921 resultados para Decoding complexity


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P>1. Proliferative kidney disease (PKD) is a disease of salmonid fish caused by the endoparasitic myxozoan, Tetracapsuloides bryosalmonae, which uses freshwater bryozoans as primary hosts. Clinical PKD is characterised by a temperature-dependent proliferative and inflammatory response to parasite stages in the kidney.;2. Evidence that PKD is an emerging disease includes outbreaks in new regions, declines in Swiss brown trout populations and the adoption of expensive practices by fish farms to reduce heavy losses. Disease-related mortality in wild fish populations is almost certainly underestimated because of e.g. oversight, scavenging by wild animals, misdiagnosis and fish stocking.;3. PKD prevalences are spatially and temporally variable, range from 0 to 90-100% and are typically highest in juvenile fish.;4. Laboratory and field studies demonstrate that (i) increasing temperatures enhance disease prevalence, severity and distribution and PKD-related mortality; (ii) eutrophication may promote outbreaks. Both bryozoans and T. bryosalmonae stages in bryozoans undergo temperature- and nutrient-driven proliferation.;5. Tetracapsuloides bryosalmonae is likely to achieve persistent infection of highly clonal bryozoan hosts through vertical transmission, low virulence and host condition-dependent cycling between covert and overt infections. Exploitation of fish hosts entails massive proliferation and spore production by stages that escape the immune response. Many aspects of the parasite's life cycle remain obscure. If infectious stages are produced in all hosts then the complex life cycle includes multiple transmission routes.;6. Patterns of disease outbreaks suggest that background, subclinical infections exist under normal environmental conditions. When conditions change, outbreaks may then occur in regions where infection was hitherto unsuspected.;7. Environmental change is likely to cause PKD outbreaks in more northerly regions as warmer temperatures promote disease development, enhance bryozoan biomass and increase spore production, but may also reduce the geographical range of this unique multihost-parasite system. Coevolutionary dynamics resulting from host-parasite interactions that maximise fitness in previous environments may pose problems for sustainability, particularly in view of extensive declines in salmonid populations and degradation of many freshwater habitats.

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A growing body of literature indicates a modestly positive association between religiosity and spirituality as predictors of psychological health (anxiety and depression), suggesting they serve as personal resiliency factors. The purpose of this study was to expand our understanding of the relationships among these constructs. Using Lazarus's Transactional Model of Stress as a theoretical framework, we examined: (a) the extent to which spirituality and religiosity mediated and/or moderated the association between perceived stress and psychological health and (b) whether there was a moderated (religiosity) mediation (spirituality) between stress and health. The Perceived Stress Scale, Daily Spiritual Experiences Scale, Religious Commitment Inventory, and Hospital Anxiety and Depression Scale were administered to measure the following constructs: stress, spirituality, religiosity, and psychological health. This study utilized a nonexperimental, quantitative, correlational, cross-sectional, moderated-mediation design, and included a convenience sample of 331 research participants. Both spirituality and religiosity moderated stress and health. However, only spirituality partially mediated the relationship. In addition, religiosity did not moderate the mediating effects of spirituality. Overall, this study confirmed the role of both religiosity and spirituality as effective resiliency resources.

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Justification Logic studies epistemic and provability phenomena by introducing justifications/proofs into the language in the form of justification terms. Pure justification logics serve as counterparts of traditional modal epistemic logics, and hybrid logics combine epistemic modalities with justification terms. The computational complexity of pure justification logics is typically lower than that of the corresponding modal logics. Moreover, the so-called reflected fragments, which still contain complete information about the respective justification logics, are known to be in~NP for a wide range of justification logics, pure and hybrid alike. This paper shows that, under reasonable additional restrictions, these reflected fragments are NP-complete, thereby proving a matching lower bound. The proof method is then extended to provide a uniform proof that the corresponding full pure justification logics are $\Pi^p_2$-hard, reproving and generalizing an earlier result by Milnikel.

