892 resultados para homogeneous mutitype Markov chains


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Major imperfections in crosslinked polymers include loose or dangling chain ends that lower the crosslink d., thereby reducing elastic recovery and increasing the solvent swelling. These imperfections are hard to detect, quantify and control when the network is initiated by free radical reactions. As an alternative approach, the sol-​gel synthesis of a model poly(ethylene glycol) (PEG-​2000) network is described using controlled amts. of bis- and mono-​triethoxy silyl Pr urethane PEG precursors to give silsesquioxane (SSQ, R-​SiO1.5) structures as crosslink junctions with a controlled no. of dangling chains. The effect of the no. of dangling chains on the structure and connectivity of the dried SSQ networks has been detd. by step-​crystn. differential scanning calorimetry. The role that micelle formation plays in controlling the sol-​gel PEG network connectivity has been studied by dynamic light scattering of the bis- and mono-​triethoxy silyl precursors and the networks have been characterized by 29Si solid state NMR, sol fraction and swelling measurements. These show that the dangling chains will increase the mesh size and water uptake. Compared to other end-​linked PEG hydrogels, the SSQ-​crosslinked networks show a low sol fraction and high connectivity, which reduces solvent swelling, degree of crystallinity and the crystal transition temp. The increased degree of freedom in segment movement on the addn. of dangling chains in the SSQ-​crosslinked network facilitates the packing process in crystn. of the dry network and, in the hydrogel, helps to accommodate more water mols. before reaching equil.

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Standard Monte Carlo (sMC) simulation models have been widely used in AEC industry research to address system uncertainties. Although the benefits of probabilistic simulation analyses over deterministic methods are well documented, the sMC simulation technique is quite sensitive to the probability distributions of the input variables. This phenomenon becomes highly pronounced when the region of interest within the joint probability distribution (a function of the input variables) is small. In such cases, the standard Monte Carlo approach is often impractical from a computational standpoint. In this paper, a comparative analysis of standard Monte Carlo simulation to Markov Chain Monte Carlo with subset simulation (MCMC/ss) is presented. The MCMC/ss technique constitutes a more complex simulation method (relative to sMC), wherein a structured sampling algorithm is employed in place of completely randomized sampling. Consequently, gains in computational efficiency can be made. The two simulation methods are compared via theoretical case studies.

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We prove that homogeneous bent functions f:GF(2)^2n --> GF(2) of degree n do not exist for n>3. Consequently homogeneous bent functions must have degree 3.

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We determine the affine equivalence classes of the eight variable degree three homogeneous bent functions using a new algorithm. Our algorithm applies to general bent functions and can systematically determine the automorphism groups. We provide a partial verification of the enumeration of eight variable degree three homogeneous bent functions obtained by Meng et al. We determine the affine equivalence classes of these functions.

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This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an extension of recent single frame, bounded error sparse decoding problems into a class of sparse estimation problems containing both temporal evolution and stochastic aspects. This paper presents two optimal estimators for compressed HMMs. The impact of measurement compression on HMM filtering performance is experimentally examined in the context of an important image based aircraft target tracking application. Surprisingly, tracking of dim small-sized targets (as small as 5-10 pixels, with local detectability/SNR as low as − 1.05 dB) was only mildly impacted by compressed sensing down to 15% of original image size.

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In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

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Both environmental economists and policy makers have shown a great deal of interest in the effect of pollution abatement on environmental efficiency. In line with the modern resources available, however, no contribution is brought to the environmental economics field with the Markov chain Monte Carlo (MCMC) application, which enables simulation from a distribution of a Markov chain and simulating from the chain until it approaches equilibrium. The probability density functions gained prominence with the advantages over classical statistical methods in its simultaneous inference and incorporation of any prior information on all model parameters. This paper concentrated on this point with the application of MCMC to the database of China, the largest developing country with rapid economic growth and serious environmental pollution in recent years. The variables cover the economic output and pollution abatement cost from the year 1992 to 2003. We test the causal direction between pollution abatement cost and environmental efficiency with MCMC simulation. We found that the pollution abatement cost causes an increase in environmental efficiency through the algorithm application, which makes it conceivable that the environmental policy makers should make more substantial measures to reduce pollution in the near future.

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Background: Cardiovascular disease is the leading cause of death in the world. Human C-reactive protein (CRP) has been used in the risk assessment of coronary events. Human saliva mirrors the body's health and well-being and is non-invasive, easy to collect and ideal for third world countries as well as for large patient screening. The aim was to establish a saliva CRP reference range and to demonstrate the clinical utility of salivary CRP levels in assessing the coronary events in a primary health care setting. Methods: We have used a homogeneous bead based assay to detect CRP levels in human saliva. We have developed a rapid 15 min (vs 90 min), sequential, one-step assay to detect CRP in saliva. Saliva was collected from healthy volunteers (n = 55, ages 20-70 years) as well as from cardiac patients (n = 28, ages 43-86 years). Results: The assay incubation time was optimised from 90 min to 15 mm and generated a positive correlation (n = 29, range 10-2189 pg/mL, r2 = 0.94; Passing Bablok slope 0.885. Intercept 0, p>0.10), meaning we could decrease the incubation time and produce equivalent results with confidence. The mean CRP level in the saliva of healthy human volunteers was 285 pg/mL and in cardiac patients was 1680 pg/mL (p<0.01). Analysis of CRP concentrations in paired serum and saliva samples from cardiac patients gave a positive correlation (r2 = 0.84, p<0.001) and the salivary CRP concentration capable of distinguishing healthy from diseased patients. Conclusions: The results suggest that this minimally invasive, rapid and sensitive assay will be useful in large patient screening studies for risk assessment of coronary events. (C) 2011 Elsevier B.V. All rights reserved.

