Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

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

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.

Modélisation incrémentale par méthode bayésienne

Les modèles incrémentaux sont des modèles statistiques qui ont été développés initialement dans le domaine du marketing. Ils sont composés de deux groupes, un groupe contrôle et un groupe traitement, tous deux comparés par rapport à une variable réponse binaire (le choix de réponses est « oui » ou « non »). Ces modèles ont pour but de détecter l’effet du traitement sur les individus à l’étude. Ces individus n’étant pas tous des clients, nous les appellerons : « prospects ». Cet effet peut être négatif, nul ou positif selon les caractéristiques des individus composants les différents groupes. Ce mémoire a pour objectif de comparer des modèles incrémentaux d’un point de vue bayésien et d’un point de vue fréquentiste. Les modèles incrémentaux utilisés en pratique sont ceux de Lo (2002) et de Lai (2004). Ils sont initialement réalisés d’un point de vue fréquentiste. Ainsi, dans ce mémoire, l’approche bayésienne est utilisée et comparée à l’approche fréquentiste. Les simulations sont e ectuées sur des données générées avec des régressions logistiques. Puis, les paramètres de ces régressions sont estimés avec des simulations Monte-Carlo dans l’approche bayésienne et comparés à ceux obtenus dans l’approche fréquentiste. L’estimation des paramètres a une influence directe sur la capacité du modèle à bien prédire l’effet du traitement sur les individus. Nous considérons l’utilisation de trois lois a priori pour l’estimation des paramètres de façon bayésienne. Elles sont choisies de manière à ce que les lois a priori soient non informatives. Les trois lois utilisées sont les suivantes : la loi bêta transformée, la loi Cauchy et la loi normale. Au cours de l’étude, nous remarquerons que les méthodes bayésiennes ont un réel impact positif sur le ciblage des individus composant les échantillons de petite taille.

An Alternative Goodness-of-fit Test for Normality with Unknown Parameters

Goodness-of-fit tests have been studied by many researchers. Among them, an alternative statistical test for uniformity was proposed by Chen and Ye (2009). The test was used by Xiong (2010) to test normality for the case that both location parameter and scale parameter of the normal distribution are known. The purpose of the present thesis is to extend the result to the case that the parameters are unknown. A table for the critical values of the test statistic is obtained using Monte Carlo simulation. The performance of the proposed test is compared with the Shapiro-Wilk test and the Kolmogorov-Smirnov test. Monte-Carlo simulation results show that proposed test performs better than the Kolmogorov-Smirnov test in many cases. The Shapiro Wilk test is still the most powerful test although in some cases the test proposed in the present research performs better.

Recent Changes in Central and Eastern Pacific El Niño

Recent research indicates that characteristics of El Niño and the Southern Oscillation (ENSO) have changed over the past several decades. Here, I examined different flavors of El Niño in the observational record and the recent changes in the character of El Niño events. The fundamental physical processes that drive ENSO were described and the Eastern Pacific (EP) and Central Pacific (CP) types or flavors of El Niño were defined. Using metrics from the peer-reviewed literature, I examined several historical data sets to interpret El Niño behavior from 1950-2010. A Monte Carlo Simulation was then applied to output from coupled model simulations to test the statistical significance of recent observations surrounding EP and CP El Niño. Results suggested that EP and CP El Niño had been occurring in a similar fashion over the past 60 years with natural variability, but no significant increase in CP El Niño behavior.