 50 kHz working frequency. The experimental test block is a C30 class concrete block with 5, 7, and 9 mm penetrating holes. The information of the experimental environment, algorithmic parameters setting and signal processing procedure are described in detail. The average recognition accuracy is 91.33% for the identification of small size concrete defects according to experimental results, which verifies the feasibility and efficiency.