Malaria prevention in north-eastern Tanzania: patterns of expenditure and determinants of demand at the household level.

Objective. This study aims to provide a better understanding of the amounts spent on different malaria prevention products and the determinants of these expenditures. Methods. 1,601 households were interviewed about their expenditure on malaria mosquito nets in the past five years, net re-treatments in the past six months and other expenditures prevention in the past two weeks. Simple random sampling was used to select villages and streets while convenience sampling was used to select households. Expenditure was compared across bed nets, aerosols, coils, indoor spraying, using smoke, drinking herbs and cleaning outside environment. Findings. 68% of households owned at least one bed net and 27% had treated their nets in the past six months. 29% were unable to afford a net. Every fortnight, households spent an average of US $0.18 on nets and their treatment, constituting about 47% of total prevention expenditure. Sprays, repellents and coils made up 50% of total fortnightly expenditure (US$0.21). Factors positively related to expenditure were household wealth, years of education of household head, household head being married and rainy season. Poor quality roads and living in a rural area had a negative impact on expenditure. Conclusion. Expenditure on bed nets and on alternative malaria prevention products was comparable. Poor households living in rural areas spend significantly less on all forms of malaria prevention compared to their richer counterparts. Breaking the cycle between malaria and poverty is one of the biggest challenges facing malaria control programmes in Africa.

User experience of mobile cloud applications - current state and future directions

The increasing penetration rate of feature rich mobile devices such as smartphones and tablets in the global population has resulted in a large number of applications and services being created or modified to support mobile devices. Mobile cloud computing is a proposed paradigm to address the resource scarcity of mobile devices in the face of demand for more computing intensive tasks. Several approaches have been proposed to confront the challenges of mobile cloud computing, but none has used the user experience as the primary focus point. In this paper we evaluate these approaches in respect of the user experience, propose what future research directions in this area require to provide for this crucial aspect, and introduce our own solution.

Determinants of bed net use in The Gambia: implications for malaria control

Malaria is still one of the biggest health threats in the developing world, with an estimated 300 million episodes per year and one million deaths, most of which are in sub-Saharan Africa. Although the efficacy and cost-effectiveness of treated bed nets has been widely reported, little is known about the range, strength, or interaction between different factors that influence their demand at the household level. This study modeled the determinants of bed net ownership as well as the factors that influence the number of bed nets purchased. Data was collected from 1,700 randomly selected households in the Farafenni region of The Gambia. Interviews were also held with 129 community spokespersons to explore the extent to which community level factors such as the quality of roads and access to market centers also influence demand for bed nets. The results of each model of demand and their policy implications are discussed.

Biomethane production from grass silage: laboratory assessment to maximise yields

On-farm biogas production is typically associated with forage maize as the biomass source. Digesters are designed and operated with the focus of optimising the conditions for this feedstock. Thus, such systems may not be ideally suited to the digestion of grass. Ireland has ca. 3.85 million ha of grassland. Annual excess grass, surplus to livestock requirements, could potentially fuel an anaerobic digestion industry. Biomethane associated with biomass from 1.1 % of grassland in Ireland, could potentially generate over 10 % renewable energy supply in transport. This study aims to identify and optimise technologies for the production of biomethane from grass silage. Mono-digestion of grass silage and co-digestion with slurry, as would occur on Irish farms, is investigated in laboratory trials. Grass silage was shown to have 7 times greater methane potential than dairy slurry on a fresh weight basis (107 m3 t-1 v 16 m3 t-1). However, comprehensive trace element profiles indicated that cobalt, iron and nickel are deficient in mono-digestion of grass silage at a high organic loading rate (OLR) of 4.0 kg VS m-3 d-1. The addition of a slurry co-substrate was beneficial due to its wealth of essential trace elements. To stimulate hydrolysis of high lignocellulose grass silage, particle size reduction (physical) and rumen fluid addition (biological) were investigated. In a continuous trial, digestion of grass silage of <1 cm particle size achieved a specific methane yield of 371 L CH4 kg-1 VS when coupled with rumen fluid addition. The concept of demand driven biogas was also examined in a two-phase digestion system (leaching with UASB). When demand for electricity is low it is recommended to disconnect the UASB from the system and recirculate rumen fluid to increase volatile fatty acid (VFA) and soluble chemical oxygen demand (SCOD) production whilst minimising volatile solids (VS) destruction. At times of high demand for electricity, connection of the UASB increases the destruction of volatiles and associated biogas production. The above experiments are intended to assess a range of biogas production options from grass silage with a specific focus on maximising methane yields and provide a guideline for feasible design and operation of on-farm digesters in Ireland.

Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand

The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.

Empirical analysis and improved modelling of natural gas demand in Ireland

Countries across the world are being challenged to decarbonise their energy systems in response to diminishing fossil fuel reserves, rising GHG emissions and the dangerous threat of climate change. There has been a renewed interest in energy efficiency, renewable energy and low carbon energy as policy‐makers seek to identify and put in place the most robust sustainable energy system that can address this challenge. This thesis seeks to improve the evidence base underpinning energy policy decisions in Ireland with a particular focus on natural gas, which in 2011 grew to have a 30% share of Ireland’s TPER. Natural gas is used in all sectors of the Irish economy and is seen by many as a transition fuel to a low-carbon energy system; it is also a uniquely excellent source of data for many aspects of energy consumption. A detailed decomposition analysis of natural gas consumption in the residential sector quantifies many of the structural drives of change, with activity (R2 = 0.97) and intensity (R2 = 0.69) being the best explainers of changing gas demand. The 2002 residential building regulations are subject to an ex-post evaluation, which using empirical data finds a 44 ±9.5% shortfall in expected energy savings as well as a 13±1.6% level of non-compliance. A detailed energy demand model of the entire Irish energy system is presented together with scenario analysis of a large number of energy efficiency policies, which show an aggregate reduction in TFC of 8.9% compared to a reference scenario. The role for natural gas as a transition fuel over a long time horizon (2005-2050) is analysed using an energy systems model and a decomposition analysis, which shows the contribution of fuel switching to natural gas to be worth 12 percentage points of an overall 80% reduction in CO2 emissions. Finally, an analysis of the potential for CCS in Ireland finds gas CCS to be more robust than coal CCS for changes in fuel prices, capital costs and emissions reduction and the cost optimal location for a gas CCS plant in Ireland is found to be in Cork with sequestration in the depleted gas field of Kinsale.

Bottom-up modelling of energy demand and technical energy savings potential in the Irish residential sector

The International Energy Agency has repeatedly identified increased end-use energy efficiency as the quickest, least costly method of green house gas mitigation, most recently in the 2012 World Energy Outlook, and urges all governing bodies to increase efforts to promote energy efficiency policies and technologies. The residential sector is recognised as a major potential source of cost effective energy efficiency gains. Within the EU this relative importance can be seen from a review of the National Energy Efficiency Action Plans (NEEAP) submitted by member states, which in all cases place a large emphasis on the residential sector. This is particularly true for Ireland whose residential sector has historically had higher energy consumption and CO2 emissions than the EU average and whose first NEEAP targeted 44% of the energy savings to be achieved in 2020 from this sector. This thesis develops a bottom-up engineering archetype modelling approach to analyse the Irish residential sector and to estimate the technical energy savings potential of a number of policy measures. First, a model of space and water heating energy demand for new dwellings is built and used to estimate the technical energy savings potential due to the introduction of the 2008 and 2010 changes to part L of the building regulations governing energy efficiency in new dwellings. Next, the author makes use of a valuable new dataset of Building Energy Rating (BER) survey results to first characterise the highly heterogeneous stock of existing dwellings, and then to estimate the technical energy savings potential of an ambitious national retrofit programme targeting up to 1 million residential dwellings. This thesis also presents work carried out by the author as part of a collaboration to produce a bottom-up, multi-sector LEAP model for Ireland. Overall this work highlights the challenges faced in successfully implementing both sets of policy measures. It points to the wide potential range of final savings possible from particular policy measures and the resulting high degree of uncertainty as to whether particular targets will be met and identifies the key factors on which the success of these policies will depend. It makes recommendations on further modelling work and on the improvements necessary in the data available to researchers and policy makers alike in order to develop increasingly sophisticated residential energy demand models and better inform policy.