Flow Regime Identification in a Bubble Column Via Nuclear Gauge Densitometry and Chaos Analysis

The Bubble Column Performance Can Change Significantly as a Result of Flow Regime Change. Since Reactor Volume Productivity, Mass and Heat Transfer as Well as Mixing Are Affected by the Prevailing Flow Regime, It is Very Important to Know How to Identify It. in This Work, Flow Regime Identification Was Performed on the Basis of the Kolmogorov Entropy (KE) Algorithm Applied to Nuclear Gauge Densitometry Data. in Addition, the Average Cycle Time Was Used for Validation of the Results. Three Transition Velocities Were Identified that Delineated the Boundaries of the Three Main Hydrodynamic Regimes. the First Two Transition Points Were Also Confirmed by the Information Entropy Concept. the Increasing KE Trend in the Bubbly Flow Regime and the Decreasing KE Trend in the Churn-Turbulent Regime Were Predicted Successfully by Means of New Semi-Theoretical Models. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Enhancing the quality of service for real time traffic over optical burst switching (OBS) networks with ensuring the fairness for other traffics

Optical burst switching (OBS) networks have been attracting much consideration as a promising approach to build the next generation optical Internet. A solution for enhancing the Quality of Service (QoS) for high priority real time traffic over OBS with the fairness among the traffic types is absent in current OBS ’ QoS schemes. In this paper we present a novel Real Time Quality of Service with Fairness Ratio (RT-QoSFR) scheme that can adapt the burst assembly parameters according to the traffic QoS needs in order to enhance the real time traffic QoS requirements and to ensure the fairness for other traffic. The results show that RT-QoSFR scheme is able to fulfill the real time traffic requirements (end to end delay, and loss rate) ensuring the fairness for other traffics under various conditions such as the type of real time traffic and traffic load. RT-QoSFR can guarantee that the delay of the real time traffic packets does not exceed the maximum packets transfer delay value. Fur- thermore, it can reduce the real time traffic packets loss, at the same time guarantee the fair- ness for non real time traffic packets by determining the ratio of real time traffic inside the burst to be 50 – 60%, 30 – 40%, and 10 – 20% for high, normal, and low traffic loads respectively

Assessment of Breast Cancer Awareness among Female University Students in Ajman, United Arab Emirates

Objectives: The aim of this study was to assess female university students’ knowledge of breast cancer and its preventative measures and to identify their main misconceptions regarding breast cancer. Methods: This cross-sectional study was conducted between April 2011 and June 2012 and included female students from three large universities in Ajman, United Arab Emirates (UAE). A stratified random sampling procedure was used. Data were collected through a validated, pilot-tested, self-administered questionnaire. The questionnaire included 35 questions testing knowledge of risk factors, warning signs and methods for the early detection of breast cancer. Participants’ opinions regarding breast cancer misconceptions were also sought. Results: The participants (n = 392) were most frequently between 18 and 22 years old (63.5%), non-Emirati (90.1%) and never married (89%). A family history of breast cancer was reported by 36 (9.2%) of the students. The percentage of participants who had low/below average knowledge scores regarding risk factors, warning signs and methods for early detection of breast cancer was 40.6%, 45.9% and 86.5%, respectively. Significantly higher knowledge scores on risk factors were noticed among participants with a family history of breast cancer (P = 0.03). The misconception most frequently identified was that “treatment for breast cancer affects a woman’s femininity” (62.5%). Conclusion: A profound lack of knowledge about breast cancer was noted among female university students in the three UAE universities studied. The most prominent gaps in knowledge identified were those concerning breast cancer screening methods

Spousal Concordance of Diabetes Mellitus among Women in Ajman, United Arab Emirates

Objectives: Spousal concordance is defined as similar behaviours and associated health statuses between spouses. This study aimed to identify the concordance of diabetes mellitus (DM) and related variables among genetically unrelated couples in Ajman, United Arab Emirates (UAE). Methods: This cross-sectional study included 270 married women attending either the Mushairef Health Center or the Gulf Medical College Hospital in Ajman between May and November 2012. A validated questionnaire was designed to determine sociodemographic characteristics and a history or family history of DM, hypertension, coronary artery disease or dyslipidaemia among the women and their husbands. The weight, height, body mass index, waist circumference, fasting blood sugar and glycated haemoglobin (HbA1c) levels of all women were measured. Results: Of the women, 39.3% of those with diabetic husbands and 39.9% of those with non-diabetic husbands were diabetic themselves (P >0.050). The prevalence of DM spousal concordance was 17.8%. A history of hypertension, coronary artery disease and dyslipidaemia was significantly more frequent among women whose husbands had a history of the same conditions (P = 0.001, 0.040 and 0.002, respectively). Spousal concordance of abnormal glycaemia among non-diabetic women with diabetic husbands was significant (P = 0.001). Having a diabetic husband (P = 0.006) and being obese (P = 0.009) were the only significant predictors of hyperglycaemia among non-diabetic women after controlling for confounding factors. Conclusion: There was significant concordance of abnormal glycaemia among non-diabetic women with diabetic husbands. The spouses of diabetic patients may therefore be a target population for regular hyperglycaemia and DM screening

