974 resultados para Complex Effective Porosity


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Abundant research has shown that poverty has negative influences on young child academic and psychosocial development, and unfortunately, disparities in school readiness between low and high income children can be seen as early the first year of life. The largest federal early care and education intervention for these vulnerable children is Early Head Start (EHS). To diminish these disparate child outcomes, EHS seeks to provide community based flexible programming for infants and toddlers and their families. Given how relatively recent these programs have been offered, little is known about the nuances of how EHS impacts infant and toddler language and psychosocial development. Using a framework of Community Based Participatory Research (CBPR) this paper had 5 goals: 1) to characterize the associations between domain specific and cumulative risk and child outcomes 2) to validate and explore these risk-outcome associations separately for Children of Hispanic immigrants (COHIs), 3) to explore relationships among family characteristics, multiple environmental factors, and dosage patterns in different EHS program types, 4) to examine the relationship between EHS dosage and child outcomes, and 5) to examine how EHS compliance impacts child internalizing and externalizing behaviors and emerging language abilities. Results of the current study showed that risks were differentially related to child outcomes. Poor maternal mental health was related to child internalizing and externalizing behaviors, but not related to emerging child language skills. Although child language skills were not related to maternal mental health, they were related to economic hardship. Additionally, parent level Spanish use and heritage orientation were associated with positive child outcomes. Results also showed that these relationships differed when COHIs and children with native-born parents were examined separately. Further, unique patterns emerged for EHS program use, for example families who participated in home-based care were less likely to comply with EHS attendance requirements. These findings provide tangible suggestions for EHS stakeholders: namely, the need to develop effective programming that targets engagement for diverse families enrolled in EHS programs.

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The United States and the European Union each have their own policy approach to protect surface water quality. Both policy approaches are similar in many ways. Both rely heavily on command and control. However, there are differences in the application of the details. Both the U.S. and E.U. began current efforts to protect surface water quality in the 1970s, yet quality continues to less than desired in both places. Both have reduced point source pollutants but have had difficulty controlling non-point source pollutants even though policies have been in place for many decades. The successes and failures of the two policies are studied in this project to determine which aspects of both policies will best protect surface water quality in an increasingly complex future.

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Aerobic Gymnastic is the ability to perform complex movements produced by the traditional aerobic exercises, in a continuous manner, with high intensity, perfectly integrated with soundtracks. This sport is performed in an aerobic/anaerobic lactacid condition and expects the execution of complex movements produced by the traditional aerobic exercises integrated with difficulty elements performed with a high technical level. An inaccuracy about this sport is related to the name itself “aerobic” because Aerobic Gymnastic does not use just the aerobic work during the competition, due to the fact that the exercises last among 1’30” and 1’45” at high rhythm. Agonistic Aerobics exploit the basic movements of amateur Aerobics and its coordination schemes, even though the agonistic Aerobics is so much intense than the amateur Aerobics to need a completely different mix of energetic mechanisms. Due to the complexity and the speed with which you perform the technical elements of Aerobic Gymnastic, the introduction of video analysis is essential for a qualitative and quantitative evaluation of athletes’ performance during the training. The performance analysis can allow the accurate analysis and explanation of the evolution and dynamics of a historical phenomenon and motor sports. The notational analysis is used by technicians to have an objective analysis of performance. Tactics, technique and individual movements can be analyzed to help coaches and athletes to re-evaluate their performance and gain advantage during the competition. The purpose of the following experimental work will be a starting point for analyzing the performance of the athletes in an objective way, not only during competitions, but especially during the phases of training. It is, therefore, advisable to introduce the video analysis and notational analysis for more quantitative and qualitative examination of technical movements. The goal is to lead to an improvement of the technique of the athlete and the teaching of the coach.

