Challenges and Opportunities for Trade and Financial Integration in Asia and the Pacific

The Asian economies have become an epitome of "trade as an engine of growth" during the past several decades as they have been expanding economic and human development, using trade as a ladder. Most of the economies have become highly integrated into the world economy, either through direct export and import or by becoming an important link in the global supply chain. However, when demand for their production and exports plunged suddenly and sharply in the last quarter of 2008, a sharp contraction in trade flows put their growth and social security under series threat. On such occasions, issues of dependency on external markets, foreign exchange, foreign direct investment (FDI) and technology rise to the surface and chosen development strategies get reviewed. In Asia and the Pacific, this is accompanied by the concerns about the inability of the region's economies to enhance and deepen their regional integration. Expectedly, the latest crisis has accentuated the concerns about low levels of existing intraregional trade and investments as well as underdeveloped financial integration in Asia and the Pacific.Trade, 2008, financial integration, intraregional trade, Asia Pacific

Modeling time series with conditional heteroscedastic structure

Models with a conditional heteroscedastic variance structure play a vital role in many applications, including modeling financial volatility. In this dissertation several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroscedastic model, are further generalized to provide more effective modeling of price range data well as count data. First, the Conditional Autoregressive Range (CARR) model is generalized by introducing a composite range-based multiplicative component formulation named the Composite CARR model. This formulation enables a more effective modeling of the long and short-term volatility components present in price range data. It treats the long-term volatility as a stochastic component that in itself exhibits conditional volatility. The Generalized Feedback Asymmetric CARR model presented in this dissertation is a generalization of the Feedback Asymmetric CARR model, with lagged cross-conditional range terms added to allow complete feedback across the two equations that model upward and downward price ranges. A regime-switching Threshold Asymmetric CARR model is also proposed. Its formulation captures both asymmetry and non-linearity, which are two main characteristics that exist in the price range data. This model handles asymmetry and non-linearity better than its range-based competitors, based on the Akaike’s Information Criteria. In addition to the above models, a Time Varying Zero Inflated Poisson Integer GARCH model is introduced. This model enables the modeling of time series of count data with excess number of zeroes where this excess varies with time. In this model, the zero inflation component is modeled either as a deterministic function of time or as a vector of stochastic variables --Abstract, page iv

Machine Learning Approach for Risk-Based Inspection Screening Assessment

Risk-based inspection (RBI) screening assessment is used to identify equipment that makes a significant contribution to the system's total risk of failure (RoF), so that the RBI detailed assessment can focus on analyzing higher-risk equipment. Due to its qualitative nature and high dependency on sound engineering judgment, screening assessment is vulnerable to human biases and errors, and thus subject to output variability and threatens the integrity of the assets. This paper attempts to tackle these challenges by utilizing a machine learning approach to conduct screening assessment. A case study using a dataset of RBI assessment for oil and gas production and processing units is provided, to illustrate the development of an intelligent system, based on a machine learning model for performing RBI screening assessment. The best performing model achieves accuracy and precision of 92.33% and 84.58%, respectively. A comparative analysis between the performance of the intelligent system and the conventional assessment is performed to examine the benefits of applying the machine learning approach in the RBI screening assessment. The result shows that the application of the machine learning approach potentially improves the quality of the conventional RBI screening assessment output by reducing output variability and increasing accuracy and precision.acceptedVersio

‘Diplomacity’ in the 21st century: why Sri Lanka’s local mayors must become global players

Rapti Ratnayake makes the case for why Sri Lanka should pursue ‘diplomacity’, where mayors and municipal leaders take a more active role in foreign policy by forging collaborative partnerships with other cities. She writes that if Colombo is to achieve its aspiration of becoming a globally competitive metropolis, embracing the opportunities afforded by paradiplomacy will be essential

Deterministic diffraction loss modelling for novel broadband communication in rural environments

This paper presents a deterministic modelling approach to predict diffraction loss for an innovative Multi-User-Single-Antenna (MUSA) MIMO technology, proposed for rural Australian environments. In order to calculate diffraction loss, six receivers have been considered around an access point in a selected rural environment. Generated terrain profiles for six receivers are presented in this paper. Simulation results using classical diffraction models and diffraction theory are also presented by accounting the rural Australian terrain data. Results show that in an area of 900 m by 900 m surrounding the receivers, path loss due to diffraction can range between 5 dB and 35 dB. Diffraction loss maps can contribute to determine the optimal location for receivers of MUSA-MIMO systems in rural areas

Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm

The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations

Strategies and techniques for fabricating MEMS bistable thermal actuators.

Bistable elements are beginning to appear in the field of MEMS as they allow engineers to design sensors and actuators which require no electrical power and possess mechanical memory. This research focuses on the development of novel strategies and techniques for fabricating MEMS bistable structures to serve as no electrical power thermal actuators. Two parallel strategies were explored for the design and fabrication of the critical bistable element. Both strategies involved an extensive material study on candidate thin film materials to determine their temperature coefficient of expansion and as-deposited internal stress properties. Materials investigated included titanium tungsten, Invar, silicon nitride and amorphous silicon deposited using either sputtering or PECVD. Deposition parameters were experimentally determined to produce tensile, compressive and stress-free films. A full set of graphs are presented. To address the 3D MEMS topology challenge required for bistability, this research explored two different strategies for fabricating 3D non-planar hemispherical dome structures using minimal processing steps. The first approach used the thermal/chemical reflow of resist, along with traditional binary lithography with a single photomask. Specific thermal/chemical reflow conditions were experimentally developed to produce hemispherical dome over a wide range. The second approach introduced a novel maskless procedure for fabricating the dome using grayscale lithography. After evaluating the above results, it was decided to use engineered compressive stress in released thin film sandwiches to form the 3D dome structures required for bistable actuation. Three different types of released multi-layer diaphragms were studied: 1) oxide-polyimide diaphragms, 2) oxide-aluminum diaphragms, and 3) oxide-aluminum-polyimide diaphragms