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There are many petrochemical industries that need adequate knowledge of multiphase flow phenomena inside pipes. In such industries, measuring the void fraction is considered to be a very challenging task. Thus, various techniques have been used for void fraction measurements. For determining more accurate multiphase flow measurements, this study employed dual non-intrusive techniques, gamma-ray and electrical capacitance sensors. The techniques using such sensors are considered non-intrusive as they do not cause any perturbation of the local structure of the phases’ flow. The first aim of this paper is to analyze both techniques separately for the void fraction data obtained from practical experiments. The second aim is to use both techniques’ data in a neural network model to analyze measurements more efficiently. Accordingly, a new system is configured to combine the two techniques’ data to obtain more precise results than they can individually. The simulations and analyzing procedures were performed using MATLAB. The model shows that using gamma-ray and capacitance-based sensors gives Mean Absolute Errors (MAE) of 3.8% and 2.6%, respectively, while using both techniques gives a lower MAE that is nearly 1%. Consequently, measurements using two techniques have the ability to enhance the multiphase flows’ observation with more accurate features. Such a hybrid measurement system is proposed to be a forward step toward an adaptive observation system within related applications of multiphase flows.

In this paper, the feasibility of using an X-ray tube instead of radioisotope sources for measuring volume fractions of gas, oil, and water in two typical flow regimes of three-phase flows, namely, annular and stratified, is evaluated. This study’s proposed detection system is composed of an X-ray tube, a 1 inch × 1 inch NaI detector, and one Pyrex-glass pipe to model different volume fractions for two flow regimes, annular and stratified. Group method of data handling (GMDH), a powerful regression tool, was also implemented to analyze the obtained data. The obtained results in this work indicate that a simple system based on an X-ray tube and just one NaI detector could be a potential alternative to radioisotope-based systems for separate measurements of gas, oil, and water volume fractions in annular and stratified flow regimes of a three-phase flow.

Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an X-ray tube rather than a radioisotope source as an X-ray generator: This choice brings some advantages that will be discussed. The study is performed through a Monte Carlo simulation and artificial intelligence. Here, the system is composed of an X-ray tube, a pipe including fluid, and a NaI detector. Two-by-two mixtures of four various oil products with different volume ratios were considered to model the pipe’s interface region. For each combination, the X-ray spectrum was recorded in the detector in all the simulations. The recorded spectra were used for training and testing the multilayer perceptron (MLP) models. After training, MLP neural networks could estimate each oil product’s volume ratio with a mean absolute error of 2.72 which is slightly even better than what was obtained in former studies using radioisotope sources.

Two phase flows are of particular importance in various research fields. In the current article, a novel system consists of an X-ray tube and one sodium iodide crystal detector with ability of determining type of flow regime as well as void fraction percentage of a two phase flow, is proposed. MCNP-X code was used for physical modelling of the proposed system and its performance. Radial basis function (RBF) was also implemented for analyzing and classifying the obtained data from the proposed system. Counts in each 1 keV energy bin of photon energy spectra in the detector were inserted in RBF as inputs data set and flow regime and void fraction percentage were obtained as the two outputs. After training the RBF network, the system could simultaneously recognize all the flow regimes and predict the void fraction percentage of a modelled liquid–gas two-phase flow with an acceptable error. The proposed methodology in the present paper has three main novelties and advantages over former studies. Firstly, in this system an X-ray tube is used compared to previous studies where one or more radioisotope sources served as radiation source in a radiation based multi-phase flow meter. Secondly, in former works at least two detectors were used to recognize type of flow pattern and meter volume fractions simultaneously, while in this study only one detector is utilized. Thirdly, in this study just one neural network is used, while in other studies more than one network was used.

Patients with cancer are purported to have poor COVID-19 outcomes. However, cancer is a heterogeneous group of diseases, encompassing a spectrum of tumour subtypes. The aim of this study was to investigate COVID-19 risk according to tumour subtype and patient demographics in patients with cancer in the UK. We compared adult patients with cancer enrolled in the UK Coronavirus Cancer Monitoring Project (UKCCMP) cohort between March 18 and May 8, 2020, with a parallel non-COVID-19 UK cancer control population from the UK Office for National Statistics (2017 data). The primary outcome of the study was the effect of primary tumour subtype, age, and sex and on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevalence and the case–fatality rate during hospital admission. We analysed the effect of tumour subtype and patient demographics (age and sex) on prevalence and mortality from COVID-19 using univariable and multivariable models. 319 (30·6%) of 1044 patients in the UKCCMP cohort died, 295 (92·5%) of whom had a cause of death recorded as due to COVID-19. The all-cause case–fatality rate in patients with cancer after SARS-CoV-2 infection was significantly associated with increasing age, rising from 0·10 in patients aged 40–49 years to 0·48 in those aged 80 years and older. Patients with haematological malignancies (leukaemia, lymphoma, and myeloma) had a more severe COVID-19 trajectory compared with patients with solid organ tumours (odds ratio [OR] 1·57, 95% CI 1·15–2·15; p<0·0043). Compared with the rest of the UKCCMP cohort, patients with leukaemia showed a significantly increased case–fatality rate (2·25, 1·13–4·57; p=0·023). After correction for age and sex, patients with haematological malignancies who had recent chemotherapy had an increased risk of death during COVID-19-associated hospital admission (OR 2·09, 95% CI 1·09–4·08; p=0·028). Patients with cancer with different tumour types have differing susceptibility to SARS-CoV-2 infection and COVID-19 phenotypes. We generated individualised risk tables for patients with cancer, considering age, sex, and tumour subtype. Our results could be useful to assist physicians in informed risk–benefit discussions to explain COVID-19 risk and enable an evidenced-based approach to national social isolation policies. University of Birmingham and University of Oxford.

