3D Reservoir Study for Yamama Formation in Nasirya Oil field in Southern of Iraq

Nasriya oil field is located at the Southern part of Iraq, this field is a giant and prolific, so it take a special are from the Oil Exploration Company for development purposes by using 3D seismic reflection. The primary objective of this thesis is to obtain reservoir properties and enhance the method of getting precise information about subsurface reservoir characterizations by improving the estimation of petrophysical properties (effective porosity, P-wave, water saturation and poisson's ratio). There are five wells in the study area penetrated the required reservoirs within Yammam Formation. The Synthetic seismogram of Nasriya wells were created to conduct well tie with seismic data. These well tie was very good matching with seismic section using best average statistical wavelet. Five main horizons were picked from the reflectors by using synthetic seismogram for wells then converted to structural maps in depth domain by using average velocity of five wells. By using petrel program TWT maps have been constructed from the picked horizons, Average velocity maps calculated from the wells velocities survey data and the sonic log information and Depth maps construction was drawn using Direct time-depth conversion and the general trend of these map was NW-SE. The model of low frequency was created from the low frequency contents from well data and the five main horizons were picked. The seismic inversion technique was performed on post-stack three dimensions (3D) seismic data in Nasriya oil field

Comparison Study of Axial Behavior of RPC-CFRP Short Columns

In this paper, the axial behaviors of reactive powder     concrete (RPC) short  columns confined with carbon fiber reinforced polymer (CFRP) were   investigated. All the specimens have square cross section of 100 mm × 100   mm and length of 400 mm with aspect ratio 4. The experimental work consists   of three groups. The first group consists of six specimens of RPC with 2% micro steel fiber, without ordinary reinforcing steel and confining by zero, one and two layer of CFRP respectively. The second group consists of six    specimens of RPC with 2% micro steel fiber and minimum ordinary reinforcing  steel and confining by zero, one and two layers of CFRP respectively. The third  group consists of four specimens of RPC without micro steel fiber and ordinary  reinforcing steel and confining by one and two layers of CFRP respectively.  Experimental data for strength, longitudinal and lateral displacement and  failure mode were obtained for each test. The toughness (area under the curve) for each test was obtained by using numerical integration. The RPC columns confined with CFRP showed stiffer behavior compared with RPC columns without CFRP. The ultimate load of the RPC columns with 2% micro steel  fiber + two layers of CFRP + minimum ordinary reinforcement were more than that of the RPC columns with 2% micro steel fiber + minimum ordinary   reinforcement and without CFRP by about 1.333. 

An unusual presentation of hemoglobin SD Punjab in a Saudi Arabian adult

HbDPunjab is an uncommon variant hemoglobin that does not result in significant pathology when inherited as a homozygous disorder. When inherited with other hemoglobinopathies, it may result in varying disease phenotypes. HbSDPunjab has been rarely reported in Saudi Arabia, coexisting with alpha or beta thalassemia. In this report, we discuss the case of a 39 years old male who presented with severe anemia and renal injury and was later diagnosed with HbSDPunjab through electropheresis and genetic testing

Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)

Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained

¹H-NMR metabolic profiling, antioxidant activity, and docking study of common medicinal plant-derived honey

The purpose of this investigation was to determine ¹H-NMR profiling and antioxidant activity of the most common types of honey, namely, citrus honey (HC1) (Morcott tangerine L. and Jaffa orange L.), marjoram honey (HM1) (Origanum majorana L.), and clover honey (HT1) (Trifolium alexandrinum L.), compared to their secondary metabolites (HC2, HM2, HT2, respectively). By using a ¹H-NMR-based metabolomic technique, PCA, and PLS-DA multivariate analysis, we found that HC2, HM2, HC1, and HM1 were clustered together. However, HT1 and HT2 were quite far from these and each other. This indicated that HC1, HM1, HC2, and HM2 have similar chemical compositions, while HT1 and HT2 were unique in their chemical profiles. Antioxidation potentials were determined colorimetrically for scavenging activities against DPPH, ABTS, ORAC, 5-LOX, and metal chelating activity in all honey extract samples and their secondary metabolites. Our results revealed that HC2 and HM2 possessed more antioxidant activities than HT2 in vitro. HC2 demonstrated the highest antioxidant effect in all assays, followed by HM2 (DPPH assay: IC50 2.91, 10.7 μg/mL; ABTS assay: 431.2, 210.24 at 50 ug/mL Trolox equivalent; ORAC assay: 259.5, 234.8 at 50 ug/mL Trolox equivalent; 5-LOX screening assay/IC50: 2.293, 6.136 ug/mL; and metal chelating activity at 50 ug/mL: 73.34526%, 63.75881% inhibition). We suggest that the presence of some secondary metabolites in HC and HM, such as hesperetin, linalool, and caffeic acid, increased the antioxidant activity in citrus and marjoram compared to clover honey

