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In view of it 's dominant role in the government' cultural and commercial spheres of the country's geography, many studies of London are concerned with an examination of the city on a national level. The nature of the conurbation itself is inherently interesting and this thesis concentrates on the geography of distributive outlets within the city. Within the present city or London I have identified various genres of distributive outlets varying in sophistication from the simplest periodic street market to the most complex shopping centre. In examining the nature of these service centres an historical link was established between them and a continuum of development suggested. described as sequential development. Each strand of the continuum is a natural phenomenon which has developed in response to the social and economic conditions of a particular period. In most spheres or life when a more advanced organism evolves its more primitive counterparts gradually disappear. In the context of London's distributive system this is only partly the case for residual elements exist today from many stages of development. In a detailed examination of each stage of the process, with reference to selected case studies I haft suggested social and economic reasons for their survival. My studies also indicated that the system was far from being static and that developments at the advanced retail levels stemming from the demands of twentieth century living, were causing tremors of change throughout the continuum. Some of these are already apparent with the plans tor removal of the central wholesale markets to larger less congested sites, and the building of squares to hold markets forced off the streets by increased pressure of traffic . From an examination of the existing situation and present plans a projection was made of the future pattern of London' s distributive system.

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No description available.

Much of the literature on the determinants of health, including alcohol consumption, has focussed on differences in individual socio-economic status as a primary risk factor. However, it has been shown that variation in health between places can be attributed to both the characteristics of the people who live in those places (composition) and also to the characteristics of the places where people live (context). From the 1990s, there has been considerable interest in the role of neighbourhoods, specifically whether their social and physical characteristics are important in explaining inequalities in health. The main aim of this thesis is to determine the influence of ‘place’ effects on alcohol-related behaviour and health and social outcomes in New Zealand. To achieve this, data was obtained for hospitalisation and mortality directly related to alcohol consumption. Age standardised rates of alcohol related hospitalisation and mortality were calculated for different census areas units over time. Secondly, a database of all alcohol outlets including type and category was obtained from the Liquor Licensing Authority and geocoded for all meshblocks in New Zealand. Using ArcGIS road network functionality, least cost distance to nearest alcohol outlets was calculated. In addition, two buffers (800 and 3000 metres) were created around the population weighted centroids of each meshblock. Statistical analysis was undertaken to examine the distribution of alcohol outlets in areas of differing socio-economic status. Thirdly, binary logistic regression was used to examine the relationship between various access measures developed and individual alcohol related behaviour from the New Zealand Health Survey (2006/07). Lastly, Ordinary Least Squares regression was used to establish the association between the density of alcohol outlets and crime, and alcohol related hospitalisation. The results reveal there is increasing geographic inequality of both hospitalisation and mortality between the most and the least deprived areas in New Zealand. Secondly, the results consistently show there is inequity in the availability of alcohol outlets; there are clear social patterns in the distribution of alcohol outlets with disproportionately high numbers in more socially deprived neighbourhoods. Thirdly, at the national level, after controlling for potential confounding factors, there was no association between either hazardous or frequent consumption of alcohol and access to alcohol outlets. However, there was an association for particular sub-populations in regards to hazardous and frequent consumption and access to alcohol outlets. Fourthly, although the explained variance was often quite low in outcome models for crime and hospitalisation, nevertheless most of the variance for crime was predicted by the density of alcohol outlets. A number of important theoretical and policy implications flow from this study. Alcohol outlets are modifiable structures in the environments that are amenable to policy interventions at a community and national level. Interventions could concentrate on three aspects to reduce excess consumption; zoning ordinances, reducing alcohol outlets in deprived areas and increased alcohol taxation. Starting with the first proposed intervention, zoning ordinances provide communities and local governments with the opportunity to regulate outlet numbers and locations as well as their trading hours. This intervention has the potential to reduce opportunities for obtaining alcohol. Secondly, a reduction in the number of alcohol outlets is likely to reduce consumption and consequently improve health and social outcomes. Finally, higher alcohol prices via increased taxation is likely to be a deterrent to excess consumption and related health outcomes. Three priority areas are identified and recommended for future research. Studies using a mixture of both qualitative and quantitative methods, to better understand the association between local purchases of alcohol, consumption and proximity to alcohol outlets would be beneficial. In addition, the use of qualitative methods to examine the influence of social capital and cohesion, culture and norms on alcohol consumption in areas with higher densities of, and better access to alcohol outlets, is imperative. Lastly, longitudinal studies are also recommended to investigate increases or decreases in the number of alcohol outlets over time and the impact of such changes on the consumption patterns of different sub-populations.

