Statistically robust representation and comparison of mortality profiles in archaeozoology

Archaeozoological mortality profiles have been used to infer site-specific subsistence strategies. There is however no common agreement on the best way to present these profiles and confidence intervals around age class proportions. In order to deal with these issues, we propose the use of the Dirichlet distribution and present a new approach to perform age-at-death multivariate graphical comparisons. We demonstrate the efficiency of this approach using domestic sheep/goat dental remains from 10 Cardial sites (Early Neolithic) located in South France and the Iberian Peninsula. We show that the Dirichlet distribution in age-at-death analysis can be used: (i) to generate Bayesian credible intervals around each age class of a mortality profile, even when not all age classes are observed; and (ii) to create 95% kernel density contours around each age-at-death frequency distribution when multiple sites are compared using correspondence analysis. The statistical procedure we present is applicable to the analysis of any categorical count data and particularly well-suited to archaeological data (e.g. potsherds, arrow heads) where sample sizes are typically small.

Heat dissipation from carbon nano-electronics

The incorporation of graphitic compounds such as carbon nanotubes (CNTs) and graphene into nano-electronic device packaging holds much promise for waste heat management given their high thermal conductivities. However, as these graphitic materials must be used in together with other semiconductor/insulator materials, it is not known how thermal transport is affected by the interaction. Using different simulation techniques, in this thesis, we evaluate the thermal transport properties - thermal boundary conductance (TBC) and thermal conductivity - of CNTs and single-layer graphene in contact with an amorphous SiO2 (a-SiO2) substrate. First, the theoretical methodologies and concepts used in our simulations are presented. In particular, two concepts are described in detail as they are necessary for the understanding of the subsequent chapters. The first is the linear response Green-Kubo (GK) theory of thermal boundary conductance (TBC), which we develop in this thesis, and the second is the spectral energy density method, which we use to directly compute the phonon lifetimes and thermal transport coefficients. After we set the conceptual foundations, the TBC of the CNT-SiO2 interface is computed using non- equilibrium molecular dynamics (MD) simulations and the new Green-Kubo method that we have developed. Its dependence on temperature, the strength of the interaction with the substrate, and tube diameter are evaluated. To gain further insight into the phonon dynamics in supported CNTs, the scattering rates are computed using the spectral energy density (SED) method. With this method, we are able to distinguish the different scattering mechanisms (boundary and CNT-substrate phonon-phonon) and rates. The phonon lifetimes in supported CNTs are found to be reduced by contact with the substrate and we use that lifetime reduction to determine the change in CNT thermal conductivity. Next, we examine thermal transport in graphene supported on SiO2. The phonon contribution to the TBC of the graphene-SiO2 interface is computed from MD simulations and found to agree well with experimentally measured values. We derive the theory of remote phonon scattering of graphene electrons and compute the heat transfer coefficient dependence on doping level and temperature. The thermal boundary conductance from remote phonon scattering is found to be an order of magnitude smaller than that of the phonon contribution. The in-plane thermal conductivity of supported graphene is calculated from MD simulations. The experimentally measured order of magnitude reduction in thermal conductivity is reproduced in our simulations. We show that this reduction is due to the damping of the flexural (ZA) modes. By varying the interaction between graphene and the substrate, the ZA modes hybridize with the substrate Rayleigh modes and the dispersion of the hybridized modes is found to linearize in the strong coupling limit, leading to an increased thermal conductance in the composite structure.

Spatial distribution of triatomines in domiciles of an urban area of the Brazilian Southeast Region

Reports of triatomine infestation in urban areas have increased. We analysed the spatial distribution of infestation by triatomines in the urban area of Diamantina, in the state of Minas Gerais, Brazil. Triatomines were obtained by community-based entomological surveillance. Spatial patterns of infestation were analysed by Ripley’s K function and Kernel density estimator. Normalised difference vegetation index (NDVI) and land cover derived from satellite imagery were compared between infested and uninfested areas. A total of 140 adults of four species were captured (100 Triatoma vitticeps, 25 Panstrongylus geniculatus, 8 Panstrongylus megistus, and 7 Triatoma arthurneivai specimens). In total, 87.9% were captured within domiciles. Infection by trypanosomes was observed in 19.6% of 107 examined insects. The spatial distributions of T. vitticeps, P. geniculatus, T. arthurneivai, and trypanosome-positive triatomines were clustered, occurring mainly in peripheral areas. NDVI values were statistically higher in areas infested by T. vitticeps and P. geniculatus. Buildings infested by these species were located closer to open fields, whereas infestations of P. megistus and T. arthurneivai were closer to bare soil. Human occupation and modification of natural areas may be involved in triatomine invasion, exposing the population to these vectors.

