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The UK housing market has faced significant challenges since the 2008 Global financial crisis, which resulted in negative financial and social impacts such as repossessions, growing social housing waiting lists, mass unemployment in the construction industries, and soaring housing costs. Many people in the UK are mishoused, ill-housed, or unhoused due to the lack of adequate housing options and ongoing lack of supply and affordability. To this end, the concept of cohousing is designed as an answer to this long-running crisis, seeking to foster meaningful relationships, social interaction, and energy efficient concepts. The terms 'cohousing' and 'collaborative housing' refer to a shared housing model or community. Through cohousing, residents are committed to living healthy lifestyle. As a contribution to this effort, this study explores the contemporary insights of UK cohousing with the aim of developing a cohousing design and developmental framework for its residents, architects and other related stakeholders. This is to investigate how can cohousing design provide a lifelong housing option and create an intergenerational community, which would lead to social engagement and low environmental impact. This study is guided by environmental psychology theory and used an inductive approach with a qualitative strategy. The research data was collected through semi-structured interviews, site observation and secondary data documents provided by the selected cohousing communities. Architects and cohousing residents are the core research participants. In total 22 participants from seven cohousing communities were interviewed in this study. Meanwhile, in-depth observations were used at two communities to understand the cohousing design, environmentally sustainable features, and residents' social interactions. The secondary data was used to supplement and validate primary data sets. The data was analysed using qualitative content analysis and ethnographic descriptive analysis to identify the core themes, and the fundamental design elements and their interrelationships. On this basis, the cohousing framework was developed using the research findings. The main findings include five sections: group motivation, sustainability, social proximity, thinking and behavioural changes, and neighbourhood design. In terms of group motivation, the study found that that the motivation for entering cohousing represents a complex decision-making process. It was identified that the social, environmental sustainability, and financial aspects were the three top priorities for people considering joining a cohousing community. Referring to the sustainability of the cohousing community, 18 categories were identified under environmental, social and economic sustainability dimensions. The categories related to 'Energy', 'Sharing', and 'Heating' were identified as influential categories with a significant impact on the community's sustainable living. Additionally, the study explored two aspects of the social environment in cohousing communities - social proximity and behavioural changes. The results show that social proximity and residents' practices are strongly associated with the community build environment and neighbourhood planning. The physical and social environments simultaneously influence each other. Finally, the cohousing framework encompassed the overall research findings and is a guiding tool that fills the knowledge gap on cohousing research. The findings of the study provided not only the comprehensive understanding of UK cohousing model, but also offered the practical design, sustainable and management guidance for wider collaborative communities in the UK and beyond. Meanwhile, the design recommendations for various stakeholders are highlighted for the future housing practice. The study results are transferable to other contexts with other stakeholders.