Objective of this thesis is the design of ventilation system for demonstration laboratory. Based on literature studies, experience and advice of experts is at work introduced the concept of design of ventilation equipment for laboratory, which will serve to demonstrate the air inlet into the room using different diffusers. Introduction of distribution elements for ventilation, priors the actual design of laboratory.

Low-cost network-connected smart outlets are now available for monitoring, controlling, and scheduling the energy usage of electrical devices. As a result, such smart outlets are being integrated into automated home management systems, which remotely control them by analyzing and interpreting their data. However, to effectively interpret data and control devices, the system must know the type of device that is plugged into each smart outlet. Existing systems require users to manually input and maintain the outlet metadata that associates a device type with a smart outlet. Such manual operation is time-consuming and error-prone: users must initially inventory all outlet-to-device mappings, enter them into the management system, and then update this metadata every time a new device is plugged in or moves to a new outlet. To address the problem, we propose AutoPlug, a system that automatically identifies and tracks the devices plugged into smart outlets in real time without user intervention. AutoPlug combines machine learning techniques with time-series analysis of device energy data in real time to accurately identify and track devices on startup, and as they move from outlet-to-outlet. We show that AutoPlug achieves ∼90% identification accuracy on real data collected from 13 distinct device types, while also detecting when a device changes outlets with an accuracy >90%. We implement an AutoPlug prototype on Raspberry Pi and deploy it live in a real home for a period of 20 days. We show that its performance enables it to monitor up to 25 outlets withlatency.

Phase-redistribution and full-phase separation data were generated for two-phase (air-water) flow splitting at an equal-sided impacting tee junction with a horizontal inlet and inclined outlets. The flow loop incorporated a tee junction machined in an acrylic block with the three sided having an equal diameter of 13.5 ± 0.1 mm I.D. Both sets of experiments were conducted at a nominal pressure (Ps) of 200 kPa (abs) and near-ambient temperature (Ts). The operating conditions for the phase-redistribution experiments were as follows: inlet superficial liquid velocities (JL1) ranging from 0.01 to 0.18 m/s, inlet qualities (x1) ranging from 0.1 to 0.9, mass split ratios (W3/W1) from 0 to 1.0, and outlet inclination angles ranging from horizontal to vertical. These inlet conditions corresponded to inlet flow regimes of stratified, wavy, and annular. Phase-redistribution data revealed that the redistribution of phases depended on the inlet conditions, the mass split ratio at the junction, and the inclination angle of the outlets. The magnitude of the inclination effect was dependent on the inlet flow regime. The phase redistribution in stratified flow was very sensitive to the outlet angle and full separation could be achieved at angles as low as 0.7°. Wavy flow was less sensitive to the outlet angle and annular flow was even less sensitive to the outlet angle. The capability of a single impacting tee junction to perform as a full phase separator has been examined. Experimental data were obtained for the limiting inlet conditions under which full separation was attainable at various outlet inclinations (θ) of 2.5°, 7.5°, 15°, 30°, 60°, 75°, and 90°. Full separation data have shown that a single impacting tee junction can perform as a full-phase separator for some inlet conditions. Flow phenomena near the limiting conditions were observed and a simple correlation based on the similarity between these flow phenomena and the phenomenon of liquid entrainment in small upward branches was developed. This correlation was capable of accurate prediction of the data in terms of magnitude and trend.

Phase-redistribution and full-phase separation data were generated for two-phase (air-water) flow splitting at an equal-sided impacting tee junction with a horizontal inlet and inclined outlets. The flow loop incorporated a tee junction machined in an acrylic block with the three sided having an equal diameter of 13.5 ± 0.1 mm I.D. Both sets of experiments were conducted at a nominal pressure (Ps) of 200 kPa (abs) and near-ambient temperature (Ts). The operating conditions for the phase-redistribution experiments were as follows: inlet superficial liquid velocities (JL1) ranging from 0.01 to 0.18 m/s, inlet qualities (x1) ranging from 0.1 to 0.9, mass split ratios (W3/W1) from 0 to 1.0, and outlet inclination angles ranging from horizontal to vertical. These inlet conditions corresponded to inlet flow regimes of stratified, wavy, and annular. Phase-redistribution data revealed that the redistribution of phases depended on the inlet conditions, the mass split ratio at the junction, and the inclination angle of the outlets. The magnitude of the inclination effect was dependent on the inlet flow regime. The phase redistribution in stratified flow was very sensitive to the outlet angle and full separation could be achieved at angles as low as 0.7°. Wavy flow was less sensitive to the outlet angle and annular flow was even less sensitive to the outlet angle. The capability of a single impacting tee junction to perform as a full phase separator has been examined. Experimental data were obtained for the limiting inlet conditions under which full separation was attainable at various outlet inclinations (θ) of 2.5°, 7.5°, 15°, 30°, 60°, 75°, and 90°. Full separation data have shown that a single impacting tee junction can perform as a full-phase separator for some inlet conditions. Flow phenomena near the limiting conditions were observed and a simple correlation based on the similarity between these flow phenomena and the phenomenon of liquid entrainment in small upward branches was developed. This correlation was capable of accurate prediction of the data in terms of magnitude and trend.

