Ultraviolet (UV) light reactors or banks are often arranged in series in order to meet microbial inactivation credit requirements. It has been assumed that UV doses given by each reactor in series are mathematically additive, though work done to substantiate the hypothesis has been inconsistent. Based on previously developed theory of reactor additivity and the reactor additivity factor (RAF), three types of UV reactors are modelled using computational fluid dynamics and their RAFs are computed. It is noted that the assumption of perfect mixing may not be valid depending on the distance between reactors in series. It is discussed that the original formulation of the RAF is inadequate when dealing with wastewater. It is shown unexpectedly that even with perfect mixing performance, worse than additivity would be achieved. A new performance factor (PF) is introduced and the implications of this are further discussed in the context of UV reactor validation.

Ultraviolet (UV) light reactors or banks are often arranged in series in order to meet microbial inactivation credit requirements. It has been assumed that UV doses given by each reactor in series are mathematically additive, though work done to substantiate the hypothesis has been inconsistent. Based on previously developed theory of reactor additivity and the reactor additivity factor (RAF), three types of UV reactors are modelled using computational fluid dynamics and their RAFs are computed. It is noted that the assumption of perfect mixing may not be valid depending on the distance between reactors in series. It is discussed that the original formulation of the RAF is inadequate when dealing with wastewater. It is shown unexpectedly that even with perfect mixing performance, worse than additivity would be achieved. A new performance factor (PF) is introduced and the implications of this are further discussed in the context of UV reactor validation.

Faculty of Science School of Statistics snd Acturial Science 9907894x craig.morgan@goldfields.co.za / This dissertation presents a detailed study of geostatistics. Included in this work are details of the development of geostatistics and its usefulness both in and outside of the mining industry, a comprehensive presentation of the theory of geostatistics, and a discussion of the application of this theory to practical situations. A published debate over the validity of geostatistics is also examined. The ultimate goal of this dissertation is to provide a thorough investigation of geostatistics from both a theoretical and a practical perspective. The theory presented in this dissertation is thus tested on various spatial data sets, and from these tests it is concluded that geostatistics can be effectively used in practice provided that the practitioner fully understands the theory of geostatistics and the spatial data being analyzed. A particularly interesting conclusion to come out of this dissertation is the importance of using additive regionalized variables in all geostatistical analyses.

In this paper, exhaustivity, continuity, and strong additivity are studied in the setting of topological Riesz spaces. Of particular interest is the link between strong additivity and exhaustive elements of Dedekind s-complete Banach lattices. There is a strong connection between the Diestel-Faires Theorem and the Meyer-Nieberg Lemma in this setting. Also, embedding properties of Banach lattices are linked to the notion of strong additivity. The Meyer-Nieberg Lemma is extended to the setting of topological Riesz spaces and uniform absolute continuity and uniformly exhaustive elements are studied in this setting. Counterexamples are provided to show that the Vitali-Hahn-Saks Theorem and the Brooks-Jewett Theorem cannot be extended to submeasures or to the setting of Banach lattices.

