The Reality of the Employees Performance in the Palestinian Cellular Telecommunications Company (Jawwal)

The aim of this study was to identify the reality of the performance of the employees in The Palestinian Cellular Telecommunications Company (Jawwal), and to find the differences between the views of the study sample on the variables of the study according to the variables (age, scientific qualification, field of work and years of service). To achieve the objectives of the study, a questionnaire was designed and developed to measure the variables of the study applied to the company's 70 employees. The Complete Census method was used and 60 samples were recovered for analysis with a recovery rate (85.7%). The SPSS statistical package was adopted. The study reached several results, the most important of which is that the degree of approval for the job performance of the employees working in The Palestinian Cellular Telecommunications Company (Jawwal) is 81.56%. The results showed that there were no statistically significant differences at the level of α≤ 0.05 between the average of the respondents' opinions on the performance of the workers in the Palestinian Cellular Telecommunications Company (Jawwal) due to the following variables (age, scientific qualification, field of work, number of years of service). The most important recommendations were to increase the efficiency of the employees of the company using the equipment of their work, and the need to pay attention to the development of the skills of employees through specialized training programs to improve their performance. And focus on moral incentives because of their role in improving the performance of employees by spreading the spirit of cooperation between

Antiepileptic drugs and bone metabolism

Anti-epileptic medications encompass a wide range of drugs including anticonvulsants, benzodiazepines, enzyme inducers or inhibitors, with a variety effects, including induction of cytochrome P450 and other enzyme, which may lead to catabolism of vitamin D and hypocalcemia and other effects that may significantly effect the risk for low bone mass and fractures. With the current estimates of 50 million people worldwide with epilepsy together with the rapid increase in utilization of these medications for other indications, bone disease associated with the use of anti-epileptic medications is emerging as a serious health threat for millions of people. Nevertheless, it usually goes unrecognized and untreated. In this review we discuss the pathophysiologic mechanisms of bone disease associated with anti-epileptic use, including effect of anti-epileptic agents on bone turnover and fracture risk, highlighting various strategies for prevention of bone loss and associated fractures a rapidly increasing vulnerable population

Deep Convolutional Neural Networks for Accurate Diagnosis of COVID-19 Patients Using Chest X-Ray Image Databases from Italy, Canada, and the USA

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), famously known as COVID-19, has quickly become a global pandemic. Chest X-ray (CXR) imaging has proven reliable, fast, and cost-effective for identifying COVID-19 infections, which proceeds to display atypical unilateral patchy infiltration in the lungs like typical pneumonia. We employed the deep convolutional neural network (DCNN) ResNet-34 to detect and classify CXR images from patients with COVID-19 and Viral Pneumonia and Normal Controls. Methods: We created a single database containing 781 source CXR images from four different international sub-databases: the Società Italiana di Radiologia Medica e Interventistica (SIRM), the GitHub Database, the Radiology Society of North America (RSNA), and the Kaggle Chest X-ray Database for COVID-19 (n = 240), Viral Pneumonia (n = 274), and Normal Controls (n = 267). Images were resized, normalized, without any augmentation, and arranged in m batches of 16 images before supervised training, testing, and cross-validation of the DCNN classifier. Results: The ResNet-34 had a diagnostic accuracy as of the receiver operating characteristic (ROC) curves of the true-positive rate versus the false-positive rate with the area under the curve (AUC) of 1.00, 0.99, and 0.99, for COVID-19 and Viral Pneumonia patient and Normal control CXR images; respectively. This accuracy implied identical high sensitivity and specificity values of 100, 99, and 99% for the three groups, respectively. ResNet-34 achieved a success rate of 100%, 99.6%, and 98.9% for classifying CXR images of the three groups, with an overall accuracy of 99.5% for the testing subset for diagnosis/prognosis. Conclusions: Based on this high classification precision, we believe the output activation map of the final layer of the ResNet-34 is a powerful tool for the accurate diagnosis of COVID-19 infection from CXR images