The retail sector accounts for more than 3% of the total electricity consumption in the UK and approximately 1% of total UK CO2 emissions. The overarching aim of this project was to understand the energy consumption of the Tesco estate (the market leader), identify best practice, and find ways to identify opportunities for energy reduction. The literature review of this work covered the topic of energy consumption in the retail sector, and reviewed benchmarks for this type of buildings from the UK, Europe and the US. Related data analysis techniques used in the industry or presented in the literature were also reviewed. This revealed that there are many different analysis and forecasting techniques available, and that they fall into two different categories: techniques that require past energy consumption data in order to calculate the future consumption, such as statistical regression, and techniques that are able to estimate the energy consumption of buildings, based on the specific building's characteristics, such as thermal simulation models. These are usually used for new buildings, but they could also be used in benchmarking exercises, in order to achieve best practice guides. Gaps in the industry knowledge were identified, and it was suggested that better analytical tools would enable the industry to create more accurate energy budgets for the year ahead leading to better operating margins. Benchmarks for the organisation's buildings were calculated. Retail buildings in the Tesco estate were found to have electrical intensity values between 230 kWh/m2 and 2000 kWh/m2 per year. Still the average electrical intensity of these buildings in 2010-11 was found to be less than the calculated UK average of the 2006-07 period. The effect of weather on gas and electricity consumption was investigated, and was found to be significant (p < 0.001). There was an effect related to the day-of-the-week, but this was found to be more related to the sales volume on those days. Sales volume was a proxy that was used to represent the number of customers walking through the stores. The built date of the building was also considered to be an interesting factor, as the building regulations changed significantly throughout the years and the sponsor did not usually carry out any fabric work when refurbishing the stores. User behaviour was also identified as an important factor that needed to be investigated further, relating to both how the staff perceives and manages the energy consumption in their work environment, as well as how the customers use the refrigeration equipment. Following a statistical analysis, significant factors were determined and used to create multiple linear regression models for electricity and gas demands in hypermarkets. Significant factors included the sales floor area of the store, the stock composition, and a factor representing the thermo-physical characteristics of the envelope. Two of the key findings are the statistical significance of operational usage factors, represented by volume of sales, on annual electricity demand and the absence of any statistically significant operational or weather related factors on annual gas demand. The results suggest that by knowing as little as four characteristics of a food retail store (size of sales area, sales volume, product mix, year of construction) one can confidently calculate its annual electricity demands (R2=0.75, p < 0.001). Similarly by knowing the size of the sales area, product mix, ceiling height and number of floors, one can calculate the annual gas demands (R2=0.5, p < 0.001). Using the models created, along with the actual energy consumption of stores, stores that are not as energy efficient as expected can be isolated and investigated further in order to understand the reason for poor energy performance. Refrigeration data from 10 stores were investigated, including data such as the electricity consumption of the pack, outside air temperature, discharge and suction pressure, as well as percentage of refrigerant gas in the receiver. Data mining methods (regression and Fourier transforms) were employed to remove known operational patterns (e.g. defrost cycles) and seasonal variations. Events that have had an effect on the electricity consumption of the system were highlighted and faults that had been identified by the existing methodology were filtered out. The resulting dataset was then analysed further to understand the events that increase the electricity demand of the systems in order to create an automatic identification method. The cases analysed demonstrated that the method presented could form part of a more advanced automatic fault detection solution; potential faults were difficult to identify in the original electricity dataset. However, treating the data with the method designed as part of this work has made it simpler to identify potential faults, and isolate probable causes. It was also shown that by monitoring the suction pressure of the packs, alongside the compressor run-times, one could identify further opportunities for electricity consumption reduction.