Essays on mixed-frequency data : forecasting and unit root testing

Doutoramento em Economia

A study on the deployment of GA in a grid computing framework

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Cost-analysis of teledentistry in residential aged care facilities

INTRODUCTION: The purpose of this research was to conduct a cost-analysis, from a public healthcare perspective, comparing the cost and benefits of face-to-face patient examination assessments conducted by a dentist at a residential aged care facility (RACF) situated in rural areas of the Australian state of Victoria, with two teledentistry approaches utilizing virtual oral examination.

METHODS: The costs associated with implementing and operating the teledentistry approach were identified and measured using 2014 prices in Australian dollars. Costs were measured as direct intervention costs and programme costs. A population of 100 RACF residents was used as a basis to estimate the cost of oral examination and treatment plan development for the traditional face-to-face model vs. two teledentistry models: an asynchronous review and treatment plan preparation; and real-time communication with a remotely located oral health professional.

RESULTS: It was estimated that if 100 residents received an asynchronous oral health assessment and treatment plan, the net cost from a healthcare perspective would be AU$32.35 (AU$27.19-AU$38.49) per resident. The total cost of the conventional face-to-face examinations by a dentist would be AU$36.59 ($30.67-AU$42.98) per resident using realistic assumptions. Meanwhile, the total cost of real-time remote oral examination would be AU$41.28 (AU$34.30-AU$48.87) per resident.

DISCUSSION: Teledental asynchronous patient assessments were the lowest cost service model. Access to oral health professionals is generally low in RACFs; however, the real-time consultation could potentially achieve better outcomes due to two-way communication between the nurse and a remote oral health professional via health promotion/disease prevention delivered in conjunction with the oral examination.

Blended learning and art education: student participation in a collaborative, authentic assessment project

This paper reports on higher education student engagement with blended learning experiences incorporating located (on campus), cloud based (online e-learning ) and graphically built, socially networked 3D multi user virtual environments (MUVES). Immersion in this environment enabled collaboration between two groups of students enrolled in separate undergraduate art education and public relations units, to identify, develop and participate in an integrated, authentic assessment project. It is contended that immersive blended learning experiences support creative problem solving and encourages synchronous and asynchronous student participation in authentic problem solving and collaborative practice. Interacting with co-learners, students gain knowledge and skills through situated learning, defined as the application of knowledge, learned in one setting and transferred to another and where immersion in a virtual learning experience leads to higher level engagement on the transfer task in a real world setting. In this project, collaborative blended learning involved the creation of a collection of digital artworks by art education students using computer software located in a real world environment. These artworks were curated and exhibited by the students in a virtual gallery they designed and built on Deakin Arts Education island in Second Life. For public relations students, the virtual art exhibition was the focus of a virtual campaign, designed, researched and developed by them to promote the Deakin Virtual Art Gallery on Deakin island in Second Life. The final promotion for the Virtual Gallery was presented by the students at a symposium in both real world and virtual world environments.

OpenDataHub: an open dataset management system

This thesis presents a cloud-based software platform for sharing publicly available scientific datasets. The proposed platform leverages the potential of NoSQL databases and asynchronous IO technologies, such as Node.JS, in order to achieve high performances and flexible solutions. This solution will serve two main groups of users. The dataset providers, which are the researchers responsible for sharing and maintaining datasets, and the dataset users, that are those who desire to access the public data. To the former are given tools to easily publish and maintain large volumes of data, whereas the later are given tools to enable the preview and creation of subsets of the original data through the introduction of filter and aggregation operations. The choice of NoSQL over more traditional RDDMS emerged from and extended benchmark between relational databases (MySQL) and NoSQL (MongoDB) that is also presented in this thesis. The obtained results come to confirm the theoretical guarantees that NoSQL databases are more suitable for the kind of data that our system users will be handling, i. e., non-homogeneous data structures that can grow really fast. It is envisioned that a platform like this can lead the way to a new era of scientific data sharing where researchers are able to easily share and access all kinds of datasets, and even in more advanced scenarios be presented with recommended datasets and already existing research results on top of those recommendations.

Average-case analysis of power consumption in embedded systems

Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is “power estimation”. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method.

Anytime and anywhere: a case study for blended learning

Using live streaming with blended learning helps engage off- and on campus students in real time and enhances the off-campus experience by incorporating synchronous activities in addition to the usual asynchronous interactions. Research into the effective use of blended learning frameworks offers opportunities to create course experiences that are personal, relevant, and engaging. Challenges include integrating appropriate technology and managing it effectively throughout the course. Results from practical experiments will likely guide future learning and teaching endeavors using technology for inclusive, interactive, and collaborative learning for on- and off-campus students.

OpenDataHub: an open dataset management system

This thesis presents a cloud-based software platform for sharing publicly available scientific datasets. The proposed platform leverages the potential of NoSQL databases and asynchronous IO technologies, such as Node.JS, in order to achieve high performances and flexible solutions. This solution will serve two main groups of users. The dataset providers, which are the researchers responsible for sharing and maintaining datasets, and the dataset users, that are those who desire to access the public data. To the former are given tools to easily publish and maintain large volumes of data, whereas the later are given tools to enable the preview and creation of subsets of the original data through the introduction of filter and aggregation operations. The choice of NoSQL over more traditional RDDMS emerged from and extended benchmark between relational databases (MySQL) and NoSQL (MongoDB) that is also presented in this thesis. The obtained results come to confirm the theoretical guarantees that NoSQL databases are more suitable for the kind of data that our system users will be handling, i. e., non-homogeneous data structures that can grow really fast. It is envisioned that a platform like this can lead the way to a new era of scientific data sharing where researchers are able to easily share and access all kinds of datasets, and even in more advanced scenarios be presented with recommended datasets and already existing research results on top of those recommendations.