United Arab Emirates University
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FOG HARVESTING USING CHEMICALLY FUNCTIONALIZED POROUS MEMBRANES
Get PDFThis work explores the use of chemically functionalized porous membranes for fog harvesting. Fog water has emerged as a valuable alternative water source in regions where traditional freshwater resources are scarce. However, conventional fog harvesting techniques often suffer from low collection efficiency and limited scalability. To address these challenges, this research investigates the potential of chemically functionalized porous membranes for enhanced fog water collection. The study focuses on the development and characterization of specialized membranes with surface properties optimized for efficient fog droplet capture.The current study proposes the use of aluminum mesh for fog collection, as it stands out for being considerably simpler and inexpensive. Chemical functionalization involves the use of composite solutions that are comprised of a polymeric matrix and a metal oxide filler. Accordingly, a dip coating approach of a metallic mesh membrane in these composite solutions results in the formation of coatings with variable gradients of hydrophilicity and hydrophobicity. The effect of varying the membrane’s surface chemistry on the efficiency of fog harvesting has been evaluated via an experimental setup for fog harvesting under controlled laboratory settings. Additionally, the thesis explored the underlying mechanism governing fog droplet capture, growth and harvesting using the chemically-functionalized membranes. The results of this study is highly believed to contribute to the advancing of fog harvesting technologies by providing a deeper understanding of the process on the chemically-functionalized membranes that have been developed towards an enhanced fog water collection efficiency. Moreover, these findings have significant implications for water resource management in arid and fog-prone regions, where fog can serve as a sustainable water source to alleviate water scarcity challenges
AN EMPIRICAL STUDY ON THE USE OF SECURE PREDICTIVE ANALYTICS FOR IMPROVING TRADE FORECASTING IN THE UAE
Get PDFTrade contributes to the United Arab Emirates\u27 economic growth. This thesis focuses on trade dynamics in the UAE using Long Short-Term Memory (LSTM) neural networks. The study focuses on both import and export activities, providing understandings into the complex patterns and impacts of international trade on the UAE\u27s economic growth. The research begins by constructing an LSTM model to forecast the UAE\u27s Gross Domestic Product (GDP) through the utilization of historical trade data. We use time series data for imports and exports as key input features. This innovative approach highlights the relevance of trade statistics as a leading indicator of economic performance.We utilize the Mean Squared Error (MSE) loss metric and evaluate the correlation matrix, thus ensuring the robustness and precision of our predictions. This evaluation process demonstrates the LSTM model\u27s ability to capture and comprehend the complex interplay of trade patterns and their subsequent impact on the UAE\u27s economic growth.This study is a significant contribution to the field of economic analysis, as it employs advanced LSTM techniques to discover previously unexplored insights into trade dynamics. By demonstrating the effectiveness of LSTM in forecasting economic variables based on trade data, this research underscores the need for artificial intelligence and machine learning and cryptography to enhance our understanding of the intricate global economic landscape and provide security and confidentiality.This thesis provides a thorough examination of the UAE\u27s import and export trends, employing LSTM models for GDP prediction, thus fostering a deeper understanding of the nation\u27s economic outlook. The work is not only expanding the horizons of predictive economic analysis but also underscores the potential for intelligent computational techniques to inform and guide policy and decision-making in the context of international trade and economic development. Finally, to ensure the confidentiality, we use homomorphic encryption
MOLECULAR CHARACTERIZATION OF GERMINATION OF DATE PALM SEEDS MAINTAINED AT MICROGRAVITY IN OUTER PACE (INTERNATIONAL SPACE STATION)
Get PDFOver the past decade, the UAE has experienced unprecedented innovations in the space sector. Collaborating closely with universities and space agencies around the world, the country\u27s efforts have advanced steadily but quietly under the public radar. The proposed research focuses on basic research to complement the UAE initiative to send date palms to Mars for future space missions. Plant growth analysis of date palm seeds was performed after maintaining at zero gravity in outer space in the International Space Station (ISS) and simultaneously under normal gravity at ground level. In this context, this work was carried out to analyze the molecular changes in the date palm seeds maintained at microgravity in comparison with the gravity seeds. Ninety seeds of different varieties of date palm were sent to space in collaboration with the UAE space agency and the seeds were returned after 6 months. The seeds returned from space did not show signs of germination and, therefore, the molecular mechanism of seed dormancy was investigated with transcriptomics. RNA was extracted from the date palm control and space-maintained seeds and transcriptome analysis was performed. From this analysis, all date palm seeds yielded Q30 values greater than 90%. The GC content of date palm seed samples ranged from 48 to 51%. The higher reads were observed in the Lulu space samples, whereas lower ones were observed in Mesalli seeds. The preprocessed and rRNA-removed reads were used for reference-based pair-wise alignment with the Date Palm NCBI reference genome. We have observed upregulated genes and downregulated genes in all the varieties. The functional profiling of the differentially expressed genes was identified in the Lulu, Majdool and Mesalli date palm seeds. We have found out there are differential gene expressions in all the date palm seeds studied, which might have prevented the seeds from germination. Thus, radiation and vibration damage might have affected both living and non-living components in many ways. We found that these factors affect seeds and their germination process. Also, maintaining seeds in space directly or indirectly affect the overall seed performance by changing the integrity of internal cell organelles
ELECTROMAGNETIC MEMS SAFETY AND ARMING DEVICES
No full textThis thesis aims to investigate the design of safety and arming devices within artillery fuzes, which rely on the spin velocity and setback acceleration of the weapon. These systems consist of two critical components: the spin lock and the inertia lock. The spin velocity and setback acceleration of the weapon induce deformations in these components, serving as key criteria for determining whether the weapon is in an armed or safe state.
The primary objective of this thesis is to comprehensively analyze and assess the design of both the spin lock and inertia lock. The research will delve into multiple factors influencing their current configurations and employ analytical techniques to gain insight into the design choices. Additionally, an evaluation will be conducted to explore the feasibility of 3D printing these components.
To validate the findings from simulations, practical tests on the spin lock will be performed to confirm the extent of deformation it undergoes. The results demonstrate a polynomial relationship between rotational velocity and setback acceleration with the deformations observed in the spin and inertia locks. Nevertheless, certain constraints limit the variables affecting the miniature design.
A trial-and-error approach was utilized to derive a design that ensures safety under conditions of less than 2000 RPM and 1000 Gs. The testing process was conducted meticulously, and light-based evidence was employed to corroborate deformation in several conceptual scenarios
CONSTRUING CHILDREN’S PERCEPTIONS OF IDEAL LEARNING SPACES BASED ON PERSONAL CONSTRUCT THEORY
No full textLearning is an intrinsic aspect of human experience, predominantly shaped by interactions within the socio-physical environment, including engagements with educators, peers, and the built surroundings. The design and character of these learning spaces influence children\u27s cognitive, perceptual, and motor development while also embodying different pedagogical philosophies. Therefore, designing these spaces to align with educational shifts and enhance learning experiences requires a deep understanding of learner’s needs and perceptions. With the COVID-19 pandemic necessitating a shift to home-learning, there arose a unique opportunity to explore children’s perception of ideal learning spaces as they experienced both home and school settings. This research aimed to identify the physical attributes of ideal learning spaces from children\u27s perceptions and place them within the context of existing learning theories. Using the principles of the personal construct theory and the repertory grid technique, a detailed qualitative approach was adopted. Thirty children aged 8 to 10 were engaged in semi-structured interviews to identify the design-related attributes they associate with ideal learning spaces. The newly developed Integrated Learning Space Framework (ILSF) was then utilised to align these attributes with the spatial implications of various learning theories. The study identified 51 key design-related attributes of an ideal learning space. Key findings highlighted children’s preferences for spacious and versatile environments, with themes of personalization, aesthetic consistency, and functional design emerging prominently. Additionally, there was a strong resonance with humanistic principles in their conceptualizations. Beyond emphasising the impact of the pandemic on children\u27s educational expectations, this research introduces the ILSF, a tool that aligns children\u27s spatial preferences with five learning theories. By linking children’s perceptions with learning theories through the ILSF, this research provides insights for the architectural design of future educational spaces that align with both theoretical foundations and learners\u27 preferences
NANO-SUPPORTED BIOCATALYSTS FOR EFFICIENT WASTEWATER REMEDIATION
No full textThe existence of various bioactive organic pollutants in wastewater and municipal water sources has raised concerns regarding their potential effects on human health. As a result, different techniques are being explored to effectively break down these persistent organic pollutants. Peroxidases have recently emerged as a new approach to remediation that may have advantages over traditional methods. However, evaluating the effectiveness of different peroxidases in breaking down various emerging pollutants can be time-consuming and difficult. In this study, a quick and reliable method was developed to test the degradability of 21 emerging pollutants by five different peroxidases (soybean peroxidase, chloroperoxidase, lactoperoxidase, manganese peroxidase, and horseradish peroxidase) using an LC-MSMS approach. Additionally, the role of a redox mediator was examined in the enzymatic degradation tests. The findings revealed that some of the organic pollutants can be easily degraded by all five of the peroxidases, while others are only degraded by a specific peroxidase or in the presence of a redox mediator. Furthermore, two support materials (hybrid nanoflowers and metal organic framework) were synthesized for efficient, recyclable, and reusable for the degradation of these pollutants, we have created, characterized, and applied hybrid nanoflowers embedded with laccase enzymes and metal organic framework embedded with horseradish peroxidase. Both nanomaterials had a large surface area. Moreover, these materials were found that they could be reused for five cycles and stored for 21 days at 4◦C. The hNFs were tested for the degradation of a set of emerging pollutants with and without a redox mediator. They were able to degrade MBT and caffeic acid with 97 and 95% efficiency, respectively. The metal organic framework was tested for the degradation of nine emerging pollutants with and without a redox mediator. They were able to degrade five pollutants very efficiently. Our findings indicate that enzymeembedded hybrid nanoflowers/ metal organic framework is a powerful remediation tool for pollutants degradation. Additionally, the successful immobilization of enzymes on hybrid nanoflowers/ metal organic framework demonstrated in this study could enable the efficient recycling of enzymes for multiple degradation cycles, potentially leading to scaling up and the creation of a bioreactor
DATA SHARING AND SCHOOL IMPROVEMENT: A PHENOMENOLOGICAL STUDY OF SOURCES, MECHANISMS, LACUNAS, AND USEFULNESS
Get PDFThe purpose of this study is to understand the process of datafication in public schools. In particular, the study aims to explore how data is collected and shared, and whether it contributes to school improvement. The investigation aims to delve into the sources and methods used for data collection, the mechanisms employed for data sharing, the gaps that exist in data sharing, and how school administrators and teachers share data to enhance schools. The use of a transcendental phenomenological approach will aid in developing a theoretical model that demonstrates the process of school data sharing and how it facilitates school improvement. To validate the results, two semistructured interviews were conducted with administrators (n=19) and teachers (n=14). The participants were purposively selected based on specific criteria from different grade levels ranging from KG to 12. Data analysis was carried out using a cluster of meaning analysis. The study\u27s findings demonstrate how sharing data can be an effective way to improve schools. To help clarify how this can be done, a model for data sharing and school improvement has been developed. The model begins by identifying two types of school data: nomothetic and ideographic sources. These sources can be shared among different parties both inside and outside of the school using various mechanisms. To make sense of the data, accurate analysis and interpretation must be conducted. This involves collecting clear evidence, evaluating school performance, identifying gaps, and making decisions based on that information. Improvement plans can then be developed collaboratively and individually to achieve school improvement. Sharing data can expand the communicative space among stakeholders, enable the exchange of experiences between different parties, and provide a holistic understanding of school performance. Future research should consider testing the theoretical model in other different contexts
AERODYNAMIC & AEROACOUSTIC PERFORMANCE OF WIND TURBINE BLADES FEATURING ENHANCED
Get PDFWind energy, being one of the cleanest and most sustainable sources, has undergone remarkable growth in recent years due to advancements in aerodynamics and increased power output. The research community is actively pursuing the development of cutting-edge solutions to further optimize wind turbine technology, ensuring its maximum efficiency and revolutionizing the landscape of wind power.This research aims to design and develop flow-control devices for wind turbine blades, employing both active and passive control mechanisms, namely morphing trailing-edge and slot-profile, respectively. The objective is to enhance wind turbine performance across a wide range of wind speeds. The morphing trailing-edge mechanism focuses on adjusting the local mean-camber through trailing-edge morphing, resulting in augmented blade lift and torque, consequently reducing the wind turbine\u27s cutin speed. Conversely, the slot-profile mechanism manages boundary layers by suppressing flow separation and delaying stall, harnessing greater lift and torque, and effectively reducing the rated wind speed.Numerical investigations form the core of the research methodology, providing insights into various flow parameters such as pressure and velocity fields, surface flow, skin friction, boundary layers, flow separation, and wake profiles to analyze the influence of developed flow-control mechanisms. These flow-control devices will eventually be integrated into the National Renewable Energy Laboratory (NREL) Phase-VI research wind turbine for performance analysis. The research promises to deliver significant power augmentation and increased productivity of wind turbines, particularly during offdesign operating conditions, a critical advantage for regions characterized by low average wind speeds, such as the Middle East and South-East Asia.Furthermore, the research outcomes hold potential for broader applications in the field of rotorcraft and unmanned aerial vehicles. These flow control techniques can be implemented on rotors and/or propellers, generating greater lift at relatively lower RPM, thereby resulting in substantial fuel/power savings and increased flight endurance
PREDICTIVE MODELLING OF ROAD DETERIORATION USING AN ARTIFICIALLY INTELLIGENT BAYESIAN BELIEF NETWORKS APPROACH
Get PDFThe ability to predict road deterioration is the cornerstone for developing a reliable Pavement Management System (PMS) that optimizes pavement maintenance programs. Such prediction capacity becomes increasingly important, especially when highway agency funds are confined. This research focuses on the development of prediction models based on an artificial intelligence technique, Bayesian Belief Networks (BBN), that aid decision-makers in forecasting expected road distress curves on the lights of various (e.g., environmental, traffic, and road-specific) factors and maintenance decisions. The novelty of this research revolves around deploying BBNs which allow analysts to yield Markovian predictions of annual road deterioration based on incomplete and/or uncertain historical data, which is probabilistically inferred based on the interrelations of factors modelled in the Bayesian Networks. Such probabilistic inferences not only tackle a gap in current road deterioration modelling literature, but are also deemed to provide a reasonable alternative over costly data collection campaigns and assist in road condition diagnoses and assessment efforts in cases where data are only partially available. The major objectives of the study are to: (1) Estimate the correlations between various deterioration factors to optimize the data collection efforts using machine learning algorithms (Correlation analysis model), (2) Develop a prediction model to estimate the probabilistic values of deterioration factors using Dynamic BBN analysis based on Markov chain process, to aid in the development of temporal graphs representing the pattern of deterioration factors in the future years (Time-series prediction model), (3) Develop a decision-support system which generates suitable alerts whenever the deterioration factors cross the safe limits, enabling the practitioners to conduct appropriate repair and maintenance activities at the right time to increase the service life of the pavements (Decision-support system). The BBN models were trained using a collection of 3,272 road sections, representing a variety of 32 arterial, collector, freeway, and expressway roads in UAE from 2013 to 2019. The BBN models developed in this study show high accuracy with a contingency table fit of over 85% for the correlation analysis models and over 80% of overall precision and reliability rate for the performance prediction models. The proposed BBN approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers to establish a decision support system that is aimed not only at prioritizing maintenance during the operation stage, but also to design pavements during the design stage, with an upfront foresight into the life-cycle implications of their design, ultimately improving and/or extending their deterioration curves
The Robot\u27s Civil Liability for Medical Errors
Get PDFThe researcher addresses a critical issue regarding robot technologies and their impact on legal liability within the healthcare sector in the research titled The Robot\u27s Civil Liability for Medical Errors . This scientific study aims to comprehend the legal challenges associated with the growing utilization of robots and intelligent systems in medical procedures, as well as how to establish medical error liability in this context .The research starts with a review of the existing legal frameworks of traditional medical liability, providing a general overview of technology developments in healthcare and their application in utilizing robots. The study sheds light on the challenges that arise when attempting to apply traditional laws to robots and explores ways to enhance legal frameworks to ensure fair and appropriate liability allocation .Furthermore, the researcher delves into the increasing role of robots in improving medical practices and preventing medical errors. This analysis looks at who should bear the liability for errors that occur in healthcare operations related to robots. A comparative analysis of various national and international legal systems in this context is conducted, highlighting the challenges that hinder the achievement of equality and justice. Moreover, practical examples and case studies are presented in the research to illustrate the legal and ethical challenges associated with civil liability related to robots in cases of medical errors. The researcher also provides recommendations for future legislation and healthcare policies to strike a balance between technological advancement and legal protection of patients and healthcare practitioners