The nature-based solutions (NBS) concept is closely related to sustainability, harmonious and green development, resource rational exploitation, coupled human health and environment, and ecological protection priority.Bio-slow sand filtration (BSSF) technology is a green water treatment technology with low energy consumption, simple operation, and a high removal rate of contaminants.To achieve low cost, easy management and secondary pollution avoidance in the process of removing contaminants in drinking water, the rational selection of biofiltration materials and the optimal combination of particle size are particularly important based on NBS.To effectively remove turbidity, organic pollutants, bacteria, and ammonia nitrogen by BSSF, three scenarios were summarized by considering the influence of sensitive parameter optimization and the external environment (temperature and velocity).We designed three BSSF water treatment testing devices, which were filled with bio-filter materials with different particle sizes (0.15-0.3 mm, 0.3-0.9mm, 0.9-1.35mm, and 0.3-0.9mm), to carry out an indoor testing comparison analysis.We optimized important parameters of BSSF water treatment technology (such as the bio-filter material particle size and filling heights, filtering velocity, and suitable temperature) to obtain the best design and operational parameters of BSSF water treatment technology.The optimum operating conditions were: filter material particle size of 0.3-0.6 mm, filling height of 0.6-0.9m, filtering velocity of 0.2-0.6 m/h, and a suitable temperature of 15-35ºC.To ensure the water quality of the filtered water, we optimized the design parameters of traditional BSSF technology, which could save land and reduce beginning time.BSSF water treatment technology based on NBS is useful for promoting the engineering application of drinking water treatment and regional water security.
This paper improved K-means clustering algorithm by using transaction recovery mechanism.The new algorithm was able to resume itself without loss of computing time after the computer running,it was shut down on purpose or by chance at any time,so that it could achieve its goal in big data sets on ordinary computers.It was verified in a clustering task which spent as long as 400 hours.
The selection of R-μ-T model is important to the elasto-plastic response spectrum analysis(EP-RSA).Various models were accordingly put forward considering ground motions,hysteric models,soil conditions,damping ratio,etc.However,there are different opinions on the effect of the site soil on the R-μ-T relationship.In this paper,the R-μ-T model,which was specifically proposed for Chinese soil condition,was selected for the EP-RSA of a hybrid structure under seismic intensity 7,7.5,8,8.5,9,respectively.The maximum inter-story drifts were obtained and compared with the results of incremental dynamic analysis(IDA).It is concluded that the slope of EP-RSA agrees well with the mean value curve of IDA.The analytical results are helpful for the wide application of EP-RSA to complex structures.
We analyze the idea of avoiding deadlock by Banker's Algorithm andpropose that Banker's Algorithm could be applied to operating system only on the premise that the numerical value of Max array could be fixed. After analyzing the problem about fixing the numerical value of Max array,we discover that thenumerical value of Max array could be fixed prior to process execution only when the quantity of requesting resource does not depend on variables in program. We propose an elementary way based on the situation,by which the numerical value of Max array could be fixed.
The dispatching automation master station system, monitors the operational status of power system equipment and plays a crucial role in the operation of the power grid. In the event of a system failure, a series of serious accidents may occur, endangering personnel safety and stable equipment operation. In response to these issues, this paper proposes a probability model for the functional failure of the dispatching automation master station system that takes into account human errors. The impact of human error on the automation master station system is comprehensively considered. Based on the risk events in the automation master station system, the risk events involved are regarded as nodes, and a probability model for the functional failure of the dispatching automation master station system based on Bayesian network is constructed. This model effectively combines qualitative expert knowledge with quantitative data information, inputs risk event nodes as evidence information into the Bayesian network model, obtains the posterior probability of event node variables through Bayesian inference, and thus realizes the probability calculation and analysis of functional failure of the automation master station system. This research has certain significance for the study of the dispatching automation master station system.
Computer technology and related Internet of things technology have penetrated into people’s daily life and industrial production; even in competitive sports training and competition, the Internet of things technology has also been a large number of applications. Traditional intelligent wearable devices are mainly used to calculate the steps of athletes or sports enthusiasts, corresponding physical data, and corresponding body indicators. The energy consumption calculated by these indexes is rough and the corresponding error is large. Based on this, this paper will design a wearable device which can accurately calculate and monitor sports energy consumption based on relevant sensors and Internet of things technology. The corresponding core algorithm is the step counting algorithm, which can accurately calculate the relationship between human motion and the corresponding energy consumption and feed back to the intelligent device. In the experiment, the wearable device designed in this paper is compared with the traditional intelligent device. The experiment shows that the wearable device proposed in this paper is more accurate in energy consumption estimation than the traditional device, and its corresponding energy consumption is relatively small.
With the promotion of market economy and education reform,university Art Design education is going through a period of comprehensive transition,yet there emerge a lot of troubles,such as over-expansion of arts design education,inaccurate orientation of talents training,weak links between basic courses and professional courses.To improve university Art Design education,we must advocate individualized model of running school and innovation of arts,construct open teaching settings.
In order to make healthcare service robots fluently and flexibly complete some tasks,a geometry model was set up to detect vision information by image processing.A kind of vision information method for predicting and processing based on Kalman filter was put forward.Moreover,a set of predicting and object tracking model was also provided in light of Kalman filter theory.Some experiments were carried out with these models in a service robot system, the experiment results show that the object status can be accurately predicted by provided method.Furthermore,the vision information can be quickly apperceived by prediction and correction of the models.On the other hand,the time lag for vision information feedback can be decreased with the provided method in the service robot system,so the communication between human and robot takes place more fluently.
A 1-D unsteady flow hydrodynamic numerical model is developed for the river network region of the Pearl River Delta, and the model is calibrated with the measured data. The research result of the 3-D hydrodynamic model for the Lingding Sea is used and coupled with the 1-D model for the Pearl River Delta network, and a coupling model is generated. Then, the time interval alternative method is used for model calculation. The calculated results by the coupling model are calibrated with the hydrological data synchronously measured in the Pearl River Delta region and Lingding Sea region in July, 1978. The good agreement of the calculated results with the measured data of the Pearl River Delta network region, Lingding Sea region, and their intersecting regions shows that the model can be used to describe the hydrodynamic process of these regions.
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