The global oil market is one of the most important markets in the world and occupies especially for countries consuming and producing countries, and the status of understanding of the mechanism for determining prices in the market help to stand on many factors affecting oil demand and supply of oil and geopolitical factors, climate and alternative sources of energy .. etc. factors, and that the main objective of the research is to study the causes and results left behind by the oil price shocks in the world market, and the movement of these factors be through a cycle of energy that explain the strength of competition between these factors and their effects on prices, when demand increases evolution Large image leads to significan

A modified Leslie-Gower predator-prey model with a Beddington-DeAngelis functional response is proposed and studied. The purpose is to examine the effects of fear and quadratic fixed effort harvesting on the system's dynamic behavior. The model's qualitative properties, such as local equilibria stability, permanence, and global stability, are examined. The analysis of local bifurcation has been studied. It is discovered that the system experiences a saddle-node bifurcation at the survival equilibrium point whereas a transcritical bifurcation occurs at the boundary equilibrium point. Additionally established are the prerequisites for Hopf bifurcation existence. Finally, using MATLAB, a numerical investigation is conducted to verify t

The detection of fungi contaminating maize grain and the effect of four plant extracts Azadirachta indica, Eucalyptus globulus Glycyrrhiza glabra and Zingiber officinale on the growth of A. flavus and its ability to produce AflatoxinB1. The results showed that the incidence of Aspergillus spp., was 52.75% of the isolated fungi, of which 29.50% was due to Aspergillus flavus, followed by Penicillium spp., with an incidence of 21.06%, and then Fusarium spp., with a rate of 18.13%. The percentage of toxin-producing A. flavus isolates reached 70.8% out of 24 isolates. The results showed the effect of alcoholic plant extracts at a concentration of 10 mg/ml on the fungal growth activity of A. flavus, the alcoholic extract of neem leaves was superi

The importance of specifying proper aggregate grading for achieving satisfactory performance in pavement applications has long been recognized. To improve the specifications for superior performance, there is a need to understand how differences in aggregate gradations within the acceptable limits may affect unbound aggregate base behavior. The effects of gradation on strength, modulus, and deformation characteristics of high-quality crushed rock base materials are described here. Two crushed rock types commonly used in constructing heavy-duty granular base layers in the State of Victoria, Australia, with three different gradations each were used in this study. The gradations used represent the lower, medium, and upper gradation li

The purpose of this study was to investigate the difference in mandibular trauma caused by two mechanisms for the delivery of missile injuries: firearms and improvised explosive devices (IEDs). The data investigated included sex, age, mechanism of injury, and other clinical and radiographic manifestations. Seventy consecutive patients, predominantly male, with a mean age of 28.6 ± 14 years (range 2–60 years) were enrolled: 38 patients (54.3%) sustained mandibular fractures caused by bullet injuries and 32 patients (45.7%) had mandibular fractures caused by IED explosion injuries. The study revealed that the differences in most of the investigated variables were not statistically significant; the only significant differences were the inci

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi