The load shedding  scheme has been extensively implemented as a fast solution for unbalance conditions. Therefore, it's crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible, however its implementation is mostly undesirable. One of the solutions to minimize the amount of load shedding is the integration renewable energy  resources, such as wind power, in the electric power generation could contribute significantly to minimizing power cuts as it is ability to positively improving the stability of the electric grid. In this paper propose a method for shedding the load base on the priority demands with incorporating the wind po

     The main goal of this work is to put a simple model of the spectral energy distribution of binary stars called WW Cha. This model is built up on the extracted data from various telescopes and archives for the target WW Cha stars and then analyzing them using a python environment. The result of the fitting proposes that there are two protoplanetary disks around the WW Cha star, with different physical properties for each disk, such as the size of the inner disk being 10 AU, while the size of the outer disk being 300 AU. The shape of the outer disk is a flaring disk not a flat disk according to the value of the power law for the surface density (1.5). The emission in the disk is caused by small amorphous olivine grains rangin

The performance of flexible pavements is significantly impacted by the permanent deformation (rutting) of asphalt pavements. Rutting shortens the pavement's useful service life and poses significant risks to those using the highway since it alters vehicle handling characteristics.. The aim of this research is to evaluate the permanent deformation of asphalt mixtures under different conditions,to achieve this aim 108 cylindrical specimens has been prepared and tested under repeated loading in uniaxial compression mode. Five factors were considered in this research, these factors represent the effect of environmental condition and traffic loading as well as mixture properties, they include testing temperature, loading condition (stress level

The aim of this research is to employ starch as a stabilizing and reducing agent in the production of CdS nanoparticles with less environmental risk, easy scaling, stability, economical feasibility, and suitability for large-scale production. Nanoparticles of CdS have been successfully produced by employing starch as a reducing agent in a simple green synthesis technique and then doped with Sn in certain proportions (1%, 2%, 3%, 4%, and 5%).According to the XRD data, the samples were crystallized in a hexagonal pattern, because the average crystal size of pure CdS is 5.6nm and fluctuates in response to the changes in doping concentration 1, 2, 3, 4, 5 %wt Sn, to become   4.8, 3.9, 11.5, 13.1, 9.3 nm respectively. An increase in crystal

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

The extraction of Basil oil from Iraqi Ocimum basillicum leaves using n-hexane and petroleum ether as organic solvents were studied and compared. The concentration of oil has been determined in a variety of extraction temperatures and agitation speed. The solvent to solid ratio effect has been studied in order to evaluate the concentration of Ocimum basillicum oil. The optimum experimental conditions for the oil extraction were established as follows: n-hexane as organic solvent, 60 °C extraction temperature, 300 rpm agitation speed and 40:1mL:g amount of solvent to solid ratio.

This paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark