Transport across nanogaps using semiclassically consistent boundary conditions

Charge particle transport across nanogaps is studied theoretically within the Schrodinger-Poisson mean field framework and the existence of limiting current investigated. It is shown that the choice of a first order WKB wavefunction as the transmitted wave leads to self consistent boundary conditions and gives results that are significantly different in the non-classical regime from those obtained using a plane transmitted wave. At zero injection energies, the quantum limiting current density, J_c, is found to obey the local scaling law J_c ~ (V_g)^alpha/(D)^{5-2alpha} with the gap separation D and voltage V_g. The exponent alpha > 1.1 with alpha --> 3/2 in the classical regime of small de Broglie wavelengths. These results are consistent with recent experiments using nanogaps most of which are found to be in a parameter regime where classical space charge limited scaling holds away from the emission dominated regime.Comment: 4 pages, 4 ps figure

Liquefaction Fragilities for Buried Lifelines

For buried structures, such as conduits and underground pipes, liquefaction induced forces will depend on the volume of soil surrounding the structure that will liquefy. Here, a methodology to calculate the probability of the onset of liquefaction at a given depth in a soil deposit is extended to assess the probability that a specified volume of soil will liquefy when liquefaction occurs at a given depth in the deposit. To account for the variability of soil properties with depth, the soil deposit is divided into horizontal layers and the volume of liquefied soil in each layer is calculated as the product of the layer thickness by the lateral extent of liquefaction. Within each layer, the horizontal variability of the soil properties is described by a homogeneous and axisymmetric random field. It is assumed that the ground motions in the horizontal direction are perfectly correlated. The results are presented in terms of the probability of liquefaction spreading over a given area (a circle of radius R) as a function of the intensity of the ground motion

Locating the ‘radical’ in 'Shoot the Messenger'

This is the author's accepted manuscript. The final published article is available from the link below, copyright 2013 @ Edinburgh University Press.The 2006 BBC drama Shoot the Messenger is based on the psychological journey of a Black schoolteacher, Joe Pascale, accused of assaulting a Black male pupil. The allegation triggers Joe's mental breakdown which is articulated, through Joe's first-person narration, as a vindictive loathing of Black people. In turn, a range of common stereotypical characterisations and discourses based on a Black culture of hypocrisy, blame and entitlement is presented. The text is therefore laid wide open to a critique of its neo-conservatism and hegemonic narratives of Black Britishness. However, the drama's presentation of Black mental illness suggests that Shoot the Messenger may also be interpreted as a critique of social inequality and the destabilising effects of living with ethnicised social categories. Through an analysis of issues of representation, the article reclaims this controversial text as a radical drama and examines its implications for and within a critical cultural politics of ‘race’ and representation

Changes in nasopharyngeal carriage and serotype distribution of antibiotic-resistant Streptococcus pneumoniae before and after the introduction of 7-valent pneumococcal conjugate vaccine in Hong Kong

This study assessed the changes in serotype distribution and antibiotic resistance of Streptococcus pneumoniae isolates in children before and after introduction of the 7-valent pneumococcal conjugate vaccine (PCV7) in Hong Kong. Nasopharyngeal specimens were collected from 1978 and 2211 children (ages, 2 to 6 years) attending day care centers or kindergartens in period 1 (1999-2000) and period 2 (2009-2010), respectively. Carriage of PCV7 serotypes decreased from 12.8% to 8.6% (P < 0.01). The relative contribution of PCV7 serotypes 14 and 18C had decreased, whereas that for non-PCV7 serotypes 19A, 6A, 6C, 23A, and 15B had increased. In period 2, PCV7 penetration rate (at least 1 dose) for children aged 2, 3, 4, and 5 years was 43%, 35.7%, 26.7%, and 20.4%, respectively. In multivariate analysis, PCV7 use was the only independent variable associated with fewer PCV7 serotype carriages (odds ratio 0.5; P = 0.001). In period 2, high rates of dual penicillin/erythromycin nonsusceptibility were found in serotypes 6B (77.3%), 14 (100%), 19F (100%), 23F (78%), 19A (75%), 6A (87.8%), 6C (59.3%), and 23A (78.9%).postprin

