Predicting the impact of subsurface heterogeneous hydraulic conductivity on the stochastic behavior of well draw down in a confined aquifer using artificial neural networks

Groundwater flow and behavior have to be investigated based on heterogeneous subsurface formation since the homogeneity assumption of this formation is not valid. Over the past twenty years, stochastic approach and Monte Carlo technique have been utilized very efficiently to understand the groundwater flow behavior. However, these techniques require lots of computational and numerical efforts according to the various researchers’ comments. Therefore, utilizing new techniques with much less computational efforts such as Artificial Neural Network (ANN) in the prediction of the stochastic behavior for the groundwater based on heterogeneous subsurface formation is highly appreciated. The current paper introduces the ANN technique to investigate and predict the stochastic behavior of a well draw down in a confined aquifer based on subsurface heterogeneous hydraulic conductivity. Several ANN models are developed in this research to predict the unsteady two dimensional well draw down and its stochastic characteristics in a confined aquifer. The results of this study showed that ANN method with less computational efforts was very efficiently capable of simulating and predicting the stochastic behavior of the well draw down resulted from the continuous constant pumping in the middle of a confined aquifer with subsurface heterogeneous hydraulic conductivity.     

Effect of grain size on the primary and secondary creep behavior of Sn–3 wt.% Bi alloy

The effect of grain size as well as creep temperature on the primary and secondary creep parameters of Sn–3 wt.% Bi alloy has been studied. It was found that the creep parameters α, β, and έ _s were decreased with increasing grain size. This was explained in view of the dislocation interaction with the defects and different inclusions in the matrix. For both the primary and secondary creep, the activation energies estimated indicate that the rate-controlling mechanism is the grain boundary-sliding mechanism.     

Coupled reliability model of biodeterioration, chloride ingress and cracking for reinforced concrete structures

Maintaining and operating civil infrastructure systems has been recognized as a critical issue worldwide. Among all possible causes of safety reduction during the structural lifetime, deterioration is particularly important. Structural deterioration is usually a slow time-dependent process controlled by safety and operation threshold specifications. This paper presents a model of RC deterioration by coupling biodeterioration (i.e., chemical, physical and mechanical action of live organisms), steel corrosion, and concrete cracking. The final purpose of the model is to compute the reduction of the concrete section and the area of steel reinforcement in order to assess the change of structural capacity with time. Given the uncertainties in both the parameters and the model, the probabilistic nature of loads, the material properties and the diffusion process are taken into account to evaluate structural reliability. The model is illustrated with an example where the inelastic behavior of a pile subject to random loading is considered. The results of the analysis have shown that the effect of biodeterioration on the structural performance is significant and can cause an important reduction of its lifetime. On the whole, the paper states that modeling the effects of biodeterioration in RC structures should be included as part of infrastructure planning and design, especially, when they are located in aggressive environments.     

CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19

Machine Learning - The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing...     

Influence of Anodizing Parameters on the Electrochemical Characteristics and Morphology of Highly Doped P-type Porous Silicon

Silicon - Controlling pore structure, e.g. pore diameter, size and distribution of porous silicon (PS) is highly needed for technological applications such as microfluidic operation and filtration...     

A novel deep learning neural network for fast-food image classification and prediction using modified loss function

Multimedia Tools and Applications - Accurate food image classification is often critical to accurately monitor the dietary assessment to reduce the risk of different heart-related diseases,...     

The microstructure of buccal cavity and alimentary canal of Siganus rivulatus: Scanning electron microscope study

The microstructure of the oral cavity and alimentary canal of herbivorous fish Siganus rivulatus collected from the Red Sea were investigated by using scanning electron microscope (SEM). The results showed that S. rivulatus has three types of teeth, tri‐cusped, bi‐cusped, and papilliform. A taste bud (Type I) was recorded in the oropharyngeal cavity. Characteristic styles of microridges on the cell's surface inside the buccal cavity were recorded. Also, the distribution of the mucous cells in the lining of the mouth cavity, alimentary canal was observed. Mucosal folds along the distinct parts of alimentary canal, showed characteristic pattern which was complex in the intestinal mucosa. The results concluded that there are characteristic microstructures according to feeding habitat compared with other bony fishes.     

The reference temperature dependence of Young’s modulus of two-temperature thermoelastic damping of gold nano-beam

This work is concerning with the study of the thermoelastic damping of a nanobeam resonator in the context of the two-temperature generalized thermoelasticity theory. An explicit formula of thermoelastic damping has been derived when Young’s modulus is a function of the reference temperature. Influences of the beam height and Young’s modulus have been studied with some comparisons between the Biot model and the Lord–Shulman model (L–S) for one- and two-temperature types. Numerical results show that the values of the thermal relaxation parameter and the two-temperature parameter have a strong influence on thermoelastic damping at nanoscales.     

Thermal blooming and photoluminescence characterizations of sol–gel CdO–SiO<Subscript>2</Subscript> with different nanocomposite

The CdO NPs was synthesized using the sol–gel method and the nanoparticles were characterized using an UV–Vis spectrophotometer, with shape and size were examined by SEM and XRD. The XRD analysis respects the Bragg’s law and confirmed the crystalline nature of CdO nanoparticles. From the XRD, the average size of CdO NPs was found to be around 41 nm. The photoluminescence spectra of the CdO NPs, as recorded at room temperature, were excited at 300 nm wavelength. The broad emission peaks were between 600 and 650 nm (orange emission). The optical limiting performance of the nanocomposite was described in the sol–gel state. Also, this study has observed and studied the diffraction rings generated in CdO NPs using the same CW laser. The number of rings increases almost exponentially with an increasing volume fraction of SiO₂ in the nanocomposites. The refractive index change, Δn, and effective nonlinear refractive index, n ₂, were found to be 10^?4 and 10^?8 cm²/W, respectively. The effective nonlinear refractive index, n ₂, was determined based on the observed number of rings. The threshold values of the CdO, CdO–2SiO₂ and CdO–5SiO₂ nanocomposites are 7.1, 6.55 and 6.34 mW, respectively. This large nonlinearity is attributed to the thermal effect. The present studies suggest that the nanocomposite is a potential candidate for optical device applications such as the optical limiters. The thermal blooming technique was applied to evaluate the thermo-optic coefficient and thermal diffusivity of the CdO NPs. In the thermal blooming experimental setup a transistor–transistor logic modulated CW laser of wavelength 532 nm was used as the excitation source.