Development of a Solar Assisted Heat Pump Desalination System

In view of the growing global demand for energy and concern expressed for environmental degradation, a clean and "free" energy source, such as solar energy, has been receiving greater attention in recent years for various applications using different techniques. The Direct Expansion Solar Assisted Heat Pump (DX-SAHP) principle is one of the most promising techniques as it makes use of both solar and ambient energy. As the system has capability to function at low temperatures, it has the potential to operate at night in the tropics. The system utilizes multi-effect distillation (MED) principle for the conversion of seawater to fresh water. An experimental setup of the DX-SAHP desalination system has been built at the Department of Mechanical Engineering, National University of Singapore (NUS). This system uses two types of flat-plate solar collectors. One is called evaporator-collector, where no glazing is used, and the efficiency varies between 80 and 90%. The other type of collector is single-glazed, where the maximum efficiency is about 60%, and it is used for feed water heating. For the heat pump cycle, refrigerant R134a is used. The present study provides a comprehensive analyses and performance evaluation of this system under different operating and meteorological conditions of Singapore. The Coefficient of Performance (COP) of the heat pump system reached a maximum value of 10. For a single effect of desalination, the system shows a Performance Ratio (PR) of around 1.3.

Grading : A Workbook of Manual Land Grading Techniques [Revised Edition]

Grading is basic to the work of Landscape Architects concerned with design on the land. Gradients conducive to easy use, rainwater drained away, and land slope contributing to functional and aesthetic use are all essential to the amenity and pleasure of external environments. This workbook has been prepared specifically to support the program of landscape construction for students in Landscape Architecture. It is concerned primarily with the technical design of grading rather than with its aesthetic design. It must be stressed that the two aspects are rarely separate; what is designed should be technically correct and aesthetically pleasing - it needs to look good as well as to function effectively. This revised edition contains amended and new content which has evolved out of student classes and discussion with colleagues. I am pleased to have on record that every delivery of this workbook material has resulted in my own better understanding of grading and the techniques for its calculation and communication.

The relationship between ambient ultraviolet radiation (UVR) and objectively measured personal UVR exposure dose is modified by season and latitude

Despite the widespread use of ambient ultraviolet radiation (UVR) as a proxy measure of personal exposure to UVR, the relationship between the two is not well-defined. This paper examines the effects of season and latitude on the relationship between ambient UVR and personal UVR exposure. We used data from the AusD Study, a multi-centre cross-sectional study among Australian adults (18-75 years), where personal UVR exposure was objectively measured using polysulphone dosimeters. Data were analysed for 991 participants from 4 Australian cities of different latitude: Townsville (19.3 °S), Brisbane (27.5 °S), Canberra (35.3 °S) and Hobart (42.8 °S). Daily personal UVR exposure varied from 0.01 to 21 Standard Erythemal Doses (median=1.1, IQR: 0.5–2.1), on average accounting for 5% of the total available ambient dose. There was an overall positive correlation between ambient UVR and personal UVR exposure (r=0.23, p<0.001). However, the correlations varied according to season and study location: from strong correlations in winter (r=0.50) and at high latitudes (Hobart, r=0.50; Canberra, r=0.39), to null or even slightly negative correlations, in summer (r=0.01) and at low latitudes (Townsville, r=-0.06; Brisbane, r=-0.16). Multiple regression models showed significant effect modification by season and location. Personal exposure fraction of total available ambient dose was highest in winter (7%) and amongst Hobart participants (7%) and lowest in summer (1%) and in Townsville (4%). These results suggest season and latitude modify the relationship between ambient UVR and personal UVR exposure. Ambient UVR may not be a good indicator for personal exposure dose under some circumstances.

Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

Identification of salivary N-glycoproteins and measurement of glycosylation site occupancy by boronate glycoprotein enrichment and liquid chromatography/electrospray ionization tandem mass spectrometry

