United Arab Emirates University
  • Al Ain, Abu Dhabi , United Arab Emirates
Recent publications
Half-Heusler (HH) alloys are a well-known and extensively researched family of thermoelectric (TE), magnetic, and spintronic materials. Doping may significantly increase the thermoelectric conversion efficiency of these materials; nevertheless, practical applications remain far. As a result, the hunt for superior parent TE alloys is critical. Using extensive first-principles density functional calculations, we predicted a novel class of vanadium-based four HH VXTe alloys, where X is one of the four elements: Cr, Mn, Fe, and Co. Their TE properties, as well as their mechanical, magnetic, electrical, and structural stability, have been studied in depth. Their mechanical and thermodynamic stability is confirmed using the predicted elastic constants, formation and cohesive energies, and phonon spectra. A comprehensive analysis of elastic constants and moduli demonstrates that HH VXTe alloys possess elastic anisotropy with reasonably good machinability, higher melting and Debye temperatures, mixed bonding characteristics with ionic and covalent contributions, and brittle nature, except for VCoTe, which is ductile. We find that the ground state of HH VCrTe and VFeTe is ferrimagnetic, while HH VCoTe is ferromagnetic and HH VMnTe is non-magnetic. Three VXTe (X = Cr, Fe, & Co) alloys demonstrated half-metallicity with 100% spin-polarization, whereas HH VMnTe is an 18-electron indirect semiconductor. In the studied HH VXTe alloys, most of the heat flow is caused by the phonon-group velocity of the acoustic phonons. Further studies on the relationship between carrier concentration and temperature dependence of TE properties reveal that the high ZT ∼1.2 at 1000 K of the pristine HH VMnTe alloy is obtained due to its high-power factor of 249.4×1012Wm−1K−2 and this value is greater than the values of some known pristine and doped HH TE materials. Our findings pave the way for further investigation into the HH VXTe alloys in the quest for improved TE and spintronic materials for use in domains that necessitate high thermoelectricity and spintronic performance.
This study aims to examine the impact of audit committee (AC) characteristics on corporate social responsibility (CSR) disclosure in the corporate annual reports of Kuwait listed firms during COVID-19. To achieve this goal, an indicator was built to measure the level of disclosure in the annual reports of a sample of 124 firms listed on the Kuwait Stock Exchange (KSE) during the period from 2017 to 2022. The study developed and tested four main hypotheses to reveal the relationship between CSR disclosure and the characteristics of the AC before, during and after the COVID-19 pandemic, namely, size, frequency of meetings, independence, and financial expertise. Using multiple regression analysis model on the data collected manually from the annual reports of those firms. The results of the study indicate that the firms listed on the KSE provide low disclosure (e.g., mean CSR index is 39%), which indicates that firms have less incentive to disclose CSR practices during the COVID-19 pandemic. The results of the statistical analysis also showed that AC characteristics such as size, frequency of meetings, and financial expertise have a significant positive influence on the level of CSR disclosure. However, there is no evidence that AC characteristics such as members’ independence affect CSR disclosure in Kuwait firms listed. The main contribution of this study is the first to be conducted on the impact of AC characteristics on CSR disclosure before and after the COVID-19 pandemic by providing analytical evidence on listed Kuwait firms. Thus, it provides insights for policymakers interested in shareholders, regulators, financial analysts, investment analysts, and managers on the rules of governance and in assessing CSR disclosure in annual reporting practices, and in strengthening the role of oversight and oversight for the accountability and transparency of ACs.
The outbreak situation of the COVID-2019 pandemic is an Unpredictable shock to the world economy. World Economy faces the slowdown of share market prices, especially the value of mutual fund value decreases. Companies and Businessmen primarily invested in the mutual funds to play a safer role, modify their risk into the return, and increase the Net Assets Value (NAV). This study attempts to describe the state of mutual funds in India during this COVID 2019 period. Thus the performance of mutual funds when compared with before and during COVID 2019, the proposed model specifies on testing the performance of mutual funds both in the public and private sectors and attains to access the impact of COVID 2019 on mutual funds. The author has used correlation for finding out the relation of COVID 2019 and Mutual Funds. This paper mainly addresses the causes of investors during economic fluctuation and the return of top mutual companies by comparing the return of 1 year and during these last three months. COVID 2019 is not only on particular sectors; it affects almost every sector like construction, manufacturing, business, agriculture. While all the sectors are affected by COVID 2019 pandemics, it hits the society and the economy; once the economy comes down, the inflation rate increase, the Forex rate will increase, and it affects our whole country. In this paper, the author included sectors that are affected and their performance now and how well the different types of funds are performing, which will be helpful for the reader to analyze the affected areas. The paper concluded with the help of a survey and statistical tools whether the investors can make a further payment and hold for some period or continue with the investment whatever situation crisis impacts our economy.
