942 resultados para Physical non-linear behaviour
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ISSUES: There have been reviews on the association between density of alcohol outlets and harm including studies published up to December 2008. Since then the number of publications has increased dramatically. The study reviews the more recent studies with regard to their utility to inform policy. APPROACH: A systematic review found more than 160 relevant studies (published between January 2009 and October 2014). The review focused on: (i) outlet density and assaultive or intimate partner violence; (ii) studies including individual level data; or (iii) 'natural experiments'. KEY FINDINGS: Despite overall evidence for an association between density and harm, there is little evidence on causal direction (i.e. whether demand leads to more supply or increased availability increases alcohol use and harm). When outlet types (e.g. bars, supermarkets) are analysed separately, studies are too methodologically diverse and partly contradictory to permit firm conclusions besides those pertaining to high outlet densities in areas such as entertainment districts. Outlet density commonly had little effect on individual-level alcohol use, and the few 'natural experiments' on restricting densities showed little or no effects. IMPLICATIONS AND CONCLUSIONS: Although outlet densities are likely to be positively related to alcohol use and harm, few policy recommendations can be given as effects vary across study areas, outlet types and outlet cluster size. Future studies should examine in detail outlet types, compare different outcomes associated with different strengths of association with alcohol, analyse non-linear effects and compare different methodologies. Purely aggregate-level studies examining total outlet density only should be abandoned. [Gmel G, Holmes J, Studer J. Are alcohol outlet densities strongly associated with alcohol-related outcomes? A critical review of recent evidence. Drug Alcohol Rev 2015].
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The least square method is analyzed. The basic aspects of the method are discussed. Emphasis is given in procedures that allow a simple memorization of the basic equations associated with the linear and non linear least square method, polinomial regression and multilinear method.
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We describe the preparation and some optical properties of high refractive index TeO2-PbO-TiO2 glass system. Highly homogeneous glasses were obtained by agitating the mixture during the melting process in an alumina crucible. The characterization was done by X-ray diffraction, Raman scattering, light absorption and linear refractive index measurements. The results show a change in the glass structure as the PbO content increases: the TeO4 trigonal bipyramids characteristics of TeO2 glasses transform into TeO3 trigonal pyramids. However, the measured refractive indices are almost independent of the glass composition. We show that third-order nonlinear optical susceptibilities calculated from the measured refractive indices using Lines' theoretical model are also independent of the glass composition.
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In this work we describe the synthesis and characterization of chalcogenide glass (0.3La2S3-0.7Ga2S 3) with low phonons frequencies. Several properties were measured like Sellmeier parameters, linear refractive index dispersion and material dispersion. Samples with the composition above were doped with Dy2S3. The absorption and emission characteristics were measured by electronic spectroscopy and fluorescence spectrum respectively. Raman and infrared spectroscopy shows that these glasses present low phonons frequencies and strucuture composed by GaS4 tetrahedrals. The Lines model was used for calculate the coefficients values of the non linear refractive index.
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Tässä työssä kehitettiin palo- ja pelastuskäyttöön tarkoitettuun henkilönostimeen teleskooppipuomin profiilit. Profiilien valmistusmateriaalina oli kuumavalssattu, ultraluja säänkestävä rakenneteräs. Työssä kehitettiin standardien ja ohjeiden pohjalta laskentapohja, jolla voidaan tutkia teleskooppipuomin jaksojen tukireaktioita, taivutus- ja vääntömomentteja ja leikkaus ja normaalivoimia. Laskentapohjassa voidaan varioida eri kuormitusten suuntia, teleskooppipuomin sivusuuntaista ulottumaa ja nostokulmaa. Profiilien alustavassa mitoituksessa hyödynnettiin paikallisen lommahduksen huomioon ottavia standardeja ja suunnitteluohjeita. Eri poikkileikkausten ominaisuuksia verrattiin keskenään ja profiili valittiin yhdessä kohdeyrityksen kanssa. Alustavan mitoituksen yhteydessä muodostettiin apuohjelma valitulle poikkileikkaukselle, jolla voitiin tutkia profiilin eri muuttujien vaikutusta mm. paikalliseen lommahdukseen ja jäykkyyteen. Laskentapohjaan sisällytettiin myös optimointirutiini, jolla voitiin minimoida poikkileikkauksen pinta-ala ja tätä kautta profiilin massa. Lopullinen mitoitus suoritettiin elementtimenetelmällä. Mitoituksessa tutkittiin alustavasti mitoitettujen profiilien paikallista lommahdusta lineaarisen stabiilius- ja epälineaarisen analyysin pohjalta. Profiilien jännityksiä tutkittiin tarkemmin mm. varioimalla kuormituksia ja osittelemalla elementtien normaalijännityksiä. Diplomityössä kehitetyllä ja analysoidulla teleskooppipuomilla voitiin keventää jaksojen painoja 15-30 %. Sivusuuntainen ulottuma parani samalla lähes 20 % ja nimelliskuorma kasvoi 25 %.