Inequalities for a new data-based method for selecting nonparametric density estimates

We continue the development of a method for the selection of a bandwidth or a number of design parameters in density estimation. We provideexplicit non-asymptotic density-free inequalities that relate the $L_1$ error of the selected estimate with that of the best possible estimate,and study in particular the connection between the richness of the classof density estimates and the performance bound. For example, our methodallows one to pick the bandwidth and kernel order in the kernel estimatesimultaneously and still assure that for {\it all densities}, the $L_1$error of the corresponding kernel estimate is not larger than aboutthree times the error of the estimate with the optimal smoothing factor and kernel plus a constant times $\sqrt{\log n/n}$, where $n$ is the sample size, and the constant only depends on the complexity of the family of kernels used in the estimate. Further applications include multivariate kernel estimates, transformed kernel estimates, and variablekernel estimates.

Study of the impact of tissue density heterogeneities on 3-dimensional abdominal dosimetry: comparison between dose kernel convolution and direct monte carlo methods.

Dose kernel convolution (DK) methods have been proposed to speed up absorbed dose calculations in molecular radionuclide therapy. Our aim was to evaluate the impact of tissue density heterogeneities (TDH) on dosimetry when using a DK method and to propose a simple density-correction method. METHODS: This study has been conducted on 3 clinical cases: case 1, non-Hodgkin lymphoma treated with (131)I-tositumomab; case 2, a neuroendocrine tumor treatment simulated with (177)Lu-peptides; and case 3, hepatocellular carcinoma treated with (90)Y-microspheres. Absorbed dose calculations were performed using a direct Monte Carlo approach accounting for TDH (3D-RD), and a DK approach (VoxelDose, or VD). For each individual voxel, the VD absorbed dose, D(VD), calculated assuming uniform density, was corrected for density, giving D(VDd). The average 3D-RD absorbed dose values, D(3DRD), were compared with D(VD) and D(VDd), using the relative difference Δ(VD/3DRD). At the voxel level, density-binned Δ(VD/3DRD) and Δ(VDd/3DRD) were plotted against ρ and fitted with a linear regression. RESULTS: The D(VD) calculations showed a good agreement with D(3DRD). Δ(VD/3DRD) was less than 3.5%, except for the tumor of case 1 (5.9%) and the renal cortex of case 2 (5.6%). At the voxel level, the Δ(VD/3DRD) range was 0%-14% for cases 1 and 2, and -3% to 7% for case 3. All 3 cases showed a linear relationship between voxel bin-averaged Δ(VD/3DRD) and density, ρ: case 1 (Δ = -0.56ρ + 0.62, R(2) = 0.93), case 2 (Δ = -0.91ρ + 0.96, R(2) = 0.99), and case 3 (Δ = -0.69ρ + 0.72, R(2) = 0.91). The density correction improved the agreement of the DK method with the Monte Carlo approach (Δ(VDd/3DRD) < 1.1%), but with a lesser extent for the tumor of case 1 (3.1%). At the voxel level, the Δ(VDd/3DRD) range decreased for the 3 clinical cases (case 1, -1% to 4%; case 2, -0.5% to 1.5%, and -1.5% to 2%). No more linear regression existed for cases 2 and 3, contrary to case 1 (Δ = 0.41ρ - 0.38, R(2) = 0.88) although the slope in case 1 was less pronounced. CONCLUSION: This study shows a small influence of TDH in the abdominal region for 3 representative clinical cases. A simple density-correction method was proposed and improved the comparison in the absorbed dose calculations when using our voxel S value implementation.

Application of the essential work of fracture method in ranking the performance in service of high-density polyethylene resins employed in pressure pipes

High-density polyethylene resins have increasingly been used in the production of pipes for water- and gas-pressurized distribution systems and are expected to remain in service for several years, but they eventually fail prematurely by creep fracture. Usual standard methods used to rank resins in terms of their resistance to fracture are expensive and non-practical for quality control purposes, justifying the search for alternative methods. Essential work of fracture (EWF) method provides a relatively simple procedure to characterize the fracture behavior of ductile polymers, such as polyethylene resins. In the present work, six resins were analyzed using the EWF methodology. The results show that the plastic work dissipation factor, beta w(p), is the most reliable parameter to evaluate the performance. Attention must be given to specimen preparation that might result in excessive dispersion in the results, especially for the essential work of fracture w(e).