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The Rh diamine complex [Rh(COD)NH2(CH2)2NH(CH2)3Si(OCH3)3] BF4 was heterogenized by covalent bonding on two carbon xerogels and on carbon nanofibers, with the objective of preparing hydrogenation hybrid catalysts. Gas adsorption, SEM, TEM, DTP, ICP-OES and XPS were used for characterization. The results indicate that the active molecule is mainly located in supermicropores and produces microporosity blockage. The hybrid catalysts are more active than the homogeneous complex, but the Rh complex is partially reduced upon reaction. This modification is related to the nature of the support, which also shows effects in the stabilization against sintering of the Rh particles formed. The support porosity is a key factor in the selectivity differences between the catalysts.

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The construction industry has long been considered as highly fragmented and non-collaborative industry. This fragmentation sprouted from complex and unstructured traditional coordination processes and information exchanges amongst all parties involved in a construction project. This nature coupled with risk and uncertainty has pushed clients and their supply chain to search for new ways of improving their business process to deliver better quality and high performing product. This research will closely investigate the need to implement a Digital Nervous System (DNS), analogous to a biological nervous system, on the flow and management of digital information across the project lifecycle. This will be through direct examination of the key processes and information produced in a construction project and how a DNS can provide a well-integrated flow of digital information throughout the project lifecycle. This research will also investigate how a DNS can create a tight digital feedback loop that enables the organisation to sense, react and adapt to changing project conditions. A Digital Nervous System is a digital infrastructure that provides a well-integrated flow of digital information to the right part of the organisation at the right time. It provides the organisation with the relevant and up-to-date information it needs, for critical project issues, to aid in near real-time decision-making. Previous literature review and survey questionnaires were used in this research to collect and analyse data about information management problems of the industry – e.g. disruption and discontinuity of digital information flow due to interoperability issues, disintegration/fragmentation of the adopted digital solutions and paper-based transactions. Results analysis revealed efficient and effective information management requires the creation and implementation of a DNS.

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Despite the enormous economic importance of Neospora caninum related veterinary diseases, the number of effective therapeutic agents is relatively small. Development of new therapeutic strategies to combat the economic impact of neosporosis remains an important scientific endeavor. This study demonstrates molecular, structural and phenotypic evidence that N. caninum calcium-dependent protein kinase 1 (NcCDPK1) is a promising molecular target for neosporosis drug development. Recombinant NcCDPK1 was expressed, purified and screened against a select group of bumped kinase inhibitors (BKIs) previously shown to have low IC50s against Toxoplasma gondii CDPK1 and T. gondii tachyzoites. NcCDPK1 was inhibited by low concentrations of BKIs. The three-dimensional structure of NcCDPK1 in complex with BKIs was studied crystallographically. The BKI-NcCDPK1 structures demonstrated the structural basis for potency and selectivity. Calcium-dependent conformational changes in solution as characterized by small-angle X-ray scattering are consistent with previous structures in low Calcium-state but different in the Calcium-bound active state than predicted by X-ray crystallography. BKIs effectively inhibited N. caninum tachyzoite proliferation in vitro. Electron microscopic analysis of N. caninum cells revealed ultra-structural changes in the presence of BKI compound 1294. BKI compound 1294 interfered with an early step in Neospora tachyzoite host cell invasion and egress. Prolonged incubation in the presence of 1294 interfered produced observable interference with viability and replication. Oral dosing of BKI compound 1294 at 50 mg/kg for 5 days in established murine neosporosis resulted in a 10-fold reduced cerebral parasite burden compared to untreated control. Further experiments are needed to determine the PK, optimal dosage, and duration for effective treatment in cattle and dogs, but these data demonstrate proof-of-concept for BKIs, and 1294 specifically, for therapy of bovine and canine neosporosis.

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Inferring the spatial expansion dynamics of invading species from molecular data is notoriously difficult due to the complexity of the processes involved. For these demographic scenarios, genetic data obtained from highly variable markers may be profitably combined with specific sampling schemes and information from other sources using a Bayesian approach. The geographic range of the introduced toad Bufo marinus is still expanding in eastern and northern Australia, in each case from isolates established around 1960. A large amount of demographic and historical information is available on both expansion areas. In each area, samples were collected along a transect representing populations of different ages and genotyped at 10 microsatellite loci. Five demographic models of expansion, differing in the dispersal pattern for migrants and founders and in the number of founders, were considered. Because the demographic history is complex, we used an approximate Bayesian method, based on a rejection-regression algorithm. to formally test the relative likelihoods of the five models of expansion and to infer demographic parameters. A stepwise migration-foundation model with founder events was statistically better supported than other four models in both expansion areas. Posterior distributions supported different dynamics of expansion in the studied areas. Populations in the eastern expansion area have a lower stable effective population size and have been founded by a smaller number of individuals than those in the northern expansion area. Once demographically stabilized, populations exchange a substantial number of effective migrants per generation in both expansion areas, and such exchanges are larger in northern than in eastern Australia. The effective number of migrants appears to be considerably lower than that of founders in both expansion areas. We found our inferences to be relatively robust to various assumptions on marker. demographic, and historical features. The method presented here is the only robust, model-based method available so far, which allows inferring complex population dynamics over a short time scale. It also provides the basis for investigating the interplay between population dynamics, drift, and selection in invasive species.

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Finite-element simulations are used to obtain many thousands of yield points for porous materials with arbitrary void-volume fractions with spherical voids arranged in simple cubic, body-centred cubic and face-centred cubic three-dimensional arrays. Multi-axial stress states are explored. We show that the data may be fitted by a yield function which is similar to the Gurson-Tvergaard-Needleman (GTN) form, but which also depends on the determinant of the stress tensor, and all additional parameters may be expressed in terms of standard GTN-like parameters. The dependence of these parameters on the void-volume fraction is found. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

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As a part of the Managing Uncertainty in Complex Models (MUCM) project, research at Aston University will develop methods for dimensionality reduction of the input and/or output spaces of models, as seen within the emulator framework. Towards this end this report describes a framework for generating toy datasets, whose underlying structure is understood, to facilitate early investigations of dimensionality reduction methods and to gain a deeper understanding of the algorithms employed, both in terms of how effective they are for given types of models / situations, and also their speed in applications and how this scales with various factors. The framework, which allows the evaluation of both screening and projection approaches to dimensionality reduction, is described. We also describe the screening and projection methods currently under consideration and present some preliminary results. The aim of this draft of the report is to solicit feedback from the project team on the dataset generation framework, the methods we propose to use, and suggestions for extensions that should be considered.

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Lifelong learning is a ‘keystone’ of educational policies (Faure, 1972) where the emphasis on learning shifts from teacher to learner. Higher Education (HE) institutions should be committed to developing lifelong learning, that is promoting learning that is flexible, diverse and relevant at different times, and in different places, and is pursued throughout life. Therefore the HE sector needs to develop effective strategies to encourage engagement in meaningful learning for diverse student populations. The use of e-portfolios, as a ‘purposeful aggregation of digital items’ (Sutherland & Powell, 2007), can meet the needs of the student community by encouraging reflection, the recording of experiences and achievements, and personal development planning (PDP). The use of e-portfolios also promotes inclusivity in learning as it provides students with the opportunity to articulate their aspirations and take the first steps along the pathway of lifelong learning. However, ensuring the uptake of opportunities within their learning is more complex than the students simply having access to the software. Therefore it is argued here that crucial to the effective uptake and engagement of the e-portfolio is embedding it purposefully within the curriculum. In order to investigate effective implementation of e-portfolios an explanatory case study on their use was carried out, initially focusing on 3 groups of students engaged in work-based learning and professional practice. The 3 groups had e-Portfolios embedded and assessed at different levels. Group 1 did not have the e-Portfolio embedded into their curriculum nor was the e-Portfolio assessed. Group 2 had the e-Portfolio embedded into the curriculum and formatively assessed. Group 3 also had the e-Portfolio embedded into the curriculum and were summatively assessed. Results suggest that the use of e-Portfolios needs to be integral to curriculum design in modules rather than used as an additional tool. In addition to this more user engagement was found in group 2 where the e-Portfolio was formatively assessed only. The implications of this case study are further discussed in terms of curriculum development.

