Egységes kockázatkezelési módszertan kialakítása a villamosenergia-ipari átviteli rendszerirányító társaságnál (Development of an integrated risk management methodology for an electricity transmission system operator (TSO) company)

A felelős vállalatirányítás egyik stratégiai jelentőségű tényezője a vállalati szintű kockázatkezelés, mely napjaink egyik legnagyobb kihívást jelentő területe a vállalatvezetés számára. A hatékony vállalati kockázatkezelés nem valósulhat meg kizárólag az általános, nemzetközi és hazai szakirodalomban megfogalmazott kockázatkezelési alapelvek követése mentén, a kockázatkezelési rendszer kialakítása során figyelembe kell venni mind az iparági, mind az adott vállalatra jellemző sajátosságokat. Mindez különösen fontos egy olyan speciális tevékenységet folytató vállalatnál, mint a villamosenergia-ipari átviteli rendszerirányító társaság (transmission system operator, TSO). A cikkben a magyar villamosenergia-ipari átviteli rendszerirányító társasággal együttműködésben készített kutatás keretében előálló olyan komplex elméleti és gyakorlati keretrendszert mutatnak be a szerzők, mely alapján az átviteli rendszerirányító társaság számára kialakítottak egy új, területenként egységes kockázatkezelési módszertant (fókuszban a kockázatok azonosításának és számszerűsítésének módszertani lépéseivel), mely alkalmas a vállalati szintű kockázati kitettség meghatározására. _______ This study handles one of today’s most challenging areas of enterprise management: the development and introduction of an integrated and efficient risk management system. For companies operating in specific network industries with a dominant market share and a key role in the national economy, such as electricity TSO’s, risk management is of stressed importance. The study introduces an innovative, mathematically and statistically grounded as well as economically reasoned management approach for the identification, individual effect calculation and summation of risk factors. Every building block is customized for the organizational structure and operating environment of the TSO. While the identification phase guarantees all-inclusivity, the calculation phase incorporates expert techniques and Monte Carlo simulation and the summation phase presents an expected combined distribution and value effect of risks on the company’s profit lines based on the previously undiscovered correlations between individual risk factors.

A comprehensive portfolio construction under stochastic environment

Prior research has established that idiosyncratic volatility of the securities prices exhibits a positive trend. This trend and other factors have made the merits of investment diversification and portfolio construction more compelling. ^ A new optimization technique, a greedy algorithm, is proposed to optimize the weights of assets in a portfolio. The main benefits of using this algorithm are to: (a) increase the efficiency of the portfolio optimization process, (b) implement large-scale optimizations, and (c) improve the resulting optimal weights. In addition, the technique utilizes a novel approach in the construction of a time-varying covariance matrix. This involves the application of a modified integrated dynamic conditional correlation GARCH (IDCC - GARCH) model to account for the dynamics of the conditional covariance matrices that are employed. ^ The stochastic aspects of the expected return of the securities are integrated into the technique through Monte Carlo simulations. Instead of representing the expected returns as deterministic values, they are assigned simulated values based on their historical measures. The time-series of the securities are fitted into a probability distribution that matches the time-series characteristics using the Anderson-Darling goodness-of-fit criterion. Simulated and actual data sets are used to further generalize the results. Employing the S&P500 securities as the base, 2000 simulated data sets are created using Monte Carlo simulation. In addition, the Russell 1000 securities are used to generate 50 sample data sets. ^ The results indicate an increase in risk-return performance. Choosing the Value-at-Risk (VaR) as the criterion and the Crystal Ball portfolio optimizer, a commercial product currently available on the market, as the comparison for benchmarking, the new greedy technique clearly outperforms others using a sample of the S&P500 and the Russell 1000 securities. The resulting improvements in performance are consistent among five securities selection methods (maximum, minimum, random, absolute minimum, and absolute maximum) and three covariance structures (unconditional, orthogonal GARCH, and integrated dynamic conditional GARCH). ^

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

An Assessment of the Performances of Several Univariate Tests of Normality

The importance of checking the normality assumption in most statistical procedures especially parametric tests cannot be over emphasized as the validity of the inferences drawn from such procedures usually depend on the validity of this assumption. Numerous methods have been proposed by different authors over the years, some popular and frequently used, others, not so much. This study addresses the performance of eighteen of the available tests for different sample sizes, significance levels, and for a number of symmetric and asymmetric distributions by conducting a Monte-Carlo simulation. The results showed that considerable power is not achieved for symmetric distributions when sample size is less than one hundred and for such distributions, the kurtosis test is most powerful provided the distribution is leptokurtic or platykurtic. The Shapiro-Wilk test remains the most powerful test for asymmetric distributions. We conclude that different tests are suitable under different characteristics of alternative distributions.