Effect of precracking method on K(Ic) results for medium-density polyethylene tested under cryogenic condition

The effect of different precracking methods on the results of linear elastic K(Ic) fracture toughness testing with medium-density polyethylene (MDPE) was investigated. Cryogenic conditions were imposed in order to obtain valid K(Ic) values from specimens of suitable size. Most conservative K(Ic) values were obtained by slow pressing a fresh razor blade at the notch root of the specimen. Due to the low deformation level imposed on the crack tip region, the slow pressing razor blade technique also produced less scatter in fracture toughness results. It has been shown that the slow stable crack growth preceding catastrophic brittle failure during K(Ic) tests in MOPE under cryogenic conditions should not be disregarded as it has relevant physical meaning and may affect the fracture toughness results. (C) 2010 Elsevier Ltd. All rights reserved.

A New Method to Determine the Density and Water Absorption of Fine Recycled Aggregates

The construction industry keeps on demanding huge quantities of natural resources, mainly minerals for mortars and concrete production. The depletion of many quarries and environmental concerns about reducing the dumping of construction and demolition waste in quarries have led to an increase in the procuring and use of recycled aggregates from this type of waste. If they are to be incorporated in concrete and mortars it is essential to know their properties to guarantee the adequate performance of the end products, in both mechanical and durability-related terms. Existing regulated tests were developed for natural aggregates, however, and several problems arise when they are applied to recycled aggregates, especially fine recycled aggregates (FRA). This paper describes the main problems encountered with these tests and proposes an alternative method to determine the density and water absorption of FRA that removes them. The use of sodium hexametaphosphate solutions in the water absorption test has proven to improve its efficiency, minimizing cohesion between particles and helping to release entrained air.

An adaptive multiscale method for density-driven instabilities

Accurate modeling of flow instabilities requires computational tools able to deal with several interacting scales, from the scale at which fingers are triggered up to the scale at which their effects need to be described. The Multiscale Finite Volume (MsFV) method offers a framework to couple fine-and coarse-scale features by solving a set of localized problems which are used both to define a coarse-scale problem and to reconstruct the fine-scale details of the flow. The MsFV method can be seen as an upscaling-downscaling technique, which is computationally more efficient than standard discretization schemes and more accurate than traditional upscaling techniques. We show that, although the method has proven accurate in modeling density-driven flow under stable conditions, the accuracy of the MsFV method deteriorates in case of unstable flow and an iterative scheme is required to control the localization error. To avoid large computational overhead due to the iterative scheme, we suggest several adaptive strategies both for flow and transport. In particular, the concentration gradient is used to identify a front region where instabilities are triggered and an accurate (iteratively improved) solution is required. Outside the front region the problem is upscaled and both flow and transport are solved only at the coarse scale. This adaptive strategy leads to very accurate solutions at roughly the same computational cost as the non-iterative MsFV method. In many circumstances, however, an accurate description of flow instabilities requires a refinement of the computational grid rather than a coarsening. For these problems, we propose a modified iterative MsFV, which can be used as downscaling method (DMsFV). Compared to other grid refinement techniques the DMsFV clearly separates the computational domain into refined and non-refined regions, which can be treated separately and matched later. This gives great flexibility to employ different physical descriptions in different regions, where different equations could be solved, offering an excellent framework to construct hybrid methods.

A nonparametric method for the measurement of size diversity with emphasis on data standardization

The most suitable method for estimation of size diversity is investigated. Size diversity is computed on the basis of the Shannon diversity expression adapted for continuous variables, such as size. It takes the form of an integral involving the probability density function (pdf) of the size of the individuals. Different approaches for the estimation of pdf are compared: parametric methods, assuming that data come from a determinate family of pdfs, and nonparametric methods, where pdf is estimated using some kind of local evaluation. Exponential, generalized Pareto, normal, and log-normal distributions have been used to generate simulated samples using estimated parameters from real samples. Nonparametric methods include discrete computation of data histograms based on size intervals and continuous kernel estimation of pdf. Kernel approach gives accurate estimation of size diversity, whilst parametric methods are only useful when the reference distribution have similar shape to the real one. Special attention is given for data standardization. The division of data by the sample geometric mean is proposedas the most suitable standardization method, which shows additional advantages: the same size diversity value is obtained when using original size or log-transformed data, and size measurements with different dimensionality (longitudes, areas, volumes or biomasses) may be immediately compared with the simple addition of ln k where kis the dimensionality (1, 2, or 3, respectively). Thus, the kernel estimation, after data standardization by division of sample geometric mean, arises as the most reliable and generalizable method of size diversity evaluation

