Compaction caused by certain intensive forest management practices can reduce tree growth, but the causes of growth reduction are usually complex interactions between soil properties and tree species. We used a 7 by 7 factorial greenhouse experiment to create a matrix of bulk density ((Ï b)) and volumetric water content (θv) to determine soil compaction effects on seedling growth of: (i) ponderosa pine (Pinus ponderosa Dougl. ex Laws) grown on Dome and Cohasset soils from California; (ii) shortleaf pine (Pinus echinata) on a Clarksville soil from Missouri; and (iii) loblolly pine (Pinus taeda) on an Argent soil from South Carolina. We also characterized soil physical properties and determined compaction effects on soil strength, air/water balance and least limiting water range (LLWR) for each of the soils. Optimum water content for compaction varied from 19%(Argent) to 34%(Cohasset). Compactive effort curves varied for the four soils;maximum Ï b were 1.33, 1.52, 1.58 and 1.65 Mg m-3 for the Cohasset, Dome, Clarksville, and Argent soils, respectively. Compression indices ranged from 0.33 to 0.38. In general, soil strength increased linearly with a θv decrease at the higher Ï b levels, but the effect varied with each soil type. Cohasset, with the lowest BD, had the highest soil strength (3.5 MPa), while strengths exceeding 2.0 MPa were not found for the Argent soil. Compaction affected the soil water retention curves and associated air/water balance parameters for all soils, particularly the Cohasset and Dome soils. Aeration porosity became limiting at Ï b of 1.3, 1.42, 1.44 and 1.55 Mg m-3 for the Cohasset, Dome, Clarksville and Argent soils respectively. The LLWR was lowest for the Dome and Argent soils (0.3 cm 3 cm-3 ) and in some cases increased with compaction. Models of root growth opportunity were developed using multiple regression. The general model of root length density (RLD) = b0 + b1 θv + b2 Ï b + b3 θv2 described rooting response for the Clarksville-shortleaf and Argent-loblolly soil-species combinations (p = 0.005). However, the root response of ponderosa pine on Cohasset was linear and pine roots in the Dome soil responded to an interaction between θv and Ï b. No model adequately described oak seedling growth as a function of BD and VW. High soil strength at low water contents and low aeration porosity at high water contents limited root growth. Shoot mass of seedlings growing within the least limiting water range (LLWR) was greater than those growing outside the range for all soil-species combinations except the Argent-loblolly pine (p = 0.05). The loblolly pines had greater shoot mass at volumetric water contents above the upper LLWR limits (aeration limiting). The LLWR is a promising method for integrating compaction's influence on soil properties and thus root growth potential since single factors did not appear to adequately explain each soil's compressibility. Furthermore, response surface models of RLD as a function of VW and BD in conjunction with the LLWR and seasonal site water data have potential for determining compaction- induced soil limitations for tree growth, but need to be calibrated for both soil and species. / Master of Science

Time delay estimation (TDE)-based algorithms for estimation of direction of arrival (DOA) have been most popular for use with speech signals. This is due to their simplicity and low computational requirements. Though other algorithms, like the steered response power with phase transform (SRP-PHAT), are available that perform better than TDE based algorithms, the huge computational load required for this algorithm makes it unsuitable for applications that require fast refresh rates using short frames. In addition, the estimation errors that do occur with SRP-PHAT tend to be large. This kind of performance is unsuitable for an application such as video camera steering, which is much less tolerant to large errors than it is to small errors. We propose an improved TDE-based DOA estimation algorithm called time delay selection (TIDES) based on either minimizing the weighted least squares error (MWLSE) or minimizing the time delay separation (MWTDS). In the TIDES algorithm, we consider not only the maximum likelihood (ML) TDEs for each pair of microphones, but also other secondary delays corresponding to smaller peaks in the generalized cross-correlation (GCC). From these multiple candidate delays for each microphone pair, we form all possible combinations of time delay sets. From among these we pick one set based on one of the two criteria mentioned above and perform least squares DOA estimation using the selected set of time delays. The MWLSE criterion selects that set of time delays that minimizes the least squares error. The MWTDS criterion selects that set of time delays that has minimum distance from a statistically averaged set of time delays from previously selected time delays. Both TIDES algorithms are shown to out-perform the ML-TDE algorithm in moderate signal to reverberation ratios. In fact, TIDES-MWTDS gives fewer large errors than even the SRP-PHAT algorithm, which makes it very suitable for video camera steering applications. Under small signal to reverberation ratio environments, TIDES-MWTDS breaks down, but TIDES-MWLSE is still shown to out-perform the algorithm based on ML-TDE. / Master of Science

