Land & Water: A History of Fifteenth-Century Vietnam from an Environmental Perspective.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Pedestrian lane detection for assistive navigation of blind people

Navigating safely in outdoor environments is a challenging activity for vision-impaired people. This paper is a step towards developing an assistive navigation system for the blind. We propose a robust method for detecting the pedestrian marked lanes at traffic junctions. The proposed method includes two stages: regions of interest (ROI) extraction and lane marker verification. The ROI extraction is performed by using colour and intensity information. A probabilistic framework employing multiple geometric cues is then used to verify the extracted ROI. The experimental results have shown that the proposed method is robust under challenging illumination conditions and obtains superior performance compared to the existing methods. © 2012 ICPR Org Committee

Applying the Fuzzy Analytic Network Process to Establish the Relative Importance of Knowledge Sharing Barriers

Knowledge sharing (KS) is the key to creativity and innovation in any organizations. Overcoming the KS barriers has created new challenges for designing in dynamic and complex environment. There may be interrelations and interdependences among the barriers. The purpose of this paper is to present a review of literature of KS barriers and impute the relative importance of them through the fuzzy analytic network process that is a generalization of the analytical hierarchy process (AHP). It helps to prioritize the barriers to find ways to remove them to facilitate KS. The study begins with a brief description of KS barriers and the most critical ones. The FANP and its role in identifying the relative importance of KS barriers are explained. The paper, then, proposes the model for research and expected outcomes. The study suggests that the use of the FANP is appropriate to impute the relative importance of KS barriers which are intertwined and interdependent. Implications and future research are also propose

Management of solar energy in microgrids using IoT-based dependable control

© 2017 IEEE. Solar energy generation requires efficient monitoring and management in moving towards technologies for net-zero energy buildings. This paper presents a dependable control system based on the Internet of Things (IoT) to control and manage the energy flow of renewable energy collected by solar panels within a microgrid. Data for optimal control include not only measurements from local sensors but also meteorological information retrieved in real-time from online sources. For system fault tolerance across the whole distributed control system featuring multiple controllers, dependable controllers are developed to control and optimise the tracking performance of photovoltaic arrays to maximally capture solar radiation and maintain system resilience and reliability in real time despite failures of one or more redundant controllers due to a problem with communication, hardware or cybersecurity. Experimental results have been obtained to evaluate the validity of the proposed approach

Cardiac-specific Conditional Knockout of the 18-kDa Mitochondrial Translocator Protein Protects from Pressure Overload Induced Heart Failure.

Heart failure (HF) is characterized by abnormal mitochondrial calcium (Ca2+) handling, energy failure and impaired mitophagy resulting in contractile dysfunction and myocyte death. We have previously shown that the 18-kDa mitochondrial translocator protein of the outer mitochondrial membrane (TSPO) can modulate mitochondrial Ca2+ uptake. Experiments were designed to test the role of the TSPO in a murine pressure-overload model of HF induced by transverse aortic constriction (TAC). Conditional, cardiac-specific TSPO knockout (KO) mice were generated using the Cre-loxP system. TSPO-KO and wild-type (WT) mice underwent TAC for 8 weeks. TAC-induced HF significantly increased TSPO expression in WT mice, associated with a marked reduction in systolic function, mitochondrial Ca2+ uptake, complex I activity and energetics. In contrast, TSPO-KO mice undergoing TAC had preserved ejection fraction, and exhibited fewer clinical signs of HF and fibrosis. Mitochondrial Ca2+ uptake and energetics were restored in TSPO KO mice, associated with decreased ROS, improved complex I activity and preserved mitophagy. Thus, HF increases TSPO expression, while preventing this increase limits the progression of HF, preserves ATP production and decreases oxidative stress, thereby preventing metabolic failure. These findings suggest that pharmacological interventions directed at TSPO may provide novel therapeutics to prevent or treat HF

A Policy Impact Evaluation Model For Scotland: Decoupling Single Farm Payments

The purpose of this paper is to assess the impacts of decoupling single farm payments in Scotland. It focuses on aggregate impacts on the agricultural products in domestic and external markets and the spill-over effect of this on the non-agricultural sector as well as an aggregate impact on the Scottish GDP. In order to capture system-wide impacts of the policy reform, a CGE model was formulated and implemented using a social accounting matrix constructed for Scotland. The simulation results suggest that the Scottish agricultural sector may encounter declines in output and factor us as a result of the policy reform. However, this critically depends on two factors: (a) the price effect of the policy reform on Scottish agricultural products relative to the EU average as well as the conditions of changes in world agricultural market prices; and (b) the extent to which customers would be sensitive to price effects of the policy reform. As far as the spill-over effect to the non-agricultural sector is concerned, decoupling of direct payments seems to have a positive spill-over effect. Similarly, the aggregate GDP effect is positive under all simulation scenarios. Critically, the simulation experiments indicate that policy shock may have a symmetrical outcome across the two sectors, with contractions in agriculture being accompanied by expansions in the non-agricultural sector, mainly because of factor market interactions between the two sectors.

Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data

Stochastic population models are widely used to model phenomena in different areas such as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond. Quantitative analysis of stochastic population models easily becomes challenging due to the combinatorial number of possible states of the population. Moreover, while the modeller easily hypothesises the mechanistic aspects of the model, the quantitative parameters associated to these mechanistic transitions are difficult or impossible to measure directly. In this paper, we investigate how formal verification methods can aid parameter inference for population discrete-time Markov chains in a scenario where only a limited sample of population-level data measurements—sample distributions among terminal states—are available. We first discuss the parameter identifiability and uncertainty quantification in this setup, as well as how the existing techniques of formal parameter synthesis and Bayesian inference apply. Then, we propose and implement four different methods, three of which incorporate formal parameter synthesis as a pre-computation step. We empirically evaluate the performance of the proposed methods over four representative case studies. We find that our proposed methods incorporating formal parameter synthesis as a pre-computation step allow us to significantly enhance the accuracy, precision, and scalability of inference. Specifically, in the case of unidentifiable parameters, we accurately capture the subspace of parameters which is data-compliant at a desired confidence level