Development of a Fully Automated Image Analysis Method for High Density cDNA and array CGH Microarray Based Genomic Studies

In this thesis, different techniques for image analysis of high density microarrays have been investigated. Most of the existing image analysis techniques require prior knowledge of image specific parameters and direct user intervention for microarray image quantification. The objective of this research work was to develop of a fully automated image analysis method capable of accurately quantifying the intensity information from high density microarrays images. The method should be robust against noise and contaminations that commonly occur in different stages of microarray development.

An approximation method using approximate approximations

The aim of this paper is to extend the method of approximate approximations to boundary value problems. This method was introduced by V. Maz'ya in 1991 and has been used until now for the approximation of smooth functions defined on the whole space and for the approximation of volume potentials. In the present paper we develop an approximation procedure for the solution of the interior Dirichlet problem for the Laplace equation in two dimensions using approximate approximations. The procedure is based on potential theoretical considerations in connection with a boundary integral equations method and consists of three approximation steps as follows. In a first step the unknown source density in the potential representation of the solution is replaced by approximate approximations. In a second step the decay behavior of the generating functions is used to gain a suitable approximation for the potential kernel, and in a third step Nyström's method leads to a linear algebraic system for the approximate source density. For every step a convergence analysis is established and corresponding error estimates are given.

A Field Evaluation of the Time-of-Detection Method to Estimate Population Size and Density for Aural Avian Point Counts

The time-of-detection method for aural avian point counts is a new method of estimating abundance, allowing for uncertain probability of detection. The method has been specifically designed to allow for variation in singing rates of birds. It involves dividing the time interval of the point count into several subintervals and recording the detection history of the subintervals when each bird sings. The method can be viewed as generating data equivalent to closed capture–recapture information. The method is different from the distance and multiple-observer methods in that it is not required that all the birds sing during the point count. As this method is new and there is some concern as to how well individual birds can be followed, we carried out a field test of the method using simulated known populations of singing birds, using a laptop computer to send signals to audio stations distributed around a point. The system mimics actual aural avian point counts, but also allows us to know the size and spatial distribution of the populations we are sampling. Fifty 8-min point counts (broken into four 2-min intervals) using eight species of birds were simulated. Singing rate of an individual bird of a species was simulated following a Markovian process (singing bouts followed by periods of silence), which we felt was more realistic than a truly random process. The main emphasis of our paper is to compare results from species singing at (high and low) homogenous rates per interval with those singing at (high and low) heterogeneous rates. Population size was estimated accurately for the species simulated, with a high homogeneous probability of singing. Populations of simulated species with lower but homogeneous singing probabilities were somewhat underestimated. Populations of species simulated with heterogeneous singing probabilities were substantially underestimated. Underestimation was caused by both the very low detection probabilities of all distant individuals and by individuals with low singing rates also having very low detection probabilities.

A novel method for production of lipid hydroperoxide- or oxysterol-rich low-density lipoprotein

LDL oxidation may be important in atherosclerosis. Extensive oxidation of LDL by copper induces increased uptake by macrophages, but results in decomposition of hydroperoxides, making it more difficult to investigate the effects of hydroperoxides in oxidised LDL on cell function. We describe here a simple method of oxidising LDL by dialysis against copper ions at 4 degrees C, which inhibits the decomposition of hydroperoxides, and allows the production of LDL rich in hydroperoxides (626 +/- 98 nmol/mg LDL protein) but low in oxysterols (3 +/- 1 nmol 7-ketocholesterol/mg LDL protein), whilst allowing sufficient modification (2.6 +/- 0.5 relative electrophoretic mobility) for rapid uptake by macrophages (5.49 +/- 0.75 mu g I-125-labelled hydroperoxide-rich LDL vs. 0.46 +/- 0.04 mu g protein/mg cell protein in 18 h for native LDL). By dialysing under the same conditions, but at 37 degrees C, the hydroperoxides are decomposed extensively and the LDL becomes rich in oxysterols. This novel method of oxidising LDL with high yield to either a hydroperoxide- or oxysterol-rich form by simply altering the temperature of dialysis may provide a useful tool for determining the effects of these different oxidation products on cell function. (C) 2007 Elsevier Ireland Ltd. All rights reserved.

Sparse kernel modelling: a unified approach

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.