Economic evaluation of smart well technology

The demand of oil and gas resources is high and the forecasts show a trend for higher requirements in the future. More unconventional resource exploitation along with an increase in the total recovery in current producing fields is required. At this pivotal time the role of emerging technologies is of at most importance. Smart or intelligent well technology is one of the up and coming technologies that have been developed to assist improvements in field development outcome. In this paper a comprehensive review of this technology has been discussed. The possible reservoir environments in which smart well technology could be used and also, the possible benefits that could be realized by utilizing smart well technology has been discussed. The economic impact of smart well technology has been studied thoroughly. Five field cases were used to evaluate the economics of smart well technology in various production environments. Real field data along with best estimate of smart well technology pricings were used in this research. I have used different comparisons between smart well cases and conventional completion to illustrate the economic differences between the different completion scenarios. Based on the research, I have realized that all the smart well cases showed a better economic return than conventional completions. The offshore cases showed a good economic environment for smart well technology. Large onshore developments with smart well technology can also provide a lucrative economic return. These situations can increase the overall economic return and ultimate recovery which will assist in meeting some of the oil demand around the globe

Steganography using dual tree complex wavelet transform with LSB indicator technique

Image steganography is undoubtedly significant in the branch of multimedia communication security. The undetectability and large payload capacity are two of the important characteristics of any form of steganography.  In this paper, the level of image security is improved by combining the steganography and cryptography techniques in order to produce the secured image. The proposed method depends on using LSBs as an indicator for hiding encrypted bits in dual tree complex wavelet coefficient DT-CWT. The cover image is split into non-overlapping blocks of size (3*3). After that, a Key is produced by extracting the center pixel (pc) from each block to encrypt each character in the secret text. The cover image is converted using DT-CWT, then the produced key is used to determine the starting pixel in each block for hiding and the direction of hiding (clockwise or anticlockwise).   The proposed method is applied on many images with different embedding rate, and many metrics are used to evaluate the performance of the proposed method, namely: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), correlation factor (CF) and Structural Similarity Index Measure (SSIM). It achieves in average 52.225 dB of PSNR, 0.3215 of MSE, 0.9952 of SSIM and 0.9997 of CF with embedding rate 1.5

Middle East respiratory syndrome coronavirus in dromedary camels: An outbreak investigation

Background: Middle East respiratory syndrome coronavirus (MERS-CoV) causes severe lower respiratory tract infection in people. Previous studies suggested dromedary camels were a reservoir for this virus. We tested for the presence of MERS-CoV in dromedary camels from a farm in Qatar linked to two human cases of the infection in October, 2013. Methods: We took nose swabs, rectal swabs, and blood samples from all camels on the Qatari farm. We tested swabs with RT-PCR, with amplification targeting the E gene (upE), nucleocapsid (N) gene, and open reading frame (ORF) 1a. PCR positive samples were tested by different MERS-CoV specific PCRs and obtained sequences were used for phylogentic analysis together with sequences from the linked human cases and other human cases. We tested serum samples from the camels for IgG immunofluorescence assay, protein microarray, and virus neutralisation assay. Findings: We obtained samples from 14 camels on Oct 17, 2013. We detected MERS-CoV in nose swabs from three camels by three independent RT-PCRs and sequencing. The nucleotide sequence of an ORF1a fragment (940 nucleotides) and a 4·2 kb concatenated fragment were very similar to the MERS-CoV from two human cases on the same farm and a MERS-CoV isolate from Hafr-Al-Batin. Eight additional camel nose swabs were positive on one or more RT-PCRs, but could not be confirmed by sequencing. All camels had MERS-CoV spike-binding antibodies that correlated well with the presence of neutralising antibodies to MERS-CoV. Interpretation: Our study provides virological confirmation of MERS-CoV in camels and suggests a recent outbreak affecting both human beings and camels. We cannot conclude whether the people on the farm were infected by the camels or vice versa, or if a third source was responsible. Funding: European Union projects EMPERIE (contract number 223498), ANTIGONE (contract number 278976), and the VIRGO consortium

Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning

Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribution, this process can be time-consuming because the server needs to capture all data distribution first from all clients to perform the correct clustering. Due to resource and time constraints at the network edge, only a fraction of devices {is} selected every round, necessitating the need for an efficient scheduling technique to address these issues. Thus, this paper introduces a two-phased client selection and scheduling approach to improve the convergence speed while capturing all data distributions. This approach ensures correct clustering and fairness between clients by leveraging bandwidth reuse for participants spent a longer time training their models and exploiting the heterogeneity in the devices to schedule the participants according to their delay. The server then performs the clustering depending on predetermined thresholds and stopping criteria. When a specified cluster approximates a stopping point, the server employs a greedy selection for that cluster by picking the devices with lower delay and better resources. The convergence analysis is provided, showing the relationship between the proposed scheduling approach and the convergence rate of the specialized models to obtain convergence bounds under non-i.i.d. data distribution. We carry out extensive simulations, and the results demonstrate that the proposed algorithms reduce training time and improve the convergence speed while equipping every user with a customized model tailored to its data distribution.Comment: To appear in IEEE Transactions on Network and Service Management, Special issue on Federated Learning for the Management of Networked System

Epidemiology of respiratory infections among adults in Qatar (2012-2017).