Building utilisation analytics: human occupancy counting and thermal comfort prediction with ambient sensing

With advancement in sensors and the Internet of Things, gathering spatiotemporal information from one&amp;rsquo;s surroundings has become more convenient. There are multiple phenomenological behaviours, such as indoor comfort and occupancy trends, that can be inferred from this information. There are multiple advantages to having an accurate indoor occupancy prediction, including better understanding of space-room utilisation, which can be used to further inform energy consumption reduction, human indoor comfort optimisation and security enhancement. We use non-intrusive ambient sensors to infer indoor occupancy patterns. Non-intrusive ambient sensors are utilised because they are commonly available in building management systems (BMSs). Machine learning techniques are applied and data-driven approaches are implemented to identify indoor human occupancy and predict comfort.These facilitate the decision-making tasks for building management professionals and are used in real-time monitoring. Our preliminary study with multiple ambient sensors reveals that carbon dioxide is one of the best predictors of indoor human occupancy. We design a seasonal trend decomposition algorithm by implementing pervasive sensing and leveraging carbon dioxide data from BMS sensors. The first model is seasonal decomposition for human occupancy counting (SD-HOC), a customised feature transformation decomposition prediction model. This provides a novel way to estimate the number of people within a closed space, using one carbon dioxide sensor. SD-HOC integrates a time lag and line of best fit model in the preprocessing algorithms and customises different regression algorithms for each subcomponent, to predict each respective human occupancy component value. Utilising several machine learning techniques, a set of prediction values for each component is obtained. Finally, additive decomposition is used to reconstruct the prediction value for human indoor occupancy. We improve the algorithm to cover multiple buildings with different contexts and locations and develop a large Room Utilisation Prediction with carbon dioxide sensor (RUP). RUP improves SD-HOC and is able to predict a larger number of occupants, up to three hundred, using data from a single carbon dioxide sensor. RUP de-noises and pre-processes the carbon dioxide data. We use multiple variants of seasonal decomposition techniques and feature factorisation for both occupant and carbon dioxide datasets, and develop a zero pattern adjustment model to increase the accuracy. We run our model in two different locations that have different contexts.The prediction accuracy results outweigh the state-of-the-art techniques for time series decomposition and regression. RUP is a reliable model for any building with adequate historical data. In the real world, this condition is not always feasible, due to several limitations such as a new building only having limited historical data, or government/military buildings that have strictly controlled access to historical ambient sensor data. One way to solve this problem is by implementing a transfer learning technique with SD-HOC. We design a semi-supervised domain adaptation method for carbon dioxide - human occupancy counter (DA-HOC) to estimate the number of people within one room, by using a carbon dioxide sensor with a limited number of training labels (as little as one day of historical data). The DA-HOC model is trained using data from a source domain that has a more complete set of training labels, and transferred to predict the occupancy of a much larger room of the target domain, with very little training data. We enhance DA-HOC into DA-HOC++ and successfully experiment with the model to transfer the knowledge from one room to five different rooms in different countries. Moving beyond indoor human occupancy, each occupant&amp;rsquo;s comfort is also a crucial problem that needs to be considered. Indoor comfort prediction is crucial for energy efficiency cost adjustment, human productivity and non-wastage of resources. Maintaining human indoor comfort levels at acceptable values is one of the primary goals in any building and room utilisation. The main problem is that everybody has a different level of acceptance of what is comfortable. We implement a machine learning algorithm to predict the thermal comfort for each occupant. Our model successfully achieves a respectable accuracy of comfort prediction to help the BMS adjust the temperature. This thesis presents several contributions in machine learning for indoor human occupancy and comfort prediction. This research implements and extends existing data mining techniques to solve problems on time series prediction. The solutions are scalable and can also work with minimal sets of historical training data with a transfer learning method. The research contributions in this thesis present multiple occupancy algorithms for both indoor human occupancy and thermal comfort. We believe that this research provides a big step towards building a robust solution for smart homes and smart buildings, in which the buildings are more aware of their occupants and can adapt to their needs