Silver nano particles have antimicrobial property which makes them appropriate for disinfection. Due to their antimicrobial feature, these particles are applicable for root canal irrigation. Fluid flow inside root canal and its appropriate circulation results in more efficient removal of microorganisms. Due to the very small dimensions of a root canal, performing experimental research is very difficult to identify the phenomena occurring in the root canal; therefore, numerical investigation will be very helpful to gain appropriate insight into the flow features of a root canal during irrigation for disinfection. Computation Fluid Dynamic (CFD) can be employed to numerically simulate the flow of irrigants inside the root canal. In the present study, the flow of Ag/water nanofluid in the root canal is numerically modeled. In order to evaluate the impact of height of injection and nanofluid concentration, two heights and concentrations are considered and compared. According to the results, lower injection height is more favorable due to better circulation of an irrigant in the root canal. Moreover, increase in the concentration of the nanofluid leads to reduction in maximum velocity of the fluid; which is attributed to higher increase in dynamic viscosity in comparison with the density. Velocity and wall shear stress contours in various cases are represented to gain better insight into the irrigant motion inside the canal. According to the results of simulation, wall shear stress of the root canal increases by increment in the concentration of the nanofluid and volumetric flow rate of the irrigants.

Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), Gene Expression Programming (GEP), and M5 model trees (MT). The results indicate that SPI delivered higher accuracy. Moreover, MT model performed better in predicting SSI by a CC of 0.8195 and a RMSE of 0.8186.

In this study, the boundary element method-finite element method (BEM-FEM) model is employed to investigate the sloshing and flexibility terms of elastic submerged structures on the behavior of a coupled domain. The methods are finite element and boundary elements which are utilized for structural dynamic and sloshing modeling, respectively. The applied models are used to assess dynamic parameters of a fluid-structure system. Based on the proposed model, a code is developed which can be applied to an arbitrary two-and three-dimensional tank with an arbitrarily shaped elastic submerged structure. Results are validated based on the existing methods represented in the literature and it is concluded that the absolute relative deviation is lower than 2%. Finally, the interactive influences of submerged components which are more meaningful are investigated.

This study explores the river-flow-induced impacts on the performance of machine learning models applied for forecasting of water quality parameters in the coastal waters in Hilo Bay, Pacific Ocean. For this purpose, hourly recorded water quality parameters of salinity, temperature and turbidity as well as the flow data of the Wailuku River were used. Several machine learning models including artificial neural network, extreme learning machine and support vector regression have been employed to investigate the river-flow-induced impact on the water quality parameters from the current time up to 2 h ahead. Following the input structure of the machine learning models, two separate models based on including and excluding the river flow were developed for each variable to quantify the importance of the flow discharge on the accuracy of the forecasting models. The performance of different machine learning models was found to be close to each other and showing similar pattern considering accuracy and uncertainty of the forecasts. The results revealed that flow discharge influenced the water salinity and turbidity of the bay in which the models including the river flow as input variables had better performance compared with those excluding the flow time series. Among the water quality parameters investigated in this research, river flow made the most and least improvement on the efficiency of the models applied for forecasting of turbidity and water temperature, respectively. Overall, it was observed that water quality parameters can be properly forecasted up to several hours ahead providing a potentially valuable tool for environmental management and monitoring in coastal areas.