Limited data is available about the etiology of influenza like illnesses (ILIs) in Qatar. This study aimed at providing preliminary estimates of influenza and other respiratory infections circulating among adults in Qatar. We retrospectively collected data of about 44,000 patients who visited Hamad General Hospital clinics, sentinel sites, and all primary healthcare centers in Qatar between 2012 and 2017. All samples were tested for influenza viruses, whereas about 38,000 samples were tested for influenza and a panel of respiratory viruses using Fast Track Diagnostics (FTD) RT-PCR kit. Among all ILIs cases, 20,278 (46.5%) tested positive for at least one respiratory pathogen. Influenza virus was predominating (22.6%), followed by human rhinoviruses (HRVs) (9.5%), and human coronaviruses (HCoVs) (5%). A detection rate of 2-3% was recorded for mycoplasma pneumonia, adenoviruses, human parainfluenza viruses (HPIVs), respiratory syncytial virus (RSV), and human metapneumovirus (HMPV). ILIs cases were reported throughout the year, however, influenza, RSV, and HMPV exhibited strong seasonal peaks in the winter, while HRVs circulated more during fall and spring. Elderly (>50 years) had the lowest rates of influenza A (13.9%) and B (4.2%), while presenting the highest rates of RSV (3.4%) and HMPV (3.3%). While males had higher rates of HRVs (11.9%), enteroviruses (1.1%) and MERS CoV (0.2%), females had higher proportions of influenza (26.3%), HPIVs (3.2%) and RSV (3.6%) infections. This report provides a comprehensive insight about the epidemiology of ILIs among adults in the Qatar, as a representative of Gulf States. These results would help in improvement and optimization of diagnostic procedures, as well as control and prevention of the respiratory infections.This study was supported by funds from Hamad Medical Corporation (grant # 16335/16) and Qatar University (grant # QUCG-BRC-2018/2019-1)

Within-Host Diversity of SARS-CoV-2 in COVID-19 Patients With Variable Disease Severities.

The ongoing pandemic of SARS-COV-2 has already infected more than eight million people worldwide. The majority of COVID-19 patients either are asymptomatic or have mild symptoms. Yet, about 15% of the cases experience severe complications and require intensive care. Factors determining disease severity are not yet fully characterized. Here, we investigated the within-host virus diversity in COVID-19 patients with different clinical manifestations. We compared SARS-COV-2 genetic diversity in 19 mild and 27 severe cases. Viral RNA was extracted from nasopharyngeal samples and sequenced using the Illumina MiSeq platform. This was followed by deep-sequencing analyses of SARS-CoV-2 genomes at both consensus and sub-consensus sequence levels. Consensus sequences of all viruses were very similar, showing more than 99.8% sequence identity regardless of the disease severity. However, the sub-consensus analysis revealed significant differences in within-host diversity between mild and severe cases. Patients with severe symptoms exhibited a significantly (-value 0.001) higher number of variants in coding and non-coding regions compared to mild cases. Analysis also revealed higher prevalence of some variants among severe cases. Most importantly, severe cases exhibited significantly higher within-host diversity (mean = 13) compared to mild cases (mean = 6). Further, higher within-host diversity was observed in patients above the age of 60 compared to the younger age group. These observations provided evidence that within-host diversity might play a role in the development of severe disease outcomes in COVID-19 patients; however, further investigations are required to elucidate this association.This work was supported by Qatar University under internal grant (QUCG-BRC-20/21-1) and Qatar National Research Fund grant under grant (RRC-2-039)

SKG4J 2020: 1st International Workshop on Semantic and Knowledge Graph Advances for Journalism

SKG4J targeted contributions at the interface between Artificial Intelligence, Data Management and its implications for journalistic practice. The first version of the workshop accepted three submissions with topics emphasising the complementary requirements for delivering realistic journalistic knowledge extraction/management platforms

Evaluating the cost-effectiveness of COVID-19 mRNA primary-series vaccination in Qatar: an integrated epidemiological and economic analysis

Qatar implemented a mass primary-series vaccination campaign to mitigate the impact of the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to retrospectively evaluate the cost-effectiveness of this program both before and after onset of the omicron wave. An economic evaluation was conducted from the public healthcare system perspective between January 5, 2021, and September 18, 2023. Cost-effectiveness was determined using an epidemiological retrospective cohort study and health economic modeling that compared the cohort of individuals who received two vaccine doses with the unvaccinated cohort with respect to incidence of infection, incidence of severe COVID-19 forms, quality-adjusted life years (QALYs), and medical costs. During the pre-omicron phase, primary-series vaccination incurred an additional cost of $104,422,358, led to a gain of 724.7 QALYs, and savings of$54,790,858 in direct medical costs. The incremental cost-effectiveness ratio (ICER) was $68,485 per QALY gained. The number needed to vaccinate was 35.4 individuals (95$3,180 (95% CI: $2,189-$4,484) and per severe COVID-19 outcome averted was $64,468 (95$42,146-$88,354). Vaccination of individuals ≥50 years of age, those more clinically vulnerable to severe COVID-19, and those with multiple coexisting conditions was substantially more cost-effective. Cost-effectiveness of primary-series vaccination was substantially reduced during the omicron phase, but vaccination remained cost-effective. Sensitivity analyses confirmed the findings. Primary-series vaccination was cost-effective with an ICER below the 1 GDP per capita threshold during the pre-omicron phase and within the 1–3 GDP per capita thresholds during the omicron phase. Targeted vaccination strategies for those most vulnerable to COVID-19 were the most cost-effective and remained essential, even in situations of moderate vaccine effectiveness or reduced infection severity