In the modern world, the stock market plays a crucial role in the country’s economic development and is amongst the most versatile sectors in the country’s financial system. It provides a platform for investors to trade shares, bonds, and debentures and allows the companies to raise the much-required funds to boost their business. In this way, the stock market plays a crucial role in enhancing the country’s industry and commerce growth. The reason for selecting this topic is that the Indian Share Market has grown unprecedented after the introduction of Dematerialization by the government of India in the mid – 90’s. Hence, to study how the Dematerialization and electronification of shares have shaped the tremendous growth and potential of the Indian Stock Market became an interesting study to explore its various possibilities. The journey traces the growth of the Indian Stock Market from its humble beginning with just a few traders to the globally competitive and colossal stock market of the present day. Currently, the Indian Stock Market boasts of having 5 70 000 traders and a total market capitalization worth 2.27 trillion dollars, leading to becoming the world’s 11th largest stock exchange. This paper explores the multiple benefits that they share trading industry gained as a result of Dematerialization.
Sentiment analysis is a popular technique for analyzing a person's behavior. Electroencephalography (EEG) is a non-invasive device for collecting brainwaves, which can be useful for identifying different emotions. The brain-computer interface (BCI) is a communication pathway between the brain's signals and an external device and can also be used to identify human emotions. Numerous studies have been conducted to distinguish human feelings using EEG signals. Deep learning (DL) algorithms are capable of identifying features from raw data. In this study, we use long short-term memory (LSTM) and a multilayer perceptron artificial neural network (MLP-ANN) to improve EEG data classification. We selected 640 datasets collected via a Muse EEG-powered headband with a global EEG position standard. We used five different combinations of activation functions with two best loss model operations and an Adam optimizer in both the LSTM and MLP-ANN algorithms, which helps in achieving better performance. We applied datasets containing different statistical features (mean median, standard deviation, etc.) from Kaggle's “EEG Brainwave Dataset: Feeling Emotions” database for the DL classifier model. We analyzed accuracy, execution time, and confusion matrix parameters and results show that both DL models achieved maximum accuracy for binary cross-entropy loss model, whereas the logcosh loss model of the MLP-ANN achieved the least accuracy.
Food waste is currently a critical global issue, even more so because waste has continued to increase despite various reduction initiatives. Deeper research insights into why people waste food are needed to formulate effective strategies to counter such behavior. This study investigated the drivers of food delivery app (FDA) users’ attitudes against food waste and their behavioral intentions to reduce it. Specifically, the study employed the theory of interpersonal behavior (TIB) to explicate the role of emotions in driving attitude and intentions. Data collected from active FDA users (N = 561) were analyzed to test the proposed associations. The other two key variables in the TIB framework – habit, operationalized in this study as ordering more food than necessary to take advantage of special offers, and facilitating conditions, captured through belief that food ordered via FDAs often goes to waste – are conceptualized and examined as moderation variables. The results confirmed the positive association of attitude and negative emotions with intentions, as well as the positive association of negative emotions and attitude. However, the negative association of positive emotions was confirmed with attitude only. In addition, attitude mediated the negative emotions-intentions association. Finally, the analysis revealed that the variable belief that food ordered via FDA often goes to waste moderated the association of positive and negative emotions with intentions. The findings of the study offer significant inferences.
