The field of climatic geography focuses on the study of the Earth's surrounding atmosphere, particularly the lower (surface) part close to the Earth's surface. This field examines the interactions within the atmosphere resulting from the solar radiation that reaches the Earth and the subsequent distribution of atmospheric pressure. Consequently, certain surface pressure systems, both high and low, emerge and vary in their temporal and spatial impacts on the climate of the Arabian Peninsula. Therefore, it is essential to conduct a comprehensive and detailed study to understand the causes, development, movement, geographical distribution, and monthly and seasonal recurrence of these pressure systems. For this purpose, a minor climatic

Liquid-side mass-transfer coefficients (K_La) were measured in air-suction type fermentors using physical absorption of oxygen.  A fermentor of 0. 5 m i.d. was used with a working capacity of 60 liters of liquid. Tap water was used as the liquid phase, and air was used as the gas phase. The bioreactor mixing system consists of shrouded-disk/curved-blade turbine with six evacuated bending blades. The effect of liquid submergence (S) was investigated. Further, the effects of the ratio of the impeller diameter (D) to the tank diameter (T), and the clearance of the impeller from the tank bottom(C) were also studied.  The agitation speed (N) was varied in the range of 50-800 rpm. It was found that the value of  K

The agricultural sector suffers from many risks and natural disasters, such as droughts and heavy rains that cause floods, as well as hail and agricultural pests, etc., that threaten agricultural activity and reduce it, which leads to the failure of farmers and peasants for fear of being subjected to continuous losses. Nevertheless, we notice almost complete reluctance to move towards agricultural insurance, due to the dependence of farmers on the government, which adopts the principle of compensation instead of agricultural insurance when natural disasters happen despite the difficulties and financial hardship as well as the suspicion of corruption that haunt the compensation process and this represents the most important problem for resea

In general, the importance of cluster analysis is that one can evaluate elements by clustering multiple homogeneous data; the main objective of this analysis is to collect the elements of a single, homogeneous group into different divisions, depending on many variables. This method of analysis is used to reduce data, generate hypotheses and test them, as well as predict and match models. The research aims to evaluate the fuzzy cluster analysis, which is a special case of cluster analysis, as well as to compare the two methods—classical and fuzzy cluster analysis. The research topic has been allocated to the government and private hospitals. The sampling for this research was comprised of 288 patients being treated in 10 hospitals. As t

Today's smart engineering systems are often faced with situations that are structurally uncertain, informationally incomplete, and non-probabilistically ambiguous, especially for electrical systems. ARDL models are limited in applications in complex computational environments where the uncertainty is due to vagueness, not randomness, and assume the exact parametric representation of the models and the structure of the stochastic uncertainty. This study proposes a new soft-computing paradigm using Fuzzy Autoregressive Distributed Lag (FARDL) models and compares the performance of the Linear Programming (LP) and Quadratic Programming (QP) estimation algorithms using large-scale parallel Monte Carlo simulations to overcome these drawba

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA