A reinforced concrete frame is referred as "RIGID FRAMES". However, researches indicate that the Beam-Column joint (BCJ) is definitely not rigid. In addition, extensive research shows that failure may occur at the joint instead of in the beam or the column. Joint failure is known to be a catastrophic type which is difficult to repair.
This study was carried out to investigate the effect of hoops and column axial load on the shear strength of high-strength fiber reinforced Beam-Column Joints by using a numerical model based on finite element method using computer program ANSYS (Version 11.0). The variables are: diameter of hoops and magnitude of column axial load.
The theoretical results obtained from ANSYS program are in a good a

Despite the broad approbation of additive manufacturing technologies over diverse industries, printed parts’ performance, quality, and related build time are still greatly influenced by printing parameters. These parameters majorly affect mechanical strength, surface finish, dimensional accuracy, and overall production time of the printed part. Customized 3D printing layer thickness, speed, and acceleration are crucial parameters that affect the speedy printing process and final product quality. The current work considers layer thickness in addition to speed and high acceleration values effect on the surface quality, surface roughness and productivity time of 3D printed Polylactic Acid (PLA). The experimental methodology implemented withi

This study delves into the design optimization of a hydropower harvesting system, exploring various parameters and their influence on system performance. By modifying the variables within the model to suit different flow conditions, a judiciously optimized design is attainable. Notably, the lift force generated is found to be intricately linked to the strategic interplay of the bluff body's location, cylinder dimensions, and flow velocity. The findings culminate in the establishment of empirical equations, one for lift force and another for displacement, based on the force equation. Many energy harvesting approaches hinge on the reciprocating motion inherent to the structural system. The methodology developed in this study emerges as a pot

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

The study included the collection of samples of raw cow milk to isolate Leuconostoc bacteria, samples were sub cultured on De-Man Rogosa Sharpe-Vancomycin medium, the pure colonies were selected and subjected to the cultural and microscopically tests, according to that 25 cocci bacterial isolates were obtained, then isolates were subjected to biochemical tests. Result of tests showed that 12 isolates belong to the genus Leuconostoc out of 25 cocci bacterial isolates, Vitek2 system was used as a supplementary step. Results of final identification showed that 3 sub species were obtained included Leuconostoc mesenteroides ssp. cremoris 9 out of 12 isolates, while it was 2 isolates of Leuconostoc mesenteroides ssp. mesenteroides and one isol

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respectively. For

This paper describes a number of new interleaving strategies based on the golden section. The new interleavers are called golden relative prime interleavers, golden interleavers, and dithered golden interleavers. The latter two approaches involve sorting a real-valued vector derived from the golden section. Random and so-called “spread” interleavers are also considered. Turbo-code performance results are presented and compared for the various interleaving strategies. Of the interleavers considered, the dithered golden interleaver typically provides the best performance, especially for low code rates and large block sizes. The golden relative prime interleaver is shown to work surprisingly well for high puncture rates. These interleav