Predicting the infiltration rate (IR) of treated wastewater (TWW) is essential in controlling clogging problems. Most researchers that predict the IR using neural network models considered the characteristics parameters of soil without considering those of TWW. Therefore, this study aims to develop a model for predicting the IR based on various combinations of TWW characteristics parameters (i.e. total suspended solids (TSS), biological oxygen demand (BOD), electric conductivity (EC), pH, total nitrogen (TN), total phosphorous (TP), and hydraulic loading rate (HLR)) as input parameters. Therefore, two different artificial neural network (ANN) architectures, multilayer perceptron model (MLP) and Elman neural network (ENN), were used to develop optimal model. The optimal model was selected through evaluating three stages: selecting the best division of data, selecting the best model, and deciding the best combination of input parameters based on several performance criteria. The study concluded that the first combination of inputs that include all the seven-parameter using MLP model associated with 90% division of data was the optimal model in predicting the IR depending on TWW characteristics parameters, achieving a promising result of 0.97 for the coefficient of determination, 0.97 for test regression, 0.012 for MSE with 32.4 of max relative percentage error. Abbreviations: IR: Infiltration Rate; TWW: Treated Wastewater; TSS: Total Suspended Solids; BOD: Biological Oxygen Demand; EC: Electric Conductivity; HC: Hydraulic Conductivity; TN: Total Nitrogen; TP: Total Phosphorous; HLR: Hydraulic Loading Rate; ANN: Artificial Neural Network; MLP: Multilayer Perceptron Model; ENN: Elman Neural Network; FFANN: Feedforward Artificial Neural Networks; R: Regression Values; SAR: Sodium Adsorption Ratio; DOC: Dissolved Organic Carbon; ANAMMOX: Anaerobic Ammonium Oxidation; CEC: Cation Exchange Capacity; BPNN: Back Propagation Neural Network; GRNN: General Regression Neural Networks; ELM: Extreme Learning Machine Neural Networks; TDNN: Time Delay Neural Network; TLRN: Time Lag Recurrent Network; NGWTP: North Gaza Wastewater Treatment Plant; MASL: Meters Above Sea Level; DNC: Dynamic Node Creation; PWA: Palestinian Water Authority; RBF: Radial Basis Function; ANFIS: Adaptive Neuro Fuzzy Inference System; BD: Bulk Density; RMSE: Root Mean Square Error; MAE: Mean Absolute Error; MSE: Mean Square Error; R ²: Determination Coefficient; LLR: Local Linear Regression; DLLR: Dynamic Linear Regression; MNN: Modular Neural Networks; RNN: Recurrent Neural Network; NARX: Nonlinear Autoregressive with Exogenous input network; WNN: Wavelet Neural Networks. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Although the UAE does not possess a long history and experience in general education, the country has recently embarked on educational reforms that emphasize the provision of quality education for all, and at the same time ensure that exceptional individuals are provided with opportunities to attain their potentials as far as they can. The main purpose of this study was to review the current situation of gifted education in the United Arab Emirates (UAE) from a learning resource perspective. In order to review the existing situation of gifted education in the UAE, the researchers utilized the procedures of document analysis and interviews. The analysis of documents and interviews revealed that the country has already made significant progress in some areas of gifted education, but there is still more work to be accomplished to attain the national goals. The results highlighted that the values of “student equity” and “student excellence” are two key features of the UAE educational system. The analysis of documents revealed that the current policy emphasizes that all students should be provided with equal opportunities and at the same time make sure that students with special needs are offered the right services and support and the needs of gifted and talented students are accounted for. It was also stated in different documents that other associations, ministries, and centers are offering some kinds of services for students with special talents. The study ended with some recommendations to aid continuous progress and direct future research. © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
The Education and Learning Capital Model (ELCM), based on the Actiotope Model of Giftedness was used as a framework to explain the current practices dedicated to serve gifted education. This article addresses the status quo of gifted education in Oman according to the ELDM. The model comprised ten components including action learning capital, economic education capital, cultural education capital, social educational capital, infrastructure education capital, organismic learning capital, telic learning capital, episodical learning capital, and attentional learning capital. Questions related to these ten elements were answered by analyzing reports from the Ministry of Education (MoE) and royal decrees that serve the education of gifted students. Also, interviews were conducted with gifted students, teachers, and administrators, and parents of gifted students to consolidate the answers to the ten questions based on the ELCM. The article concluded with some recommendations and suggestions of supporting gifted education in Oman.
