Defect detection and classification in semiconductor wafers has received an increasing attention from both industry and academia alike. Wafer defects are a serious problem that could cause massive losses to the companies' yield. The defects occur as a result of a lengthy and complex fabrication process involving hundreds of stages, and they can create unique patterns. If these patterns were to be identified and classified correctly, then the root of the fabrication problem can be recognized and eventually resolved. Machine learning (ML) techniques have been widely accepted and are well suited for such classification-/identification problems. In this study, we develop a state-of-the-art classifying algorithm using multiple ML techniques, relying on a general-regression-network-based consensus learning model along with a powerful randomization technique. Our method has proved to be more accurate and stable as compared to any of the existing algorithms reported in the literature, achieving accuracy of 99.8%.
Valve Regulated Lead-acid (VRLA) batteries are one of the most common rechargeable battery technologies used in energy backup systems, and are widely used in several applications. The steady demand on those batteries adds more burdens on improving the performance and determining the state-of-health of those batteries. At harsh environmental conditions such as in UAE where the temperature can go to extremely high levels most of the year, the battery performance and replacement increases the cost and raises the concerns about the reliability and economic feasibility of those batteries. It is extremely beneficial to predict battery behavior to allow the investigation of possible scenarios during operation run time. This paper investigates the discharge performance with 0.01Hz to 1kHz frequency range of two major VRLA batteries, namely 12V/150Ah Gel and Absorbed glass mat (AGM) sealed lead-acid batteries, when operating in stand-alone mode in photovoltaic (PV) based systems in United Arab Emirates.
The United Arab Emirates (UAE) is considered one of the major oil producers and suppliers in the world. However, the increasing pollution rates around the globe resulted in a major move towards the exploration and deployment of different types of green technologies in the UAE. Fuel cells are one of those green technologies currently being explored in the region. However, despite their promising features, fuel cell systems still lack a solid fault diagnosis and predictive maintenance study that would reflect the course climate conditions of the UAE. Moreover, in order to develop an accurate fault diagnosis study, a fault diagnosis oriented model of the fuel cell system under study is to be developed.
Biodiesel is a leading biofuel in the alternative fuels market with a surge in its worldwide production recently with the purpose of replacing crude-based diesel as a transportation fuel. In this work, two reactive distillation processes for biodiesel production have been simulated: the first process uses an alkali while the second includes a heterogeneous catalyst with both processes yielding a high purity biodiesel product. Aspen Plus v8.4 was used as the process simulation tool in this particular work. Comparison between the two production processes were made in terms of the annual production costs and total capital expenditure. The heterogeneous-catalyzed process for the reactive distillation was found to be more economically advantageous than the alkali-catalyzed process due to a much lower annual cost per unit of biodiesel produced and lower annual production cost calculated in USD/year.
Several sand samples from the desert of the United Arab Emirates (UAE) were analyzed using a thermogravimetric analyzer (TGA) and a differential scanning calorimeter (DSC). Loss of mass was observed in all the samples above 600 ?C, although no further mass loss could be observed in following thermal cycles. DSC analysis of cycled samples allowed for the determination of their heat capacities and revealed the existence of a solid phase transformation during the heating process at 575 ?C. This transformation was identified as the transition from ?-quartz to ?-quartz in the sample.
Exergy analysis plays an important role in the measurement of the true performance of engineering systems, aiding in the identification and minimization of losses within. In this work, a Matlab model is developed for exergy analysis and is applied to Proton Exchange Membrane Fuel Cell (PEMFC) and Solid Oxide Fuel Cell (SOFC), the respective work potential is determined, and the components contributing to the thermodynamic losses within the systems are identified. Furthermore, the effects of the variation of operating conditions on the performance are analyzed, identifying opportunities for efficiency improvement and therefore maximizing the fuel cell output. Significant differences have been seen between our results and the recent literature. The variations in results are discussed with emphasis on the PEM fuel cell model.
Sand from the United Arab Emirates (UAE) desert was mechanically characterized to determine its effective angle of internal friction as well as its angle of wall friction against two different metallic surfaces and a concrete surface. The Jenike method for silo design was used to calculate the minimum required angle leading to mass flow (first-in-first-out) discharge regime of a conical hopper with such sand particles.
yearly in the Emirate of Abu Dhabi. As an innovative and sustainable approach for waste recycling, four types of waste have been identified as inexpensive materials for high-temperature thermal energy storage applications: aluminum white dross (AWD) and aluminum post skimming (APS) from Emirates Aluminium, and electric arc furnace (EAF) slags, ladle furnace (LF) slags from Emirates Steel. Results of chemical analysis revealed that these materials are chemically stable with a negligible quantity of heavy metals. Excluding APS, the rest materials are thermally stable at high temperatures up to 1100 ?C.
The purpose of this study is to assess the factors that contribute to the effectiveness of the competency-based models used in an oil company in the United Arab Emirates. A questionnaire method is used to get the perception of trainees in an oil company who are still in or completed the program. A structured equation model is used to analyze the data. The factors that contribute to the effectiveness of the competency-based model are the competency based goals, content and framework, the assessment conducted to the trainees and minimum coaching. The study was conducted in one oil company and for that it can't be generalized to other contexts that implement similar program. This study will help to close the gap that previous studies have indicated on the application of competency models and their effectiveness. It could add a value to the efforts of the National Qualification Authority in Abu Dhabi.
There was a debate concerning polygamy at the FNC of the UAE. Polygamy has been suggested as a solution to the rising number of spinsters and it was blamed to be a cause of divorce. My research is an examination of current Emirati perspectives on polygamous marriage.The issue I am trying to tackle in this research is the causes, effects of polygamous marriages in the UAE, and if pushing polygamy at the national agenda would eliminate or cause issues. I collected data from preexisting research and conducted my own fieldwork. I surveyed a sample of over 200 willing participants with guaranteed anonymity, using a snowball technique.With an aim to build a link between perceptions of polygamy and age, gender, region lived in and economic status. I also carried out semi-structured interviews with 40 participants which were then thematically analyzed to further investigate the acceptance of the UAE society for polygamy.