Time, Cost, and Environmental Impact Analysis for Sustainable Design at Multiple Building Levels

Construction projects are complex endeavors that require the involvement of different professional disciplines in order to meet various project objectives that are often conflicting. The level of complexity and the multi-objective nature of construction projects lend themselves to collaborative design and construction such as integrated project delivery (IPD), in which relevant disciplines work together during project conception, design and construction. Traditionally, the main objectives of construction projects have been to build in the least amount of time with the lowest cost possible, thus the inherent and well-established relationship between cost and time has been the focus of many studies. The importance of being able to effectively model relationships among multiple objectives in building construction has been emphasized in a wide range of research. In general, the trade-off relationship between time and cost is well understood and there is ample research on the subject. However, despite sustainable building designs, relationships between time and environmental impact, as well as cost and environmental impact, have not been fully investigated. The objectives of this research were mainly to analyze and identify relationships of time, cost, and environmental impact, in terms of CO2 emissions, at different levels of a building: material level, component level, and building level, at the pre-use phase, including manufacturing and construction, and the relationships of life cycle cost and life cycle CO2 emissions at the usage phase. Additionally, this research aimed to develop a robust simulation-based multi-objective decision-support tool, called SimulEICon, which took construction data uncertainty into account, and was capable of incorporating life cycle assessment information to the decision-making process. The findings of this research supported the trade-off relationship between time and cost at different building levels. Moreover, the time and CO2 emissions relationship presented trade-off behavior at the pre-use phase. The results of the relationship between cost and CO2 emissions were interestingly proportional at the pre-use phase. The same pattern continually presented after the construction to the usage phase. Understanding the relationships between those objectives is a key in successfully planning and designing environmentally sustainable construction projects.

Formulações e algoritmos para o problema das p-medianas heterogêneo livre de penalidade

This work presents a new model for the Heterogeneous p-median Problem (HPM), proposed to recover the hidden category structures present in the data provided by a sorting task procedure, a popular approach to understand heterogeneous individual’s perception of products and brands. This new model is named as the Penalty-free Heterogeneous p-median Problem (PFHPM), a single-objective version of the original problem, the HPM. The main parameter in the HPM is also eliminated, the penalty factor. It is responsible for the weighting of the objective function terms. The adjusting of this parameter controls the way that the model recovers the hidden category structures present in data, and depends on a broad knowledge of the problem. Additionally, two complementary formulations for the PFHPM are shown, both mixed integer linear programming problems. From these additional formulations lower-bounds were obtained for the PFHPM. These values were used to validate a specialized Variable Neighborhood Search (VNS) algorithm, proposed to solve the PFHPM. This algorithm provided good quality solutions for the PFHPM, solving artificial generated instances from a Monte Carlo Simulation and real data instances, even with limited computational resources. Statistical analyses presented in this work suggest that the new algorithm and model, the PFHPM, can recover more accurately the original category structures related to heterogeneous individual’s perceptions than the original model and algorithm, the HPM. Finally, an illustrative application of the PFHPM is presented, as well as some insights about some new possibilities for it, extending the new model to fuzzy environments

Um controlador dual subótimo com fator de peso variável no tempo

This work concerns a refinement of a suboptimal dual controller for discrete time systems with stochastic parameters. The dual property means that the control signal is chosen so that estimation of the model parameters and regulation of the output signals are optimally balanced. The control signal is computed in such a way so as to minimize the variance of output around a reference value one step further, with the addition of terms in the loss function. The idea is add simple terms depending on the covariance matrix of the parameter estimates two steps ahead. An algorithm is used for the adaptive adjustment of the adjustable parameter lambda, for each step of the way. The actual performance of the proposed controller is evaluated through a Monte Carlo simulations method.

