The present study considers to confirming the applicability of flow with double-sided square lid driven cavity flow by using the lattice Boltzmann equation with moment-based boundary conditions for no slip boundaries.  The boundary conditions are applied over the hydrodynamic moments of the lattice Boltzmann equations locally at each node. The investigation is carried out numerically for both single and multiple relaxation time models. To simulate two-sided lid driven-cavity flow, the top and bottom walls are moving with constant velocity while other walls are stationary. Various Reynolds numbers are used in a range of 100 and up to 5000. The present method shows the effect of the moving boundaries on the two symmetrical cavities t

This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to

KE Sharquie, HR Al-Hamamy, AA Noaimi, AF Tahir, Journal of Cosmetics, Dermatological Sciences and Applications, 2012 - Cited by 2

ABSTRACT Background: One of the challenges to use chlorhexidine is its effect on the amount of microleakage after restoration; however, use of the materials with antibacterial properties after tooth preparation and before restoration has been widespread. The objective of this, in-vitro, study was to evaluate the influence of consepsis (chlorhexidine gloconate disinfectant) application on microleakage in class II cavities restored with light cured composite using universal adhesive system; etch and rinse technique –self etch technique. Materials and Methods: Forty class II cavities were prepared on mesial and distal surfaces of 20 non-carious mandibular third molars. The cavities were divided into four groups; (n =10 for each group).

In this search, a new pyrophosphate technique was proved. The technique was employed to single- nucleotide polymorphisms (SNPs), which diagnosis using a one-base extension reaction. Three Mycobacterium tuberculosis genes were chosen (Rpob, InhA, KatG) genes. Fifty-four specimens were used in this study fifty-three proved as drug-resistant specimens by The Iraqi Institute of Chest and Respiratory Diseases in Baghdad.; also one specimen was used as a negative control. The steps of this technique were by used a specific primer within each aliquot that has a short 3-OH end of the base of the target gene that was hybridized to the single-stranded DNA template. Then, the Taq polymerase enzyme and one of either α-thio-dATP, dTTP, dGTP, or dCTP

APDBN Rashid, Review of International Geographical Education Online (RIGEO), 2021