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In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

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The fragmentation of previously integrated systems of production and service delivery has been an important feature of organisational restructuring over the last three decades. This article highlights the adverse implications of this development for the health and safety of workers, examines the extent to which current British health and safety law provides an adequate framework for addressing these outcomes and explores whether its capacity to do so could be enhanced through the introduction of new statutory provisions on the regulation of supply chains. It concludes that, in terms of both structure and operation, the present framework of law is problematic. It further argues that recent international initiatives show that it is feasible to develop such statutory provisions and that existing evidence suggests that provisions of this type could usefully be introduced in respect of a number of areas of activity where the implications of the externalisation of production and service delivery seem particularly problematic.

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Over the past 20 years the labour market, workforce and work organisation of most if not all industrialised countries have been significantly refashioned by the increased use of more flexible work arrangements, variously labelled as precarious employment or contingent work. There is now a substantial and growing body of international evidence that many of these arrangements are associated with a significant deterioration in occupational health and safety (OHS), using a range of measures such as injury rates, disease, hazard exposures and work-related stress. Moreover, there is an emerging body of evidence that these arrangements pose particular problems for conventional regulatory regimes. Recognition of these problems has aroused the concern of policy makers - especially in Europe, North America and Australia - and a number of responses have been adopted in terms of modifying legislation, producing new guidance material and codes of practice and revised enforcement practices. This article describes one such in itiative in Australia with regard to home-based clothing workers. The regulatory strategy developed in one Australian jurisdiction (and now being ‘exported’ into others) seeks to counter this process via contractual tracking mechanisms to follow the work, tie in liability and shift overarching legal responsibility to the top of the supply chain. The process also entails the integration of minimum standards relating to wages, hours and working conditions; OHS and access to workers’ compensation. While home-based clothing manufacture represents a very old type of ‘flexible’ work arrangement, it is one that regulators have found especially difficult to address. Further, the elaborate multi-tiered subcont racting and diffuse work locations found in this industry are also characteristic of newer forms of contingent work in other industries (such as some telework) and the regulatory challenges they pose (such as the tendency of elaborate supply chains to attenuate and fracture statutory responsibilities, at least in terms of the attitudes and behaviour of those involved).

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This paper investigates how social and environmental non-government organisations (NGOs) use the news media in an endeavour to create changes in the social performance and associated accountabilities of multinational buying companies’ (MBCs’) supply chains located in the developing country of Bangladesh. In this research, we explicitly seek the views of senior officers from global and local NGOs operating in Bangladesh, as well as the views of journalists from major global and local news media organisations. Our results show that social and environmental NGOs strategically use the news media in an effort to effect changes in corporate labour practices and related disclosure practices. More particularly, both the NGOs and the news media representatives stated that NGOs would be relatively powerless to create change in corporate without media coverage. This is the first known study to specifically address the joint and complementary role of NGOs and the news media in potentially creating changes in the social and environmental operating and disclosure practices of supply chains emanating from a developing country.

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Digital innovation is transforming the media and entertainment industries. The professionalization of YouTube’s platform is paradigmatic of that change. The 100 original channel initiative launched in late 2011 was designed to transform YouTube’s brand through production of a high volume of quality premium video content that would more deeply engage its audience base and in the process attract big advertisers. An unanticipated by-product has been the rapid growth of a wave of aspiring next-generation digital media companies from within the YouTube ecosystem. Fuelled by early venture capital some have ambitious goals to become global media corporations in the online video space. A number of larger MCNs (Multi-Channel Networks) - BigFrame, Machinima, Fullscreen, AwesomenessTV, Maker Studios , Revision3 and DanceOn - have attracted interest from media incumbents like Warner Brothers, DreamWorks, Discovery, Bertlesmann, Comcast and AMC, and two larger MCNs Alloy and Break Media have merged. This indicates that a shakeout is underway in these new online supply chains, after rapid initial growth. The higher profile MCNs seek to rapidly develop scale economies in online distribution and facilitate audience growth for their member channels, helping channels optimize monetization, develop sustainable business models and to facilitate producer-collaboration within a growing online community of like-minded content creators. Some MCNs already attract far larger online audiences than any national TV network. The speed with which these developments have occurred is reminiscent of the 1910s, when Hollywood studios first emerged and within only a few years replaced the incumbent film studios as the dominant force within the film industry.

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Thermal transport in graphene-polymer nanocomposite is complicated and has not been well understood. The interfacial thermal transport between graphene nanofiller and polymer matrix is expected to play a key role in controlling the overall thermal performance of graphene-polymer nanocomposite. In this work, we investigated the thermal transport across graphene-polymer interfaces functionalized with end-grafted polymer chains using molecular dynamics simulations. The effects of grafting density, chain length and initial morphology on the interfacial thermal transport were systematically investigated. It was found that end-grafted polymer chains could significantly enhance interfacial thermal transport and the underlying mechanism was considered to be the enhanced vibration coupling between graphene and polymer. In addition, a theoretical model based on effective medium theory was established to predict the thermal conductivity in graphene-polymer nanocomposites.