Estimação e previsão no processo INARCH(2)

In the last decades the study of integer-valued time series has gained notoriety due to its broad applicability (modeling the number of car accidents in a given highway, or the number of people infected by a virus are two examples). One of the main interests of this area of study is to make forecasts, and for this reason it is very important to propose methods to make such forecasts, which consist of nonnegative integer values, due to the discrete nature of the data. In this work, we focus on the study and proposal of forecasts one, two and h steps ahead for integer-valued second-order autoregressive conditional heteroskedasticity processes [INARCH (2)], and in determining some theoretical properties of this model, such as the ordinary moments of its marginal distribution and the asymptotic distribution of its conditional least squares estimators. In addition, we study, via Monte Carlo simulation, the behavior of the estimators for the parameters of INARCH(2) processes obtained using three di erent methods (Yule- Walker, conditional least squares, and conditional maximum likelihood), in terms of mean squared error, mean absolute error and bias. We present some forecast proposals for INARCH(2) processes, which are compared again via Monte Carlo simulation. As an application of this proposed theory, we model a dataset related to the number of live male births of mothers living at Riachuelo city, in the state of Rio Grande do Norte, Brazil.

Estimação e previsão no processo INARCH(2)

In the last decades the study of integer-valued time series has gained notoriety due to its broad applicability (modeling the number of car accidents in a given highway, or the number of people infected by a virus are two examples). One of the main interests of this area of study is to make forecasts, and for this reason it is very important to propose methods to make such forecasts, which consist of nonnegative integer values, due to the discrete nature of the data. In this work, we focus on the study and proposal of forecasts one, two and h steps ahead for integer-valued second-order autoregressive conditional heteroskedasticity processes [INARCH (2)], and in determining some theoretical properties of this model, such as the ordinary moments of its marginal distribution and the asymptotic distribution of its conditional least squares estimators. In addition, we study, via Monte Carlo simulation, the behavior of the estimators for the parameters of INARCH(2) processes obtained using three di erent methods (Yule- Walker, conditional least squares, and conditional maximum likelihood), in terms of mean squared error, mean absolute error and bias. We present some forecast proposals for INARCH(2) processes, which are compared again via Monte Carlo simulation. As an application of this proposed theory, we model a dataset related to the number of live male births of mothers living at Riachuelo city, in the state of Rio Grande do Norte, Brazil.

Numerical study of the enlarged O(5) symmetry of the 3D antiferromagnetic RP^(2) spin model

We investigate by means of Monte Carlo simulation and finite-size scaling analysis the critical properties of the three dimensional O (5) non-linear σ model and of the antiferromagnetic RP^(2) model, both of them regularized on a lattice. High accuracy estimates are obtained for the critical exponents, universal dimensionless quantities and critical couplings. It is concluded that both models belong to the same universality class, provided that rather non-standard identifications are made for the momentum-space propagator of the RP^(2) model. We have also investigated the phase diagram of the RP^(2) model extended by a second-neighbor interaction. A rich phase diagram is found, where most of the phase transitions are of the first order.

Comparison of available measurements of the absolute air-fluorescence yield and determination of its global average value

Experimental results of the absolute air-fluorescence yield are given very often in different units (photons/MeV or photons/m) and for different wavelength intervals. In this work we present a comparison of available results normalized to its value in photons/MeV for the 337 nm band at 1013 hPa and 293 K. The conversion of photons/m to photons/MeV requires an accurate determination of the energy deposited by the electrons in the field of view of the experimental set-up. We have calculated the energy deposition for each experiment by means of a detailed Monte Carlo simulation and the results have been compared with those assumed or calculated by the authors. As a result, corrections to the reported fluorescence yields are proposed. These corrections improve the compatibility between measurements in such a way that a reliable average value with uncertainty at the level of 5% is obtained.