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One of the main problems in quantitative analysis of complex samples by x-ray fluorescence is related to interelemental (or matrix) effects. These effects appear as a result of interactions among sample elements, affecting the x-ray emission intensity in a non-linear manner. Basically, two main effects occur; intensity absorption and enhancement. The combination of these effects can lead to serious problems. Many studies have been carried out proposing mathematical methods to correct for these effects. Basic concepts and the main correction methods are discussed here.
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Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.
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Rosin is a natural product from pine forests and it is used as a raw material in resinate syntheses. Resinates are polyvalent metal salts of rosin acids and especially Ca- and Ca/Mg- resinates find wide application in the printing ink industry. In this thesis, analytical methods were applied to increase general knowledge of resinate chemistry and the reaction kinetics was studied in order to model the non linear solution viscosity increase during resinate syntheses by the fusion method. Solution viscosity in toluene is an important quality factor for resinates to be used in printing inks. The concept of critical resinate concentration, c crit, was introduced to define an abrupt change in viscosity dependence on resinate concentration in the solution. The concept was then used to explain the non-inear solution viscosity increase during resinate syntheses. A semi empirical model with two estimated parameters was derived for the viscosity increase on the basis of apparent reaction kinetics. The model was used to control the viscosity and to predict the total reaction time of the resinate process. The kinetic data from the complex reaction media was obtained by acid value titration and by FTIR spectroscopic analyses using a conventional calibration method to measure the resinate concentration and the concentration of free rosin acids. A multivariate calibration method was successfully applied to make partial least square (PLS) models for monitoring acid value and solution viscosity in both mid-infrared (MIR) and near infrared (NIR) regions during the syntheses. The calibration models can be used for on line resinate process monitoring. In kinetic studies, two main reaction steps were observed during the syntheses. First a fast irreversible resination reaction occurs at 235 °C and then a slow thermal decarboxylation of rosin acids starts to take place at 265 °C. Rosin oil is formed during the decarboxylation reaction step causing significant mass loss as the rosin oil evaporates from the system while the viscosity increases to the target level. The mass balance of the syntheses was determined based on the resinate concentration increase during the decarboxylation reaction step. A mechanistic study of the decarboxylation reaction was based on the observation that resinate molecules are partly solvated by rosin acids during the syntheses. Different decarboxylation mechanisms were proposed for the free and solvating rosin acids. The deduced kinetic model supported the analytical data of the syntheses in a wide resinate concentration region, over a wide range of viscosity values and at different reaction temperatures. In addition, the application of the kinetic model to the modified resinate syntheses gave a good fit. A novel synthesis method with the addition of decarboxylated rosin (i.e. rosin oil) to the reaction mixture was introduced. The conversion of rosin acid to resinate was increased to the level necessary to obtain the target viscosity for the product at 235 °C. Due to a lower reaction temperature than in traditional fusion synthesis at 265 °C, thermal decarboxylation is avoided. As a consequence, the mass yield of the resinate syntheses can be increased from ca. 70% to almost 100% by recycling the added rosin oil.
