945 resultados para nth characteristic of a real number


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A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest than the test cases they consider. In this paper, the technique is applied to the subset sum problem, which is a combinatorial optimization problem with a strongly non-linear energy (fitness) function and many local minima under single spin flip dynamics. It is a problem which exhibits an interesting dynamics, reminiscent of stabilizing selection in population biology. The dynamics are solved under certain simplifying assumptions and are reduced to a set of difference equations for a small number of relevant quantities. The quantities used are the population's cumulants, which describe its shape, and the mean correlation within the population, which measures the microscopic similarity of population members. Including the mean correlation allows a better description of the population than the cumulants alone would provide and represents a new and important extension of the technique. The formalism includes finite population effects and describes problems of realistic size. The theory is shown to agree closely to simulations of a real genetic algorithm and the mean best energy is accurately predicted.

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The revolution in the foundations of physics at the beginning of the twentieth century suggested to several of its most prominent workers that biology was ripe for something similar. In consequence, a number of physicists moved into biology. They were highly influential in initiating a molecular biology in the 1950s. Two decades later it seemed to several of these migrants, and those they had influenced, that the major problems in molecular biology had been solved, and that it was time to move on to what seemed to them the final problem: the nervous system, consciousness, and the age-old mind-body problem. This paper reviews this "double migration" and shows how the hopes of the first generation of physicist-biologists were both realized and dashed. No new physical principles were discovered at work in the foundations of biology or neuroscience. On the other hand, the mind-set of those trained in physics proved immensely valuable in analyzing fundamental issues in both biology and neuroscience. It has been argued that the outcome of the molecular biology of the 1950s was a change in the concept of the gene from that of "a mysterious entity into that of a real molecular object" (Watson, 1965, p.6); the gates and channels which play such crucial roles in the functioning of nervous systems have been transformed in a similar way. Studies on highly simplified systems have also opened the prospect of finding the neural correlatives of numerous behaviors and neuropathologies. This increasing understanding at the molecular level is invaluable not only in devising rational therapies but also, by defining the material substrate of consciousness, in bringing the mind-body problem into sharper focus. Copyright © Taylor & Francis Inc.

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The rapid global loss of biodiversity has led to a proliferation of systematic conservation planning methods. In spite of their utility and mathematical sophistication, these methods only provide approximate solutions to real-world problems where there is uncertainty and temporal change. The consequences of errors in these solutions are seldom characterized or addressed. We propose a conceptual structure for exploring the consequences of input uncertainty and oversimpli?ed approximations to real-world processes for any conservation planning tool or strategy. We then present a computational framework based on this structure to quantitatively model species representation and persistence outcomes across a range of uncertainties. These include factors such as land costs, landscape structure, species composition and distribution, and temporal changes in habitat. We demonstrate the utility of the framework using several reserve selection methods including simple rules of thumb and more sophisticated tools such as Marxan and Zonation. We present new results showing how outcomes can be strongly affected by variation in problem characteristics that are seldom compared across multiple studies. These characteristics include number of species prioritized, distribution of species richness and rarity, and uncertainties in the amount and quality of habitat patches. We also demonstrate how the framework allows comparisons between conservation planning strategies and their response to error under a range of conditions. Using the approach presented here will improve conservation outcomes and resource allocation by making it easier to predict and quantify the consequences of many different uncertainties and assumptions simultaneously. Our results show that without more rigorously generalizable results, it is very dif?cult to predict the amount of error in any conservation plan. These results imply the need for standard practice to include evaluating the effects of multiple real-world complications on the behavior of any conservation planning method.

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The aim of the investigation was to study the problem of colonization of shipboard fuel systems and to examine the effect of a number of environmental factors on microbial growth and survival in order to find potential preservative treatments. A variety of microbial species were isolated from samples taken from fuel storage tanks. Bacteria were more numerous than yeasts or fungi and most microorganisms were found at the fuel/water interface. 1he salinity, pH and phosphate concentration of some water bottoms were characteristic of sea water. Others were brackish, acidic and varied in phosphate content. Microorganisms were cultured under a number of environmental conditions. After prolonged incubation, the inoculum size had no effect on the final biomass of Cladosporium resinae but the time required to achieve the final mass decreased with increasing spore number. Undecane supported better growth of the fungus than diesel fuel and of four types of diesel fuel, two allowed more profuse growth. With sea water as the aqueous phase, a number of isolates were inhibited but the addition of nutrients allowed the development of many of the organisms. Agitation increased the growth of C. resinae on glucose but inhibited it on hydrocarbons. The optimum temperature fgr growth of C. resinae on surface culture lay between 25º C and 30º C and growth was evident at 5º C but not at 45º C. In aqueous suspension, 90% of spores were inactivated in around 60 hours at 45ºC and the same proportion of spores of C. resinae and Penicillium corylophilum were destroyed after about 30 seconds at 65ºC. The majority of bacteria and all yeasts in a water bottom sample were killed within 10 seconds at this temperature. An increase in the concentration of an organo-boron compound caused more rapid inactivation of C. resinae spores and raising the temperature from 25ºC to 45°C significantly enhanced the potency of the biocide.

