Theoretical and experimental approaches towards the determination of solute effective diffusivities in foods

The aim of this review paper is to present experimental methodologies and the mathematical approaches used to determine effective diffusivities of solutes in food materials. The paper commences by describing the diffusion phenomena related to solute mass transfer in foods and effective diffusivities. It then focuses on the mathematical formulation for the calculation of effective diffusivities considering different diffusion models based on Fick's second law of diffusion. Finally, experimental considerations for effective diffusivity determination are elucidated primarily based on the acquirement of a series of solute content versus time curves appropriate to the equation model chosen. Different factors contributing to the determination of the effective diffusivities such as the structure of food material, temperature, diffusion solvent, agitation, sampling, concentration and different techniques used are considered. (c) 2005 Elsevier Inc. All rights reserved.

Valuing emerging markets companies : new approaches to determine the effective exposure to country risk

The aim of this paper is to propose new methods to measure the effective exposure to country risk of emerging-market companies. Starting from Damodaran (2003), we propose seven new approaches and a revised CAPM for emerging markets companies. The “Prospective Lambda” represents the effective exposure according to analysts’ estimates of growth. The “Relative Lambda” relies on the firm value estimated through a relative valuation. The “Retrospective Lambda” represents the ex-post effective exposure to country risk. The “Company Effective Risk Premium” is a generalization of the Retrospective Lambda, and expresses the premium effectively requested by investors to invest in that specific company in the past year. “The Actual Lambda” and the “Company Actual Risk Premium” represent, respectively, the actual exposure to country risk of a company and the actual premium requested by investors to invest in that specific company. The “Industry Lambda” reflects the median exposure to country risk of the industry in which the company belongs. We tested our new measures of exposure to country risk on the Latin American emerging markets companies according to the classification of the MSCI Emerging Markets Latin America Index. The results confirm that the new approaches can be effectively applied by financial analysts to stable-growth companies that operate in emerging markets and to mature markets companies that operate in emerging markets, providing with a more reliable estimate of both the premium effectively requested by investors in the past and the actual premium. Applying the new approaches, the cost of equity reflects the effective exposure of a company to country risk without being over- or underestimated, as is the case with other existing approaches.

New ICT Literacies and Holistic Approaches for the Effective Learning of Languages in Tertiary Education

Abstract: This paper summarizes the evolution of different subjects of English for Specific Purposes and English for Academic and Professional Purposes. The aim here is to show a continuum of changes that have not started and nished in one subject alone but affect the whole curriculum. After the discussion section where advantages and drawbacks of the changes introduced are analyzed, we arrive at some conclusions regarding this ve year period of development in the approach to the teaching and learning of the specific or academic English language in the Escuela Universitaria de Ingeniería Técnica de Telecomunicación, Universidad Politécnica de Madrid. Resumen: Este trabajo resume la evolución que han experimentado distintas asignaturas de Inglés para Fines Especí cos e Inglés para Fines Académicos y Profesionales. El objetivo principal es mostrar cómo el esfuerzo por mejorar las asignaturas afecta al currículo como un todo y no sólo a cada una de las asignaturas. Tras el análisis de algunas de las ventajas e inconvenientes de los cambios introducidos, se alcanzan algunas conclusiones sobre la evolución que han sufrido este tipo de asignaturas durante los últimos cinco años en la Escuela Universitaria de Ingeniería Técnica de Telecomunicación, Universidad Politécnica de Madrid.

Effective Cell-Centred Time-Domain Maxwell's Equations Numerical Solvers

This research work analyses techniques for implementing a cell-centred finite-volume time-domain (ccFV-TD) computational methodology for the purpose of studying microwave heating. Various state-of-the-art spatial and temporal discretisation methods employed to solve Maxwell's equations on multidimensional structured grid networks are investigated, and the dispersive and dissipative errors inherent in those techniques examined. Both staggered and unstaggered grid approaches are considered. Upwind schemes using a Riemann solver and intensity vector splitting are studied and evaluated. Staggered and unstaggered Leapfrog and Runge-Kutta time integration methods are analysed in terms of phase and amplitude error to identify which method is the most accurate and efficient for simulating microwave heating processes. The implementation and migration of typical electromagnetic boundary conditions. from staggered in space to cell-centred approaches also is deliberated. In particular, an existing perfectly matched layer absorbing boundary methodology is adapted to formulate a new cell-centred boundary implementation for the ccFV-TD solvers. Finally for microwave heating purposes, a comparison of analytical and numerical results for standard case studies in rectangular waveguides allows the accuracy of the developed methods to be assessed.

