Does phylogeographic structure relate to climatic niche divergence? A test using maritime pine (Pinus pinaster Ait.)

Aim To disentangle the effects of environmental and geographical processes driving phylogenetic distances among clades of maritime pine (Pinus pinaster). To assess the implications for conservation management of combining molecular information with species distribution models (SDMs; which predict species distribution based on known occurrence records and on environmental variables). Location Western Mediterranean Basin and European Atlantic coast. Methods We undertook two cluster analyses for eight genetically defined pine clades based on climatic niche and genetic similarities. We assessed niche similarity by means of a principal component analysis and Schoener's D metric. To calculate genetic similarity, we used the unweighted pair group method with arithmetic mean based on Nei's distance using 266 single nucleotide polymorphisms. We then assessed the contribution of environmental and geographical distances to phylogenetic distance by means of Mantel regression with variance partitioning. Finally, we compared the projection obtained from SDMs fitted from the species level (SDMsp) and composed from the eight clade-level models (SDMcm). Results Genetically and environmentally defined clusters were identical. Environmental and geographical distances explained 12.6% of the phylogenetic distance variation and, overall, geographical and environmental overlap among clades was low. Large differences were detected between SDMsp and SDMcm (57.75% of disagreement in the areas predicted as suitable). Main conclusions The genetic structure within the maritime pine subspecies complex is primarily a consequence of its demographic history, as seen by the high proportion of unexplained variation in phylogenetic distances. Nevertheless, our results highlight the contribution of local environmental adaptation in shaping the lower-order, phylogeographical distribution patterns and spatial genetic structure of maritime pine: (1) genetically and environmentally defined clusters are consistent, and (2) environment, rather than geography, explained a higher proportion of variation in phylogenetic distance. SDMs, key tools in conservation management, better characterize the fundamental niche of the species when they include molecular information.

Ecological and physiological variance in T-cell mediated immune response in Cory's shearwaters

T-cell mediated immune response (CMI) hasbeen widely studied in relation to individual andfitness components in birds. However, few studieshave simultaneously examined individual and socialfactors and habitat-mediated variance in theimmunity of chicks and adults from the samepopulation and in the same breeding season. Weinvestigated ecological and physiological variancein CMI of male and female nestlings and adults in abreeding population of Cory's Shearwaters(Calonectrisdiomedea) in theMediterranean Sea. Explanatory variables includedindividual traits (body condition, carbon andnitrogen stable isotope ratios, plasma totalproteins, triglycerides, uric acid, osmolarity,β-hydroxy-butyrate, erythrocyte meancorpuscular diameter, hematocrit, andhemoglobin) and burrow traits(temperature, isolation, and physicalstructure). During incubation, immune responseof adult males was significantly greater than thatof females. Nestlings exhibited a lower immuneresponse than adults. Ecological and physiologicalfactors affecting immune response differed betweenadults and nestlings. General linear models showedthat immune response in adult males was positivelyassociated with burrow isolation, suggesting thatmales breeding at higher densities suffer immunesystem suppression. In contrast, immune response inchicks was positively associated with bodycondition and plasma triglyceride levels.Therefore, adult immune response appears to beassociated with social stress, whereas a trade-offbetween immune function and fasting capability mayexist for nestlings. Our results, and those fromprevious studies, provide support for anasymmetrical influence of ecological andphysiological factors on the health of differentage and sex groups within a population, and for theimportance of simultaneously considering individualand population characteristics in intraspecificstudies of immune response.

Ecological and physiological variance in T-cell mediated immune response in Cory's shearwaters

T-cell mediated immune response (CMI) hasbeen widely studied in relation to individual andfitness components in birds. However, few studieshave simultaneously examined individual and socialfactors and habitat-mediated variance in theimmunity of chicks and adults from the samepopulation and in the same breeding season. Weinvestigated ecological and physiological variancein CMI of male and female nestlings and adults in abreeding population of Cory's Shearwaters(Calonectrisdiomedea) in theMediterranean Sea. Explanatory variables includedindividual traits (body condition, carbon andnitrogen stable isotope ratios, plasma totalproteins, triglycerides, uric acid, osmolarity,β-hydroxy-butyrate, erythrocyte meancorpuscular diameter, hematocrit, andhemoglobin) and burrow traits(temperature, isolation, and physicalstructure). During incubation, immune responseof adult males was significantly greater than thatof females. Nestlings exhibited a lower immuneresponse than adults. Ecological and physiologicalfactors affecting immune response differed betweenadults and nestlings. General linear models showedthat immune response in adult males was positivelyassociated with burrow isolation, suggesting thatmales breeding at higher densities suffer immunesystem suppression. In contrast, immune response inchicks was positively associated with bodycondition and plasma triglyceride levels.Therefore, adult immune response appears to beassociated with social stress, whereas a trade-offbetween immune function and fasting capability mayexist for nestlings. Our results, and those fromprevious studies, provide support for anasymmetrical influence of ecological andphysiological factors on the health of differentage and sex groups within a population, and for theimportance of simultaneously considering individualand population characteristics in intraspecificstudies of immune response.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

Methods and Models for Accelerating Dynamic Simulation of Fluid Power Circuits

The objective of this dissertation is to improve the dynamic simulation of fluid power circuits. A fluid power circuit is a typical way to implement power transmission in mobile working machines, e.g. cranes, excavators etc. Dynamic simulation is an essential tool in developing controllability and energy-efficient solutions for mobile machines. Efficient dynamic simulation is the basic requirement for the real-time simulation. In the real-time simulation of fluid power circuits there exist numerical problems due to the software and methods used for modelling and integration. A simulation model of a fluid power circuit is typically created using differential and algebraic equations. Efficient numerical methods are required since differential equations must be solved in real time. Unfortunately, simulation software packages offer only a limited selection of numerical solvers. Numerical problems cause noise to the results, which in many cases leads the simulation run to fail. Mathematically the fluid power circuit models are stiff systems of ordinary differential equations. Numerical solution of the stiff systems can be improved by two alternative approaches. The first is to develop numerical solvers suitable for solving stiff systems. The second is to decrease the model stiffness itself by introducing models and algorithms that either decrease the highest eigenvalues or neglect them by introducing steady-state solutions of the stiff parts of the models. The thesis proposes novel methods using the latter approach. The study aims to develop practical methods usable in dynamic simulation of fluid power circuits using explicit fixed-step integration algorithms. In this thesis, twomechanisms whichmake the systemstiff are studied. These are the pressure drop approaching zero in the turbulent orifice model and the volume approaching zero in the equation of pressure build-up. These are the critical areas to which alternative methods for modelling and numerical simulation are proposed. Generally, in hydraulic power transmission systems the orifice flow is clearly in the turbulent area. The flow becomes laminar as the pressure drop over the orifice approaches zero only in rare situations. These are e.g. when a valve is closed, or an actuator is driven against an end stopper, or external force makes actuator to switch its direction during operation. This means that in terms of accuracy, the description of laminar flow is not necessary. But, unfortunately, when a purely turbulent description of the orifice is used, numerical problems occur when the pressure drop comes close to zero since the first derivative of flow with respect to the pressure drop approaches infinity when the pressure drop approaches zero. Furthermore, the second derivative becomes discontinuous, which causes numerical noise and an infinitely small integration step when a variable step integrator is used. A numerically efficient model for the orifice flow is proposed using a cubic spline function to describe the flow in the laminar and transition areas. Parameters for the cubic spline function are selected such that its first derivative is equal to the first derivative of the pure turbulent orifice flow model in the boundary condition. In the dynamic simulation of fluid power circuits, a tradeoff exists between accuracy and calculation speed. This investigation is made for the two-regime flow orifice model. Especially inside of many types of valves, as well as between them, there exist very small volumes. The integration of pressures in small fluid volumes causes numerical problems in fluid power circuit simulation. Particularly in realtime simulation, these numerical problems are a great weakness. The system stiffness approaches infinity as the fluid volume approaches zero. If fixed step explicit algorithms for solving ordinary differential equations (ODE) are used, the system stability would easily be lost when integrating pressures in small volumes. To solve the problem caused by small fluid volumes, a pseudo-dynamic solver is proposed. Instead of integration of the pressure in a small volume, the pressure is solved as a steady-state pressure created in a separate cascade loop by numerical integration. The hydraulic capacitance V/Be of the parts of the circuit whose pressures are solved by the pseudo-dynamic method should be orders of magnitude smaller than that of those partswhose pressures are integrated. The key advantage of this novel method is that the numerical problems caused by the small volumes are completely avoided. Also, the method is freely applicable regardless of the integration routine applied. The superiority of both above-mentioned methods is that they are suited for use together with the semi-empirical modelling method which necessarily does not require any geometrical data of the valves and actuators to be modelled. In this modelling method, most of the needed component information can be taken from the manufacturer’s nominal graphs. This thesis introduces the methods and shows several numerical examples to demonstrate how the proposed methods improve the dynamic simulation of various hydraulic circuits.