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The ability of biomolecules to catalyze chemical reactions is due chiefly to their sensitivity to variations of the pH in the surrounding environment. The reason for this is that they are made up of chemical groups whose ionization states are modulated by pH changes that are of the order of 0.4 units. The determination of the protonation states of such chemical groups as a function of conformation of the biomolecule and the pH of the environment can be useful in the elucidation of important biological processes from enzymatic catalysis to protein folding and molecular recognition. In the past 15 years, the theory of Poisson-Boltzmann has been successfully used to estimate the pKa of ionizable sites in proteins yielding results, which may differ by 0.1 unit from the experimental values. In this study, we review the theory of Poisson-Boltzmann under the perspective of its application to the calculation of pKa in proteins.
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Dynamic mechanical analysis (DMA) is widely used in materials characterization. In this work, we briefly introduce the main concepts related to this technique such as, linear and non-linear viscoelasticity, relaxation time, response of material when it is submitted to a sinusoidal or other periodic stress. Moreover, the main applications of this technique in polymers and polymer blends are also presented. The discussion includes: phase behavior, crystallization; spectrum of relaxation as a function of frequency or temperature; correlation between the material damping and its acoustic and mechanical properties.
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It is a well known phenomenon that the constant amplitude fatigue limit of a large component is lower than the fatigue limit of a small specimen made of the same material. In notched components the opposite occurs: the fatigue limit defined as the maximum stress at the notch is higher than that achieved with smooth specimens. These two effects have been taken into account in most design handbooks with the help of experimental formulas or design curves. The basic idea of this study is that the size effect can mainly be explained by the statistical size effect. A component subjected to an alternating load can be assumed to form a sample of initiated cracks at the end of the crack initiation phase. The size of the sample depends on the size of the specimen in question. The main objective of this study is to develop a statistical model for the estimation of this kind of size effect. It was shown that the size of a sample of initiated cracks shall be based on the stressed surface area of the specimen. In case of varying stress distribution, an effective stress area must be calculated. It is based on the decreasing probability of equally sized initiated cracks at lower stress level. If the distribution function of the parent population of cracks is known, the distribution of the maximum crack size in a sample can be defined. This makes it possible to calculate an estimate of the largest expected crack in any sample size. The estimate of the fatigue limit can now be calculated with the help of the linear elastic fracture mechanics. In notched components another source of size effect has to be taken into account. If we think about two specimens which have similar shape, but the size is different, it can be seen that the stress gradient in the smaller specimen is steeper. If there is an initiated crack in both of them, the stress intensity factor at the crack in the larger specimen is higher. The second goal of this thesis is to create a calculation method for this factor which is called the geometric size effect. The proposed method for the calculation of the geometric size effect is also based on the use of the linear elastic fracture mechanics. It is possible to calculate an accurate value of the stress intensity factor in a non linear stress field using weight functions. The calculated stress intensity factor values at the initiated crack can be compared to the corresponding stress intensity factor due to constant stress. The notch size effect is calculated as the ratio of these stress intensity factors. The presented methods were tested against experimental results taken from three German doctoral works. Two candidates for the parent population of initiated cracks were found: the Weibull distribution and the log normal distribution. Both of them can be used successfully for the prediction of the statistical size effect for smooth specimens. In case of notched components the geometric size effect due to the stress gradient shall be combined with the statistical size effect. The proposed method gives good results as long as the notch in question is blunt enough. For very sharp notches, stress concentration factor about 5 or higher, the method does not give sufficient results. It was shown that the plastic portion of the strain becomes quite high at the root of this kind of notches. The use of the linear elastic fracture mechanics becomes therefore questionable.
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B3LYP/6-31G(d,p) calculations were used to determine the optimized geometries of the C2H4O-C2H2 and C2H4S-C2H2 heterocyclic hydrogen-bonded complexes. Results of structural, rotational, electronic and vibrational parameters indicate that the hydrogen bonding is non-linear due to the pi bond of the acetylene interacting with the hydrogen atoms of the methyl groups of the three-membered rings. Moreover, the theoretical investigation showed that the non-linearity is much more intriguing, since there is a structural disjunction on the acetylene within the heterocyclic system.
