It is considered as one of the statistical methods used to describe and estimate the relationship between randomness (Y) and explanatory variables (X). The second is the homogeneity of the variance, in which the dependent variable is a binary response takes two values  (One when a specific event occurred and zero when that event did not happen) such as (injured and uninjured, married and unmarried) and that a large number of explanatory variables led to the emergence of the problem of linear multiplicity that makes the estimates inaccurate, and the method of greatest possibility and the method of declination of the letter was used in estimating A double-response logistic regression model by adopting the Jackna

NeuroAIDS refers to the spectrum of neuropsychiatric complications observed in approximately 30–50% of HIV/AIDS patients. Although the advent of antiretroviral therapy has reduced the severity of AIDS, mild cognitive deficits persist in nearly half of treated patients, highlighting limitations in central nervous system (CNS) drug delivery. These limitations are attributed to poor blood-brain barrier (BBB) permeability, P-glycoprotein efflux, low solubility, first-pass metabolism, and short half-lives of many ART drugs. Nose-to-brain drug delivery has emerged as a promising alternative, bypassing the BBB via olfactory and trigeminal pathways. This route offers direct CNS access while minimizing systemic exposure. Factors such as dr

This research investigated the isomerization of n-hexane using molybdenum trioxide supported on titanium dioxide as a catalyst. The study explored the effect of liquid hourly space velocity (LHSV) and reaction temperature on n-hexane isomerization. The synthesized catalyst contained 20.46 wt.% molybdenum trioxide, with LHSV values ranging from 0.5 to 2 hr-1 and temperatures between 200oC and 275oC. The research aimed to identify the best reaction conditions, specifically temperature and LHSV, for maximizing the conversion of n-hexane to isomers. The results indicated that the highest conversion, 53.21%, occurred at an LHSV of 0.5 hr⁻¹ and a temperature of 275°C. The reaction followed first-order kinetics with an activation energ

Wind turbine (WT) is now a major renewable energy resource used in the modern world. One of the most significant technologies that use the wind speed (WS) to generate electric power is the horizontal-axis wind turbine. In order to enhance the output power over the rated WS, the blade pitch angle (BPA) is controlled and adjusted in WT. This paper proposes and compares three different controllers of BPA for a 500-kw WT. A PID controller (PIDC), a fuzzy logic controller (FLC) based on Mamdani and Sugeno fuzzy inference systems (FIS), and a hybrid fuzzy-PID controller (HFPIDC) have been applied and compared. Furthermore, Genetic Algorithm (GA) and Particle swarm optimization (PSO) have been applied and compared in order to identify the

   In the present study, semi – batch experiments were conducted to investigate the efficiency of ozone microbubbles (OMBs) in the treatment of aqueous dye solutions methylene orange under different reaction conditions such as  effect of initial solution pH , ozone generation rate and initial MO-concentration. The results showed that the removal of MO by OMBs were very high at the acidic and alkaline media and upon increasing the generation rate of ozone from 0.498 to 0.83 mg/s, the removal efficiency dramatically increased from 75to 100% within 15 min. The rate of oxidation reaction followed a pseudo first- order kinetic model. The results demonstrated that OMBs is efficient in terms of the decline of methylene orange c

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob