PREPARATION OF RED MUD/GRAPHENE COMPOSITE AND ITS APPLICATION FOR ADSORPTION OF As(III) FROM AQUEOUS SOLUTION

In this study, we produce a red mud/graphene composite (REEG) via electrochemical activation graphite in basic red mud slurry. The adsorption properties for As(III) on REEG were investigated by batch method. The influence of pH (2-12), contact time (0-300 min), and the amount of adsorbent (0.02-0.1 g) on As(III) removal efficiency by the REEG were also determined. Results showed that the equilibrium time, the optimal pH, and mass of adsorbent were 240 min, pH 3.0 and 0.05 g, respectively. The maximum adsorption capacity (qmax) calculated by Langmuir isotherm model was found to be 21.367 mg/g. The results showed that REEG promises to be a good absorbent for As(III) removal from aqueous solution.

Doctor of Philosophy

dissertationStochastic methods, dense free-form mapping, atlas construction, and total variation are examples of advanced image processing techniques which are robust but computationally demanding. These algorithms often require a large amount of computational power as well as massive memory bandwidth. These requirements used to be ful lled only by supercomputers. The development of heterogeneous parallel subsystems and computation-specialized devices such as Graphic Processing Units (GPUs) has brought the requisite power to commodity hardware, opening up opportunities for scientists to experiment and evaluate the in uence of these techniques on their research and practical applications. However, harnessing the processing power from modern hardware is challenging. The di fferences between multicore parallel processing systems and conventional models are signi ficant, often requiring algorithms and data structures to be redesigned signi ficantly for efficiency. It also demands in-depth knowledge about modern hardware architectures to optimize these implementations, sometimes on a per-architecture basis. The goal of this dissertation is to introduce a solution for this problem based on a 3D image processing framework, using high performance APIs at the core level to utilize parallel processing power of the GPUs. The design of the framework facilitates an efficient application development process, which does not require scientists to have extensive knowledge about GPU systems, and encourages them to harness this power to solve their computationally challenging problems. To present the development of this framework, four main problems are described, and the solutions are discussed and evaluated: (1) essential components of a general 3D image processing library: data structures and algorithms, as well as how to implement these building blocks on the GPU architecture for optimal performance; (2) an implementation of unbiased atlas construction algorithms|an illustration of how to solve a highly complex and computationally expensive algorithm using this framework; (3) an extension of the framework to account for geometry descriptors to solve registration challenges with large scale shape changes and high intensity-contrast di fferences; and (4) an out-of-core streaming model, which enables developers to implement multi-image processing techniques on commodity hardware

Carbon Footprint Assessment of A Green Building: Case Study of Intel Corporation Fitness Center in Ho Chi Minh City, Vietnam

In this thesis, a carbon footprint (CFP) assessment is performed on a building during the design phase. The building is planned to be located in Ho Chi Minh City and to function as a fitness center. The goal of the assessment was to determine which building materials and life-cycle stages contributed the most to the environmental impacts. The functional unit (FU) is set as 60 years of 1m2 floor area. The building environmental impacts were calculated in One Click LCA – a building life cycle metrics software with integrated database from Gabi and Ecoinvent. The building data was taken from Bill of Quantities and Architecture design provided by Intel Corporation – the building owner and Greenviet Consultancy Co., Ltd – the consultant of this project. The total global warming potential (GWP) throughout the building’s life cycle is 9.32*105 kgCO2eq/FU. Speaking of life cycle stages, energy consumption in the use phase generates the highest amount of emissions, accounting for 56.2% of the total amount, followed by the production phase, with 40.6%. Speaking of building materials, the largest environmental impact is caused by concrete with 2.44*105 kgCO2eq/FU, which is 60% of the total emissions from the life cycle of the building. In conclusion, the major contributors are the concrete production processes and energy consumption, particularly electricity, during the use phase. The CFP assessment consists of limitations and assumptions; therefore, an uncertainty analysis was carried out for transparent results and possible improvements in future assessment

Applied Epidemiology during COVID-19 in Vietnam

This thesis presents projects I conducted between February 2020 to December 2021 to meet the competencies of the Master of Applied Epidemiology Program (MAE) of the Australian National University (ANU). During this period, I was employed at the Department of Communicable Disease Control of the National Institute of Hygiene and Epidemiology (NIHE) in Hanoi, Vietnam. Chapter 1 – Introduction. This chapter provides an overview of my field placement, describes how I met the different MAE requirements, and presents other side projects I have been involved in during my MAE that do not fall under the MAE competencies. Chapter 2 – Investigate an outbreak: In-flight transmission of SARS-CoV-2 in Vietnam: Results from an outbreak investigation and containment measures. This project covers the investigation of and response to an in-flight COVID-19 transmission event in Vietnam in March 2020. I took part in the investigations from the beginning which resulted in the publication of two journal articles: one on the in-depth investigation of the on-board transmission event (Journal Article 1), and another one describing the response activities to prevent community transmission (Journal Article 2). We found that one infectious passenger in business class very likely infected at least 12 other passengers during a 10-hour flight, many of whom were seated beyond the two-row/seat distance threshold that is usually used for contact tracing among airplane passengers. Timely, systematic and comprehensive contact tracing of all passengers and their close contacts was needed to prevent widespread community transmission. Chapter 3 – Analyze a public health dataset: Association of public health interventions and COVID19 incidence in Vietnam, January to December 2020. In this project I analyzed the relation between public health interventions and COVID-19 incidence in Vietnam over the course of 2020, which culminated in the publication of Journal Article 3. This analysis, which was the first of its kind in Vietnam and the region, identified important associations between the timing of public health interventions and changes in the reproductive number of SARSCoV-2. Chapter 4 – Evaluate a surveillance system: After action review of the COVID-19 surveillance system in Quang Ninh Province, Vietnam in 2020. For this project I conducted a Literature Review on the World Health Organization (WHO)’s After Action Review (AAR) toolkit, and used it to evaluate the effectiveness of the surveillance system in Quang Ninh province, Vietnam in preventing and controlling COVID-19. This evaluation, presented as a Final Report in this chapter, was part of a WHO-funded initiative to learn lessons from the COVID19 response in Vietnam. While central coordination and adaptive capacity during the emergency were identified as strengths, my evaluation also provides important recommendations on how to improve the surveillance system in Quang Ninh province, in particular through improved integration of different data systems and communication channels between health jurisdictions of Quang Ninh’s healthcare system. Chapter 5 – Design an epidemiological project: User-generated online information in response to a COVID-19 outbreak in Vietnam in July – September 2020. In this project I investigated so-called ‘infodemics’ related to a COVID-19 outbreak in Da Nang province, Vietnam between July and September 2020 by applying content analysis and semantic network analysis to publicly available user-generated information from the internet. I conducted two separate analyses: one on ‘infodemics’ related to COVID-19 incidence and mortality, which resulted in Submitted Article Manuscript 1; and another on ‘infodemics’ in relation to public health interventions, which resulted in Submitted Article Manuscript 2. Findings showed that public awareness and perceptions were highly correlated with the evolution of COVID-19 incidence and mortality (at first) during the outbreak, while misinformation and unverified information related to public health interventions that were implemented in response to the outbreak were also prevalent. Chapter 6 – Other MAE requirements. In this chapter I report on other MAE requirements which I completed during my fellowship, namely the publication of a Lay Audience Report, the Lesson From The Field, and the Teaching Experienc

Efficient probabilistic and geometric anatomical mapping using particle mesh approximation on GPUs

pre-printDeformable image registration in the presence of considerable contrast differences and large size and shape changes presents significant research challenges. First, it requires a robust registration framework that does not depend on intensity measurements and can handle large nonlinear shape variations. Second, it involves the expensive computation of nonlinear deformations with high degrees of freedom. Often it takes a significant amount of computation time and thus becomes infeasible for practical purposes. In this paper, we present a solution based on two key ideas: a new registration method that generates a mapping between anatomies represented as a multicompartment model of class posterior images and geometries and an implementation of the algorithm using particle mesh approximation on Graphical Processing Units (GPUs) to fulfill the computational requirements. We show results on the registrations of neonatal to 2-year old infant MRIs. Quantitative validation demonstrates that our proposed method generates registrations that better maintain the consistency of anatomical structures over time and provides transformations that better preserve structures undergoing large deformations than transformations obtained by standard intensity-only registration. We also achieve the speedup of three orders of magnitudes compared to a CPU reference implementation, making it possible to use the technique in time-critical applications