FORMULATION AND OPTIMIZATION OF ITRACONAZOLE PRONIOSOMES USING BOX BEHNKEN DESIGN

Objective: The aim of the present study was to obtain an optimized formula of itraconazole (ITC) proniosomes using Box Behnken design.Methods: Itraconazole proniosomes were prepared using span 60 and/or brij 35 as surfactants, cholesterol and lecithin as a penetration enhancer by slurry method. Various trials have been carried out for investigation of proniosomes. Parameters such as entrapment efficiency (EE%), in vitro drug release, zeta potential, vesicle size and Transmission Electron Microscope were assessed for evaluation of proniosomes.Results: Entrapment efficiency (EE%) was found to be between 78.56% and 95.46%. The release profile of itraconazole proniosomes occurred in two distinct phases, an initial phase for about 8 h, followed by a slow phase for 16 h. The release pattern shown by these formulations was Higuchi diffusion controlled mechanism. The zeta potential values for all itraconazole proniosomes were in the range of-21.71 to-34.53 mV which confirms their stability. All itraconazoleproniosomes formula was found to be nano-sized and were appeared to be spherical in shape with sharp boundaries. One way analysis of variance (ANOVA) study showed that HLB (X1) had the main effects on most responses (Y).Conclusion: Box behnken design facilitates optimization of the formulation ingredients on entrapment efficiency, in vitro release of itraconazole proniosomes, zeta potential and vesicle size. Finally, an optimum level of factors was provided by the optimization process

Monitoring Changes and Soil Characterization in Mangrove Forests of the United Arab Emirates Using the Canonical Correlation Forest Model by Multitemporal of Landsat Data

Mangrove forests are an important indicator of blue carbon storage and biodiversity and provide several benefits to the environment. This study showed the first attempt to apply the canonical correlation forest (CCF) model to classify mangroves and monitor changes in the mangrove forests of the entire region. The CCF model obtained a satisfactory accuracy with an F1 score of more than 0.90. Compared to Sentinel-2, Landsat 8 exhibited good temporal resolution with relatively little mangrove details. The resultant mangrove maps (1990–2020) were used to monitor changes in mangrove forests by applying a threshold value ranging from +1 to −1. The results showed a significant increase in the UAE mangroves over the period from 1990 to 2020. To characterize soil in mangrove forests, a set of interpolated maps for calcium carbonate, salinity concentration, nitrogen, and organic matter content was constructed. The results showed that there is a positive relationship between mangrove distribution and the calcium carbonate, nitrogen, salinity, and organic matter concentrations in the soil of the mangrove forests. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization

Classification of Apple Diseases Using Deep Learning

Abstract: In this study, we explore the challenge of identifying and preventing diseases in apple trees, which is a popular activity but can be difficult due to the susceptibility of these trees to various diseases. To address this challenge, we propose the use of Convolutional Neural Networks, which have proven effective in automatically detecting plant diseases. To validate our approach, we use images of apple leaves, including Apple Rot Leaves, Leaf Blotch, Healthy Leaves, and Scab Leaves collected from Kaggle which is part from the Plant Village dataset. We generate a comprehensive training dataset using techniques such as image filtering, compression, and generation. Our model achieves impressive accuracy scores for all classes, with an overall accuracy of 99.93% on a dataset of 10,000 labeled images

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

© Copyright © 2020 Elmahdy, Ali, Mohamed, Howari, Abouleish and Simonet. Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates. The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, and recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990–2000, 2000–2010, 2010–2019, and 1990–2019) was used to image difference algorithm to monitor mangrove extent by applying a threshold ranges from +1 to −1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization

Effects of High-Mobility Group A Protein Application on Canine Adipose-Derived Mesenchymal Stem Cells In Vitro