Background: We know a lot about illness, but we do not know as much about wellness. The positive psychology movements over recent decades have improved our understanding of wellness a lot. However, the current research on mental well-being (MWB) is still lacking in integrated evidence on the multifactorial association between MWB and its determinants and the incorporated effects of these determinants on MWB. Moreover, there is a dearth of research on the consequences of MWB in later life beyond associations with mortality. Furthermore, what is new to the current knowledge of MWB is whether MWB could mediate and moderate the association between later-life risk factors and ageing well. Aim: This thesis draws on the central question of the epidemiology of MWB in older age, from its distribution, determinants and consequences. The overall aim of this PhD research is to discover whether MWB influences outcomes associated with ageing and how it is associated with ageing well. Methods: This thesis provided integrated quantitative evidence drawing on the Health Survey for England and the UK Household Longitudinal Study. It consisted of three phases: Phase 1. Preliminary research which included two parallel systematic reviews of the literature on the relationship between later life risk factors and MWB, and epidemiological findings on MWB consequences. Phase 2. Secondary data analysis to describe the distribution, investigate the determinants and explore the possible consequences of MWB in later life, and unveil the impact of MWB on ageing well. Multiple regression analysis and mixed-effects modelling were used to examine the determinants of older people's MWB. The association between MWB and its consequences was analysed using a random-effects model. The mediation effect of MWB 20 on the association between general health and physically ageing well (PCS) was analysed using structural equation modelling and path analysis, while the moderation effect of MWB was analysed using multiple regression and logistic regression. The Cox proportional hazards regression and a Kaplan-Meier survival curve were used to investigate the lost to follow-ups. Phase 3. Discussion and integrated findings with policy implementation ideas. Results: The distribution of MWB generally displayed a slightly inverted 'U-shape' (convex relationship) in old age after controlling for physical health and demographic & socio-economic variables. The results from the cross-sectional analysis and longitudinal analysis provided statistical evidence of the determinants of older people's MWB. In summary, ill-health (both physically and mentally), poor sleep quality and financial situation were all significantly associated with low MWB. On the other hand, being retired or remain employed, being married, being female, having a higher qualification level and being religious were all significantly associated with high MWB. Mental illness and physical health were demonstrated to be the most influential factors over all other determinants of older people's MWB. This PhD also confirmed that retaining MWB was a vital criterion of ageing well. MWB could enhance physically ageing well longitudinally. Moreover, what is new to the current knowledge of MWB is that MWB could mediate and moderate the association between general health and physically ageing well (PCS) even after controlling for demographic & socio-economic variables and psychological variables. Conclusion: This PhD underlined a critical message that MWB plays both roles as a critical outcome and a determinant of physical wellness. Furthermore, MWB was a protector against later-life risks. This PhD thesis is the first research that links the 21 distribution, determinants and consequences of MWB in old age. It could add value to the knowledge of MWB and promote MWB through policy initiatives.

Economists in different fields have been developing theoretical models and conducting experimental studies to understand how people are making decisions over various products and different domains. The first essay explores how individual choices may change over time and differ across multiple domains. This study also shows evidence for domain-specific temporal discounting and suggests that consumers are generally more impatient for health rewards but more patient for environmental rewards. The second essay aims to explore which models, the traditional temporal discounting models, or machine learning models, can better predict individuals’ intertemporal choices. Results suggests that some machine learning algorithms, for example, random forest, have better prediction powers compared to the temporal discounting models. Although machine learning method sometimes suffer from overfitting problems, it may have the potential to give more accurate predictions for individual choices when the training data have enough information on individual previous choices or behaviors. The third essay examines how information framing and consumers’ neighborhood attachment impact their product choices. In particular, the product we focus on is low-input turfgrass. We find the presence of environmental impact information in the choice experiment has impacts on homeowners’ preferences for low-input turfgrass. We also find the homeowners’ neighborhood attachment affects their lawn maintenance behavior.

Functional cardiovascular assessment is important in the study of fetal pathology, and is becom- ing an increasingly popular tool in clinical practice. The myocardial performance index (MPI) is one particularly important index for evaluating global myocardial function, as it combines assessment of both systolic and diastolic function and has been shown to be a sensitive indicator of cardiac dysfunction. Moreover, the MPI can be obtained non-invasively, and it is indepen- dent of ventricular geometry and heart rate. However, the current method for MPI calculation, which is obtained by manually annotating the time intervals required for the MPI calculation, can be time-consuming and demonstrates poor inter-operator repeatability; this is evidenced by a broad variation in normal ranges of MPI values reported in the literature. Therefore, there is motivation to develop an automated method for MPI calculation, which o↵ers a convenient and consistent way to estimate the MPI, hence making fetal cardiac functional evaluation based on the MPI easier and more reliable. In this thesis, automated fetal MPI calculation algorithms for both left and right ventricles have been developed by investigating manual measurement as well as exploring morphological characteristics of ultrasound images. The performance of these algorithms has been validated, demonstrating excellent agreement with aggregated manual an- notation performed by experts. These algorithms have also been deployed in clinical studies evaluating fetal MPI beat-to-beat variation, tissue Doppler-derived MPI, and developing normal MPI reference ranges.