How effective are heuristic solutions for electricity planning in developing countries

Acknowledgement The first author would like to acknowledge the University of Aberdeen and the Henderson Economics Research Fund for funding his PhD studies in the period 2011-2014 which formed the basis for the research presented in this paper. The first author would also like to acknowledge the Macaulay Development Trust which funds his postdoctoral fellowship with The James Hutton Institute, Aberdeen, Scotland. The authors thank two anonymous referees for valuable comments and suggestions on earlier versions of this paper. All usual caveats apply

The reliability mathematical model of load-sharing parallel systems based on fatigue cumulative damage

Conventional reliability models for parallel systems are not applicable for the analysis of parallel systems with load transfer and sharing. In this short communication, firstly, the dependent failures of parallel systems are analyzed, and the reliability model of load-sharing parallel system is presented based on Miner cumulative damage theory and the full probability formula. Secondly, the parallel system reliability is calculated by Monte Carlo simulation when the component life follows the Weibull distribution. The research result shows that the proposed reliability mathematical model could analyze and evaluate the reliability of parallel systems in the presence of load transfer.

Advanced Applications of 3D Dosimetry and 3D Printing in Radiation Therapy

As complex radiotherapy techniques become more readily-practiced, comprehensive 3D dosimetry is a growing necessity for advanced quality assurance. However, clinical implementation has been impeded by a wide variety of factors, including the expense of dedicated optical dosimeter readout tools, high operational costs, and the overall difficulty of use. To address these issues, a novel dry-tank optical CT scanner was designed for PRESAGE 3D dosimeter readout, relying on 3D printed components and omitting costly parts from preceding optical scanners. This work details the design, prototyping, and basic commissioning of the Duke Integrated-lens Optical Scanner (DIOS).

The convex scanning geometry was designed in ScanSim, an in-house Monte Carlo optical ray-tracing simulation. ScanSim parameters were used to build a 3D rendering of a convex ‘solid tank’ for optical-CT, which is capable of collimating a point light source into telecentric geometry without significant quantities of refractive-index matched fluid. The model was 3D printed, processed, and converted into a negative mold via rubber casting to produce a transparent polyurethane scanning tank. The DIOS was assembled with the solid tank, a 3W red LED light source, a computer-controlled rotation stage, and a 12-bit CCD camera. Initial optical phantom studies show negligible spatial inaccuracies in 2D projection images and 3D tomographic reconstructions. A PRESAGE 3D dose measurement for a 4-field box treatment plan from Eclipse shows 95% of voxels passing gamma analysis at 3%/3mm criteria. Gamma analysis between tomographic images of the same dosimeter in the DIOS and DLOS systems show 93.1% agreement at 5%/1mm criteria. From this initial study, the DIOS has demonstrated promise as an economically-viable optical-CT scanner. However, further improvements will be necessary to fully develop this system into an accurate and reliable tool for advanced QA.

Pre-clinical animal studies are used as a conventional means of translational research, as a midpoint between in-vitro cell studies and clinical implementation. However, modern small animal radiotherapy platforms are primitive in comparison with conventional linear accelerators. This work also investigates a series of 3D printed tools to expand the treatment capabilities of the X-RAD 225Cx orthovoltage irradiator, and applies them to a feasibility study of hippocampal avoidance in rodent whole-brain radiotherapy.

As an alternative material to lead, a novel 3D-printable tungsten-composite ABS plastic, GMASS, was tested to create precisely-shaped blocks. Film studies show virtually all primary radiation at 225 kVp can be attenuated by GMASS blocks of 0.5cm thickness. A state-of-the-art software, BlockGen, was used to create custom hippocampus-shaped blocks from medical image data, for any possible axial treatment field arrangement. A custom 3D printed bite block was developed to immobilize and position a supine rat for optimal hippocampal conformity. An immobilized rat CT with digitally-inserted blocks was imported into the SmART-Plan Monte-Carlo simulation software to determine the optimal beam arrangement. Protocols with 4 and 7 equally-spaced fields were considered as viable treatment options, featuring improved hippocampal conformity and whole-brain coverage when compared to prior lateral-opposed protocols. Custom rodent-morphic PRESAGE dosimeters were developed to accurately reflect these treatment scenarios, and a 3D dosimetry study was performed to confirm the SmART-Plan simulations. Measured doses indicate significant hippocampal sparing and moderate whole-brain coverage.

