997 resultados para Forecast methods


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Compression ignition (CI) engine design is subject to many constraints which presents a multi-criteria optimisation problem that the engine researcher must solve. In particular, the modern CI engine must not only be efficient, but must also deliver low gaseous, particulate and life cycle greenhouse gas emissions so that its impact on urban air quality, human health, and global warming are minimised. Consequently, this study undertakes a multi-criteria analysis which seeks to identify alternative fuels, injection technologies and combustion strategies that could potentially satisfy these CI engine design constraints. Three datasets are analysed with the Preference Ranking Organization Method for Enrichment Evaluations and Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) algorithm to explore the impact of 1): an ethanol fumigation system, 2): alternative fuels (20 % biodiesel and synthetic diesel) and alternative injection technologies (mechanical direct injection and common rail injection), and 3): various biodiesel fuels made from 3 feedstocks (i.e. soy, tallow, and canola) tested at several blend percentages (20-100 %) on the resulting emissions and efficiency profile of the various test engines. The results show that moderate ethanol substitutions (~20 % by energy) at moderate load, high percentage soy blends (60-100 %), and alternative fuels (biodiesel and synthetic diesel) provide an efficiency and emissions profile that yields the most “preferred” solutions to this multi-criteria engine design problem. Further research is, however, required to reduce Reactive Oxygen Species (ROS) emissions with alternative fuels, and to deliver technologies that do not significantly reduce the median diameter of particle emissions.

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This paper describes an approach to investigate the adoption of Web 2.0 in the classroom using a mixed methods study. By using a combination of qualitative or quantitative data collection and analysis techniques, we attempt to synergize the results and provide a more valid understanding of Web 2.0 adoption for learning by both teachers and students. This approach is expected to yield a better holistic view on the adoption issues associated with the e-learning 2.0 concept in current higher education as opposed to single method studies done previously. This paper also presents some early findings of e-learning 2.0 adoption using this research method

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Purpose. The purpose of this article was to present methods capable of estimating the size and shape of the human eye lens without resorting to phakometry or magnetic resonance imaging (MRI). Methods. Previously published biometry and phakometry data of 66 emmetropic eyes of 66 subjects (age range [18, 63] years, spherical equivalent range [−0.75, +0.75] D) were used to define multiple linear regressions for the radii of curvature and thickness of the lens, from which the lens refractive index could be derived. MRI biometry was also available for a subset of 30 subjects, from which regressions could be determined for the vertex radii of curvature, conic constants, equatorial diameter, volume, and surface area. All regressions were compared with the phakometry and MRI data; the radii of curvature regressions were also compared with a method proposed by Bennett and Royston et al. Results. The regressions were in good agreement with the original measurements. This was especially the case for the regressions of lens thickness, volume, and surface area, which each had an R2 > 0.6. The regression for the posterior radius of curvature had an R2 < 0.2, making this regression unreliable. For all other regressions we found 0.25 < R2 < 0.6. The Bennett-Royston method also produced a good estimation of the radii of curvature, provided its parameters were adjusted appropriately. Conclusions. The regressions presented in this article offer a valuable alternative in case no measured lens biometry values are available; however care must be taken for possible outliers.

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Metrics such as passengers per square metre have been developed to define optimum or crowded rail passenger density. Whilst such metrics are important to operational procedures, service evaluation and reporting, they fail to fully capture and convey the ways in which passengers experience crowded situations. This paper reports findings from a two year study of rail passenger crowding in five Australian capital cities which involved a novel mixed-methodology including ethnography, focus groups and an online stated preference choice experiment. The resulting data address the following four fundamental research questions: 1) to what extent are Australian rail passengers concerned by crowding, 2) what conditions exacerbate feelings of crowdedness, 3) what conditions mitigate feelings of crowdedness, and 4) how can we usefully understand passengers’ experiences of crowdedness? It concludes with some observations on the significance and implications of these findings for customer service provision. The findings outlined in this paper demonstrate that the experience of crowdedness (including its tolerance) cannot be understood in isolation from other customer services issues such as interior design, quality of environment, safety and public health concerns. It is hypothesised that tolerance of crowding will increase alongside improvements to overall customer service. This was the first comprehensive study of crowding in the Australian rail industry.

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This study uses borehole geophysical log data of sonic velocity and electrical resistivity to estimate permeability in sandstones in the northern Galilee Basin, Queensland. The prior estimates of permeability are calculated according to the deterministic log–log linear empirical correlations between electrical resistivity and measured permeability. Both negative and positive relationships are influenced by the clay content. The prior estimates of permeability are updated in a Bayesian framework for three boreholes using both the cokriging (CK) method and a normal linear regression (NLR) approach to infer the likelihood function. The results show that the mean permeability estimated from the CK-based Bayesian method is in better agreement with the measured permeability when a fairly apparent linear relationship exists between the logarithm of permeability and sonic velocity. In contrast, the NLR-based Bayesian approach gives better estimates of permeability for boreholes where no linear relationship exists between logarithm permeability and sonic velocity.