Yes / This study estimates output market power in the Chinese banking industry using the multi-output Lerner index. We propose a minimum distance cost function approach, which allows us to determine not only the level of market power but also the non-profit maximizers and efficiency level of Chinese banks. Following the first-stage analysis, we employ the generalized method of moment system estimator to evaluate the impacts of bank innovation and trade openness on market power in a multi-output banking context. In particular, we innovatively propose a causal structure analysis based on Wang and Blei (2019) to validate and verify the robustness of our results. We also assess this relationship for different types of bank ownership in China. The findings suggest that Chinese banks exhibit high market power in loans. Furthermore, the results show that bank innovation and trade openness have a significant negative impact on market power in loans, but a significant positive impact on market power in securities. The results also indicate a significantly negative impact of trade openness on overall market power. We find that higher levels of innovation among state-owned and joint-stock commercial banks improve the overall level of market power. The results suggest that, for all bank ownership types, trade openness has a significant negative impact on market power in loans but a significant positive impact on market power in securities. The impact on the overall level of market power is consistently significant and negative. / The full-text of this article will be released for public view at the end of the publisher embargo on 11 Aug 2025.

Yes / The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen – nicotinamide (IBU-NIC) and 1:1 carbamazepine – nicotinamide (CBZ-NIC) has been evaluated. A Partial Least Squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals.

Cookies är idag ett utbrett fenomen som nyttjas vid digital insamling av information. Informationen som samlas in är ofta av personlig karaktär och används bland annat för att individanpassa användarupplevelser på webbsidor. Ovissheten kring digital insamling av personlig information skapar en oro som idag är mycket omdebatterad. Detta arbete utgörs av en kvantitativ studie med syfte att undersöka möjliga samband mellan universitetsstudenters medvetenhet, attityd och beteende gällande digital insamling av information. Undersökningen består av en onlinebaserad enkät. Det insamlade materialet har analyserats utifrån det teoretiska ramverket Communication Privacy Management (CPM) med hjälp av analysmetoden Partial Least Squares (PLS) samt IBM Statistical Package for Social Sciences (SPSS) för att finna relevanta samband. Resultat påvisade att majoriteten universitetsstudenter känner till fenomenet cookies, men färre känner till dess användningsområden. Det konstaterades även att medvetenhet kring cookies har en påverkan på både beteende och attityd. Det fastställdes även att oavsett om universitetsstudenter har en negativ attityd förändras inte dess beteende. / Cookies are a widespread phenomenon and the main technique for digital collection of information. The collected information is often of personal nature and is used, among other things, to personalize user experiences on web pages. The uncertainty regarding digital collection of personal information creates privacy concerns that is significantly debated today. This essay consists of a quantitative study with the aim to investigate possible relations between university students awareness, attitude and behaviour regarding digital collection of information. The survey consist of an online-based poll. The gathered material has been analyzed by the theoretical framework Communication Communication Management (CPM) with the analysis method Partial Least Squares (PLS) and the IBM Statistical Package for Social Sciences (SPSS) to find relevant relations. Results showed that the majority of university students are familiar with the phenomenon of cookies, but few are aware of its area of use. It was also found that awareness of cookies has an influence on both behaviour and attitude. It can be established that regardless of whether university students have a negative or positive attitude regarding cookies, it does not affect their behaviour.