Many chemical and physical factors govern the mobility of metal contaminants in soils and clay systems; some of these chemical factors include mineralogical composition, cation exchange capacity, organic matter content, pH and the ionic strength of soil water. This makes understanding and therefore predicting the fate of metal contaminants in soils a complex undertaking. There were two broad objectives in this study. The first was to investigate binary and ternary sorption systems, with the aim to understand the effects that factors such as pH, ionic strength, organic matter and metal concentrations, have on sorption of simple clay minerals (bentonite and kaolinite) with metals (cadmium, caesium, nickel and strontium). The second was to investigate the retention of heavy metals and radionuclides by well characterised organic-rich and organic-poor clay soils, breaking them down to their individual components to help understand the effects of each component separately, the study also tested to see if the additivity principle holds for these heavy metals and radionuclides, the additivity principle presumes that the overall sorption behaviour of a complex mixture is a summation of the weighted individual sorption behaviours of its constituents. The study also determined the relationship between the natural organic matter (NOM) content and cation exchange capacity (CEC) of the soils and how these affect the mobility of the metals, while also considering the relative importance of the speciation of the metals. Two British clay soils (Mercia Mudstone and London Clay) and two Nigerian soils (an organic-rich Ikeja Loam and the other organic-poor Magodo Laterite) were characterised by X-ray diffraction (XRD) analysis. Batch techniques were employed for the sorption studies, and radiometric techniques, ICP-OES and ICP-MS were used to quantify metal distribution between phases. The sorption of dissolved organic matter to clay minerals is very dependent on pH; this dependence is greater with increasing concentration of organic matter. The formation of metal-humate complexes is dependent on the nature of the metal and pH. Caesium exhibits no discernible sorption to humic acid, cadmium sorption is enhanced by increasing alkalinity but this enhancement is slightly reduced in higher concentrations of humic acid, nickel sorption is mostly unaffected by pH except in higher concentrations of humic acid and enhanced only under very low concentrations of humic acid, while strontium sorption to humic acid is reduced with increasing alkalinity. The nature and preference of humic acid sorption for these metals are vital to understanding the role played by humic acid in the ternary sorption studies of metals, humic acid and clay minerals. Strongly acidic conditions dominate other factors affecting sorption such as ionic strength in sorption of metals to clay minerals. Caesium sorption to bentonite at pH 4 is poor and almost unaffected by ionic strength of the electrolyte solution; saturation of sorption sites is reached with low amounts of adsorbed caesium. Strontium also binds poorly to bentonite at pH 4, more so than caesium, but an ionic strength effect on sorption still exists and is discernible even under these conditions. Increasing alkalinity has the expected effect of increasing sorption capacity of bentonite for both metals. Bentonite has poor sorption properties, having low affinity for caesium and strontium, but has greater affinity for caesium than strontium. The presence of humic acid can enhance or suppresses sorption, this varies from metal to metal and from solid to solid, the degree of enhancement or suppression also depends on humic acid concentration. Kaolinite has better sorption properties than bentonie. Cadmium has greater sorption affinity for kaolinite in the absence of humic acid, but nickel sorption is more enhanced in the presence of humic acid. Although the presence of humic acid enhances cadmium and nickel sorption to kaolinite, low humic acid concentration provides the best conditions for maximum sorption of both metals. High concentrations of humic acid lead to colloid formation which block access of metals with larger hydration radii to sorption sites while also encouraging the formation of humic acid-metal complexes. As with bentonite, caesium and strontium are both poorly sorbed by kaolinite. Unlike with cadmium and nickel however, the enhancement of strontium sorption is supported by higher concentrations of humic acid, these confirm a greater preference for the formation of S-HA-Sr ternary and lower preference for the formation HA-Sr binary complexes. The presence of humic acid inhibits caesium binding, and retention but this reduces with increasing alkalinity, while the reverse is the case with strontium whose retention is very poor and improved in the presence of humic acid and increasing alkalinity. Both caesium and strontium are poorly sorbed and retained by kaolinite but their retention improves with increasing humic acid concentration and pH. Cadmium and nickel also exhibit poor retention to kaolinite but their retentions are more improved under alkaline conditions and higher humic acid concentrations, nickel more so than cadmium. Similar sorption affinities were exhibited by the British (London Clay and Mercia Mudstone) and Nigerian soils (Magodo Laterite and Ikeja Loam) for the metals studied, showing the significance of soil constituent contribution to sorption behaviour. Both British clay matrices have affinity for the metals in the order Cs >> Cd ≈ Ni >> Sr, London Clay has the greater sorption capacity for all the metals, the clay mineral content of Mercia Mudstone is almost entirely made up of Illite while London Clay contains a mixture of smectite, illite and kaolinite with smectite being the most prevalent clay mineral phase. These confirm that smectite-rich clay systems will exhibit better sorption and retention capacities for metal contaminants. Both Nigerian soils showed the same order of affinity for all the metals Cd >> Ni >> Cs >> Sr, cadmium s preferential sorption to kaolinite was observed in its greater sorption and retention by the Nigerian soils which are kaolinite-rich. This preferential sorption of cadmium by kaolinite is confirmed by the Standard Addition experiment where its contribution to cadmium sorption is clearly evident, a trend not replicated with nickel as the sorbing metal. The presence of organic matter in soils or clay systems improves their metal sorption capacity significantly, especially true for insoluble organic matter, however its significance is reduced as pH increases. The contribution of organic matter also depends on the sorption affinity of the metal for organic matter; metals such like strontium are more affected by organic matter presence. The overall sorption behaviour of complex systems such as soils is difficult to attribute to their individual constituents. The laterite soils can be considered a relatively simple soil system containing only four constituents in significant quantities, yet it is difficult to replicate its sorption behaviour using a replicate proportioned mixture of its constituent phases. The additively calculated sorption profiles for the synthetic laterite were different from those obtained experimentally, the reason for this is that the existence of significant particle size differences between the natural and synthetic soils give rise to differences in the availability of sorption sites which is evident from the different CEC values measured for both systems.