RATIONALE Diseases including cancer and congenital disorders of glycosylation have been associated with changes in the site-specific extent of protein glycosylation. Saliva can be non-invasively sampled and is rich in glycoproteins, giving it the potential to be a useful biofluid for the discovery and detection of disease biomarkers associated with changes in glycosylation. METHODS Saliva was collected from healthy individuals and glycoproteins were enriched using phenylboronic acid based glycoprotein enrichment resin. Proteins were deglycosylated with peptide-N-glycosidase F and digested with AspN or trypsin. Desalted peptides and deglycosylated peptides were separated by reversed-phase liquid chromatography and detected with on-line electrospray ionization quadrupole-time-of-flight mass spectrometry using a 5600 TripleTof instrument. Site-specific glycosylation occupancy was semi-quantitatively determined from the abundance of deglycosylated and nonglycosylated versions of each given peptide. RESULTS Glycoprotein enrichment identified 67 independent glycosylation sites from 24 unique proteins, a 3.9-fold increase in the number of glycosylation sites identified. Enrichment of glycoproteins rather than glycopeptides allowed detection of both deglycosylated and nonglycosylated versions of each peptide, and thereby robust measurement of site-specific occupancy at 21 asparagines. Healthy individuals showed limited biological variability in occupancy, with partially modified sites having characteristics consistent with inefficient glycosylation by oligosaccharyltransferase. Inclusion of negative controls without enzymatic deglycosylation controlled for spontaneous chemical deamidation, and identified asparagines previously incorrectly annotated as glycosylated. CONCLUSIONS We developed a sample preparation and mass spectrometry detection strategy for rapid and efficient measurement of site-specific glycosylation occupancy on diverse salivary glycoproteins suitable for biomarker discovery and detection of changes in glycosylation occupancy in human disease.

The impact of Cool Roof applications on energy performance : results from Australian sub-tropical and tropical field studies

Cool roof coatings are identified by their solar reflectance index. They have been reported to have multiple benefits, the extent of which are strongly dependent on the peculiarities of the local climate, building stock and electricity network. This paper presents measured and simulated data from residential, educational and commercial buildings involved in recent field trials in Australia. The purpose of the field trials was to evaluate the impact of such coatings on electricity demand and load and to assess their potential application to improve comfort whilst avoiding the need for air conditioners. Measured reductions in temperature, power (kW) and energy (kWh) were used to develop a predictive model that correlates ambient temperature distribution profiles, building demand reduction profiles and electricity network peak demand times. Combined with simulated data, the study indicates the types of buildings that could be targeted in Demand Management programs for the mutual benefit of electricity networks and building occupants.

The effects of metabolic work rate and ambient environment on physiological tolerance times while wearing explosive and chemical personal protective equipment

This study evaluated the physiological tolerance times when wearing explosive and chemical (>35kg) personal protective equipment (PPE) in simulated environmental extremes across a range of differing work intensities. Twelve healthy males undertook nine trials which involved walking on a treadmill at 2.5, 4 and 5.5 km.h-1 in the following environmental conditions, 21, 30 and 37 °C wet bulb globe temperature (WBGT). Participants exercised for 60 min or until volitional fatigue, core temperature reached 39 °C, or heart rate exceeded 90% of maximum. Tolerance time, core temperature, skin temperature, mean body temperature, heart rate and body mass loss were measured. Exercise time was reduced in the higher WBGT environments (WBGT37

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

Deterrence of drug driving : the impact of the ACT drug driving legislation and detection techniques

Overarching Research Questions Are ACT motorists aware of roadside saliva based drug testing operations? What is the perceived deterrent impact of the operations? What factors are predictive of future intentions to drug drive? What are the differences between key subgroups

Identifying product families using data mining techniques in manufacturing paradigm

Identifying product families has been considered as an effective way to accommodate the increasing product varieties across the diverse market niches. In this paper, we propose a novel framework to identifying product families by using a similarity measure for a common product design data BOM (Bill of Materials) based on data mining techniques such as frequent mining and clus-tering. For calculating the similarity between BOMs, a novel Extended Augmented Adjacency Matrix (EAAM) representation is introduced that consists of information not only of the content and topology but also of the fre-quent structural dependency among the various parts of a product design. These EAAM representations of BOMs are compared to calculate the similarity between products and used as a clustering input to group the product fami-lies. When applied on a real-life manufacturing data, the proposed framework outperforms a current baseline that uses orthogonal Procrustes for grouping product families.

Investigations of ultrafine particles emitted by ships and other transport modes in the port of Brisbane

This research measured particle and gaseous emissions from ships and trains operating within the Port of Brisbane, and explored their influence on ambient air composition at a downwind suburban measurement site. The ship and train emission factor investigations resulted in the development of novel measurement techniques which permit the quantification of particle and gaseous emission factors using samples collected from post-emission exhaust plumes. The urban influence investigation phase of the project produced a new approach to identifying influences from ship emissions.