• Publicationsclose
• Research softwareclose
• Other research productsclose
• 2019-2023close
• AE close

Background: Smoking is now prohibited in all educational institutions and other public places in the United Arab Emirates (UAE), but shisha smoking is considered as one of the major problems among the students population. This study aimed to identify the (a) prevalence of ever shisha, current shisha and shisha dependency smokers among university students in the University of Sharjah (UOS), (b) knowledge and belief differences among ever shisha, current shisha as well as shisha dependency smoking students, (c) relationship between precipitating factors and shisha dependency and (d) precipitating factors (stimulation, handling, pleasure, tension reduction, addiction (dependency), automatism (habit) and social interaction, parents smoking behavior, knowledge and beliefs about smoking predict shisha dependency among students in UOS. Materials and Methods: In this cross sectional study, 633 students participated from UOS, UAE. Knowledge and Belief scale, Modified Reason for Smoking Scale and Fagerstrom Test for Nicotine Dependence (FTND) were used to measure knowledge, beliefs, shisha dependency and predictive factors of smoking behavior among undergraduate students in UOS. Results: Nearly103 (16.3%) of students were addictive to shisha smoking based on FTND. Students had adequate knowledge that smoking led to cardiac problems; however, their knowledge about the other consequences of smoking was inadequate and believed that smoking was not harmful. There was a significant positive relationship between addiction, pleasure, social interaction, habit, parental smoking behavior and shisha dependency behavior among current shisha dependency students. Habit, addiction, pleasure, social interaction and parental smoking were the predictors of shisha smoking dependency among this population. Step wise multiple regressions showed that social interaction was the highest significant predictor for shisha dependency behavior. Conclusions: Hence, there is a need to enhance the knowledge and modify irrational beliefs about shisha smoking as these students possess inadequate knowledge about consequences of shisha smoking.

Abstract Maghemite nanoparticles were synthesized using sol–gel method. The structural study of the samples was done using XRD technique. Zero-field-cooled (ZFC) and field-cooled (FC) magnetization versus applied field (M−H) measurements were conducted between −5 T and +5 T at several temperatures and several field cooled values. The XRD study revealed that the average diameter of the nanoparticles to be around 20 nm. XRD patterns show mainly maghemite phase with very slight hematite phase. The coercively was found to decrease sharply with temperature up to 200 K above which it remains almost constant. Negative and positive horizontal exchange bias was obtained in the ZFC state and only negative exchange bias was obtained at all field cooled values. The magnitudes of the exchange bias were found to decrease sharply with temperature and disappearing above 100 K. Negative and positive vertical loop shifts were obtained in the ZFC state and only positive loop shifts were obtained at all field cooled values. The magnitudes of the loop shifts were found to decrease sharply with temperature and disappearing above 50 K. These results are discussed and attributed mainly to the exchange coupling between the maghemite (ferrimagnetic) and hematite (antiferromagnetic) phases in the nanoparticle system.

© 1982-2012 IEEE. Back propagation (BP) based on stochastic gradient descent is the prevailing method to train multilayer neural networks (MNNs) with hidden layers. However, the existence of the physical separation between memory arrays and arithmetic module makes it inefficient and ineffective to implement BP in conventional digital hardware. Although CMOS may alleviate some problems of the hardware implementation of MNNs, synapses based on CMOS cost too much power and areas in very large scale integrated circuits. As a novel device, memristor shows promises to overcome this shortcoming due to its ability to closely integrate processing and memory. This paper proposes a novel circuit for implementing a synapse based on a memristor and two MOSFET tansistors (p-type and n-type). Compared with a CMOS-only circuit, the proposed one reduced the area consumption by 92%-98%. In addition, we develop a fuzzy method for the adjustment of the learning rates of MNNs, which increases the learning accuracy by 2%-3% compared with a constant learning rate. Meanwhile, the fuzzy adjustment method is robust and insensitive to parameter changes due to the approximate reasoning. Furthermore, the proposed methods can be extended to memristor-based multilayer convolutional neural network for complex tasks. The novel architecture behaves in a human-liking thinking process.

Engaging students in university mathematics classes can be a challenge for professors. One pedagogical technique is the use of pre-class videos in a flipped classroom. The students are exposed to the concepts and theories before attending class so that class time can be devoted to interacting with the content to better understand it. Most of the research into the flipped classroom shows that the students generally like the idea and feel they benefit from the approach; but to date, there is no conclusive research showing that students’ improve their grades. This research is a precursor to a larger study on the flipped classroom in university mathematics classes and investigates the types of videos undergraduate students prefer to help guide the development of a pre-class videos library. Eight-one students in three university mathematics classes in a private university in the United Arab Emirates were involved in the study. Electronic supplementary material The online version of this article (doi:10.1186/s41039-015-0026-9) contains supplementary material, which is available to authorized users.

