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Enhanced Cooperation in the EU Abstract Enhanced cooperation has been a widely discussed in the context of the further possible development of the European integration and its direction. It represents a mechanism which introduces elements of flexible integration into the EU law. This thesis aims to provide a complete view of the process of enhanced cooperation. The first part focuses on the definition of flexibility and its theoretical concepts. The following parts of the thesis are then fully focused on enhanced cooperation as a specific legal instrument regulated by the Treaties. First of all, it mentions its historical development from the Treaty of Amsterdam to the Treaty of Lisbon and the changes it has undergone. After that there is the analysis of the legal provisions on enhanced cooperation itself. It discusses the whole process of establishing enhanced cooperation and the conditions necessary for that. There are also discussed special rules for activation of enhanced cooperation in the area of the common foreign and security policy and the police and judicial cooperation in criminal matters. Furthermore, a substantial part of the work is devoted to the application of enhanced cooperation in practice. Five cases of enhanced cooperation established so far are analysed in that part. In each case, the...

Thermal energy is an important resource for many industrial processes and is usually produced by combustion of hydrocarbon fuels with air. These processes could beneficiate from the use of oxygen-enhanced combustion (OEC), whose benefits (pollutants emissions reduction, fuel savings, productivity increase and volumes reduction) are already known. However, low costs oxygen production is still a challenge as the currently most used technique, cryogenics, is very energy consuming and costly. So, the present work proposes the thermodynamic analysis of two different techniques for production of oxidant required for the OEC process, the first one including air separation by polymeric membrane and the second one by PSA. Both systems were simulated on the software EES. Results show an increase of the energetic efficiency in both of the systems (from 22% to 58% in the membrane case and 66% in the PSA case) and of the exergetic efficiencies (from 18% to 48.5% and 54% respectively). A reduction of more than 60% of specific pollutants emissions was shown. The assessed techniques were shown to be energetically more attractive than cryogenics for small plants, the size limit depending on operating conditions.

The rational combination of dielectric and metallic nano particles brings novel optical properties to conventional subwavelength structures. This thesis introduces the optoplasmonic geometries demonstrating versatile ability in both far and near field modification within nano scale. Template-assisted self-assembly approaches are applied creating nano entities with titanium dioxide and gold nano spheres. A top-bottom mono hybrid unit and interdigitated array are developed. With the examination of the elastic and inelastic response of these hybrid materials, physical models are simulated to depict the scenario of varied geometry and combination of nano particles. In contrast to solely metal or dielectric particle arrays, this type of artificial material not only enhances the near electric field intensity within the metal nano cluster hot spots, but also expands the overall volume of enhanced electric field. Further study reveals that the additional enhancement and redistribution of near field are derived from the coupling between the nano gold cluster plasmon resonance and the in-plane diffractive mode of the dielectric array. The redirected emission profile of the fluorescent dyes within the hybrid array is explored.

Advances in surgical technique and medical management have led to fundamental changes in surgical care allowing for a paradigm shift from inpatient to outpatient surgery. Enhanced recovery pathways have moved surgical recovery from inpatient to outpatient settings requiring informal caregiver support. The purpose of this study was to determine the prevalence of caregiver burden in this patient population and to explore whether caregiver burden contributes to preventable use of emergency room services. The conceptual framework supporting this retrospective cross-sectional study was Andersen's behavioral model of health services utilization. Data collected from 28 urologic patient/caregiver pairs were analyzed using descriptive statistics and linear and logistic regression. Findings indicated measurable caregiver burden in 2 of the 5 Caregiver Reaction Assessment (CRA) subscales: impact on schedule and impact on health. Findings also indicated a measurable protective effect of high socioeconomic status of caregivers and the CRA subscale of impact on finances, and a possible protective effect of caregiver self-esteem as measured by the CRA subscale and emergency room utilization within the first 30 days after enhanced recovery surgery. Social change implications include improving the surgical experience of patients and caregivers and enhancing the use of health care resources.

This study explored the preventive impact of a brief Hat ha yoga program on stress-reduction and enhanced wellbeing in a sample of healthy African American college athletes. African Americans suffer higher rates of hypertension and sudden cardiac death syndrome, both linked to stress. Study design was single case, with six replications, utilizing comparison of pre- and post-intervention scores on physiological and psychological indicators of distress and wellbeing. Findings were inconsistent across participants and measures, though generally included beneficial increases in positive affect (i.e ., C = 0.567 , Z = 2 071, p < 0.05) and perceived physical health (i.e., C = 0489 , Z = 1. 721, p < 0.05). Increased fatigue over the course of the semester may have confounded results. Given the brevity and cost-effectiveness of the intervention, limited positive results suggest more intensive studies are warranted.

