Overcoming the supply inconsistencies of tidal current energy conversion devices

The adoption of a sustainable approach to meeting the energy needs of society has recently taken on a more central and urgent place in the minds of many people. There are many reasons for this including ecological, environmental and economic concerns. One particular area where a sustainable approach has become very relevant is in the production of electricity. The contribution of renewable sources to the energy mix supplying the electricity grid is nothing new, but the focus has begun to move away from the more conventional renewable sources such as wind and hydro. The necessity of exploring new and innovative sources of renewable energy is now seen as imperative as the older forms (i.e. hydro) reach the saturation point of their possible exploitation. One such innovative source of energy currently beginning to be utilised in this regard is tidal energy. The purpose of this thesis is to isolate one specific drawback to tidal energy, which could be considered a roadblock to this energy source being a major contributor to the Irish national grid. This drawback presents itself in the inconsistent nature in which a tidal device generates energy over the course of a 24 hour period. This inconsistency of supply can result in the cycling of conventional power plants in order to even out the supply, subsequently leading to additional costs. The thesis includes a review of literature relevant to the area of tidal and other marine energy sources with an emphasis on the state of the art devices currently in development or production. The research carried out included tidal data analysis and manipulation into a model of the power generating potential at specific sites. A solution is then proposed to the drawback of inconsistency of supply, which involves the positioning of various tidal generation installations at specifically selected locations around the Irish coast. The temporal shift achieved in the power supply profiles of the individual sites by locating the installations in the correct locations, successfully produced an overall power supply profile with the smoother curve and a consistent base load energy supply. Some limitations to the method employed were also outlined, and suggestions for further improvements to the method were made.

The challenges of establishing the electric vehicle in Ireland

This thesis investigates the challenges of establishing the electric vehicle (EV) in Ireland and how the Irish government and industry are trying to meet them. It further seeks to provide information on Irish consumers’ attitudes towards the electric vehicle and their willingness to purchase it. The review of the literature showed that the Irish government is investing significant funds in trying to establish the market for the electric vehicle and position itself as a world leader in adopting the electric vehicle. The EV will also have an important role to play in how Ireland meets its targets for CO2 reductions towards 2020. Climate change and use of fossil fuels are driving the need for increased use of renewable energy and increased energy independence while reducing the greenhouse gas emissions that are the leading cause of climate change. The transport sector is almost completely dependent on the use of fossil fuel and resultantly is one of the largest sources of these GHG emissions. These issues are leading to the design and production of more energy efficient and environmentally friendly vehicles. The ultimate goal is to achieve a zero emissions vehicle. The electric vehicle is presently the only vehicle being mass produced that has the potential to be zero emissions. There are however issues that customers may not be willing to overlook such as the lower range of the vehicle and the length of time it takes to recharge. Vehicle cost is also an important issue that customers may not overlook. Knowing what the consumer’s attitudes are towards the EV and their willingness to purchase them is important as these new vehicles begin to appear in the showrooms. The consumers will be vital to how successful this market becomes. Using an online questionnaire methodology, in a sample of 118 consumers, the major conclusion to be drawn from the research is that the vehicle price, the convenience to recharge and vehicle range were the three most essential issues for the consumers if they were purchasing an EV. The success of the electric vehicle market may depend on what measures are taken to overcome them.

Influence of Aerosol Pollution of Atmosphere in Tbilisi on Air Electric Conductivity in Dusheti

The results of investigations of the influence of aerosol pollution of atmosphere in Tbilisi (the capital of the Georgia, city with the greatest level of air pollution) on the total air electric conductivity in Dusheti (the small city, located in 40-45 km to the north of Tbilisi) are represented.

Long-term Forecasting of Frame Alignment Losses for Circuit Emulation Implementation

The article provides a method for long-term forecast of frame alignment losses based on the bit-error rate monitoring for structure-agnostic circuit emulation service over Ethernet in a mobile backhaul network. The developed method with corresponding algorithm allows to detect instants of probable frame alignment losses in a long term perspective in order to give engineering personnel extra time to take some measures aimed at losses prevention. Moreover, long-term forecast of frame alignment losses allows to make a decision about the volume of TDM data encapsulated into a circuit emulation frame in order to increase utilization of the emulated circuit. The developed long-term forecast method formalized with the corresponding algorithm is recognized as cognitive and can act as a part of network predictive monitoring system.

pressure resistance of hollow glass fibers at internal pressure load

Otto-von-Guericke-Universität Magdeburg, Fakultät für Maschinenbau, Univ., Dissertation, 2015

Forecasting volatility using a continuous time model

This paper evaluates the forecasting performance of a continuous stochastic volatility model with two factors of volatility (SV2F) and compares it to those of GARCH and ARFIMA models. The empirical results show that the volatility forecasting ability of the SV2F model is better than that of the GARCH and ARFIMA models, especially when volatility seems to change pattern. We use ex-post volatility as a proxy of the realized volatility obtained from intraday data and the forecasts from the SV2F are calculated using the reprojection technique proposed by Gallant and Tauchen (1998).