Abstract Freshwater scarcity is one of the grand challenges that has posed threats to the global economy, societal stability and ecosystem balance. Desalination has been long recognized as an effective approach for fresh water production from seawater and brackish water. Forward osmosis (FO) is currently one of the most studied technologies for seawater and brackish water desalination due to its intrinsic advantages compared to reverse osmosis. On the other hand, membrane distillation (MD) is an emerging technology to offer a potentially cost effective thermally-driven desalination process, especially when coupled with waste heat and solar thermal. One major and inevitable challenge for FO and MD is membrane fouling. For decades, fouling and its related topics have gained extensive attention from the desalination communities and various strategies have been implemented to tackle this long standing issue. In this contribution, the fouling mitigation strategies in terms of pretreatment, membrane surface modification and operating conditions for both FO and MD processes are comprehensively reviewed.

The dissipative particle dynamics (DPD) technique was employed to design multiple microfluidic devices for investigating the motion of bioparticles at low Reynolds numbers. A DPD in-house FORTRAN code was developed to simulate the trajectories of two microparticles in the presence of hydrodynamic and transverse deflecting force fields via considering interparticle interaction forces. The particle-particle interactions were described by using a simplified version of the Morse potential. The transverse deflecting force considered in this microfluidic application was the dielectrophoresis (DEP) force. Multiple microfluidic devices with different configurations of microelectrodes were numerically designed to investigate the dielectrophoretic behavior of bioparticles for their trajectories and the focusing of bioparticles into a single stream in the middle of the microchannel. The DPD simulation results were verified and validated against previously reported numerical and experimental works in the literature. The computationally designed microdevices were fabricated by employing standard lithographic techniques, and experiments were conducted via taking red blood cells as the representative bioparticles. The experimental results for the trajectories and focusing showed good agreement with the numerical results.

Industrial Control Systems (ICS) have evolved in the last decade, shifting from proprietary software/hardware to contemporary embedded architectures paired with open-source operating systems. In contrast to the IT world, where continuous updates and patches are expected, decommissioning always-on ICS for security assessment can incur prohibitive costs to their owner. Thus, a solution for routinely assessing the cybersecurity posture of diverse ICS without affecting their operation is essential. Therefore, in this paper we introduce IFFSET, a platform that leverages full system emulation of Linux-based ICS firmware and utilizes fuzzing for security evaluation. Our platform extracts the file system and kernel information from a live ICS device, building an image which is emulated on a desktop system through QEMU. We employ fuzzing as a security assessment tool to analyze ICS specific libraries and find potential security threatening conditions. We test our platform with commercial PLCs, showcasing potential threats with no interruption to the control process.

AbstractThe challenge was to achieve more reservoir contact in a cretaceous tight reservoir to improve production and maximize recovery. Multilateral well campaigns were performed to meet these objectives.This case study describes an effective workflow for performing openhole sidetracks in this challenging medium-hard carbonate formation. The workflow maintained reservoir contact and achieved the desired production objectives.Two 6-in. multilateral drain sections were successfully drilled by performing openhole sidetrack using the continuous proportional steering method (CPSM). This method is not new to the industry, but this case study describes the systematic, unique workflow that was designed and followed to ensure a successful sidetrack in this low-porosity, hard formation.The sidetrack implementation started by creating humps at inclinations ranging from 88 to 91 degrees into the formation in the original 6-in. section. These humps were confirmed using near-bit inclination data (4.5 ft from the bit) and were identified as the sites for initiating a sidetrack. This paper discusses the best practices that were key to the successful execution of the project in one run on the first attempt.After the sidetrack, use of appropriate combinations of shallow and deep logging-while-drilling (LWD) measurements in the same bottom hole assembly (BHA) enabled the direct geosteering of the well, exposing more reservoir surface area than planned.A reduction of 10% from the planned well duration was achieved. Two 6-in. laterals, each approximately 4000 ft, were drilled in a single run and 100% reservoir contact was achieved.This experience proved that planning and precise execution could enable drilling of openhole sidetracks, even through hard formations. These sidetracks can then achieve fishbone wells with desired reservoir contact and realize the field development objectives in a technically robust and cost-efficient manner.CPSM does not rely on a pressure drop for steering. Although this proof of concept was performed in a relatively hard formation, similar workflows with appropriate drilling engineering may be applied to less-competent formations as well. The detailed procedure and flowchart created from the experience with the sample well can be adopted for use in similar applications.

Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embedding, and it is hard for the models to handle difficult situations with different illumination, large pose variance and occlusion. In this work, we take a step further and consider employing context information for person search. For a probe-gallery pair, we first propose a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene. We also build a graph learning framework to effectively employ context pairs to update target similarity. These two modules are built on top of a joint detection and instance feature learning framework, which improves the discriminativeness of the learned features. The proposed framework achieves state-of-the-art performance on two widely used person search datasets. To appear in CVPR 2019