Category: Ankle Arthritis; Ankle Introduction/Purpose: Total Ankle Replacement (TAR) has rapidly grown in popularity over the last fifteen years as a treatment for ankle arthritis. With this growth, it is imperative to understand risk factors that may predispose patients to complications following the procedure. This information can be valuable in generating treatment plans and facilitating conversation regarding risks and benefits of those plans. Current literature on TAR-related outcomes is limited in timeframe and scope and does not investigate the association of diabetes with common post operative complications. The objective of this study was to perform a retrospective database analysis to quantify the risk of 90-day complications in patients with diabetes undergoing TAR when compared to patients without diabetes undergoing TAR. Methods: The TriNetX database was utilized for this study. Patients who underwent TAR were identified and stratified by those with a history of diabetes mellitus and those without a history of diabetes mellitus of any type. The cohorts were 1:1 propensity matched based on demographic information and medical history. The 90-day complication rates for readmission, inpatient service use, opioid use, anesthesia use, periprosthetic fracture, deep vein thrombosis, and wound dehiscence were calculated using chi-square analysis. Results: n=5,984 TAR patients were identified. 52% were male and 48% female with mean age of 73±10 years. There was a n=1,331 patients in each the patients with diabetes and without diabetes groups after cohort analysis and propensity matching. TAR patients with diabetes had a significantly higher 90-day odds ratio of inpatient service use (OR: 1.675, 95% CI 1.3082-2.144; p< 0.001). There was no significant difference between groups in opioid use (OR: 1.13, 95% CI 0.941-1.357; p=0.1908), anesthesia use (OR: 1.335, 95% CI 0.906-1.965; p=0.1424), and wound dehiscence (OR: 1.313, 95% CI 0.829-2.08; p=0.2445); Table 1. There was insufficient data to determine the role of diabetes on critical care services, periprosthetic fracture, and DVT within 90 days of TAR. Conclusion: TAR is a developing operation for ankle arthritis and little data exists on diabetes related perioperative complications. The results of this study show that although diabetes is significantly associated with a higher rate of inpatient service use, it may not have as large a role in opioid use, anesthesia use, and wound dehiscence in the immediate postoperative period. More information is still needed to address long term outcomes and the role diabetes plays in other postoperative complications.

This study aimed to identify Sarcocystis species isolated from macroscopic sarcocysts of naturally infected sheep and goats using histopathological and molecular studies. A total of 260 macrosarcocyst samples were randomly collected from 1,337 infected sheep and goats slaughtered at different abattoirs in Duhok province, Iraq, from May 2021 to June 2022. The macroscopic cysts, which were found in the esophagus, diaphragm, and abdominal muscles, were classified into fat and thin cysts. Histopathological examination of the observed fat and thin cysts showed a thick eosinophilic wall, several internal septa-forming compartments enclosing numerous bradyzoites, and mild mononuclear inflammatory cells infiltrating around the cysts. The 18 Subunit ribosomal RNA (18S rRNA) and 28 Subunit ribosomal RNA (28S rRNA) genes of Sarcocystis spp. were amplified by polymerase chain reaction (PCR) from 200 macrosarcocysts samples. Molecularly, the DNA sequencing results obtained from fat macrocysts of sheep and goats were found to be identical to Sarcocystis gigantea, and from thin cysts of sheep proved to be similar to Sarcocystis medusiformis, while from thin macrocysts of goats were found to be identical to Sarcocystis moulei. Alignment and phylogenetic analysis observed a very close relationship between identified species of Sarcocystis and other Sarcocystis DNA sequences of sheep and goats across the world. To our knowledge, this is the first histopathological and molecular study for identification of Sarcocystis spp. isolated from different macroscopic forms of sarcocysts of sheep and goats in Iraq.

Background IOM piloted the use of Ag RDTs for COVID-19 in Iraq, in collaboration with FIND, the Global Alliance for Diagnostics, to facilitate access to testing and understand barriers and opportunities for testing in a displacement context. The purpose was to (i) evaluate the performance metrics of the Sure Status COVID-19 Antigen Card Test in this population; (ii) compare Ag RDT results across high- and low-probability cases, vaccination status, and symptom severity; and (iii) report participant perspectives on Ag RDT use for COVID-19 and other diseases. Methods Secondary analysis was conducted using de-identified cross-sectional data collected from November 2022-March 2023 in four IDP camps in the Kurdistan region of Iraq. Data was collected during the recording, reporting, and monitoring processes of the pilot project using Ag RDTs for COVID-19 among displaced populations. Descriptive statistics and bivariate analyses were conducted in Stata (version 17). Results 9,346 Sure Status Ag RDTs were performed from November 2022-March 2023. Observed performance metrics were as follows: sensitivity = 72.73% (95% CI: 69.56–75.80%); specificity = 99.74% (95% CI: 99.39–100%), PPV = 92.31% (95% CI: 92.31–94.15%), and NPV = 98.85% (95% CI: 98.11–99.58%). Ag RDT uptake was higher among women and symptomatic individuals, and preference for Ag RDTs was especially strong among illiterate respondents. The overall positivity of Ag RDTs and PCR results in this population remained lower than national averages. Concerningly, 23.39% of respondents declined an Ag RDT because they did not believe in COVID-19. Conclusions This pilot program was among the first to explore Ag RDT use in Iraqi Kurdistan. Over 99% of respondents reported satisfaction with their experience, and over 90% wanted to see Ag RDTs available for other diseases. Findings can inform implementation of RDTs and screening protocols for other infectious diseases, and patient perspectives on Ag RDTs for testing and screening of COVID-19 among displaced populations can inform health programming within Iraq and globally.