This research focuses on finding the optimal maintenance policy for an item with varying failure behavior. We analyze several types of item failure rates and develop methods to solve for optimal maintenance schedules. We also illustrate nonparametric modeling techniques for failure rates, and utilize these models in the optimization methods. The general problem falls under the umbrella of stochastic optimization under uncertainty. / text

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This paper considers Bayesian estimation of the threshold vector error correction (TVECM) model in moderate to large dimensions. Using the lagged cointegrating error as a threshold variable gives rise to additional difficulties that are typically solved by relying on large sample approximations. Relying on Markov chain Monte Carlo methods we circumvent these issues by avoiding computationally prohibitive estimation strategies like the grid search. Due to the proliferation of parameters we use novel global-local shrinkage priors in the spirit of Griffin and Brown (2010). We illustrate the merits of our approach in an application to five exchange rates vis-á-vis the US dollar and assess whether a given currency is over or undervalued. Moreover, we perform a forecasting comparison to investigate whether it pays off to adopt a non-linear modeling approach relative to a set of simpler benchmark models. / Series: Department of Economics Working Paper Series

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The nitrification process, the biologically mediated process of ammonia treatment in water resources recovery facilities (WRRF), remains the most common treatment process to mitigate the adverse effects of effluent ammonia discharges in surface water. However, it is well established that the temperature-sensitive process of nitrification remains hindered at low temperatures in conventional suspended growth technologies; specifically, passive treatment systems such as the lagoons, representing over 50% of Canadian treatment facilities in operation. As such, nitrification in lagoon facilities remains unreliable during the cold seasons with no nitrification occurring at 1°C. In contrast to suspended growth systems, attached growth technologies such as the moving bed biofilm reactors (MBBR) have recently been proven capable of achieving significant nitrification rates at temperatures as low as 1°C and are proposed as suitable upgrade systems to the common lagoon facility to reach year-long ammonia treatment targets. As such, the main objective of this research is to investigate and expand the current knowledge by investigating the key research questions lacking in the current literature on post-carbon, low temperature nitrifying MBBR systems. With this aim, a temperature-controlled study of attached growth nitrification kinetics was conducted to isolate the effects of low temperatures on nitrifying MBBR system performance down to 1°C. A removal rate of 98.44 ± 4.69 gN/m³d is identified as the 1°C intrinsic removal rate and the design removal rate for nitrifying MBBR systems at low temperatures. Considering this intrinsic rate at 1°C, an assessment of reactor efficiency at elevated TAN concentrations typical of non-combined sewer systems indicates that a two reactor in-series MBBR system configuration is recommended for retrofitting lagoon facilities connected to sanitary sewers. The study of the reactor performance to temperatures as low as 1°C demonstrates a non-linear decline in removal efficiency between 10°C and 1°C, with the existence of a kinetic threshold temperature delineated between 4°C and 2°C. As such, this delineated temperature range accounts for a significant decline in the performance of low carbon nitrifying MBBR systems during the onset of the cold seasons. This research identifies new recommended Arrhenius correction coefficient values taking into account this kinetic threshold temperature, with a coefficient of 1.049 being recommended above the kinetic threshold (≥4°C) and 1.149 below the threshold temperature at 1°C. Moreover, since the elapsed time to low temperature was identified as a key factor of attached growth nitrification kinetics, a modified theta model accounting for temperature and time is proposed in this research to accurately model the rate of nitrifying MBBR systems between 4°C and 1°C. Finally, with the severe adverse effects of sudden decreases in temperature, or cold shocks, on nitrification kinetics being previously demonstrated but not well understood, this research compares acclimatized and cold shocked MBBR reactors down to 1°C. The findings indicate 21% lower kinetics in the cold shocked reactor with reactor efficiencies never reaching those of the acclimatized reactor despite extended operation at 1°C. Thus, the research delineates the potentially lasting effects of extreme weather events such as cold air outbreaks and snowmelt periods on nitrifying MBBR system performance. On the other hand, these same findings demonstrate the resiliency of nitrifying MBBR reactors as nitrification was maintained within these systems despite being cold-shocked down from 10°C and 1°C. This study of attached growth kinetics was coupled with an investigation of the nitrifying biofilms, biomass, and microbiome responses to low temperatures and cold shock down to 1°C to provide an understanding of the changes occurring in these systems down to the cellular level. Comparisons of acclimatized and cold shocked nitrifying biofilms responses down to 1°C were characterized by increases in biofilm thickness, increases in biomass viability; and, greater shifts in microbiome communities occurring above 4°C in the acclimatized biofilm. Considering these observations, results also indicated a significant increase in nitrifiers per carrier above 4°C. As such, these findings suggested that the bulk of nitrifying biofilm adaptation to cold temperatures occurs above 4°C, a crucial adaptation phase in acclimatized systems. This adaptation phase is shown to be lacking in cold-shocked systems, with the cold shocked biofilm and microbiome demonstrating significant differences with the acclimatized systems’ biofilm and microbiome. This research was performed to answer the critical research questions relating to the design and operation of the post-carbon, low temperature nitrifying MBBR systems, with the first low temperature MBBR systems being scheduled to begin operation in the fall of 2020. This research expands the current knowledge on low temperature attached growth nitrification kinetics as well as cold shocked attached growth nitrification kinetics in MBBR systems down to 1°C. In addition, this research delineates the effects of low temperatures and cold shocks on the nitrifying MBBR system’s biofilms and their embedded cells.