Filamentous fungi produce a wide variety of bioactive secondary metabolites, including polyketides and polyketide-nonribosomal peptide hybrids. These compounds are synthesized by the highly-programmed and iterative multifunctional megasynthases: polyketide synthases (PKSs) and polyketide-nonribosomal peptide synthases (PKS-NRPSs). Compared to their well-studied bacterial counterparts, the biosynthetic programming rules utilized by the fungal PKSs remain mostly unknown. To obtain a better understanding of the biosynthetic logic of PKSs and to decipher the relationship between protein sequences and the chemical structures of fungal secondary metabolites, this dissertation will aim at studying one fungal biosynthetic system involving PKSs and probe the enzymatic programming rules in highly reducing PKSs (HR-PKS). Specifically, the biochemistry of the HR-PKS Hpm8 from a tandem fungal iterative PKS (IPKS) system for the formation of the resorcylic acid lactone hypothemycin will be extensively investigated with the help of a series of synthetically-prepared labeled intermediates.

The world is experiencing fossil fuel depletion, global warming and environmental deterioration due to the overuse of fossil fuels. Biodiesel, as an alternative fuel, is considered as part of the solution. Biodiesel has experienced rapid development and commercialization in the past decade, and the technology for biodiesel production has greatly improved in handling multiple feedstocks. But the development of the biodiesel industry is still facing challenges. The major obstacle to the wide use of biodiesel is that biodiesel is not cost-competitive compared with diesel fuels. The industry is constantly searching for low-cost, or even no-cost feedstocks. Therefore, trap grease can potentially serve as a promising biodiesel feedstock to boost the biodiesel industry. Trap grease is a mixture of oils, food debris and kitchen wastes. It is generated in grease traps in restaurants. In most municipalities in the US, trap grease, after being pumped out from grease traps by grease haulers, is either sent to wastewater treatment plant or directly to landfills. An intensive literature review has been conducted and the following facts have been obtained. In the US, grease is the number one cause of clogging of public sewers, which results in costly sanitary overflow or combined sewer overflows. Various utilization ways of trap grease include composting, land application, anaerobic co-digestion, making biodiesel, combustion, incineration and rendering, etc. If trap grease is utilized as a feedstock for biodiesel production, it is not only beneficial to the biodiesel industry, but also helps solve the trap grease disposal issue. However, the challenge lies in the extraction of the oil fractions from this highly heterogeneous low grade feedstock.The goal of this study is to evaluate the feasibility of a community scale conversion of trap grease to biodiesel. Trap grease sample used for this study was obtained from the Metropolitan Sewer District of Greater Cincinnati (designated as MSD-TG). The MSD-TG mainly consists of water (58.94%), free fatty acids (FFAs) (20.69%) and unextractable part/solids (18.17%), with the lipid part of the MSD-TG being almost all FFAs. This study uses waste cooking oil (WCO) to extract the oil faction from the MSD-TG. WCO is also a low-cost biodiesel feedstock oil and using WCO as the solvent removes the solvent recovery step in the pretreatment process. The optimum extraction conditions were studied. It was found that 60° is the optimum extraction temperature. And at 60°, the optimum extraction duration is 90 minutes and the optimum extraction ratio is 4:1 (for every 10g of the MSD-TG 40mL (36g) of WCO is used). The extraction performance of WCO was evaluated by comparing it with three other organic solvents, methanol, hexane and isopropyl alcohol/hexane (2:1, v/v). WCO shows comparable performance among the four solvents. Based on the survey from trap grease haulers, the quantity of the grease trap waste (as semi solids) generated in Cincinnati is estimated to vary from 1.48 to 3.97 million pounds annually.