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DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT This thesis is a cross-disciplinary study of the empirical impact of real options theory in the fields of decision sciences and performance management. Borrowing from the economics, strategy and operations research literature, the research examines the risk and performance implications of real options in firms’ strategic investments and multinational operations. An emphasis is placed on the flexibility potential and competitive advantage of multinational corporations to explore the extent to which real options analysis can be classified as best practice in management research. Using a combination of qualitative and quantitative techniques the evidence suggests that, if real options are explored and exploited appropriately, real options management can result in superior performance for multinational companies. The qualitative findings give an overview of the practical advantages and disadvantages of real options and the statistical results reveal that firms which have developed a high awareness of their real options are, as predicted by the theory, able to reduce their downside risk and increase profits through flexibility, organisational slack and multinationality. Although real options awareness does not systematically guarantee higher returns from operations, supplementary findings indicate that firms with evidence of significant investments in the acquisition of real options knowledge tend to outperform competitors which are unaware of their real options. There are three contributions of this research. First, it extends the real options and capacity planning literature to path-dependent contingent-claims analysis to underline the benefits of average type options in capacity allocation. Second, it is thought to be the first to explicitly examine the performance effects of real options on a sample of firms which have developed partial capabilities in real options analysis suggesting that real options diffusion can be key to value creation. Third, it builds a new decision-aiding framework to facilitate the use of real options in projects appraisal and strategic planning.

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The number of remote sensing platforms and sensors rises almost every year, yet much work on the interpretation of land cover is still carried out using either single images or images from the same source taken at different dates. Two questions could be asked of this proliferation of images: can the information contained in different scenes be used to improve the classification accuracy and, what is the best way to combine the different imagery? Two of these multiple image sources are MODIS on the Terra platform and ETM+ on board Landsat7, which are suitably complementary. Daily MODIS images with 36 spectral bands in 250-1000 m spatial resolution and seven spectral bands of ETM+ with 30m and 16 days spatial and temporal resolution respectively are available. In the UK, cloud cover may mean that only a few ETM+ scenes may be available for any particular year and these may not be at the time of year of most interest. The MODIS data may provide information on land cover over the growing season, such as harvest dates, that is not present in the ETM+ data. Therefore, the primary objective of this work is to develop a methodology for the integration of medium spatial resolution Landsat ETM+ image, with multi-temporal, multi-spectral, low-resolution MODIS \Terra images, with the aim of improving the classification of agricultural land. Additionally other data may also be incorporated such as field boundaries from existing maps. When classifying agricultural land cover of the type seen in the UK, where crops are largely sown in homogenous fields with clear and often mapped boundaries, the classification is greatly improved using the mapped polygons and utilising the classification of the polygon as a whole as an apriori probability in classifying each individual pixel using a Bayesian approach. When dealing with multiple images from different platforms and dates it is highly unlikely that the pixels will be exactly co-registered and these pixels will contain a mixture of different real world land covers. Similarly the different atmospheric conditions prevailing during the different days will mean that the same emission from the ground will give rise to different sensor reception. Therefore, a method is presented with a model of the instantaneous field of view and atmospheric effects to enable different remote sensed data sources to be integrated.

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Automotive catalysts are the most effective short-term answer to air pollution from automobiles. Since strict control of exhaust emissions is, or will be,covered by legislation in most developed countries in the world, catalytic devices will be increasingly fitted to cars. There is consequently an urgent need for the development of catalysts that will not compete for scarce precious metal resources. A number of problems have already been identified in connection with base metal catalysts but quantitative investigations are lacking. The base metal reduction catalysts developed by Imperial Chemical Industries Limited, catalysts and Chemical Group, in collaboration with the Air Pollution Control Laboratory, B L Cars Limited for automotive emission control, are susceptible to de-activation by three major mechanisms. These are: physical loss of the wash-coat (a high surface area coating which supports the active species), aggregation of the active species and poisoning by fuel and engine oil additives. This thesis is especially concerned with the first two of these and attempts to indicate the relative magnitude .of their effect on the activity of. the catalysts. Aggregation of the active species or sintering, as it is loosely called, was studied by using impregnated granules to overcome effects due to the loss of the wash-coat. Samples were aged in a synthetic exhaust gas, free from poisons, and metal crystallite sizes were measured by scanning-electron microscopy. The increase in particle size was correlated with the loss in catalytic activity. In order to maintain a link with the real conditions of service a number of monolithic catalysts were tested in an engine-dynamometer and several previously tested endurance catalysts were examined. A mechanism is proposed for the break-up and subsequent 10s.5 of the wash-coat and suggestions for improved resistance to loss of the' coating and active species are proposed.