The energy retrieval process in an Enhanced Geothermal System (EGS) depends on fracture networks to facilitate fluid movement, thereby enabling the extraction of heat from adjacent rocks matrix. Nonetheless, due to the inherent heterogeneity and intricate multi-physics characteristics of these systems, high-fidelity physics-based forward simulations ($f_h$) can be computationally demanding. This presents a considerable obstacle to the efficient management of these reservoirs. Therefore, creating an effective and robust optimization framework is essential, with the primary aim being to maximize the thermal extraction from Enhanced Geothermal Systems (EGS). A deep learning-assisted reservoir management framework incorporating a low-fidelity forward surrogate model ($f_l$) alongside gradient-based optimizers is developed to expedite reservoir management. A thermo-hydro-mechanical (THM) model for EGS is established by utilizing finite element-based reservoir simulation techniques. By parameterizing the fracture aperture and well controls, we carried out the THM simulation to produce 2500 datasets. Subsequently, we employed these datasets to train two distinct deep neural network (DNN) architectures to predict the variations in pressure and temperature distributions. Ultimately, these predictions from the forward model are used in calculating the total net energy. Instead of executing the optimization workflow with a large number of simulations from $f_h$, we directly optimize the well control parameters relative to the geological parameters using $f_l$. Since $f_l$ is efficient and fully differentiable, it could be combined with various gradient-based or gradient-free optimization algorithms to maximize the total net energy by determining the optimal decision parameters. Drawing from the simulation datasets, we analysed the effect of fracture aperture variation on temperature and pressure evolution. Our investigation revealed that the spatial distribution of the fracture aperture is a predominant factor in controlling the propagation of the thermal front. Variations of the fracture aperture exhibit a strong correlation with temperature fluctuations within the fracture, primarily due to thermal stress changes. When compared with a comprehensive physics simulator, our DNN-based forward surrogate model offers a significant computational acceleration, approximately 1500 times faster, without compromising predictive accuracy, achieving an $R^2$ value of 99%. The forward model $f_l$, when combined with gradient-based optimizers, enables optimization to proceed 10 to 68 times faster than when using derivative-free global optimizers. The proposed reservoir management framework exhibits both efficiency and scalability, facilitating the real-time execution of each optimization process.

Forests cover a large portion of the global land area and provide critical resources such as timber, food, and medicine in addition to playing a significant role in the global carbon cycle. As such, sustainable forest management practices are required to balance forest economies and climate change mitigation with other non-timber objectives. A key aspect of many sustainable forest management programs is forest monitoring, for which technological and methodological development has led to enhanced forest inventory (EFI) methods, many of which rely on remote sensing data from high-resolution light detection and ranging (LiDAR) and optical imagery. However, to date, current applications of EFI methods have mostly focused on timber attributes with limited research on non-timber attributes or analyses regarding multi-temporal monitoring, method scaling, or method transferability. The objective of this thesis is to expand applications of EFIs in monitoring and analysis through two distinct studies, first evaluating the utility of LiDAR-based EFI methods in multi-temporal silvicultural treatment assessment and secondly in the pre-harvest estimation of merchantable wood and non-merchantable wood left as logging residues. The first study evaluates a process that expands the sampling of fertilization treatment effects on forest stands to the wider treatment area by utilizing paired LiDAR blocks made up of raster cell estimates from a multi-temporal area-based model. Results showed promise for detecting treatment impacts on stand volume, biomass, and height and highlights the potential for the methods to be used as a means to rapidly expand analysis from sample plots to the entire treatment area. The second study focuses on the use of a hybrid area-based and individual tree EFI approach to model merchantable and non-merchantable forest wood volumes while exploring the scalability of these models to harvest blocks and the transferability to additional blocks without prior training. Results from this study indicated that models for both volume attributes are successfully scalable and transferable to harvest blocks. Overall, the research results presented in this thesis demonstrate the potential of enhanced forest inventory methods for the monitoring and assessment of timber attributes, such as wood volume or biomass, as well as alternative attributes, such as stand height, or non-merchantable wood volume, over multiple years. This work further demonstrates the potential for these methods to expand areas of assessment and increase prediction accuracies. / Graduate / 2022-08-17

Nonpoint Source (NPS) pollution, pollution that comes from diffuse sources that are difficult to trace back to a single point such as farm fields, is a major concern affecting America's water ways. Nationally, agriculture is the leading source of water impairment and has also been identified as the largest source of pollution affecting the Chesapeake Bay. There are proposals now for a program that would pay farmers to use 15 percent less than the extension recommended amount of fertilizer. Known as the Yield Reserve Program, or Enhanced Nutrient Management, this policy could possibly help to reduce the nutrient loads. The objective of this study was to evaluate the effects of a Yield Reserve Program on farm yields and net revenues. Costs were estimated for different policy implementations for the Virginia Coastal Plain to better understand problems with this type of program. The effects of Yield Reserve on enterprise net returns, yields, and N applications for corn under varying weather and soil conditions were estimated. Based on experimental trials from North Carolina and Virginia from 2000 to 2004, yield response functions were calculated for corn. The costs of the Yield Reserve Program vary depending on its implementation, but under current proposals these costs were estimated at $113 per hectare, and $10,855,000 for the coastal plain. This proposal is a more costly program than would be necessary to compensate farmers for yield losses based on estimates in this study. / Master of Science