L'imagerie cérébrale connaît un intérêt grandissant, en tant que phénotype intermédiaire, dans la compréhension du chemin complexe qui relie les gènes à un phénotype comportemental ou clinique. Dans ce contexte, un premier objectif est de proposer des méthodes capables d'identifier la part de variabilité génétique qui explique une certaine part de la variabilité observée en neuroimagerie. Les approches univariées classiques ignorent les effets conjoints qui peuvent exister entre plusieurs gènes ou les covariations potentielles entre régions cérébrales.Notre première contribution a été de chercher à améliorer la sensibilité de l'approche univariée en tirant avantage de la nature multivariée des données génétiques, au niveau local. En effet, nous adaptons l'inférence au niveau du cluster en neuroimagerie à des données de polymorphismes d'un seul nucléotide (SNP), en cherchant des clusters 1D de SNPs adjacents associés à un même phénotype d'imagerie. Ensuite, nous prolongeons cette idée et combinons les clusters de voxels avec les clusters de SNPs, en utilisant un test simple au niveau du "cluster 4D", qui détecte conjointement des régions cérébrale et génomique fortement associées. Nous obtenons des résultats préliminaires prometteurs, tant sur données simulées que sur données réelles.Notre deuxième contribution a été d'utiliser des méthodes multivariées exploratoires pour améliorer la puissance de détection des études d'imagerie génétique, en modélisant la nature multivariée potentielle des associations, à plus longue échelle, tant du point de vue de l'imagerie que de la génétique. La régression Partial Least Squares et l'analyse canonique ont été récemment proposées pour l'analyse de données génétiques et transcriptomiques. Nous proposons ici de transposer cette idée à l'analyse de données de génétique et d'imagerie. De plus, nous étudions différentes stratégies de régularisation et de réduction de dimension, combinées avec la PLS ou l'analyse canonique, afin de faire face au phénomène de sur-apprentissage dû aux très grandes dimensions des données. Nous proposons une étude comparative de ces différentes stratégies, sur des données simulées et des données réelles d'IRM fonctionnelle et de SNPs. Le filtrage univarié semble nécessaire. Cependant, c'est la combinaison du filtrage univarié et de la PLS régularisée L1 qui permet de détecter une association généralisable et significative sur les données réelles, ce qui suggère que la découverte d'associations en imagerie génétique nécessite une approche multivariée. / Brain imaging is increasingly recognised as an interesting intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. Our first contribution is to improve the sensitivity of the univariate approach by taking advantage of the multivariate nature of the genetic data in a local way. Indeed, we adapt cluster-inference techniques from neuroimaging to Single Nucleotide Polymorphism (SNP) data, by looking for 1D clusters of adjacent SNPs associated with the same imaging phenotype. Then, we push further the concept of clusters and we combined voxel clusters and SNP clusters, by using a simple 4D cluster test that detects conjointly brain and genome regions with high associations. We obtain promising preliminary results on both simulated and real datasets .Our second contribution is to investigate exploratory multivariate methods to increase the detection power of imaging genetics studies, by accounting for the potential multivariate nature of the associations, at a longer range, on both the imaging and the genetics sides. Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse genetic and transcriptomic data. Here, we propose to transpose this idea to the genetics vs. imaging context. Moreover, we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA, to face the overfitting issues due to the very high dimensionality of the data. We propose a comparison study of the different strategies on both a simulated dataset and a real fMRI and SNP dataset. Univariate selection appears to be necessary to reduce the dimensionality. However, the generalisable and significant association uncovered on the real dataset by the two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful imaging genetics associations calls for a multivariate approach.

Considerando as micorregiões do Censo Agropecuário de 1995-1996 e dos Censos Demográficos de 1991 e 2000, este trabalho estima, primeiramente, uma função de produção Cobb-Douglas. Para tal, controla-se o efeito dos preços regionais sobre o valor da produção. São levados em conta fatores tradicionais (mão-de-obra, terra, insumos químicos e capital) e o capital humano (nível de escolaridade formal e conhecimento de mercado). A estimativa foi obtida através do método de mínimos quadrados em três estágios – MQ3E. O método se justifica pelo modelo econômico proposto ser um sistema de equações (estima-se também a demanda por capital e o custo relativo da utilização deste fator) e pela forte presença de heteroscedasticia na estimação em mínimos quadrados ordinários – MQO. Em seguida, estima-se a Produtividade Total dos Fatores – PTF por estado, verificando-se a contribuição relativa do capital humano. / Using data from the Agricultural Census of 1995-96 and the Demographic Censuses of 1991 and 2000, this dissertation first estimate a Cobb-Douglas production function. The value of production is calculated in order to control the effect of regional prices. Traditional factors (labor, land, fertilizers and capital) and human capital (years of schooling and market knowledge) are used as explanatory variables in the production function. The economic model is a simultaneous equations system that considers both decisions to produce and maximize profits. Results of the proposed economic model are obtained by using the three-stage least squares (3-SLS) method due to presence of heteroskedasticity. Afterwards, total productivity factor (TFP) is estimated to each of the Brazilian states and the relative contribution of human capital is analyzed.