Image registration driven by combined probabilistic and geometric descriptors

pre-printDeformable image registration in the presence of considerable contrast dierences and large-scale size and shape changes represents a signicant challenge for image registration. A representative driving application is the study of early brain development in neuroimaging, which requires co-registration of images of the same subject across time or building 4-D population atlases. Growth during the first few years of development involves significant changes in size and shape of anatomical structures but also rapid changes in tissue properties due to myeli-nation and structuring that are reflected in the multi-modal Magnetic Resonance (MR) contrast measurements. We propose a new registration method that generates a mapping between brain anatomies represented as a multi-compartment model of tissue class posterior images and geometries. We transform intensity patterns into combined probabilistic and geometric descriptors that drive the matching in a diffeomorphic framework, where distances between geometries are represented using currents which does not require geometric correspondence. We show preliminary results on the registrations of neonatal brain MRIs to two-year old infant MRIs using class posteriors and surface boundaries of structures undergoing major changes. Quantitative validation demonstrates that our proposed method generates registrations that better preserve the consistency of anatomical structures over time

Targeting TBK1 inhibits migration and resistance to MEK inhibitors in mutant NRAS melanoma.

UNLABELLED: Melanoma is a devastating form of skin cancer with limited therapeutic options. Fifteen to 20% of patients with melanoma have an activating mutation in the GTPase, NRAS. The major downstream effectors of RAS are RAFs (ARAF, BRAF, and CRAF), phosphoinositide 3-kinase (PI3K), and the Ral guanine exchange factors (RalGEF). TANK-binding kinase 1 (TBK1) is an atypical IκB kinase family member that acts downstream of RalGEFs. Whereas many studies have analyzed RAF and PI3K signaling in mutant NRAS melanoma, the role of RalGEF/Ral is understudied and TBK1 has not been examined. To address this, TBK1 was modulated with knockdown approaches and targeted therapies to determine the role of TBK1 in motility, apoptosis, and signaling. In melanoma, NRAS overexpression increased TBK1 phosphorylation. TBK1 depletion inhibited migration and invasion, whereas its constitutive overexpression led to an increase in invasion. In three-dimensional systems that mimic the dermal microenvironment, TBK1 depletion or inhibition cooperated with MEK inhibitors to promote apoptosis, particularly in the context of MEK-insensitive mutant NRAS. This effect was absent in melanoma cells that are wild-type for NRAS. These results suggest the utility of TBK1 inhibitors as part of a treatment regimen for patients with mutant NRAS melanoma, for whom there are no current effective therapies. IMPLICATIONS: TBK1 promotes the malignant properties of NRAS-mutant melanoma and its targeting, in combination with MEK, promotes apoptosis, thus providing a potential novel targeted therapeutic option

ISP: An optimal out-of-core image-set processing streaming architecture for parallel heterogeneous systems

Journal ArticleImage population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets

NUMERICAL SIMULATION OF THE BULK FORMING PROCESS TO MANUFACTURE COUPLING DETAILS FROM TUBE BILLET

Currently, most coupling details in the actuators are made by traditional methods such as bulk forming from block billet and then metal cutting. Such manufacturing methods often lead to material wastes due to cutting a large amount of excess processing. To save material and improve production efficiency, tube billet would be selected for bulk forming. However, when tube billet is used for bulk forming, it should be carefully calculated to avoid instability and folding defects in workpiece. This article presents the research on the forming process of the coupling details using numerical simulation and based on the obtained results, the suitable geometric size of tube billet for the forming operation in closed die can be determined