Spousal Concordance of Diabetes Mellitus among Women in Ajman, United Arab Emirates

Objectives: Spousal concordance is defined as similar behaviours and associated health statuses between spouses. This study aimed to identify the concordance of diabetes mellitus (DM) and related variables among genetically unrelated couples in Ajman, United Arab Emirates (UAE). Methods: This cross-sectional study included 270 married women attending either the Mushairef Health Center or the Gulf Medical College Hospital in Ajman between May and November 2012. A validated questionnaire was designed to determine sociodemographic characteristics and a history or family history of DM, hypertension, coronary artery disease or dyslipidaemia among the women and their husbands. The weight, height, body mass index, waist circumference, fasting blood sugar and glycated haemoglobin (HbA1c) levels of all women were measured. Results: Of the women, 39.3% of those with diabetic husbands and 39.9% of those with non-diabetic husbands were diabetic themselves (P >0.050). The prevalence of DM spousal concordance was 17.8%. A history of hypertension, coronary artery disease and dyslipidaemia was significantly more frequent among women whose husbands had a history of the same conditions (P = 0.001, 0.040 and 0.002, respectively). Spousal concordance of abnormal glycaemia among non-diabetic women with diabetic husbands was significant (P = 0.001). Having a diabetic husband (P = 0.006) and being obese (P = 0.009) were the only significant predictors of hyperglycaemia among non-diabetic women after controlling for confounding factors. Conclusion: There was significant concordance of abnormal glycaemia among non-diabetic women with diabetic husbands. The spouses of diabetic patients may therefore be a target population for regular hyperglycaemia and DM screening

Website Phishing Detection Using Machine Learning Techniques

Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods

The Role of Business Intelligence adoption as a Mediator of Big Data Analytics in the Management of Outsourced Reverse Supply Chain Operations

The fluctuating and disorganized state of todays global markets is the result of several factors. COVID-19 is an illustration. Supply chain managers should re-evaluate their competitive strategy and leverage big data analytics in light of the rising volatility in demand and supply, rivalry among supply chain partners, and the requirement to deliver tailored goods and services (BDA). Supply chain firms require sophisticated BDA processes and procedures to provide useful insights from big data to better decision-making and supply chain operations, as many leaders in the sector have acknowledged the necessity for improving with data (SCO). This research gives theoretical justification for the influence that BDA has on SCO

Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

The Industrial Internet of Things (IIoT) refers to the employment of the Internet of Things in industrial management, where a substantial number of machines and devices are linked and synchronized with the help of software programs and third platforms to improve the overall productivity. The acquisition of the industrial IoT provides benefits that range from automation and optimization to eliminating manual processes and improving overall efficiencies, but security remains to be forethought. The absence of reliable security mechanisms and the magnitude of security features are significant obstacles to enhancing IIoT security. Over the last few years, alarming attacks have been witnessed utilizing the vulnerabilities of the IIoT network devices. Moreover, the attackers can also sink deep into the network by using the relationships amidst the vulnerabilities. Such network security threats cause industries and businesses to suffer financial losses, reputational damage, and theft of important information. This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. SDN is an approach that enables the network to be centrally and intelligently controlled through software applications. In our framework, the SDN controller employs a machine-learning algorithm to monitor the behavior of industrial IoT devices and networks by analyzing traffic flow data and ultimately determining the flow rules for SDN switches. We use SVM and Decision Tree classification models to analyze our framework’s network intrusion and attack detection performance. The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%

Epidemiology of Diabetes Mellitus in Oman : Results from two decades of research

Objectives: This study aimed to describe the epidemiology of diabetes mellitus over the past two decades in Oman, particularly in terms of its prevalence and incidence. In addition, the study sought to estimate the future incidence of diabetes in Oman. Methods: Three national and three regional surveys conducted between 1991 and 2010 were analysed to obtain the age-adjusted prevalence and undiagnosed proportion of type 2 diabetes mellitus (T2DM) among Omani subjects aged ≥20 years. Diabetes mellitus registers and published studies were used to determine incidence rates of both type 1 diabetes mellitus (T1DM) and T2DM in Oman. Linear regression was used to determine trends and projections for diabetes in 2050. Results: The age-adjusted prevalence of T2DM in Oman varied from 10.4% to 21.1%, while the highest prevalence of impaired fasting glucose was found in males (35.1%). In comparison to men, higher incidence rates of T2DM were found in women (2.7 cases compared to 2.3 cases per 1,000 person-years, respectively). No significant trends were observed for the prevalence or incidence of T2DM in both genders. Undiagnosed T2DM was more common in men (range: 33–68%) than women (range: 27–53%). The results of this study show that by 2050, there will be an estimated 350,000 people with T2DM living in Oman (a 174% increase compared to estimates for 2015). Conclusion: Health authorities need to prioritise diabetes prevention and control in order to prevent or delay long-term complications and avert a potential epidemic of diabetes in Oman