To promote sustainable steel-concrete composite structures, it is essential to develop special shear connectors that facilitate accelerated construction and deconstruction. A lockbolt demountable shear connector (LBDSC) was recently proposed. While the LBDSC has been evaluated using horizontal and vertical (standard) push-out tests, it is essential to further assess the disassembly mechanism and the positive flexural performance of prefabricated demountable composite beams (PDCBs) under both serviceability and ultimate limit states. Two full-scale test specimens of PDCBs with LBDSC were designed with partial shear connections and assessed using a three or four-point load beam setup under both cyclic and static monotonic loading conditions.

In the current study, synthesis and characterization of silver nanoparticles (AgNPs) before and after functionalization with ampicillin antibiotic and their application as anti-pathogenic agents towards bacteria were investigated. AgNPs were synthesized by a green method from AgNO3 solution with glucose subjected to microwave radiation. Characterization of the nanoparticles was conducted using UV-Vis spectroscopy, scanning electron microscopy (SEM), zeta potential determination and Fourier transform infrared (FTIR) spectroscopy. From SEM analysis, the typical silver nanoparticle particle size was found to be 30 nm and Zeta potential measurements gave information about particle stability. Analysis of FTIR patterns and UV-VIS spectroscopy con

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

Background: pregnancy as a systemic condition causes changes in the functioning of human body as a whole and specifically in the oral cavity and it also is considered as a stressful condition. These changes may favor the increase of oxidative stress. Aim: The aim of this study was to estimate the level of marker of oxidative stress (malondialdehyde) and antioxidant (uric acid) in saliva of pregnant compared to non-pregnant women and to assess the gingival health condition in both groups. Additionally, unstimulated salivary flow rate was determined in both groups. Subjects, materials and methods: The study group consisted of sixty pregnant women, they were divided into three equal groups according to trimester (20 pregnant women for each

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p