In this paper, two new simple, fast and efficient block matching algorithms are introduced, both methods begins blocks matching process from the image center block and moves across the blocks toward image boundaries. With each block, its motion vector is initialized using linear prediction that depending on the motion vectors of its neighbor blocks that are already scanned and their motion vectors are assessed. Also, a hybrid mechanism is introduced, it depends on mixing the proposed two predictive mechanisms with Exhaustive Search (ES) mechanism in order to gain matching accuracy near or similar to ES but with Search Time ST less than 80% of the ES. Also, it offers more control capability to reduce the search errors. The experimental tests

In composite steel-concrete structures, shear connectors in the form of headed steel studs are commonly utilized to transfer longitudinal shear force developed at the interface between the two materials. To overcome the shortcomings of design codes, which frequently understate shear capacity and fail to take advantage of sophisticated computational methods, this paper presents an optimization attempt to estimate the shear strength of headed steel studs utilizing the Grey Wolf Optimizer (GWO) technique using MATLAB software. Data from 234 experimental tests are employed to identify and highlight key input parameters influencing the shear strength of headed steel studs. These key parameters include concrete compressive strength (f’c

Abstract

Most universities in the world are largely committed to creating credible and transparent admission standards that provide justice in admission and have the ability to predict students' performance in their chosen programs. Hence, this study aimed to reveal the predictive ability of the acceptance criteria for the level of performance of master's students in the College of Education at Sultan Qaboos University. Quantitative data were collected from (115) students' admission documents for those accepted in the postgraduate programs for the academic year 2019-2020, and GPA data was collected from students’ transcripts for the fall semester of 2019. Qualitative data were also collected from the interviews

The study aimed at identifying the strategic gaps in the actual reality of the management of public organizations investigated to determine the strategy used based on the study model. The study relied on the variable of the general organization strategy in its dimensions (the general organization strategy, the organization's political strategy and the defense strategy of the organization) The sample of the study was (General Directorate of Traffic, Civil Status Directorate and Civil Defense Directorate), formations affiliated to the Ministry of the Interior, for the importance of the activity carried out by these public organizations by providing them In order to translate the answers into a quantitative expression in the analysi

The study aimed to detect the VrPIP2;7 gene using PCR approach, as well as to know the effect of the treatment with four increased melatonin concentrations of 50, 100, 150 and 200 ppm in addition to control treatment were 0 ppm on the gene expression of plasma membrane intrinsic proteins (PIP) genes in Vigna radiata L. plant exhibition for five periods of drought which is irrigation every 24 hours, 48 hours, 5 days, 10 days and every 15 days. The electrophoresis of agarose gel at a concentration of 2% showed one band when detecting the VrPIP2;7 gene with a sizeable 732 bp and using the 100 bp volume index. This gene was selected for sequencing study based on its importance as well as on the results of its gene expression. The sequencing of

Dual-species biofilms of Pseudomonas aeruginosa and Staphylococcus aureus generate difficult-to-treat illnesses. Nutrition stress in biofilms affects physiology, microbial metabolism, and species interactions, impacting bacteria growth and survival. Furthermore, the function of alginate, which is encoded by the algD gene, in the production of biofilms has been established. The present study aimed at investigating the impact of starvation on algD gene expression in single-species biofilm of P. aeruginosa and dual-species biofilms of P. aeruginosa and S. aureus from hospital sewage. A total of six P. aeruginosa and six S. aureus isolates were obtained from the microbiology laboratory at the Department of Biology, College of Science, Universit

It is important that real time stability in smart grids is ensured as the integration of renewables and the complexity of the systems grows. In this paper, we provide a solid architecture, which combines a Residual CNNLSTM deep neural network predictor, FPGA-accelerated Model Predictive Control (MPC), and SHAP-based explainability. The proposed method predicted with 99.8% accuracy using the Electrical grid Stability Simulated Dataset (UCI) and minimized the instability rates surpassing 85 percent in all operating conditions. Meeting real-time operating needs, FPGA deployment on a Xilinx Zynq UltraScale+ provided 3.1 ms latency and 5 times reduced energy consumption against CPU processing. By emphasizing bus voltage and frequency as major in

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 Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm