Environmental and human determinates of vegetation distribution in the Hadhramaut region, Republic of Yemen

The principal objectives of the research are to analyse the distribution and dynamics of plants of the Hadhramaut region and to evaluate the role of the physical parameters and human action on their distribution, survival and conservation. The study area is located in Hadhramaut Governorate and lies in the eastern part of the Republic of Yemen. This is a remote and inaccessible region; however, there has been rapid development in recent years with the discovery of oil, which has had a significant effect on the vegetation and landscape. The Hadhramaut region represents an important area of eastern Yemen, linking eastern and western phyto-geographical units, representing a key transition zone between northeast Africa and Southeast Asia. Previous studies in the study area have only dealt with individual species and there has been no complete botanical survey. Recent floristic studies are turning up new species with many endemic and near endemic plant species. The Hadhramaut region is a desert region, dissected by deep valleys where agriculture is possible and the main towns are surrounded by rocky, dry limestone plateaus. The northern section passes into the deserts of the Rub ‘al Khali or Empty Quarter. Hadhramaut has a long history of human occupation with ancient civilisations well reflected in the archaeological records. Archaeological sites suggest that agriculture, with a related development of irrigation technology, was more widespread during a period when rainfall was more abundant. Initially, a reconnaissance survey of the whole Hadhramaut Governorate was undertaken, leading to the selection the Wadi Hadhramaut for detailed study. Within this study area, three sites were selected for intensive survey. These sites were considered representative of the major landforms and vegetation of the area and reflect the principal patterns of land use. The three sites represent tracts of land that were either unaffected, undergoing change or already altered as a result of oil-related development. Transects were designed to cross each site, from the valley bottoms to the plateau surfaces, passing across the representative landforms and vegetation. Surveys were made of the vegetation associations, their structure and biodiversity, as well as their relationship with environment and human impact. Two preliminary transects were made across the entire region, from the southern coast to the plateau in the northwest and from east to west, in order to place the study area in a regional context. The research is the first detailed vegetation survey in the Hadhramaut region and has revealed relevant data that can be used for further studies in similar habitats or for further management and conservation activities. In the study area, major vegetation associations, their composition and biodiversity were identified and in addition, vegetation and land use maps were generated including local endemic, near-endemic and rare plant species. About 469 plant species have been identified from the Hadhramaut region. There are 107 taxa which are endemic and near-endemic; 68 of these are endemic to Yemen, of these 41 are confined to Hadhramaut region. A total of 134 species belonging to 42 families (about 30% of flora of Hadhramaut region) were recorded in the study area and, of these, seven species are endemic to Yemen (four of them endemic to Hadhramaut region). The study revealed 15 vegetation associations and thirty sociological species groups. The main wadis are covered by desert alluvial shrubland comprising Fagonia indica, Tephrosia apollinea, Cymbopogon schoenanthus, Boerhavia elegans and Dichanthium insculptum with scattered trees of Acacia campoptila. In contrast, much of the fertile lands of the main wadis, such as the bottom of the rocky slopes, are intensively cultivated with palm trees and other annual crops, notably sorghum and wheat. The rocky slopes facing the main wadis and the plateau surface are covered by stony and gravelly desert vegetation dominated by herbaceous plants, namely Stipagrostis hirtigluma, Farsetia linearis, Aristida triticoides, Fagonia paulayana, Boerhavia elegans and Dichanthium insculptum. Within the plateau there are some sloping sites and secondary wadis which support dense vegetation. The vegetation here comprises shrubland or grassland dominated by Jatropha spinosa with Zygophyllum decumbens, Commiphora foliacea, Commiphora kua, Maerua crassifolia. Dichanthium insculptum, Stipagrostis hirtigluma and Farsetia linearis. The research in the Hadhramaut region has revealed the importance of this region in terms of plant biodiversity, and particularly of endemic, rare and near-endemic species, which urgently require further management and conservation activities

Detection of Covid-19 and chest pneumonia based on X-ray images using Deep-Transfer Learning

