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【Objective】 Non-pressurized mesh filters are often installed in front of pumps to filter sediments and debris. In this paper, we study the influence of different irrigation water sources and backwash device parameters on hydraulic performance of a new network filter. 【Method】 The water flow rate in the experiment ranged from 120 to 160 m3/h, and the sediment concentration in the water varied from 0.2 to 0.8 g/L. The backwash varied from 1 to 20 m3/h, and the velocity gradient in the filter box changed from 1 to 4 r/min. A multi factor dispersion analysis method was used to analyze the experimental results. 【Result】 ① The initial sediment concentration had minimal impact on hydraulic performance of the mesh filter, contributing only 0.010 8 to the overall effect. However, the influence of water flow duration on the initial sediment concentration was significant, exceeding 159%. ② The fourth model provided the best fit for the overall regression analysis, with a residual value of 0.804 4, significantly higher than other models; its coefficient of determination R2 was 0.986 1. ③ the optimal performance was achieved when flow rate was 120.198 m3/h, velocity was 1.112 r/min, and self-cleaning flow rate was 19.934 m3/h. The associated a minimum energy loss was 16.024 kPa. 【Conclusion】 The model we proposed can accurately predict the impact of operation conditions and sediment concentration in the water on the performance of the non-pressurized mesh filters. Selecting optimal operation conditions can minimize the energy loss across the filter.

To explore the characterization of the transport and deposition of sludge particles in the mesh filter of water-saving irrigation systems, taking the inlet flow, particle concentration, and calculation time as experimental factors, a filtration test was carried out by computational fluid dynamics-discrete element method model (CFD-DEM) coupling and physical testing. Through the test results, deposition distribution, average particle size, and geometric standard deviation of the filter cake are analyzed. The cake pressure drop model of the filter cake's average particle size and the filter cake's geometric standard deviation are established. The results showed that the deposition of filter particles is triangular during the filtration process, and the axial deposition particles of the filter screen gradually increase. The proportion of small particles (smaller than 0.6 mm) in the cake layer formed by particles is the largest, greater than 70%; the main deposition area of particles is the lower filter screen (90%), and the particles deposited on the upper filter screen are all small particles. The increase in flow significantly affects the distribution of different sediment particles in the inlet and middle sections of the filter cake layer; the average particle size and geometric standard deviation of the filter cake increase with the increase of flow. An analysis of pressure drop was also conducted. The pressure drop of the filter cake decreases with the increase in geometric standard deviation and average particle size. Compared with the test pressure drop, the calculated pressure drop is consistent. The obtained results have important theoretical and practical significance for establishing a screen filter screen and cake pressure drop model, as well as optimizing filter media filtration efficiency and cleaning efficiency, and the relative error does not exceed 10%. [ABSTRACT FROM AUTHOR]

Mesh filters are frequently employed in water-saving irrigation fields. Studies addressing the method of cake formation and the characteristics of the cake during the mesh filter's growing phase are still missing. One-way and orthogonal experiments were carried out using mesh filters with 220 μm and 320 μm aperture sizes as the research objects, taking particle concentrations, inlet flow, and growth phases as experimental factors. According to the variation rule of seed pressure drop in the formation process of filter cake, the growth process of filter cake is divided into four stages, which are as follows: slow blockage first and second stages (M1, M2), fast blockage stage (M3), and filter cake filtration stage (M4). Moreover, the size distribution, porosity (ε), pore-to-particle ratio (KP), and median size (d50) of the filter cake were used to represent the structural characteristics. The results show that the growth of filter cake was a process that started with the filling of mesh pores by intercepted particles and progressed to the filling of large-particle skeleton pores by subsequently filtered particles. During this process, the proportion of intercepted particles gradually decreased, while the proportion of filtered particles increased incrementally, and the median size (d50) and porosity (ε) decreased. Meanwhile, the smaller the aperture size of the screen, the smaller the filter cake's median size (d50) was, but the larger the pore-to-particle ratio (KP) was. As the flow rate increased, the porosity (ε) was augmented in the M1 and M2 stages; however, it decreased in the M3 and M4 stages. The concentration had a minor influence on the filter cake's porosity. Lastly, the regression model for filter cake porosity under two aperture size conditions was established, based on factors such as flow rate, concentration, and growth stage. The coefficients of determination, R2, for the model were 90.33% and 80.73%, indicating a good fit. [ABSTRACT FROM AUTHOR]

BackgroundThere is a growing trend in the use of mobile health (mHealth) technologies in traditional Chinese medicine (TCM) and telemedicine, especially during the coronavirus disease (COVID-19) outbreak. Tongue diagnosis is an important component of TCM, but also plays a role in Western medicine, for example in dermatology. However, the procedure of obtaining tongue images has not been standardized and the reliability of tongue diagnosis by smartphone tongue images has yet to be evaluated. ObjectiveThe first objective of this study was to develop an operating classification scheme for tongue coating diagnosis. The second and main objective of this study was to determine the intra-rater and inter-rater reliability of tongue coating diagnosis using the operating classification scheme. MethodsAn operating classification scheme for tongue coating was developed using a stepwise approach and a quasi-Delphi method. First, tongue images (n=2023) were analyzed by 2 groups of assessors to develop the operating classification scheme for tongue coating diagnosis. Based on clinicians’ (n=17) own interpretations as well as their use of the operating classification scheme, the results of tongue diagnosis on a representative tongue image set (n=24) were compared. After gathering consensus for the operating classification scheme, the clinicians were instructed to use the scheme to assess tongue features of their patients under direct visual inspection. At the same time, the clinicians took tongue images of the patients with smartphones and assessed tongue features observed in the smartphone image using the same classification scheme. The intra-rater agreements of these two assessments were calculated to determine which features of tongue coating were better retained by the image. Using the finalized operating classification scheme, clinicians in the study group assessed representative tongue images (n=24) that they had taken, and the intra-rater and inter-rater reliability of their assessments was evaluated. ResultsIntra-rater agreement between direct subject inspection and tongue image inspection was good to very good (Cohen κ range 0.69-1.0). Additionally, when comparing the assessment of tongue images on different days, intra-rater reliability was good to very good (κ range 0.7-1.0), except for the color of the tongue body (κ=0.22) and slippery tongue fur (κ=0.1). Inter-rater reliability was moderate for tongue coating (Gwet AC2 range 0.49-0.55), and fair for color and other features of the tongue body (Gwet AC2=0.34). ConclusionsTaken together, our study has shown that tongue images collected via smartphone contain some reliable features, including tongue coating, that can be used in mHealth analysis. Our findings thus support the use of smartphones in telemedicine for detecting changes in tongue coating.