Drug prescribing patterns in the General Medical Services Scheme in Ireland and projected costs to 2026

The primary objective is to investigate the main factors contributing to GMS expenditure on pharmaceutical prescribing and projecting this expenditure to 2026. This study is located in the area of pharmacoeconomic cost containment and projections literature. The thesis has five main aims: 1. To determine the main factors contributing to GMS expenditure on pharmaceutical prescribing. 2. To develop a model to project GMS prescribing expenditure in five year intervals to 2026, using 2006 Central Statistics Office (CSO) Census data and 2007 Health Service Executive{Primary Care Reimbursement Service (HSE{PCRS) sample data. 3. To develop a model to project GMS prescribing expenditure in five year intervals to 2026, using 2012 HSE{PCRS population data, incorporating cost containment measures, and 2011 CSO Census data. 4. To investigate the impact of demographic factors and the pharmacology of drugs (Anatomical Therapeutic Chemical (ATC)) on GMS expenditure. 5. To explore the consequences of GMS policy changes on prescribing expenditure and behaviour between 2008 and 2014. The thesis is centered around three published articles and is located between the end of a booming Irish economy in 2007, a recession from 2008{2013, to the beginning of a recovery in 2014. The literature identified a number of factors influencing pharmaceutical expenditure, including population growth, population aging, changes in drug utilisation and drug therapies, age, gender and location. The literature identified the methods previously used in predictive modelling and consequently, the Monte Carlo Simulation (MCS) model was used to simulate projected expenditures to 2026. Also, the literature guided the use of Ordinary Least Squares (OLS) regression in determining demographic and pharmacology factors influencing prescribing expenditure. The study commences against a backdrop of growing GMS prescribing costs, which has risen from e250 million in 1998 to over e1 billion by 2007. Using a sample 2007 HSE{PCRS prescribing data (n=192,000) and CSO population data from 2008, (Conway et al., 2014) estimated GMS prescribing expenditure could rise to e2 billion by2026. The cogency of these findings was impacted by the global economic crisis of 2008, which resulted in a sharp contraction in the Irish economy, mounting fiscal deficits resulting in Ireland's entry to a bailout programme. The sustainability of funding community drug schemes, such as the GMS, came under the spotlight of the EU, IMF, ECB (Trioka), who set stringent targets for reducing drug costs, as conditions of the bailout programme. Cost containment measures included: the introduction of income eligibility limits for GP visit cards and medical cards for those aged 70 and over, introduction of co{payments for prescription items, reductions in wholesale mark{up and pharmacy dispensing fees. Projections for GMS expenditure were reevaluated using 2012 HSE{PCRS prescribing population data and CSO population data based on Census 2011. Taking into account both cost containment measures and revised population predictions, GMS expenditure is estimated to increase by 64%, from e1.1 billion in 2016 to e1.8 billion by 2026, (ConwayLenihan and Woods, 2015). In the final paper, a cross{sectional study was carried out on HSE{PCRS population prescribing database (n=1.63 million claimants) to investigate the impact of demographic factors, and the pharmacology of the drugs, on GMS prescribing expenditure. Those aged over 75 (ẞ = 1:195) and cardiovascular prescribing (ẞ = 1:193) were the greatest contributors to annual GMS prescribing costs. Respiratory drugs (Montelukast) recorded the highest proportion and expenditure for GMS claimants under the age of 15. Drugs prescribed for the nervous system (Escitalopram, Olanzapine and Pregabalin) were highest for those between 16 and 64 years with cardiovascular drugs (Statins) were highest for those aged over 65. Females are more expensive than males and are prescribed more items across the four ATC groups, except among children under 11, (ConwayLenihan et al., 2016). This research indicates that growth in the proportion of the elderly claimants and associated levels of cardiovascular prescribing, particularly for statins, will present difficulties for Ireland in terms of cost containment. Whilst policies aimed at cost containment (co{payment charges, generic substitution, reference pricing, adjustments to GMS eligibility) can be used to curtail expenditure, health promotional programs and educational interventions should be given equal emphasis. Also policies intended to affect physicians prescribing behaviour include guidelines, information (about price and less expensive alternatives) and feedback, and the use of budgetary restrictions could yield savings.