Visible Light Photocatalytic Activity of Ag/WO3 Nanoparticles and its Antibacterial Activity Under Ambient Light and in The Dark

Nanomaterial such as metals and metal oxide photocatalysts have emerged as important tools for removing contaminants from wastewater and as antibacterial agents to prevent infections; this is mainly due to their stability under different irradiation conditions. Herein, the catalytic and antimicrobial activities of nanocrystalline silver (Ag), supported on tungsten oxide (WO3) nanoparticles prepared using the deposition-precipitation synthesis technique, are studied. The synthesized material was characterized as XRD, XPS, TEM, and TEM-EDS to investigate their physio-chemical properties. HRTEM, XPS analysis shows that the photocatalyst has a large sheet-like morphology with well-dispersed small metallic Ag particles (<3 nm) on the WO3 nanoparticle's surface, with most particles near the edges. Ultraviolet–visible spectra analysis observed a large redshift in the absorbing band edge and decreased bandgap energy from 2.6 to 2.1 eV. Photocatalytic analysis at different concentrations of 1% Ag/WO3 under visible light indicated a high degradation efficiency. The largest degradation efficiency of Methylene Blue (MB) under visible light irradiation was (∼80%) in 120 min at 1 g/L catalyst dosage. The photodegradation of MB under visible light as a function of catalyst dose followed the pseudo-first-order kinetics. In addition, the catalyst shows high degradation efficiency and significant dose-dependent inhibition of Gram-negative E. Coli and the Gram-positive S. aureus. Furthermore, the catalyst showed excellent stability and recyclability

FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

BackgroundForests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset.ResultsThe test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with the given data.ConclusionThe integration of the robust feature extraction of FireXnet with the transparency of explainable AI using SHAP enhances the model’s interpretability and allows for the identification of key characteristics triggering wildfire detections. Extensive experimentation reveals that in addition to being accurate, FireXnet has reduced computational complexity due to considerably fewer training and non-training parameters and has significantly fewer training and testing times

A Proposed Vision of the Transformation of the Arab Universities into Smart Digital Universities

This research develops a proposed vision to transform Arab universities into smart digital universities. The descriptive research approach is used to achieve the research objectives. The research sample consists of 450 faculty members and 75 educational experts randomly selected by stratified random method. The questionnaire is adopted as a research instrument. The findings indicate that a proposed vision can be developed to transform Arab universities into smart digital universities by addressing several themes; the philosophical premises of the proposed vision, the features of the proposed vision “smart university administration, smart people, smart university environment, and knowledge network”, determining the requirements necessary to implement the proposed vision, setting the appropriate foundations for the proposed implementation and success in Arab universities, and demonstrating the potential challenges and threats that may stand in the way of implementing the proposed vision and methods to overcome them

Demographics and Epidemiology of Hepatitis B in the State of Qatar: A Five-Year Surveillance-Based Incidence Study

Background: Expatriates represent >80% of Qatar’s population, mostly arriving from countries in Africa and Asia that are endemic with many diseases. This increases the risk for introducing new pathogens into the country and provides a platform for maintenance of endemic pathogen circulation. Here, we report on the incidence and epidemiological characteristics of hepatitis B in Qatar between 2010 and 2014. Methods: We performed a retrospective epidemiological data analysis using the data available at the surveillance system of the Ministry of Public Health (MOPH) in Qatar. Data were collected from distinctive public and private incorporates around the nation. Reported cases of hepatitis B patients represent those who met the stringent case definition as per World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) guidelines and eventually reported to MOPH. Results: The annual incidence rates of hepatitis B cases were 30.0, 34.2, 30.5, 39.4, and 19.8 per 100,000 population in 2010, 2011, 2012, 2013, and 2014, respectively. There was no specific trend or seasonality for the reported cases. The incidence rates were higher in females compared to males between 2010 and 2012, but similar in 2013 and 2014. The highest incidence rates were reported among individuals between 25 and 34 years of age. No cases were reported in children younger than five years in 2013 and 2014. Rates of hepatitis B cases declined dramatically in 2014, in both Qataris and non-Qataris, as compared to the previous years. Conclusion: Our results indicate a dramatic decline of hepatitis B cases in Qatar but mandate improved surveillance and vaccination efforts in expatriates in the nation. View Full-TextMOP