Multipotency and self-renewal are considered as most important features of stem cells to persist throughout life in tissues. In this context, the role of HMGA proteins to influence proliferation of adipose-derived mesenchymal stem cell (ASCs) while maintaining their multipotent and self-renewal capacities has not yet been investigated. Therefore, extracellular HMGA1 and HMGA2 application alone (10–200 ng/mL) and in combination with each other (100, 200 ng/mL each) was investigated with regard to proliferative effects on canine ASCs (cASCs) after 48 hours of cultivation. Furthermore, mRNA expression of multipotency marker genes in unstimulated and HMGA2-stimulated cASCs (50, 100 ng/mL) was analyzed by RT-qPCR. HMGA1 significantly reduced cASCs proliferation in concentrations of 10–200 ng/mL culture medium. A combination of HMGA1 and HMGA2 protein (100 and 200 ng/mL each) caused the same effects, whereas no significant effect on cASCs proliferation was shown after HMGA2 protein application alone. RT-qPCR results showed that expression levels of marker genes including KLF4, SOX2, OCT4, HMGA2, and cMYC mRNAs were on the same level in both HMGA2-protein-stimulated and -unstimulated cASCs. Extracellular HMGA protein application might be valuable to control proliferation of cASCs in context with their employment in regenerative approaches without affecting their self-renewal and multipotency abilities

Paraoxonase 2 protein is spatially expressed in the human placenta and selectively reduced in labour

Humans parturition involves interaction of hormonal, neurological, mechanical stretch and inflammatory pathways and the placenta plays a crucial role. The paraoxonases (PONs 1–3) protect against oxidative damage and lipid peroxidation, modulation of endoplasmic reticulum stress and regulation of apoptosis. Nothing is known about the role of PON2 in the placenta and labour. Since PON2 plays a role in oxidative stress and inflammation, both features of labour, we hypothesised that placental PON2 expression would alter during labour. PON2 was examined in placentas obtained from women who delivered by cesarean section and were not in labour and compared to the equivalent zone of placentas obtained from women who delivered vaginally following an uncomplicated labour. Samples were obtained from 12 sites within each placenta: 4 equally spaced apart pieces were sampled from the inner, middle and outer placental regions. PON2 expression was investigated by Western blotting and real time PCR. Two PON2 forms, one at 62 kDa and one at 43 kDa were found in all samples. No difference in protein expression of either isoform was found between the three sites in either the labour or non-labour group. At the middle site there was a highly significant decrease in PON2 expression in the labour group when compared to the non-labour group for both the 62 kDa form (p = 0.02) and the 43 kDa form (p = 0.006). No spatial differences were found within placentas at the mRNA level in either labour or non-labour. There was, paradoxically, an increase in PON2 mRNA in the labour group at the middle site only. This is the first report to describe changes in PON2 in the placenta in labour. The physiological and pathological significance of these remains to be elucidated but since PON2 is anti-inflammatory further studies are warranted to understand its role

A Knowledge Based System for Cucumber Diseases Diagnosis

The cucumber is a creeping vine that roots in the ground and grows up trellises or other supporting frames, wrapping around supports with thin, spiraling tendrils. The plant may also root in a soilless medium, whereby it will sprawl along the ground in lieu of a supporting structure. The vine has large leaves that form a canopy over the fruits. Among these common diseases, we single out the diseases that affect the cucumber, which is affected by about 22 diseases, with different symptoms for each disease. Today, technology is facilitating human life in all areas of life, and among these facilities are expert systems that have become an integral part of human life as they contain several systems and areas, for example: Artificial Intelligence (AI), which refers to systems or devices that simulate Human intelligence to perform tasks that can improve itself based on some human information, and other areas, and with reference to expert systems and their importance to humans, an integrated expert system has been created in the agricultural field that diagnoses cucumber diseases using CLIPS Expert System language Delphi language. The system was used to design and implement the proposed expert system. The system facilitates the diagnosis of cucumber-related diseases. There is no doubt that this expert system will help farmers and those involved in the agricultural field to diagnose cucumber-related diseases. Objectives: is to help farmers diagnose pear diseases in the correct way and how to treat these diseases. Method: The system contains a program that diagnoses 22 diseases that affect cucumber. Results: The expert system was evaluated by farmers and praised for helping them with it. Conclusion: The expert system for diagnosing cucumber diseases is effective and usable