The electrical power grid is one of the most important infrastructures in the modernsociety. It supplies industrial and private customers with electricity and supportsother critical infrastructures such as the water supply. Thus, it is significant that the power grid is a reliable system. However, the power system experiences a hugetransition from classical production methods such as coal and nuclear power plantsto distributed renewable energy forms such as wind energy and photovoltaic. This change to a more distributed system challenges the existing power grid as well as the traditional business models of electric utilities. Thus, cost minimization to increase profitability and improvement of the power grid to increase customer satisfactionare in the focus. One approach to increase the reliability of the grid and decrease maintenance costs is a condition-based maintenance approach which requirescondition monitoring techniques. This thesis introduces into failure rate modelling for individual power system components and develops a method to calculate individual failure rates based onthe average failure rate, failure statistics, and condition monitoring data. This approach includes the analysis of failure statistics to identify failure causes and failure locations which are population characteristics but can be utilized to describe the heterogeneity within the population. Thus, the thesis first introduces into the topic of failure analysis and heterogeneity in populations. Different factors are identified and categorized which describe the condition development of a component overtime. Then, the literature within failure rate estimation is reviewed to present the factors which are used within failure rate modelling and to outline the existingmethods which consider the individual. However, limitations are discussed which emphasize the demand for a new approach. Consequently, this thesis introduce intoa new approach for estimating the failure rate for individual components. / <p>QC 20160526</p> / Energiforsk AB risk analysis program

The objective of this report is to carry out a pre-study and develop a framework for how the liquidity and interest rate risk of a bank's demand deposits can be modeled. This is done by first calibrating a Vasicek short rate model and then deriving models for the bank's deposit volume and deposit rate using multiple regression. The volume model and the deposit rate model are used to determine the liquidity and interest rate risk, which is done separately. The liquidity risk is determined by a liquidity quantile which estimates the minimum deposit volume that is expected to remain in the bank over a given time period. The interest rate risk is quantified by an arbitrage-free valuation of the demand deposit which can be used to determine the sensitivity of the net present value of the demand deposit caused by a parallel shift in the market rates. Furthermore, an immunization and a replicating portfolio are constructed and the performances of these are tested when introducing the same parallel shifts in the market rates as in the valuation of the demand deposit. The conclusion of this thesis is that the framework for the liquidity risk management that is developed gave satisfactory results and could be used by the bank if the deposit volume is estimated on representative data and a more accurate model for the short rate is used. The interest rate risk framework did however not yield as reliable results and would be more challenging to implement as a more advanced model for the deposit rate is required. / Målet med denna rapport är att utveckla ett ramverk för att bestämma likviditets-och ränterisken som är relaterad till en banks inlåningsvolym. Detta görs genom att först ta fram en modell för korträntan via kalibrering av en Vasicek modell. Därefter utvecklas, genom multipelregression, modeller för att beskriva bankens inlåningsvolym och inlåningsränta. Dessa modeller används för att kvantifiera likviditets- och ränterisken för inlånings-volymen, vilka beräknas och presenteras separat. Likviditetsrisken bestäms genom att en likviditetskvantil tas fram, vilken estimerar den minimala inlånings-volymen som förväntas kvarstå hos banken över en given tidsperiod. Ränterisken kvantifieras med en arbitragefri värdering av inlåningen och resultatet används för att bestämma känsligheten för hur nuvärdet av inlåningsvolymen påverkas av ett parallellskifte. Utöver detta bestäms en immuniseringsportfölj samt en rep-likerande portfölj och resultatet av dessa utvärderas mot hur nuvärdet förändras givet att samma parallellskifte i ränteläget som tidigare introduceras. Slutsatsen av projektet är att det framtagna ramverket för att bestämma likviditetsrisken för inlåningen gav bra resultat och skulle kunna implementeras i dagsläget av banken, förutsatt att volymmodellen estimeras på representativ data samt att en bättre modell för korträntan används. Ramverket för att bestämma ränterisken gav dock inte lika tillförlitliga resultat och är mer utmanande att implementera då en mer avancerad modell för inlåningsräntan krävs.