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Marine mammals are exposed to persistent organic pollutants (POPs), which may be biotransformed to metabolites some of which are highly toxic. Both POPs and their metabolites may lead to adverse health effects, which have been studied using various biomarkers. Changes in endocrine homeostasis have been suggested to be sensitive biomarkers for contaminant-related effects. The overall objective of this doctoral thesis was to investigate biotransformation capacity of POPs and their potential endocrine disruptive effects in two contrasting ringed seal populations from the low contaminated Svalbard area and from the highly contaminated Baltic Sea. Biotransformation capacity was studied by determining the relationships between congener-specific patterns and concentrations of polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), polybrominated diphenyl ethers (PBDEs) and their hydroxyl (OH)- and/or methylsulfonyl (MeSO2)-metabolites, and catalytic activities of hepatic xenobiotic-metabolizing phase I and II enzymes. The results suggest that the biotransformation of PCBs, PBDEs and toxaphenes in ringed seals depends on the congener-specific halogen-substitution pattern. Biotransformation products detected in the seals included OH-PCBs, MeSO2-PCBs and –DDE, pentachlorophenol, 4-OHheptachlorostyrene, and to a minor extent OH-PBDEs. The effects of life history state (moulting and fasting) on contaminant status and potential biomarkers for endocrine disruption, including hormone and vitamin homeostasis, were investigated in the low contaminated ringed seal population from Svalbard. Moulting/fasting status strongly affected thyroid, vitamin A and calcitriol homeostasis, body condition and concentrations of POPs and their OH-metabolites. In contrast, moulting/fasting status was not associated with variations in vitamin E levels. Endocrine disruptive effects on multiple endpoints were investigated in the two contrasting ringed seal populations. The results suggest that thyroid, vitamin A and calcitriol homeostasis may be affected by the exposure of contaminants and/or their metabolites in the Baltic ringed seals. Complex and non-linear relationships were observed between the contaminant levels and the endocrine variables. Positive relationships between circulating free and total thyroid hormone concentration ratios and OH-PCBs suggest that OH-PCBs may mediate the disruption of thyroid hormone transport in plasma. Species differences in thyroid and bone-related effects of contaminants were studied in ringed and grey seals from low contaminated references areas and from the highly contaminated Baltic Sea. The results indicate that these two species living at the same environment approximately at the same trophic level respond in a very different way to contaminant exposure. The results of this thesis suggest that the health status of the Baltic ringed seals has still improved during the last decade. PCB and DDE levels have decreased in these seals and the contaminant-related effects are different today than a decade ago. The health of the Baltic ringed seals is still suggested to be affected by the contaminant exposure. At the present level of the contaminant exposure the Baltic ringed seals seem to be at a zone where their body is able to compensate for the contaminant-mediated endocrine disruption. Based on the results of this thesis, several recommendations that could be applied on monitoring and assessing risk for contaminant effects are provided. Circulating OH-metabolites should be included in monitoring and risk assessment programs due to their high toxic potential. It should be noted that endogenous variables may have complex and highly variable responses to contaminant exposure including non-linear responses. These relationships may be further confounded by life history status. Therefore, it is highly recommended that when using variables related to endocrine homeostasis to investigate/monitor or assess the risk of contaminant effects in seals, the life history status of the animal should be carefully taken into consideration. This applies especially when using thyroid, vitamin A or calcitriolrelated parameters during moulting/fasting period. Extrapolations between species for assessing risk for contaminant effects in phocid seals should be avoided.
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A procedure for determining of the isotope ratio 235U/238U in UF6 samples was established using a quadrupole mass spectrometer with ionization by electron impact. The following items were optimized in the spectrometer: the parameters in the ion source that provided the most intense peak, with good shape, for the most abundant isotope; the resolution that reduced the non linear effects and the number of analytical cycles that reduced the uncertainty in the results. The measurement process was characterized with respect to the effects of mass discrimination, linearity and memory effect.
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The most widespread literature for the evaluation of uncertainty - GUM and Eurachem - does not describe explicitly how to deal with uncertainty of the concentration coming from non-linear calibration curves. This work had the objective of describing and validating a methodology, as recommended by the recent GUM Supplement approach, to evaluate the uncertainty through polynomial models of the second order. In the uncertainty determination of the concentration of benzatone (C) by chromatography, it is observed that the uncertainty of measurement between the methodology proposed and Monte Carlo Simulation, does not diverge by more than 0.0005 unit, thus validating the model proposed for one significant digit.