This study aimed to determine whether the Hamad Medical Corporation Ambulance Service (HMCAS) personnel fulfil the pre-hospital readiness requirements for hazardous material and chemical, biological, radiological, and nuclear (HazMat-CBRN) incidents. This cross-sectional study performed an online assessment of non-specialist paramedics’ behaviour and knowledge about HazMat-CBRN incident management, followed by a ‘HazMat-CBRN incident management’ course with pre-and post-activity assessments. The validity and reliability of the knowledge assessment questions were also tested. The pre-and-post course assessement responses revealed certain deficiencies in staff knowledge. The multiple linear regression and paired groups t-test demonstrated that this was rectified after the training intervention. The results indicate that the implemented course helped HMCAS staff acquire a satisfactory level of knowledge to ensure their readiness for safe and effective responses to potential HazMat-CBRN incidents in Qatar. KEYWORDS: AmbulanceHazMat-CBRNexposureknowledgetraining
Modeling wind speed has a significant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine learning approaches – Gaussian process regression (GPR), bagged regression trees (BTs) and support vector regression (SVR) – were applied for prediction of the weekly wind speed (maximum, mean, minimum) of the target station using other stations, which were specified as reference stations. Daily wind speed data, gathered via the Malaysian Meteorological Department at 14 measuring stations in Malaysia covering the period between 2000 and 2019, were used. The results showed that the average weekly wind speed had superior performance to the maximum and minimum wind speed prediction. In general, the GPR model could effectively predict the weekly wind speed of the target station using the measured data of other stations. Errors found in this model were within acceptable limits. The findings of this model were compared with the measured data, and only Kota Kinabalu station showed an unacceptable range of prediction. To investigate the prediction performance of the proposed model, two models were used as the comparison models: the BTs model and SVR model. Although the comparison of GPR with the BTs model at Kuching station showed slightly better performance for the BTs model in maximum and minimum wind speed prediction, the prediction outcomes of the other 13 stations showed better performance for the proposed GPR model. Moreover, the proposed model generated smaller prediction errors than the SVR model at all stations.
Martian dust plays a crucial role in the meteorology and climate of the Martian atmosphere. It heats the atmosphere, enhances the atmospheric general circulation, and affects spacecraft instruments and operations. Compliant with that, studying dust is also essential for future human exploration. In this work, we present a method for the deep-learning-based detection of the areal extent of dust storms in Mars satellite imagery. We use a mask regional convolutional neural network, consisting of a regional-proposal network and a mask network. We apply the detection method to Mars daily global maps of the Mars global surveyor, Mars orbiter camera. We use center coordinates of dust storms from the eight-year Mars dust activity database as ground-truth to train and validate the method. The performance of the regional network is evaluated by the average precision score with $$50\%$$ 50 % overlap ( $$mAP_{50}$$ m A P 50 ), which is around $$62.1\%$$ 62.1 % .
Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.
With the widespread adoption of social networking sites among college students, discerning the relationship between social networking sites use and college students’ academic performance has become a major research endeavor. However, much of the available research in this area rely on student self-reports and findings are notably inconsistent. Further, available studies typically cast the relationship between social networking sites use and college students’ academic performance in linear terms, ignoring the potential moderating role of the intensity of social networking sites use. In this study, we draw on contrasting arguments in the literature predicting positive and negative effects of social networking sites use on college students’ academic performance to propose an inverted U-shaped relationship. We collected data on social networking sites use by having college students install a tracking app on their smartphones for 1 week and data on academic performance from internal college records. Our findings indicate that social networking sites use indeed exhibits an inverted U-shaped relationship with college students’ academic performance. Specifically, we find that spending up to 88.87 min daily on social networking sites is positively associated with academic performance, but beyond that, social networking sites use is negatively associated with academic performance. We discuss the implications of our findings.