A bondingness term is further developed to aid in heat of formation (ΔfHº) calculations for C, N, O and S containing molecules. Bondingness originated from qualitative investigations into the antibonding effect in the occupied MOs of ethane. Previous work used a single parameter for bondingness to calculate ΔfHº in an alkane homologous series using an additivity scheme. This work modifies the bondingness algorithm and uses the term to parameterise a test group of 345 molecules consisting of 17 subgroups that include alkanes, alkenes, alkynes, alcohols, ethers, aldehydes, ketones, carboxylic acids, esters, amines, amides, diazenes, nitriles, nitroalkanes, nitrates, thiols and benzenoids. Comparing experimental with calculated ΔfHº values, a standard deviation for the residuals of 6.3 kJ mol 1 can be achieved using bondingness with a simple steric repulsion term (SSR) in a bond additivity scheme, and a standard deviation of 5.2 kJ mol 1 can be achieved using a Lennard-Jones potential. The method is compared with the group method of Pedley, which for a slightly smaller set of 338 molecules, a subset of the test set of 345 molecules, gives a standard deviation of 7.0 kJ mol 1. Bondingness, along with SSR or a Lennard-Jones potential, is parameterised in the lowest level of ab initio (HF-SCF) or semiempirical quantum chemical calculations. It therefore may be useful in determining the ΔfHº values for the largest molecules that are amenable to quantum chemical calculation. As part of our analysis we calculated the difference between the lowest energy conformer and the average energy of a mixture populated with higher energy conformers. This is the difference between the experimental ΔfHº value and the ΔfHº calculated for a single conformer. Example calculations which we have followed are given by Dale and Eliel et al.. Dale calculates the energy difference for molecules as large as hexane using relative energies based on the number of 1,4 gauche interactions. We have updated these values with constant increments ascertained by Klauda et al. as well as ab initio MP2 cc-pVDZ relative energies and have included calculations for heptane and octane.