Methods for construction and analysis of computational models in systems biology : applications to the modelling of the heat shock response and the self-assembly of intermediate filaments

Systems biology is a new, emerging and rapidly developing, multidisciplinary research field that aims to study biochemical and biological systems from a holistic perspective, with the goal of providing a comprehensive, system- level understanding of cellular behaviour. In this way, it addresses one of the greatest challenges faced by contemporary biology, which is to compre- hend the function of complex biological systems. Systems biology combines various methods that originate from scientific disciplines such as molecu- lar biology, chemistry, engineering sciences, mathematics, computer science and systems theory. Systems biology, unlike “traditional” biology, focuses on high-level concepts such as: network, component, robustness, efficiency, control, regulation, hierarchical design, synchronization, concurrency, and many others. The very terminology of systems biology is “foreign” to “tra- ditional” biology, marks its drastic shift in the research paradigm and it indicates close linkage of systems biology to computer science. One of the basic tools utilized in systems biology is the mathematical modelling of life processes tightly linked to experimental practice. The stud- ies contained in this thesis revolve around a number of challenges commonly encountered in the computational modelling in systems biology. The re- search comprises of the development and application of a broad range of methods originating in the fields of computer science and mathematics for construction and analysis of computational models in systems biology. In particular, the performed research is setup in the context of two biolog- ical phenomena chosen as modelling case studies: 1) the eukaryotic heat shock response and 2) the in vitro self-assembly of intermediate filaments, one of the main constituents of the cytoskeleton. The range of presented approaches spans from heuristic, through numerical and statistical to ana- lytical methods applied in the effort to formally describe and analyse the two biological processes. We notice however, that although applied to cer- tain case studies, the presented methods are not limited to them and can be utilized in the analysis of other biological mechanisms as well as com- plex systems in general. The full range of developed and applied modelling techniques as well as model analysis methodologies constitutes a rich mod- elling framework. Moreover, the presentation of the developed methods, their application to the two case studies and the discussions concerning their potentials and limitations point to the difficulties and challenges one encounters in computational modelling of biological systems. The problems of model identifiability, model comparison, model refinement, model inte- gration and extension, choice of the proper modelling framework and level of abstraction, or the choice of the proper scope of the model run through this thesis.

Integration of existing military capability models into the Comprehensive Capability Meta-model

Abstract—This paper discusses existing military capability models and proposes a comprehensive capability meta-model (CCMM) which unites the existing capability models into an integrated and hierarchical whole. The Zachman Framework for Enterprise Architecture is used as a structure for the CCMM. The CCMM takes into account the abstraction level, the primary area of application, stakeholders, intrinsic process, and life cycle considerations of each existing capability model, and shows how the models relate to each other. The validity of the CCMM was verified through a survey of subject matter experts. The results suggest that the CCMM is of practical value to various capability stakeholders in many ways, such as helping to improve communication between the different capability communities.