This thesis presents a study of impedance optimisation of active microstrip patch antennas to multiple frequency points. A single layered aperture coupled microstrip patch antenna has been optimised to match the source reflection coefficient of a transistor in designing an active antenna. The active aperture coupled microstrip patch antenna was optimised to satisfy Global Positioning System (GPS) frequency specifications. A rudimentary aperture coupled microstrip patch antenna consists of a rectangular antenna element etched on the top surface of two dielectric substrates. The substrates are separated by a ground plane and a microstrip feed is etched on the bottom surface. A rectangular aperture in the ground plane provides coupling between the feed and the antenna element. This type of antenna, which conveniently isolates any circuit at the feed from the antenna element, is suitable for integrated circuit design and is simple to fabricate. An active antenna design directly couples an antenna to an active device, therefore saving real estate and power. This thesis focuses on designing an aperture coupled patch antenna directly coupled to a low noise amplifier as part of the front end of a GPS receiver. In this work an in-house software package, dubbed ACP by its creator Dr Rod Waterhouse, for calculating aperture coupled microstrip patch antenna performance parameters was linked to HP-EEsof, a microwave computer aided design and simulation package by Hewlett-Packard. An ANSI C module in HP-EEsof was written to bind the two packages. This process affords the client the benefit of powerful analysis tools offered in HP-EEsof and the fast analysis of ACP for seamless system design. Moreover, the optimisation algorithms in HP-EEsof were employed to investigate which algorithms are best suited for optimising patch antennas. The active antenna design presented in this study evades an input matching network, which is accomplished by designing the antenna to represent the desired source termination of a transistor. It has been demonstrated that a dual-band microstrip patch antenna can be successfully designed to match the source reflection coefficient, avoiding the need to insert a matching network. Maximum power transfer in electrical circuits is accomplished by matching the impedance between entities, which is generally acheived with the use of a matching network. Passive matching networks employed in amplifier design generally consist of discrete components up to the low GHz frequency range or distributed elements at greater frequencies. The source termination for a low noise amplifier will greatly influence its noise, gain and linearity which is controlled by designing a suitable input matching network. Ten diverse search methods offered in HP-EEsof were used to optimise an active aperture coupled microstrip patch antenna. This study has shown that the algorithms based on the randomised search techniques and the Genetic algorithm provide the most robust performance. The optimisation results were used to design an active dual-band antenna.

<p>In computational science it is common to describe dynamic systems by mathematical models in forms of differential or integral equations. These models may contain parameters that have to be computed for the model to be complete. For the special type of ordinary differential equations studied in this thesis, the resulting parameter estimation problem is a separable nonlinear least squares problem with equality constraints. This problem can be solved by iteration, but due to complicated computations of derivatives and the existence of several local minima, so called short-cut methods may be an alternative. These methods are based on simplified versions of the original problem. An algorithm, called the modified Kaufman algorithm, is proposed and it takes the separability into account. Moreover, different kinds of discretizations and formulations of the optimization problem are discussed as well as the effect of ill-conditioning.</p><p>Computation of parameters often includes as a part solution of linear system of equations <i>Ax = b</i>. The corresponding pseudoinverse solution depends on the properties of the matrix <i>A</i> and vector <i>b</i>. The singular value decomposition of <i>A</i> can then be used to construct error propagation matrices and by use of these it is possible to investigate how changes in the input data affect the solution <i>x</i>. Theoretical error bounds based on condition numbers indicate the worst case but the use of experimental error analysis makes it possible to also have information about the effect of a more limited amount of perturbations and in that sense be more realistic. It is shown how the effect of perturbations can be analyzed by a semi-experimental analysis. The analysis combines the theory of the error propagation matrices with an experimental error analysis based on randomly generated perturbations that takes the structure of <i>A</i> into account</p>

In computational science it is common to describe dynamic systems by mathematical models in forms of differential or integral equations. These models may contain parameters that have to be computed for the model to be complete. For the special type of ordinary differential equations studied in this thesis, the resulting parameter estimation problem is a separable nonlinear least squares problem with equality constraints. This problem can be solved by iteration, but due to complicated computations of derivatives and the existence of several local minima, so called short-cut methods may be an alternative. These methods are based on simplified versions of the original problem. An algorithm, called the modified Kaufman algorithm, is proposed and it takes the separability into account. Moreover, different kinds of discretizations and formulations of the optimization problem are discussed as well as the effect of ill-conditioning. Computation of parameters often includes as a part solution of linear system of equations Ax = b. The corresponding pseudoinverse solution depends on the properties of the matrix A and vector b. The singular value decomposition of A can then be used to construct error propagation matrices and by use of these it is possible to investigate how changes in the input data affect the solution x. Theoretical error bounds based on condition numbers indicate the worst case but the use of experimental error analysis makes it possible to also have information about the effect of a more limited amount of perturbations and in that sense be more realistic. It is shown how the effect of perturbations can be analyzed by a semi-experimental analysis. The analysis combines the theory of the error propagation matrices with an experimental error analysis based on randomly generated perturbations that takes the structure of A into account