I study, develop and implement selected interest rate models. I begin with a simple categorization of interest rate models and with an explanation why interest rate models are useful. I explain and discuss the notion of arbitrage. I use Oldrich Vasicek's seminal model (Vasicek; 1977) to develop the idea of no-arbitrage term structure modeling. I introduce both the partial di erential equation and the risk-neutral approach to zero-coupon bond pricing. I briefly comment on affine term structure models, a general equilibrium term structure model, and HJM framework. I present the Czech Treasury yield curve estimates at a daily frequency from 1999 to the present. I use the parsimonious Nelson-Siegel model (Nelson and Siegel; 1987), for which I suggest a parameter restriction that avoids abrupt changes in parameter estimates and thus allows for the economic interpretation of the model to hold. The Nelson-Siegel model is shown to fit the Czech bond price data well without being over-parameterized. Thus, the model provides an accurate and consistent picture of the Czech Treasury yield curve evolution. The estimated parameters can be used to calculate spot rates and hence par rates, forward rates or discount function for practically any maturity. To my knowledge, consistent time series of spot rates are not available for the Czech economy. I introduce two estimation techniques of the short-rate process. I begin with the maximum likelihood estimator of a square root diff usion. A square root di usion serves as the short rate process in the famous CIR model (Cox, Ingersoll and Ross; 1985b). I develop and analyze two Matlab implementations of the estimation routine and test them on a three-month PRIBOR time series. A square root diff usion is a restricted version of, so called, CKLS di ffusion (Chan, Karolyi, Longsta and Sanders; 1992). I use the CKLS short-rate process to introduce the General Method of Moments as the second estimation technique. I discuss the numerical implementation of this method. I show the importance of the estimator of the GMM weighting matrix and question the famous empirical result about the volatility speci cation of the short-rate process. Finally, I develop a novel yield curve model, which is based on principal component analysis and nonlinear stochastic di erential equations. The model, which is not a no-arbitrage model, can be used in areas, where quantification of interest rate dynamics is needed. Examples, of such areas, are interest rate risk management, or the pro tability and risk evaluation of interest rate contingent claims, or di erent investment strategies. The model is validated by Monte Carlo simulations.

This thesis describes and models wirelesss transmission intereferences in real communication channels. A signal received on a fading channel is subjected to a multiplicative distortion and to the usual additive noise. Real channel adds to the signal noise and fadings. Gaussian noise is a result of channel awgn. Fadings is implicated by multipath propagation of signal in Rayleigh and Rician channels. Main goal of this project is to program BERsolve, which is created in tool GUIDE in Matlab. User program BERsolve makes it possible to analyse bit error rate and symbol error rate for some types of channel models, which are defined by standard COST207. BERsolve offers some other functions i.e. display time behaviour, constellation diagram and spectrum. This program provides us with an overview representation of problem by multipath interferences in communication channels.