Background Chest radiographs are frequently used to evaluate pediatric patients with COVID-19 infection during the current pandemic. Despite the minimal radiation dose associated with chest radiography, children are far more sensitive to ionizing radiation's carcinogenic effects than adults. This study aimed to examine whether serum biochemical markers could be potentially used as a surrogate for imaging findings to reduce radiation exposure. Methods The retrospective posthoc analysis of 187 pediatric patients who underwent initial chest radiographs and serum biochemical parameters on the first day of emergency department admission. The cohort was separated into two groups according to whether or not the initial chest radiograph revealed evidence of pneumonia. Spearman's rank correlation was used to connect serum biochemical markers with observations on chest radiographs. The Student's t-test was employed for normally distributed data, and for non-normally distributed data, the Mann–Whitney U test was used. A simple binary logistic regression was used to determine the importance of LDH in predicting chest radiographs. The discriminating ability of LDH in predicting chest radiographs was determined using receiver operating characteristics (ROC) analysis. The cut-off value was determined using Youden's test. Interobserver agreement was quantified using the Cohen k coefficient. Results 187 chest radiographs from 187 individual pediatric patients (95 boys and 92 girls; mean age ± SD, 10.1 ± 6.0 years; range, nine months–18 years) were evaluated. The first group has 103 patients who did not have pneumonia on chest radiographs, while the second group contains 84 patients who had evidence of pneumonia on chest radiographs. GGO, GGO with consolidation, consolidation, and peri-bronchial thickening were deemed radiographic evidence of pneumonia in group 2 patients. Individuals in group 2 with radiological indications of pneumonia had significantly higher LDH levels ( p = 0.001) than patients in group 1. The Spearman's rank correlation coefficient between LDH and chest radiography score is 0.425, showing a significant link. With a p -value of < 0.001, the simple binary logistic regression analysis result validated the relevance of LDH in predicting chest radiography. An abnormal chest radiograph was related to LDH > 200.50 U/L (AUC = 0.75), according to the ROC method. Interobserver agreement between the two reviewers was almost perfect for chest radiography results in both groups ( k = 0.96, p = 0.001). Conclusion This study results show that, compared to other biochemical indicators, LDH has an 80.6% sensitivity and a 62% specificity for predicting abnormal chest radiographs in a pediatric patient with confirmed COVID-19 infection. It also emphasizes that biochemical measures, rather than chest radiological imaging, can detect the pathogenic response to COVID-19 infection in the chest earlier. As a result, we hypothesized LDH levels might be potentially used instead of chest radiography in children with COVID-19, reducing radiation exposure.
This paper examines the dynamics of a time-delay differential model of the tumour immune system with random noise. The model describes the interactions between healthy tissue cells, tumour cells, and activated immune system cells. We discuss stability and Hopf bifurcation of the deterministic system. We then explore stochastic stability, and the dynamics of the system in view of environmental fluctuations. Criteria for persistence and sustainability are discussed. Using multiple Lyapunov functions, some sufficient criteria for tumour cell persistence and extinction are obtained. Under certain circumstances, stochastic noise can suppress tumour cell growth completely. In contrast to the deterministic model which shows no stable tumour-free state, the white noise can either lead to tumour dormancy or tumour elimination. Some numerical simulations, by using Milstein’s scheme, are carried out to show the effectiveness of the obtained results.
Background The overuse of short-acting β 2 -agonists (SABA) is associated with poor asthma control. However, data on SABA use in the Gulf region are limited. Herein, we describe SABA prescription practices and clinical outcomes in patients with asthma from the Gulf cohort of the SABA use IN Asthma (SABINA) III study. Methods In this cross-sectional study conducted at 16 sites across Kuwait, Oman, and the United Arab Emirates, eligible patients (aged ≥ 12 years) with asthma were classified based on investigator-defined disease severity guided by the 2017 Global Initiative for Asthma report and by practice type, i.e., respiratory specialist or primary care physician. Data on demographics, disease characteristics, and prescribed asthma treatments, including SABA, in the 12 months prior to a single, prospective, study visit were transcribed onto electronic case report forms (eCRFs). All analyses were descriptive in nature. Continuous variables were summarized by the number of non-missing values, given as mean (standard deviation [SD]) and median (range). Categorical variables were summarized by frequency counts and percentages. Results This study analyzed data from 301 patients with asthma, 54.5% of whom were treated by respiratory specialists. Most patients were female (61.8%), with a mean age of 43.9 years, and 84.4% were classified with moderate-to-severe disease, with a mean (SD) asthma duration of 14.8 (10.8) years. Asthma was partly controlled or uncontrolled in 51.2% of patients, with 41.9% experiencing ≥ 1 severe exacerbation in the 12 months preceding their study visit. Overall, 58.5% of patients were prescribed ≥ 3 SABA canisters, 19.3% were prescribed ≥ 10 canisters, and 13.3% purchased SABA over-the-counter (OTC) in the 12 months before the study visit. Most patients who purchased OTC SABA (92.5%) also received SABA prescriptions. Inhaled corticosteroid/long-acting β 2 -agonist combinations and oral corticosteroid bursts were prescribed to 87.7% and 22.6% of patients, respectively. Conclusions SABA over-prescription was highly prevalent in the Gulf region, compounded by purchases of nonprescription SABA and suboptimal asthma-related outcomes. Increased awareness among policymakers and healthcare practitioners is needed to ensure implementation of current, evidence-based, treatment recommendations to optimize asthma management in this region. Trial registration NCT03857178 (ClinicalTrials.gov).
