This study was carried out from February to October 2012 in six semi salty ponds in Gwer sub-district which is the first work in the area. A total of 32 species and 2 genera of algae where reported as the new records. Mostly the non diatoms are belonging to Cyanophyta, Chlorophyta, Euglenophyta, Cryptophyta, Chrysophyceae, while diatoms or Bacilariophyceae are belong to pennals- order.

In this paper, the solar surface magnetic flux transport has been simulated by solving the diffusion–advection equation utilizing numerical explicit and implicit methods in 2Dsurface. The simulation was used to study the effect of bipolar tilted angle on the solar flux distribution with time. The results show that the tilted angle controls the magnetic distribution location on the sun’s surface, especially if we know that the sun’s surface velocity distribution is a dependent location. Therefore, the tilted angle parameter has distribution influence.

Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contras

Experimental results for the density of states of hydrogenated amorphous silicon due to Jackson et al near the valence and conduction band edges were analyzed using Levenberg-Marquardt nonlinear fitting method. It is found that the density of states of the valence band and the conduction band can be fitted to a simple power law, with a power index 0.60 near the valence band edge, and 0.55 near the conduction band edge. These results indicate a modest but noticeable deviation from the square root law (power index=0.5) which is found in crystalline semiconductors. Analysis of Jackson et al density of states integral J(E) data over about (1.4 eV) of photon energy range, showed a significant fit to a simple power law with a power index of 2.11

RNA Sequencing (RNA-Seq) is the sequencing and analysis of transcriptomes. The main purpose of RNA-Seq analysis is to find out the presence and quantity of RNA in an experimental sample under a specific condition. Essentially, RNA raw sequence data was massive. It can be as big as hundreds of Gigabytes (GB). This massive data always makes the processing time become longer and take several days. A multicore processor can speed up a program by separating the tasks and running the tasks’ errands concurrently. Hence, a multicore processor will be a suitable choice to overcome this problem. Therefore, this study aims to use an Intel multicore processor to improve the RNA-Seq speed and analyze RNA-Seq analysis's performance with a multiproce

Cloud storage provides scalable and low cost resources featuring economies of scale based on cross-user architecture. As the amount of data outsourced grows explosively, data deduplication, a technique that eliminates data redundancy, becomes essential. The most important cloud service is data storage. In order to protect the privacy of data owner, data are stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for data storage. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. This paper proposes a combined compressive sensing and video deduplication to maximize

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Cardiovascular disease is one of the most common comorbidities associated with enlarged extremities, occurring in 60 % of patients with acromegaly. The aim of this study is to evaluate the relationship of growth hormone and insulin such as growth factor-1 with obesity, dyslipidemia, hyperglycemia, and pro-inflammatory cytokines (IL-2, IL-6, IL-10), as risk factors for cardiovascular disorder in acromegaly patients. Eighty subjects were included and categorized into two groups: 40 acromegaly patients and 40 of the control group. The results indicated weight excess, hyperglycemia, hypertension, lipid disorder, and elevated levels of interleukins (2, 6, and 10). The correlation of both GH and IGF-1 with each of weight, BMI, systolic blood p

Background Depression is one of the most common medical illnesses worldwide. Medical students during their training will be subjected to many stressors, both mentally and emotionally. Objectives Assess the prevalence of depressive symptoms among medical students and to identify any association between depressive symptoms and some socio-demographic factors.

Objectives: To highlight the knowledge of Al-Kindy medical students regarding varicella infection and to assess their knowledge and attitude toward its prevention. Methodology: This descriptive cross-sectional study enrolled academic year 2020/2021 students at Al-Kindy College of Medicine from first to final year. We used an online questionnaire by Google forms to collect data, which were analyzed with SPSS 26. Results: A total of 208 students participated with 126 in the preclinical and 82 in the clinical grades. Generally, most clinical students were more aware about epidemiology, clinical manifestations, and complications of varicella infection than the preclinical students. Conclusion: Educational stage is a significant variab