Phenotypic heterogeneity of antigen-specific CD4 cells under different conditions of antigen persistence and antigen load

RESUME Nous n'avons pas de connaissance précise des facteurs à l'origine de l'hétérogénéité phénotypique des cellules T CD4 mémoires. Une troisième population phénotypique des cellules T CD4 mémoires, caractérisée par les marqueurs CD45RA+CCR7- a été identifiée dans cette étude. Cette population présente un état de différentiation avancée, comme en témoigne son histoire de réplication, ainsi que sa capacité de prolifération homéostatique. Les réponses des cellules T CD4 mémoires à différentes conditions de persistance et charge antigénique ont trois patterns phénotypiques différents, caractérisés par les marqueurs CD45RA et CCR7. La réponse CD4 mono -phénotypique CD45RA-CCR7+ ou CD45RA- CCR7- est associée à des conditions d'élimination de l'antigène (telle la réponse CD4 tétanos spécifique) ou à des conditions de persistance antigénique et de virémie élevée (telle la réponse HIV chronique ou la primo-infection CMV) respectivement. D'autre part, les réponses T CD4 multi -phénotypiques CD45RA-CCR7+ sont associées à des conditions d'exposition antigénique prolongée et de faible virémie (telles les infections CMV, EBV et HSV ou les infections HIV chez les long term non progressons). La réponse mono -phénotypique CD45RA- CCR7+ est propre aux cellules T CD4 secrétant de IL2, définies également comme centrales mémoires, la réponse CD45RA- CCR7- aux cellules T CD4 secrétant de l'IFNγ et finalement la réponse mufti-phénotypique aux cellules T CD4 secrétant à la fois de l'IL2 et de l' IFNγ. En conclusion, ces résultats témoignent d'une régulation de l'hétérogénéité phénotypique par l'exposition et la charge antigénique. ABSTRACT The factors responsible for the phenotypic heterogeneity of memory CD4 T cells are unclear. In the present study, we have identified a third population of memory CD4 T cells characterized as CD45RA+CCRT that, based on its replication history and the homeostatic proliferative capacity, was at an advanced stage of differentiation. Three different phenotypic patterns of memory CD4 T cell responses were delineated under different conditions of antigen (Ag) persistence and load using CD45RA and CCR7 as markers of memory T cells. Mono-phenotypic CD45RA'CCR7+ or CD45RA'CCR7' CD4 T cell responses were associated with conditions of Ag clearance (tetanus toxoid-specific CD4 T cell response) or Ag persistence and high load (chronic HIV-1 and primary CMV infections), respectively. Multi-phenotypic CD45RA CCR7+, CD45RA'CCRT and CD45RA+CCRT CD4 T cell responses were associated with protracted Ag exposure and low load (chronic CMV, EBV and HSV infections and HIV-1 infection in long-term nonprogressors). The mono-phenotypic CD45RA'CCR7+ response was typical of central memory (TCM) IL-2-secreting CD4 T cells, the mono-phenotypic CD45RA CCRT response of effector memory (TEM) IFN-γ -secreting CD4 T cells and the multi-phenotypic response of both IL-2- and IFN-γ -secreting cells. The present results indicate that the heterogeneity of different Ag-specific CD4 T cell responses is regulated by Ag exposure and Ag load.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Forecasting the European Carbon Market

In an effort to meet its obligations under the Kyoto Protocol, in 2005 the European Union introduced a cap-and-trade scheme where mandated installations are allocated permits to emit CO2. Financial markets have developed that allow companies to trade these carbon permits. For the EU to achieve reductions in CO2 emissions at a minimum cost, it is necessary that companies make appropriate investments and policymakers design optimal policies. In an effort to clarify the workings of the carbon market, several recent papers have attempted to statistically model it. However, the European carbon market (EU ETS) has many institutional features that potentially impact on daily carbon prices (and associated nancial futures). As a consequence, the carbon market has properties that are quite different from conventional financial assets traded in mature markets. In this paper, we use dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has three advantages over conventional approaches. First, it allows the coefficients on the predictors in a forecasting model to change over time. Second, it allows for the entire fore- casting model to change over time. Third, it surmounts statistical problems which arise from the large number of potential predictors that can explain carbon prices. Our empirical results indicate that there are both important policy and statistical bene ts with our approach. Statistically, we present strong evidence that there is substantial turbulence and change in the EU ETS market, and that DMA can model these features and forecast accurately compared to conventional approaches. From a policy perspective, we discuss the relative and changing role of different price drivers in the EU ETS. Finally, we document the forecast performance of DMA and discuss how this relates to the efficiency and maturity of this market.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.