The present article includes an experimental study of the behavior of dry and saturated dense sandy soil under the action of a single impulsive load. Dry and saturated dense sand models were tested under impact loads. Different falling masses from different heights were conducted using the falling weight deflectometer (FWD) to provide the single pulse energy. The responses of dense soils were evaluated at surface of soil under impact load. These responses include; displacements, velocities, and accelerations that are developed due to the impact acting at top and the displacement at different depths within the soil using the falling weight deflectometer (FWD) and accelerometers (ARH-500A waterproof, and low capacity acceleration transducer) that are embedded in the soil in addition to soil pressure gauges and then recorded using the multi-recorder TMR-200. Based on the experimental test results, it was found that as the sand becomes saturated, the amplitude of the force-time history decreases by about 10-22% since the voids are filled with water which lead to less contact points between particles. Moreover, the resulting vertical displacement due to impact increases by about 20-60% as compared to the case of dry sand at a depth B (where B is the diameter of the bearing plate) from the bearing plate. Such a behavior is related to two compressive waves through the saturated medium; the fluid wave and the soil skeleton wave with a coupled motion of those two waves hence, makes the displacement to be larger in the saturated soil. The horizontal displacement within the soil medium at a distance B away from the edge of the footing are less than the displacements in dry state. The excess pore water pressure increases by about 40% as the amplitude of the impact force increases due to the increase of the contact pressure.
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
This work includes the synthesis of new ester compounds containing two 1,3,4-oxadiazole rings, 15a-c and 16a-c. This was done over seven steps, starting with p-acetamido-phenol 1 and 2-mercaptobenzoimidazole 2. The structure of the products was determined using FT-IR, 1H NMR, and mass spectroscopy. The evaluation of the antimicrobial activities of some prepared compounds was achieved against four types of bacteria (two types of gram-positive bacteria; Staphylococcus aureus and Bacillus subtilis, and two types of gram-negative bacteria, Pseudomonas aeruginosa and E. Coli), as well as against one types of fungus (C. albino). The results show moderate activit against the study bacteria, and the theoretical analysis of the toxi
... Show MoreAn improved Metal Solar Wall (MSW) with integrated thermal energy storage is presented in this research. The proposed MSW makes use of two, combined, enhanced heat transfer methods. One of the methods is characterized by filling the tested ducts with a commercially available copper Wired Inserts (WI), while the other one uses dimpled or sinusoidal shaped duct walls instead of plane walls. Ducts having square or semi-circular cross sectional areas are tested in this work.
A developed numerical model for simulating the transported thermal energy in MSW is solved by finite difference method. The model is described by system of three governing energy equations. An experimental test rig has been built and six new duct configurations have b
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
... Show MoreA Ligand (ECA) methyl 2-((1-cyano-2-ethoxy-2-oxoethyl)diazenyl)benzoate with metals of (Co2+, Ni2+, Cu2+) were prepared and characterization using H-NMR, atomic absorption spectroscopy, ultra violet (UV) visible, magnetic moments measurements, bioactivity, and Molar conductivity measurements in soluble ethanol. Complexes have been prepared using a general formula which was suggested as [M (ECA)2] Cl2, where M = (Cobalt(II), Nickel(II) and Copper(II), the geometry shape of the complexes is octahedral.