لقي العديد من الأشخاص حتفهم نتيجة تفشي فيروس كورونا في عام 2019 (كوفيد-19)، والذي أثر أيضًا على ملايين آخرين في جميع أنحاء العالم. تنتشر العدوى بسرعة. ولذلك، فإن التكنولوجيا التي تتيح الكشف السريع عن الفيروسات ستوفر لمتخصصي الرعاية الصحية المساعدة التي هم في أمس الحاجة إليها. تهدف هذه الدراسة إلى التعرف على مرض كوفيد-19 من صور الأشعة السينية للأشخاص الأصحاء والمصابين بالالتهاب الرئوي باستخدام نموذج VGG16 المعدل. حقق النموذج المقترح نتائج أفضل من الدراسات السابقة المقدمة بدقة 99.13% واستدعاء 99% ودقة 98.70%.Numerous people have died as a result of the coronavirus outbreak in 2019 (COVID-19), which also affected millions of others worldwide. The infection spreads quickly. Therefore, technology that enables quick virus detection will offer healthcare professionals much-needed assistance. This study aims to identify COVID-19 disease from X-ray images of healthy and infected people with pneumonia by using a modified VGG16 model. The proposed model achieved better results than previous studies presented with an accuracy of 99.13%, a recall of 99%, and a precision of 98.70%

Speech to text translation for Malay Language

The speech recognition system is a front end and a back-end process that receives an audio signal uttered by a speaker and converts it into a text transcription. The speech system can be used in several fields including: therapeutic technology, education, social robotics and computer entertainments. In most cases in control tasks, which is the purpose of proposing our system, wherein the speed of performance and response concern as the system should integrate with other controlling platforms such as in voiced controlled robots. Therefore, the need for flexible platforms, that can be easily edited to jibe with functionality of the surroundings, came to the scene; unlike other software programs that require recording audios and multiple training for every entry such as MATLAB and Phoenix. In this paper, a speech recognition system for Malay language is implemented using Microsoft Visual Studio C#. 90 (ninety) Malay phrases were tested by 10 (ten) speakers from both genders in different contexts. The result shows that the overall accuracy (calculated from Confusion Matrix) is satisfactory as it is 92.69%

DETECTION OF PNEUMONIA BY USING NINE PRE-TRAINED TRANSFER LEARNING MODELS BASED ON DEEP LEARNING TECHNIQUES

Pneumonia is a serious chest disease that affects the lungs. This disease has become an important issue that must be taken care of in the field of medicine due to its rapid and intense spread, especially among people who are addicted to smoking. This paper presents an efficient prediction system for detecting pneumonia using nine pre-trained transfer learning models based on deep learning technique (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, and SqueezeNet). The dataset in this study consisted of 5856 chest x-rays, which were divided into 5216 for training and 624 for the test. In the training phase, the images were pre-processed by resizing the input images to the same dimensions to reduce complexity and computation. The images are then forwarded to the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121, DenseNet-169, AlexNet, SqueezeNet) to extract features and classify the images as normal or pneumonia. The results of the proposed models (Inception v4, SeNet-154, Xception, PolyNet, ResNet-50, DenseNet-121 DenseNet-169, AlexNet and SqueezeNet) give accuracies (98.72%, 98.94%, 98.88%, 98.72%, 96.2%, 94.69%, 96.29%, 95.01% and 96.10%) respectively. We found that the SeNet-154 model gave the best result with an accuracy of 98.94% with a validation loss (0.018103). When comparing our results with older studies, it should be noted that the proposed method is superior to other methods

Economic potential of renewable energy sources use in Yemen

The article represents the evaluation of opportunities of economical potential use of renewed energy sources (solar, wind, hydro resources, tidal and thermal energy of the seas, biomass, high and low potential energy of geothermal) based on climatic data of Yemen. Technical and economic data on various device are generalized. Lacks and problems are indicated, the basic strategy priorities of power branch development of the country are defined. Favorable climatic conditions allow to speak about high technical and economic potential of renewed energy sources, and also about the high social importance of their use development

ANALYSIS OF EXISTING SOFTWARE PACKAGES IN THE CLUSTER SYSTEMS

The existing software packages in the cluster systems are analyzed. The characteristics of clusters and cluster distributives are given, topological variants are considered, distinctive characteristics of the main software packages are determined

SOFTWARE PACKAGES FOR PARALLEL COMPUTING

Grid development has approached the phase of realization of projects aimed at deployment of the large-scale operating  infrastructures. Reality of practical implementation has not only brought in a whole new set of tasks, but also demands reevaluation of the methods that were previously developed and successfully tested in pilot experiments. One of the main component of such infrastructure is the software discussed in this article

ELABORATION OF TASK CONTROL ALGORITHMS WITH PARALLEL COMPUTING IN CLUSTER COMPUTING SYSTEMS

The optimum problem of the allocation of tasks for executing in the cluster is considered. Task control algorithms are elaborated. The algorithms validation is done in the performance of the transportation problem on the local network cluster. The data obtained permit to judge efficiency of the suggested algorithms