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This paper focuses on adolescents who live in divided societies and how they navigate those divisions as they develop as civic actors. The study sites are Northern Ireland, South Africa, and the United States. In each setting we collected surveys, conducted focus groups with teachers and students, and followed students through the 9th and 10th grades in a case study classroom. In all locales, the students used materials from Facing History and Ourselves, and their teachers had participated in workshops on using those materials. In this paper we follow a case study student from the United States who provides a particularly complex look at issues of division and ethical civic development. The student, Pete, is a white immigrant from South Africa, studying in a multi-ethnic and multi-racial school in the United States. He confronts his South African legacies in the context of a foreign school system, which is working to help U.S. students confront their own legacies. Across two, one-semester, citizenship classes, Pete shows us the tension between an academic stance and a moral/emotional stance. When moral dilemmas become complex for him, he begins to lose his ability to judge. Teacher support and guidance is critical to help students like Pete learn to hold their moral ground, while understanding why others act as they do.

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Our approaches to the use of EEG studies for the understanding of the pathogenesis of schizophrenic symptoms are presented. The basic assumptions of a heuristic and multifactorial model of the psychobiological brain mechanisms underlying the organization of normal behavior is described and used in order to formulate and test hypotheses about the pathogenesis of schizophrenic behavior using EEG measures. Results from our studies on EEG activity and EEG reactivity (= EEG components of a memory-driven, adaptive, non-unitary orienting response) as analyzed with spectral parameters and "chaotic" dimensionality (correlation dimension) are summarized. Both analysis procedures showed a deviant brain functional organization in never-treated first-episode schizophrenia which, within the framework of the model, suggests as common denominator for the pathogenesis of the symptoms a deviation of working memory, the nature of which is functional and not structural.

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Although assessment of asthma control is important to guide treatment, it is difficult since the temporal pattern and risk of exacerbations are often unpredictable. In this Review, we summarise the classic methods to assess control with unidimensional and multidimensional approaches. Next, we show how ideas from the science of complexity can explain the seemingly unpredictable nature of bronchial asthma and emphysema, with implications for chronic obstructive pulmonary disease. We show that fluctuation analysis, a method used in statistical physics, can be used to gain insight into asthma as a dynamic disease of the respiratory system, viewed as a set of interacting subsystems (eg, inflammatory, immunological, and mechanical). The basis of the fluctuation analysis methods is the quantification of the long-term temporal history of lung function parameters. We summarise how this analysis can be used to assess the risk of future asthma episodes, with implications for asthma severity and control both in children and adults.

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Multi-input multi-output (MIMO) technology is an emerging solution for high data rate wireless communications. We develop soft-decision based equalization techniques for frequency selective MIMO channels in the quest for low-complexity equalizers with BER performance competitive to that of ML sequence detection. We first propose soft decision equalization (SDE), and demonstrate that decision feedback equalization (DFE) based on soft-decisions, expressed via the posterior probabilities associated with feedback symbols, is able to outperform hard-decision DFE, with a low computational cost that is polynomial in the number of symbols to be recovered, and linear in the signal constellation size. Building upon the probabilistic data association (PDA) multiuser detector, we present two new MIMO equalization solutions to handle the distinctive channel memory. With their low complexity, simple implementations, and impressive near-optimum performance offered by iterative soft-decision processing, the proposed SDE methods are attractive candidates to deliver efficient reception solutions to practical high-capacity MIMO systems. Motivated by the need for low-complexity receiver processing, we further present an alternative low-complexity soft-decision equalization approach for frequency selective MIMO communication systems. With the help of iterative processing, two detection and estimation schemes based on second-order statistics are harmoniously put together to yield a two-part receiver structure: local multiuser detection (MUD) using soft-decision Probabilistic Data Association (PDA) detection, and dynamic noise-interference tracking using Kalman filtering. The proposed Kalman-PDA detector performs local MUD within a sub-block of the received data instead of over the entire data set, to reduce the computational load. At the same time, all the inter-ference affecting the local sub-block, including both multiple access and inter-symbol interference, is properly modeled as the state vector of a linear system, and dynamically tracked by Kalman filtering. Two types of Kalman filters are designed, both of which are able to track an finite impulse response (FIR) MIMO channel of any memory length. The overall algorithms enjoy low complexity that is only polynomial in the number of information-bearing bits to be detected, regardless of the data block size. Furthermore, we introduce two optional performance-enhancing techniques: cross- layer automatic repeat request (ARQ) for uncoded systems and code-aided method for coded systems. We take Kalman-PDA as an example, and show via simulations that both techniques can render error performance that is better than Kalman-PDA alone and competitive to sphere decoding. At last, we consider the case that channel state information (CSI) is not perfectly known to the receiver, and present an iterative channel estimation algorithm. Simulations show that the performance of SDE with channel estimation approaches that of SDE with perfect CSI.