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An increasing body of research is highlighting the involvement of illicit drugs in many road fatalities. Deterrence theory has been a core conceptual framework underpinning traffic enforcement as well as interventions designed to reduce road fatalities. Essentially the effectiveness of deterrence-based approaches is predicated on perceptions of certainty, severity, and swiftness of apprehension. However, much less is known about how the awareness of legal sanctions can impact upon the effectiveness of deterrence mechanisms and whether promoting such detection methods can increase the deterrent effect. Nevertheless, the implicit assumption is that individuals aware of the legal sanctions will be more deterred. This study seeks to explore how awareness of the testing method impacts upon the effectiveness of deterrence-based interventions and intentions to drug drive again in the future. In total, 161 participants who reported drug driving in the previous six months took part in the current study. The results show that awareness of testing had a small effect upon increasing perceptions of the certainty of apprehension and severity of punishment. However, awareness was not a significant predictor of intentions to drug drive again in the future. Importantly, higher levels of drug use were a significant predictor of intentions to drug drive in the future. Whilst awareness does have a small effect on deterrence variables, the influence of levels of drug use seems to reduce any deterrent effect.

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Qualitative research methods are widely accepted in Information Systems and multiple approaches have been successfully used in IS qualitative studies over the years. These approaches include narrative analysis, discourse analysis, grounded theory, case study, ethnography and phenomenological analysis. Guided by critical, interpretive and positivist epistemologies (Myers 1997), qualitative methods are continuously growing in importance in our research community. In this special issue, we adopt Van Maanen's (1979: 520) definition of qualitative research as an umbrella term to cover an “array of interpretive techniques that can describe, decode, translate, and otherwise come to terms with the meaning, not the frequency, of certain more or less naturally occurring phenomena in the social world”. In the call for papers, we stated that the aim of the special issue was to provide a forum within which we can present and debate the significant number of issues, results and questions arising from the pluralistic approach to qualitative research in Information Systems. We recognise both the potential and the challenges that qualitative approaches offers for accessing the different layers and dimensions of a complex and constructed social reality (Orlikowski, 1993). The special issue is also a response to the need to showcase the current state of the art in IS qualitative research and highlight advances and issues encountered in the process of continuous learning that includes questions about its ontology, epistemological tenets, theoretical contributions and practical applications.

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This paper explores what we are calling “Guerrilla Research Tactics” (GRT): research methods that exploit emerging mobile and cloud based digital technologies. We examine some case studies in the use of this technology to generate research data directly from the physical fabric and the people of the city. We argue that GRT is a new and novel way of engaging public participation in urban, place based research because it facilitates the co- creation of knowledge, with city inhabitants, ‘on the fly’. This paper discusses the potential of these new research techniques and what they have to offer researchers operating in the creative disciplines and beyond. This work builds on and extends Gauntlett’s “new creative methods” (2007) and contributes to the existing body of literature addressing creative and interactive approaches to data collection.

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Now as in earlier periods of acute change in the media environment, new disciplinary articulations are producing new methods for media and communication research. At the same time, established media and communication studies meth- ods are being recombined, reconfigured, and remediated alongside their objects of study. This special issue of JOBEM seeks to explore the conceptual, political, and practical aspects of emerging methods for digital media research. It does so at the conjuncture of a number of important contemporary trends: the rise of a ‘‘third wave’’ of the Digital Humanities and the ‘‘computational turn’’ (Berry, 2011) associated with natively digital objects and the methods for studying them; the apparently ubiquitous Big Data paradigm—with its various manifestations across academia, business, and government — that brings with it a rapidly increasing interest in social media communication and online ‘‘behavior’’ from the ‘‘hard’’ sciences; along with the multisited, embodied, and emplaced nature of everyday digital media practice.

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Fractional mathematical models represent a new approach to modelling complex spatial problems in which there is heterogeneity at many spatial and temporal scales. In this paper, a two-dimensional fractional Fitzhugh-Nagumo-monodomain model with zero Dirichlet boundary conditions is considered. The model consists of a coupled space fractional diffusion equation (SFDE) and an ordinary differential equation. For the SFDE, we first consider the numerical solution of the Riesz fractional nonlinear reaction-diffusion model and compare it to the solution of a fractional in space nonlinear reaction-diffusion model. We present two novel numerical methods for the two-dimensional fractional Fitzhugh-Nagumo-monodomain model using the shifted Grunwald-Letnikov method and the matrix transform method, respectively. Finally, some numerical examples are given to exhibit the consistency of our computational solution methodologies. The numerical results demonstrate the effectiveness of the methods.

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Transport processes within heterogeneous media may exhibit non-classical diffusion or dispersion; that is, not adequately described by the classical theory of Brownian motion and Fick's law. We consider a space fractional advection-dispersion equation based on a fractional Fick's law. The equation involves the Riemann-Liouville fractional derivative which arises from assuming that particles may make large jumps. Finite difference methods for solving this equation have been proposed by Meerschaert and Tadjeran. In the variable coefficient case, the product rule is first applied, and then the Riemann-Liouville fractional derivatives are discretised using standard and shifted Grunwald formulas, depending on the fractional order. In this work, we consider a finite volume method that deals directly with the equation in conservative form. Fractionally-shifted Grunwald formulas are used to discretise the fractional derivatives at control volume faces. We compare the two methods for several case studies from the literature, highlighting the convenience of the finite volume approach.

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Rayleigh–Stokes problems have in recent years received much attention due to their importance in physics. In this article, we focus on the variable-order Rayleigh–Stokes problem for a heated generalized second grade fluid with fractional derivative. Implicit and explicit numerical methods are developed to solve the problem. The convergence, stability of the numerical methods and solvability of the implicit numerical method are discussed via Fourier analysis. Moreover, a numerical example is given and the results support the effectiveness of the theoretical analysis.

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Spreadsheet for Creative City Index 2012