Vibrations in Rotating Machinery Arising from Minor Imperfections in Component Geometries

Vibrations in machines can cause noise, decrease the performance, or even damage the machine. Vibrations appear if there is a source of vibration that excites the system. In the worst case scenario, the excitation frequency coincides with the natural frequency of the machine causing resonance. Rotating machines are a machine type, where the excitation arises from the machine itself. The excitation originates from the mass imbalance in the rotating shaft, which always exists in machines that are manufactured using conventional methods. The excitation has a frequency that is dependent on the rotational speed of the machine. The rotating machines in industrial use are usually designed to rotate at a constant rotational speed, the case where the resonances can be easily avoided. However, the machines that have a varying operational speed are more problematic due to a wider range of frequencies that have to be avoided. Vibrations, which frequencies equal to rotational speed frequency of the machine are widely studied and considered in the typical machine design process. This study concentrates on vibrations, which arise from the excitations having frequencies that are multiples of the rotational speed frequency. These vibrations take place when there are two or more excitation components in a revolution of a rotating shaft. The dissertation introduces four studies where three kinds of machines are experiencing vibrations caused by different excitations. The first studied case is a directly driven permanent magnet generator used in a wind power plant. The electromagnetic properties of the generator cause harmonic excitations in the system. The dynamic responses of the generator are studied using the multibody dynamics formulation. In another study, the finite element method is used to study the vibrations of a magnetic gear due to excitations, which frequencies equal to the rotational speed frequency. The objective is to study the effects of manufacturing and assembling inaccuracies. Particularly, the eccentricity of the rotating part with respect to non-rotating part is studied since the eccentric operation causes a force component in the direction of the shortest air gap. The third machine type is a tube roll of a paper machine, which is studied while the tube roll is supported using two different structures. These cases are studied using different formulations. In the first case, the tube roll is supported by spherical roller bearings, which have some wavinesses on the rolling surfaces. Wavinesses cause excitations to the tube roll, which starts to resonate at the frequency that is a half of the first natural frequency. The frequency is in the range where the machine normally operates. The tube roll is modeled using the finite element method and the bearings are modeled as nonlinear forces between the tube roll and the pedestals. In the second case studied, the tube roll is supported by freely rotating discs, which wavinesses are also measured. The above described phenomenon is captured as well in this case, but the simulation methodology is based on the flexible multibody dynamics formulation. The simulation models that are used in both of the last two cases studied are verified by measuring the actual devices and comparing the simulated and measured results. The results show good agreement.

Statistical segmentation methods and color variance analysis of retinal images

In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.

Numerical Studies of Dark Energy Models and Observations

The cosmological standard view is based on the assumptions of homogeneity, isotropy and general relativistic gravitational interaction. These alone are not sufficient for describing the current cosmological observations of accelerated expansion of space. Although general relativity is extremely accurately tested to describe the local gravitational phenomena, there is a strong demand for modifying either the energy content of the universe or the gravitational interaction itself to account for the accelerated expansion. By adding a non-luminous matter component and a constant energy component with negative pressure, the observations can be explained with general relativity. Gravitation, cosmological models and their observational phenomenology are discussed in this thesis. Several classes of dark energy models that are motivated by theories outside the standard formulation of physics were studied with emphasis on the observational interpretation. All the cosmological models that seek to explain the cosmological observations, must also conform to the local phenomena. This poses stringent conditions for the physically viable cosmological models. Predictions from a supergravity quintessence model was compared to Supernova 1a data and several metric gravity models were studied with local experimental results. Polytropic stellar configurations of solar, white dwarf and neutron stars were numerically studied with modified gravity models. The main interest was to study the spacetime around the stars. The results shed light on the viability of the studied cosmological models.

Hydraulic and fluvial geomorphological models for a bedrock channel reach of the Twenty Mile Creek /