A network of early psychosis-specific intervention programs at the University of Montreal in Montreal, Quebec, Canada, conducted a longitudinal naturalistic five-year study at two Urban Early Intervention Services (EIS). In this study, 198 patients were recruited based on inclusion/exclusion criteria and agreed to participate. Our objectives were to assess the subjective cognition complaints of schizophrenic patients assessed by Subjective Scale to Investigate Cognition in Schizophrenia (SSTICS) in their first-episode psychosis (FEP) in relation to their general characteristics. We also wanted to assess whether there are sex-based differences in the subjective cognitive complaints, as well as differences in cognitive complaints among patients who use alcohol in comparison to those who are abstainers. Additionally, we wanted to monitor the changes in the subjective complaints progress for a period of five years follow-up. Our findings showed that although women expressed more cognitive complaints than men [mean (SD) SSTICS, 28.2 (13.7) for women and 24.7 (13.2) for men], this difference was not statistically significant (r = −0.190, 95 % CI, −0. 435 to 0. 097). We also found that abstainers complained more about their cognition than alcohol consumers [mean (SD) SSTICS, 27.9 (13.4) for abstainers and 23.7 (12.9) for consumers], a difference which was statistically significant (r = −0.166, 95 % CI, −0. 307 to −0.014). Our findings showed a drop in the average score of SSTICS through study follow-up time among FEP patients. In conclusion, we suggest that if we want to set up a good cognitive remediation program, it is useful to start with the patients' demands. This demand can follow the patients' complaints. Further investigations are needed in order to propose different approaches between alcohol users and abstinent patients concerning responding to their cognitive complaints.
This study explored whether children's second language (L2) vocabulary, syntactic awareness, and reading comprehension contributed to the growth of each other. A total of 184 Chinese primary school children (91 girls) aged 8–10 years old in Hong Kong participated in the pre-test of this study. Among them, 88 were in Grade 3 and 96 were in Grade 4. One year later, 178 of these children also participated in the post-test. These children learned English as an L2 at school. They completed a series of English language tasks. The results from a cross-lagged panel model show that vocabulary predicted the growth of syntactic awareness and reading comprehension. Syntactic awareness predicted the growth of vocabulary and reading comprehension. Reading comprehension facilitated the growth of vocabulary, and it also predicted the growth of syntactic awareness in Grade 4 students but not in Grade 3 students. Implications for teaching children an L2 are discussed.
Background Availability of stage information by population-based cancer registries (PBCR) remains scarce for diverse reasons. Nevertheless, stage is critical cancer control information particularly for cancers amenable to early detection. In the framework of the Global Initiative for Cancer Registry Development (GICR), we present the status of stage data collection and dissemination among registries in the Middle East and Northern Africa (MENA) region as well as the stage distribution of breast cancer patients. Methods A web-based survey exploring staging practices and breast cancer stage was developed and sent to 30 PBCR in 18 countries of the MENA region. Results Among 23 respondent PBCR, 21 collected stage data, the majority (80%) for all cancers. Fourteen registries used a single classification (9 TNM and 5 SEER), 7 used both staging systems in parallel. Out of 12,888 breast cancer patients (seven registries) 27.7% had unknown TNM stage (11.1% in Oman, 46% in Annaba). When considering only cases with known stage, 65.3% were early cancers (TNM I+II), ranging from 57.9% in Oman to 83.3% in Batna (Algeria), and 9.9% were stage IV cancers. Among the nine registries providing SEER Summary stage for breast cancer cases, stage was unknown in 19% of the cases, (0 in Bahrain, 39% in Kuwait). Stage data were largely absent from the published registry reports. Conclusion Despite wide stage data collection by cancer registries, missing information and low dissemination clearly limit informing efforts on early detection. The use of two classification systems in parallel implies additional workload and might undermine completeness. The favourable results of early cancer (TNM I+II) in two thirds of breast cancer patients needs to be interpreted with caution and followed up in time. Although efforts to improve quality of stage data are needed, our findings are particularly relevant to the WHO Global Breast Cancer Initiative.
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4,736 members
Khaled Galal Ahmed
  • Department of Architectural Engineering
Kilani Ghoudi
  • Department of Statistics
Omran Bakoush
  • Department of Internal Medicine
Synan AbuQamar
  • Department of Biology
15551, Al Ain, Abu Dhabi , United Arab Emirates