21,812 research outputs found

    Analysis of reliable deployment of TDOA local positioning architectures

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    .Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applicationS

    Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis

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    Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases

    Non-tariff Measures and Productivity of Ukrainian Food Processing Firms

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    Using detailed data on veterinary, ecological, sanitary, phytosanitary and mandatory certification measures, this paper studies the effect of non-tariff measures (NTMs) on firm productivity in the food-processing industry through forward and backward linkages. Using quantity and value of output at product level, we calculate and compare quantity- and revenue-based measures of total factor productivity (TFP). Exploiting the episode of NTM liberalisation in Ukraine in 2008–2012, we find that NTMs on intermediate inputs have a negative effect on quantity-based TFP. Other trade policy variables, including input tariffs and output NTMs also negatively influence productivity. The effect on the revenue-based TFP is weaker due to price and quality adjustments. Interacting changes in input NTMs with import intensity prior to trade liberalisation, we find that firms that used imported inputs more intensively tend to have lower long-run TFP growth

    The Regional Dimension of the Distribution and Effects of Public Incentives Directed towards Innovation of Firms

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    This study is based on the recent vision that the innovative activity is a territorial phenomenon which is enhanced by the cooperation between actors and local infrastructures. The aim of this study is to determine whether the specific economic and institutional conditions of a region have an influence on the results of a national policy intended to support entrepreneurial innovation. The analysis is directed towards comparing the effect of this policy between firms located in Madrid, Catalonia and the Basque country, regions which concentrate around 70% of Spain’s innovative activity. The type of analysis undertaken allows to approach a situation which lies close to solving two of the most important methodological problems which arise when the evaluation of innovation policies is put into practice: the lack of control over the aid distribution process, and the non-estimation of a counterfactual state (the scenario without public support). The results of this study allow to conclude that the region plays an important differentiating role in the final result of the national innovation policy. Therefore, this study recommends to include the localization of the firm in future evaluations

    Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis

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    Infectious diseases that incorporate presymptomatic transmission are challenging to monitor, model, predict, and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks, offering a significantly improved accuracy with respect to individual-based mean-field approaches while requiring a much lower computational cost compared to numerical simulations. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic

    Field synergy analysis of pollutant dispersion in street canyons and its optimization by adding wind catchers

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    The microenvironment, which involves pollutant dispersion of the urban street canyon, is critical to the health of pedestrians and residents. The objectives of this work are twofold: (i) to effectively assess the pollutant dispersion process based on a theory and (ii) to adopt an appropriate stratigy, i.e., wind catcher, to alleviate the pollution in the street canyons. Pollutant dispersion in street canyons is essentially a convective mass transfer process. Because the convective heat transfer process and the mass transfer process are physically similar and the applicability of field synergy theory to turbulence has been verified in the literature, we apply the field synergy theory to the study of pollutant dispersion in street canyons. In this paper, a computational fluid dynamics (CFD) simulation is conducted to investigate the effects of wind catcher, wind speed and the geometry of the street canyons on pollutant dispersion. According to the field synergy theory, Sherwood number and field synergy number are used to quantitatively evaluate the wind catcher and wind speed on the diffusion of pollutants in asymmetric street canyons. The results show that adding wind catchers can significantly improve the air quality of the step-down street canyon and reduce the average pollutant concentrations in the street canyon by 75%. Higher wind speed enhances diffusion of pollutants differently in different geometric street canyons

    Renormalization of Quantum Fields in Curved Spacetime

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    Quantum fields in curved spacetime undergo fluctuations that produce non-vanishing vacuum expectation values of the stress-energy tensor, i.e., energy can be generated due to the gravitational field. The same happens for other type of background fields like gauge or scalars. This effect plays an important role in the early Universe, in astrophysical compact objects, and in strong electromagnetic phenomena. However, the computation of the stress-energy tensor, among others, is a highly nontrivial issue. In particular, non-trivial divergences appear when computing expectation values of local observables. The objective of my thesis is to tackle this issue by studying regularization and renormalization mechanisms for quantum fields in curved spacetime, especially in Friedman-Robertson-Walker-Lemaitre spacetimes. On the one hand, this will be done by extending adiabatic regularization to include interacting fields (scalar, gauge fields). On the other hand, running of the coupling constant by introducing a mass parameter will be computed for general curved spacetime and a subtraction scheme, that naturally incorporates decoupling for higher massive fields will be obtained. A particular application will be given in the context of the cosmological constant problem

    In pursuit of eco-innovation

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    This study makes theoretical and methodological contributions to the field of eco-innovation research. The first theoretical contribution pertains to the literature review, which offers a synthesis regarding eco-innovation definitions, the main dimensions of eco-innovation, eco-innovation features, eco-innovation drivers and eco-innovation outcomes. This is followed by a proposal of our own definition of eco-innovation, developed based on the results and findings of this study

    Hegartymaths: gimmick or game changer?

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    This research examines the effectiveness of Hegartymaths, an online platform comprised of mathematical instructional video tutorials and quizzes used in approximately a third of mainstream secondary schools in England. The study employed mixed methods from an objectivist epistemological standpoint and used quasiexperiments to assess how schools who use Hegartymaths compare with ones that do not, as well as exploring how schools’ implementation of Hegartymaths impacts GCSE performance for the pupils that use it. In order to explore the impact Hegartymaths on GCSE performance, and specifically on the topics/types of questions which are part of the GCSE, data was collected and cleaned from the Gov.uk website, which publishes KS4 data for all 5,512 schools in England, and the Hegartymaths data team, who shared a snapshot of the big data analytics for 37 United Learning schools (30,501 pupils). A teacher survey, which included 106 responses from United Learning teachers of mathematics, considered which Hegartymaths practices increase its efficacy. The findings indicate that there are significant and positive relationships between the time spent on Hegartymaths and the performance of students in several categories, and the time spent completing quizzes was more effective than watching the video tutorials. Hegartymaths was seen to be more aligned to questions that test for procedural knowledge, rather than conceptual knowledge, and the schools that were identified to be more successful indicate there seems to be merit in the following practices: setting more Hegartymaths tasks at a time; allowing some topics to be taught solely through Hegartymaths; directing pupils to write notes when watching the tutorials. The research design and analyses in the study harness the power of big data with learning analytics to contribute to the literature from a methodological point of view, whereas the findings contribute to the limited existing literature on using video tutorials within a blended learning approach

    Scale coding a bag of words for real-time video-based action recognition.

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    Masters Degree.University of KwaZulu- Natal, Durban.Abstract available in PDF
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