Bedrock channels have been considered challenging geomorphic settings for the application of numerical models. Bedrock fluvial systems exhibit boundaries that are typically less mobile than alluvial systems, yet they are still dynamic systems with a high degree of spatial and temporal variability. To understand the variability of fluvial systems, numerical models have been developed to quantify flow magnitudes and patterns as the driving force for geomorphic change. Two types of numerical model were assessed for their efficacy in examining the bedrock channel system consisting of a high gradient portion of the Twenty Mile Creek in the Niagara Region of Ontario, Canada. A one-dimensional (1-D) flow model that utilizes energy equations, HEC RAS, was used to determine velocity distributions through the study reach for the mean annual flood (MAF), the 100-year return flood and the 1,000-year return flood. A two-dimensional (2-D) flow model that makes use of Navier-Stokes equations, RMA2, was created with the same objectives. The 2-D modeling effort was not successful due to the spatial complexity of the system (high slope and high variance). The successful 1 -D model runs were further extended using very high resolution geospatial interpolations inherent to the HEC RAS extension, HEC geoRAS. The modeled velocity data then formed the basis for the creation of a geomorphological analysis that focused upon large particles (boulders) and the forces needed to mobilize them. Several existing boulders were examined by collecting detailed measurements to derive three-dimensional physical models for the application of fluid and solid mechanics to predict movement in the study reach. An imaginary unit cuboid (1 metre by 1 metre by 1 metre) boulder was also envisioned to determine the general propensity for the movement of such a boulder through the bedrock system. The efforts and findings of this study provide a standardized means for the assessment of large particle movement in a bedrock fluvial system. Further efforts may expand upon this standardization by modeling differing boulder configurations (platy boulders, etc.) at a high level of resolution.

Effect of iron on reactivity of vanillin in wine models /

Phenolic compounds are important components of grapes and wines. They have been found to have important roles in grape and wine systems and properties that are beneficial for human health. Vanillin (3-methoxy-4-hydroxybenzaldehyde) is a phenolic compound coming from the oxidative degradation of lignin in oak-barrels during the aging of wine. Vanillin is an important flavour component of wine and its concentration in wine influences significantly the aroma and flavour of wine. The concentration of vanillin in wine is affected by various factors including the presence of metal ions. In this work, by using HPLC, HPLC-MS, and MS technologies, iron (III) cations were found to affect the oxidation of vanillin in a model system of wine, and the product of the oxidation was identified as divanillin. The mechanism of the redox reaction between vanillin and Fe^"^ is thought to follow that of other phenol oxidations. Increasing the concentration of Fe ^ in the model system accelerates divanillin production. The best pH condition for the divanillin production in the system is the range of 3.0 ~ 3.5. Increasing temperature from 20°C to 40°C accelerates the divanillin production. Divanillin was found to exist in three commercial red wines in this work. Keeping the storage temperature cool and decreasing the contact of grapes and wines with iron are two major measures suggested by this work in order to decrease the oxidation of vanillin during the making and aging of wine.

A comparison of self-determined motivational experiences between participants and non-participants following the physical activity component of the CATCH Kid Club

The CATCH Kids Club (CKC) is an after-school intervention that has attempted to address the growing obesity and physical inactivity concerns publicized in current literature. Using Self-Determination Theory (SDT: Deci & Ryan, 1985) perspective, this study's main research objective was to assess, while controlling for gender and age, i f there were significant differences between the treatment (CKC program participants) and control (non- eKC) groups on their perceptions of need satisfaction, intrinsic motivation and optimal challenge after four months of participation and after eight months of participation. For this study, data were collected from 79 participants with a mean age of9.3, using the Situational Affective State Questionnaire (SASQ: Mandigo et aI., 2008). In order to determine the common factors present in the data, a principal component analysis was conducted. The analysis resulted in an appropriate three-factor solution, with 14 items loading onto the three factors identified as autonomy, competence and intrinsic motivation. Initially, a multiple analysis of co-variance (MANCOY A) was conducted and found no significant differences or effects (p> 0.05). To further assess the differences between groups, six analyses of co-variance (ANeOY As) were conducted, which also found no significant differences (p >0 .025). These findings suggest that the eKC program is able to maintain the se1fdetermined motivational experiences of its participants, and does not thwart need satisfaction or self-determined motivation through its programming. However, the literature suggests that the CKe program and other P A interventions could be further improved by fostering participants' self-determined motivational experiences, which can lead to the persistence of healthy PA behaviours (Kilpatrick, Hebert & Jacobsen, 2002).

Aggregations and Marginalization of GARCH and Stochastic Volatility Models

The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.