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This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

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In recent years, UK industry has seen an explosive growth in the number of `Computer Aided Production Management' (CAPM) system installations. Of the many CAPM systems, materials requirement planning/manufacturing resource planning (MRP/MRPII) is the most widely implemented. Despite the huge investments in MRP systems, over 80 percent are said to have failed within 3 to 5 years of installation. Many people now assume that Just-In-Time (JIT) is the best manufacturing technique. However, those who have implemented JIT have found that it also has many problems. The author argues that the success of a manufacturing company will not be due to a system which complies with a single technique; but due to the integration of many techniques and the ability to make them complement each other in a specific manufacturing environment. This dissertation examines the potential for integrating MRP with JIT and Two-Bin systems to reduce operational costs involved in managing bought-out inventory. Within this framework it shows that controlling MRP is essential to facilitate the integrating process. The behaviour of MRP systems is dependent on the complex interactions between the numerous control parameters used. Methodologies/models are developed to set these parameters. The models are based on the Pareto principle. The idea is to use business targets to set a coherent set of parameters, which not only enables those business targets to be realised, but also facilitates JIT implementation. It illustrates this approach in the context of an actual manufacturing plant - IBM Havant. (IBM Havant is a high volume electronics assembly plant with the majority of the materials bought-out). The parameter setting models are applicable to control bought-out items in a wide range of industries and are not dependent on specific MRP software. The models have produced successful results in several companies and are now being developed as commercial products.

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Thirteen experiments investigated the dynamics of stream segregation. Experiments 1-6b used a similar method, where a same-frequency induction sequence (usually 10 repetitions of an identical pure tone) promoted segregation in a subsequent, briefer test sequence (of alternating low- and high-frequency tones). Experiments 1-2 measured streaming using a direct report of perception and a temporal-discrimination task, respectively. Creating a single deviant by altering the final inducer (e.g. in level or replacement with silence) reduced segregation, often substantially. As the prior inducers remained unaltered, it is proposed that the single change actively reset build-up. The extent of resetting varied gradually with the size of a frequency change, once noticeable (experiments 3a-3b). By manipulating the serial position of a change, experiments 4a-4b demonstrated that resetting only occurred when the final inducer was replaced with silence, as build-up is very rapid during a same-frequency induction sequence. Therefore, the observed resetting cannot be explained by fewer inducers being presented. Experiment 5 showed that resetting caused by a single deviant did not increase when prior inducers were made unpredictable in frequency (four-semitone range). Experiments 6a-6b demonstrated that actual and perceived continuity have a similar effect on subsequent streaming judgements promoting either integration or segregation, depending on listening context. Experiment 7 found that same-frequency inducers were considerably more effective at promoting segregation than an alternating-frequency inducer, and that a trend for deviant-tone resetting was only apparent for the same-frequency case. Using temporal-order judgments, experiments 8-9 demonstrated the stream segregation of pure-tone-like percepts, evoked by sudden changes in amplitude or interaural time difference for individual components of a complex tone, Active resetting was observed when a deviant was inserted into a sequence of these percepts (Experiment 10). Overall, these experiments offer new insight into the segregation-promotIng effect of induction sequences, and the factors which can reset this effect.

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Formulating complex queries is hard, especially when users cannot understand all the data structures of multiple complex knowledge bases. We see a gap between simplistic but user friendly tools and formal query languages. Building on an example comparison search, we propose an approach in which reusable search components take an intermediary role between the user interface and formal query languages.

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This paper is dedicated to modelling of network maintaining based on live example – maintaining ATM banking network, where any problems are mean money loss. A full analysis is made in order to estimate valuable and not-valuable parameters based on complex analysis of available data. Correlation analysis helps to estimate provided data and to produce a complex solution of increasing network maintaining effectiveness.