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We address the question of how to communicate among distributed processes valuessuch as real numbers, continuous functions and geometrical solids with arbitrary precision, yet efficiently. We extend the established concept of lazy communication using streams of approximants by introducing explicit queries. We formalise this approach using protocols of a query-answer nature. Such protocols enable processes to provide valid approximations with certain accuracy and focusing on certain locality as demanded by the receiving processes through queries. A lattice-theoretic denotational semantics of channel and process behaviour is developed. Thequery space is modelled as a continuous lattice in which the top element denotes the query demanding all the information, whereas other elements denote queries demanding partial and/or local information. Answers are interpreted as elements of lattices constructed over suitable domains of approximations to the exact objects. An unanswered query is treated as an error anddenoted using the top element. The major novel characteristic of our semantic model is that it reflects the dependency of answerson queries. This enables the definition and analysis of an appropriate concept of convergence rate, by assigning an effort indicator to each query and a measure of information content to eachanswer. Thus we capture not only what function a process computes, but also how a process transforms the convergence rates from its inputs to its outputs. In future work these indicatorscan be used to capture further computational complexity measures. A robust prototype implementation of our model is available.

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The analysis of bacterial genomes for epidemiological purposes often results in the production of a banding profile of DNA fragments characteristic of the genome under investigation. These may be produced using various methods, many of which involve the cutting or amplification of DNA into defined and reproducible characteristic fragments. It is frequently of interest to enquire whether the bacterial isolates are naturally classifiable into distinct groups based on their DNA profiles. A major problem with this approach is whether classification or clustering of the data is even appropriate. It is always possible to classify such data but it does not follow that the strains they represent are ‘actually’ classifiable into well-defined separate parts. Hence, the act of classification does not in itself answer the question: do the strains consist of a number of different distinct groups or species or do they merge imperceptibly into one another because DNA profiles vary continuously? Nevertheless, we may still wish to classify the data for ‘convenience’ even though strains may vary continuously, and such a classification has been called a ‘dissection’. This Statnote discusses the use of classificatory methods in analyzing the DNA profiles from a sample of bacterial isolates.

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This paper contributes to the debate on the role of real options theory in business strategy and organizational decision-making. It analyses and critiques the decision-making and performance implications of real options within the management theories of the (multinational) firm, reviews and categorizes the organizational, strategic and operational facets of real options management in large business settings. It also presents the views of scholars and practitioners regarding the incorporation and validity of real options in strategy, international management and business processes. The focus is particularly on the decision-making and performance attributes of the real options logic concerning strategic investments, governance modes and multinational operations management. These attributes are examined from both strategic and operating perspectives of decision-making in organizations, also with an overview of the empirical evidence on real options decision-making and performance.

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The role of the Sm3+ ions in the structure of vitreous Sm2O3•4P2O5 has been investigated using the neutron diffraction anomalous dispersion technique, which employs the wavelength dependence of the real and imaginary parts of the neutron scattering length close to an absorption resonance. The data described here represent the first successful complete neutron anomalous dispersion study on an amorphous material. This experimental methodology permits one to determine exclusively the closest Sm• •• Sm separation. Knowledge of the R•••R (R = rare-earth) pairwise correlation is key to understanding the optical and magnetic properties of rare-earth phosphate glasses. The anomalous difference correlation function, ΔT''(r), shows a dominant feature pertaining to a Sm•••Sm separation, centred at 4.8 Å. The substantial width and marked asymmetry of this peak indicates that the minimum approach of Sm3+ ions could be as close as 4 Å. Information on other pairwise correlations is also revealed via analysis of T (r) and ΔT (r) correlation functions: Sm3+ ions display an average co-ordination number, n Sm(O), of 7, with a mean Sm–O bond length of 2.375(5) Å whilst the PO4 tetrahedra have a mean P–O bond length of 1.538(2) Å. Second- and third-neighbour correlations are also identified. These results corroborate previous findings. Such consistency lends support to the application of the anomalous dispersion technique to determine separations.

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Calibration of stochastic traffic microsimulation models is a challenging task. This paper proposes a fast iterative probabilistic precalibration framework and demonstrates how it can be successfully applied to a real-world traffic simulation model of a section of the M40 motorway and its surrounding area in the U.K. The efficiency of the method stems from the use of emulators of the stochastic microsimulator, which provides fast surrogates of the traffic model. The use of emulators minimizes the number of microsimulator runs required, and the emulators' probabilistic construction allows for the consideration of the extra uncertainty introduced by the approximation. It is shown that automatic precalibration of this real-world microsimulator, using turn-count observational data, is possible, considering all parameters at once, and that this precalibrated microsimulator improves on the fit to observations compared with the traditional expertly tuned microsimulation. © 2000-2011 IEEE.

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Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

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A spatial object consists of data assigned to points in a space. Spatial objects, such as memory states and three dimensional graphical scenes, are diverse and ubiquitous in computing. We develop a general theory of spatial objects by modelling abstract data types of spatial objects as topological algebras of functions. One useful algebra is that of continuous functions, with operations derived from operations on space and data, and equipped with the compact-open topology. Terms are used as abstract syntax for defining spatial objects and conditional equational specifications are used for reasoning. We pose a completeness problem: Given a selection of operations on spatial objects, do the terms approximate all the spatial objects to arbitrary accuracy? We give some general methods for solving the problem and consider their application to spatial objects with real number attributes. © 2011 British Computer Society.

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2000 Mathematics Subject Classification: 42B10, 43A32.