Animal waste from animal feeding operations (AFOs) is a significant contributor to nitrate contamination of groundwater. Some animal waste also contains heavy metals and salts that may build up in cropland and underlying aquifers. This thesis focuses on pollution reduction from the largest AFOs, in particular, Concentrated Animal Feeding Operations (CAFOs), which present the greatest potential risk among all AFOs to environmental quality and public health. To find cost-effective policies for controlling pollution at the field level and at the farm level, a dynamic environmental-economic modeling framework for representative CAFOs is developed. The framework incorporates four models (i.e., animal model, crop model, hydrologic model, and economic model) that includes various components such as herd management, manure handling system, crop rotation, water sources, irrigation system, waste disposal options, and pollutant emissions. The operator maximizes discounted total farm profit over multiple periods subject to environmental regulations. Decision rules from the dynamic optimization problem demonstrate best management practices for CAFOs to improve their economic and environmental performance. Results from policy simulations suggest that direct quantity restrictions of emission or incentive-based emission policies are much more cost-effective than the standard approach of limiting the amount of animal waste that may be applied to fields; reason being, policies targeting intermediate pollution and final pollution create incentives for the operator to examine the effects of other management practices to reduce pollution in addition to controlling the polluting inputs. Incentive-based emission policies are shown to have advantages over quantity restrictions over multiple years when seasonal or annual emissions fluctuate either due to inherent operation practices or the accumulation of precursors to the pollution. My approach demonstrates the importance of taking into account the integrated effects of water, nitrogen, and salinity on crop yield and nitrate leaching as well as the spatial heterogeneity of nitrogen/water application. It also suggests that ecosystem services can play an important role in pollution control and thus deserve more attention when designing policies.

Application of biosolids and animal manures to agriculture fields has become important low-cost approach towards the improvement of soil fertility. However, nutrient and heavy metal concentration in biosolids and animal manure are a possible hazard for humans and the environment. Once satellite monitoring methods have been proven, multispectral remote sensing data would be an efficient and effective way to monitor the application of biosolids and animal manures to agriculture fields, especially since LANDSAT TM data have become free of charge from the U. S. Geological Survey's EROS Data Center. The objectives of this study are: 1) to examine the feasibility of using spectral ratio combination of R(7,5), R(5,4) and R(3,2), respectively, in blue, green and red to monitor sewage sludge, chicken manure, and cow manure application on agriculture fields; 2) to examine whether the Total Phosphorus (TP) algorithm developed on bare corn and soybean fields can be used map biosolids, cow-manure, and chicken-manure application on winter wheat stubble fields; 3) to create an inventory of biosolids application on winter wheat fields in Wood County Ohio from 1999 to 2008. For this study, Field 25B in Lucas County was selected for biosolids application, and 2 different fields in Cygnet of Wood County were selected for animal manure application. Both field based and laboratory spectral reflectance measurements, LANDSAT data, and chemical concentration analysis were employed in this study to verify the spectral ratio combination method and the TP algorithm. The result showed the dark-object-subtracted spectral ratio combination of R(7,5), R(5,4) and R(3,2) represented as blue, green and red, respectively, was robust for discrimination of untreated winter wheat fields, biosolids-applied winter wheat fields, chicken-manure applied fields and cow-manure applied agriculture fields. In this spectral ratio combination image, untreated winter wheat fields appear yellow; biosolids-applied fields appear gray, and biosolids-applied fields appear grayish purple when water, clouds, and vegetation masks are used; chicken manure appears dark red; and cow manure appears light brown. Even though the result showed that the TP algorithm was only effective in predicting the P concentration between 700-3000 mg/kg, it still can be used as a supplementary method to discriminate biosolids application and cow-manure application to agriculture fields. Therefore, the dark-object-subtracted spectral ratio combination of R(7,5), R(5,4) and R(3,2) represented as blue, green and red, respectively was used to define the untreated winter wheat fields and the biosolids-applied winter wheat fields in Wood County by minimum distance supervised classification with 3 standard deviations. The classification results finally were used to create the inventory of biosolids application on winter wheat fields in Wood County Ohio from 1999 to 2008.