The development and approval of new drugs is an expensive process. The total cost for the approval of a new compound is on average 1.0 - 1.2 billion dollars and the entire process lasts about 12 - 15 years. The main difficulties are related to poor pharmacokinetics, lack of efficacy and unwanted side effects. These problems have naturally led to the question if new and alternative methodologies can be developed to find reliable and low cost alternatives to existing practices. Nowadays, computer-assisted tools are used to support the decision process along the early stages of the drug discovery path leading from the identification of a suitable biomolecular target to the design/optimization of drug-like molecules. This process includes assessments about target druggability, screening of molecular libraries and the optimization of lead compounds where new drug-like molecules able to bind with sufficiently affinity and specificity to a disease-involved protein are designed. Existing computational methods used by the pharmaceutical industry are usually focused on the screening of library compounds such as docking, chemoinformatics and other ligand-based methods to predict and improve binding affinities, but their reliable application requires improvements in accuracy. New quantitative methods based on molecular simulations of drug binding to a protein could greatly improve prospects for the reliable in-silico design of new potent drug candidates. A common parameter used by medicinal chemists to quantify the affinity between candidate ligands and a target protein is represented by the free energy of binding. However, despite the increased amount of structural information, predicting binding free energy is still a challenge and this technique has found limited use beyond academia. A major reason for limited adoption in the industry is that reliable computer models of drug binding to a protein must reproduce the change in molecular conformations of the drug and protein upon complex formation and this includes the correct modelling of weak non-covalent interactions such as hydrogen bonds, burials of hydrophobic surface areas, Van der Waals interactions, fixations of molecular degrees of freedom solvation/desolvation of polar groups and different entropy contributions related to the solvent and protein interactions. For several classes of proteins these phenomena are not easy to model and often require extremely computationally intensive simulations. The main goal of the thesis was to explore efficient ways of computing binding affinities by using molecular simulations. With this aim, novel software to compute relative binding free energies has been developed. The implementation is based on alchemical transformations and it extended a preexisted piece of software Sire, a molecular modeling framework, by using the OpenMM APIs to run fast molecular dynamics simulations on the latest GPGPU technology. This new piece of software has equipped the scientific community with a flexible and fast tool, not only to predict relative binding affinities, but also a starting point to develop new sampling methods for instance hybrid molecular dynamics and Monte Carlo. The implementation has been validated on the prediction of relative hydration free energy of small molecules, showing good agreement with experimental data. In addition, non-additive effects to binding affinities in series of congeneric Thrombin inhibitors were investigated. Although excellent agreement between predicted and experimental relative binding affinities was achieved, it was not possible to accurately predict the non-additivity levels in most of the examined inhibitors, thus suggesting that improved force fields are required to further advance the state-of-the art of the field.

The link between the genotype and the phenotype of an organism is immensely complex. Despite this it can, to a great extent, be captured using models that assume that gene variants combine their effects in an additive manner. This thesis explores aspects of genetics that cannot be fully captured using such additive models. Using experimental data from three different model organisms, I study two phenomena that fall outside of the additive paradigm: genetic interactions and genetic variance heterogeneity. Using the model plant Arabidopsis thaliana, we show how important biological insights can be reached by exploring loci that display genetic variance heterogeneity. In the first study, this approach identified alleles in the gene CMT2 associated with the climate at sampling locations, suggesting a role in climate adaption. These alleles affected the genome wide methylation pattern, and a complete knock down of this gene increased the plants heat tolerance. In the second study, we demonstrate how the observed genetic variance heterogeneity was the result of the partial linkage of many functional alleles near the gene MOT1, all contributing to Molybdenum levels in the leaves. Further, we explore genetic interactions using data from dogs and budding yeast (Saccharomyces cerevisiae). In the dog population, two interacting loci were associated with fructosamine levels, a biomarker used to monitor blood glucose. One of the loci displayed the pattern of a selective sweep in some of the studied breeds, suggesting that the interaction is important for the phenotypic breed-differences. In a cross between two strains of yeast, with the advantage of large population size and nearly equal allele frequencies, we identified large epistatic networks. The networks were largely centered on a number of hub-loci and altogether involved hundreds of genetic interactions. Most network hubs had the ability to either suppress or uncover the phenotypic effects of other loci. Many multi-locus allele combinations resulted in phenotypes that deviated significantly from the expectations, had the loci acted in an additive manner. Critically, this thesis demonstrates that non-additive genetic mechanisms often need to be considered in order to fully understand the genetics of complex traits.

Orientador: Nelson Henrique Morgon / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Quimica / Made available in DSpace on 2018-08-07T03:03:20Z (GMT). No. of bitstreams: 1 Sanvido_CibelledeSouza_M.pdf: 3049772 bytes, checksum: 93473c5a86f022408435d3d8d4bfadd4 (MD5) Previous issue date: 2006 / Mestrado / Físico-Química / Mestre em Química

In variety testing as well as in psychological assessment, the situation occurs that in a two-way ANOVA-type model with only one replication per cell, analysis is done under the assumption of no interaction between the two factors. Tests for this situation are known only for fixed factors and normally distributed outcomes. In the following we will present five additivity tests and apply them to fixed and mixed models and to quantitative as well as to Bernoulli distributed data. We consider their performance via simulation studies with respect to the type-I-risk and power. Furthermore, two new approaches will be presented, one being a modification of Tukey's test and the other being a new experimental design to test for interactions.