Barriers to effective knowledge transfer in project-based organisations

Effective knowledge transfer can prevent the reinvention of systems and ideas as well as the repetition of errors. Doing so will save substantial time, as well as contribute to better performance of projects and project-based organisations (PBOs). Despite the importance of knowledge, PBOs face serious barriers to the effective transfer of knowledge, while their characteristics, such as unique and innovative approaches taken during every project, mean they have much to gain from knowledge transfer. As each new project starts, there is the strong potential to reinvent the process, rather than utilise learning from previous projects. In fact, rework is one of the primary factors contributing to construction industry's poor performance and productivity. Current literature has identified several barriers to knowledge transfer in organisational settings in general, and not specifically PBOs. However, PBOs significantly differ from other types of organisations. PBOs operate mainly on temporary projects, where time is a crucial factor and people are more mobile than in other organisational settings. The aim of this research is to identify the key barriers that prevent effective knowledge transfer for PBOs, exclusively. Interviews with project managers and senior managers of PBOs complement the analysis of the literature and provide professional expertise. This research is crucial to gaining a better understanding of obstacles that hinder knowledge transfer in projects. The main contribution of this research is exclusive for PBO, list of key barriers that organisation and project managers need to consider to ensure effective knowledge transfer and better project management.

Innovative approaches to intervention for problem drinking

Purpose of review: To critique the recent literature on telephone, correspondence-based, and computerized interventions for alcohol problems, which enhance or substitute for practitioner-delivered treatments. Recent findings: There is an unmet need for screening, assessment and intervention for alcohol problems, in part because of the difficulty in accessing such treatment within the current health care system. Research on the efficacy of correspondence or electronic (for example Internet-based) interventions is beginning to emerge. In the period 2003–2004 we identified nine acceptability or feasibility studies of these approaches and seven efficacy trials covering a wide range of settings. These modes of intervention are acceptable to patients and the public, and with careful planning, can be implemented in a variety of settings. Treatment trials demonstrate the efficacy of these interventions in reducing hazardous drinking by university students, in delaying initiation of heavy drinking in children and adolescents, and, intriguingly, in addressing insomnia among recovering alcoholics. Summary: There is strong support among potential users for alcohol interventions that employ telephone assistance, written correspondence, and the Internet. These new technologies offer the prospect of increasing the reach of interventions for problem drinking and being cost- effective alternatives or supplements to face-to-face health service delivery.

Effectiveness of current treatment approaches for benzodiazepine discontinuation : a meta-analysis.

Aims: To assess the effectiveness of current treatment approaches to assist benzodiazepine discontinuation. Methods: A systematic review of approaches to benzodiazepine discontinuation in general practice and out-patient settings was undertaken. Routine care was compared with three treatment approaches: brief interventions, gradual dose reduction (GDR) and psychological interventions. GDR was compared with GDR plus psychological interventions or substitutive pharmacotherapies. Results: Inclusion criteria were met by 24 studies, and a further eight were identified by future search. GDR [odds ratio (OR) = 5.96, confidence interval (CI) = 2.08–17.11] and brief interventions (OR = 4.37, CI = 2.28–8.40) provided superior cessation rates at post-treatment to routine care. Psychological treatment plus GDR were superior to both routine care (OR = 3.38, CI = 1.86–6.12) and GDR alone (OR = 1.82, CI = 1.25–2.67). However, substitutive pharmacotherapies did not add to the impact of GDR (OR = 1.30, CI = 0.97– 1.73), and abrupt substitution of benzodiazepines by other pharmacotherapy was less effective than GDR alone (OR = 0.30, CI = 0.14–0.64). Few studies on any technique had significantly greater benzodiazepine discontinuation than controls at follow-up. Conclusions: Providing an intervention is more effective than routine care. Psychological interventions may improve discontinuation above GDR alone. While some substitutive pharmacotherapies may have promise, current evidence is insufficient to support their use.

Effective pattern taxonomy mining in text documents

Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.

Different approaches to reduce shaft voltage in AC generators

This paper presents several shaft voltage reduction techniques for doubly-fed induction generators in wind turbine applications. These techniques includes: pulse width modulated voltage without zero vectors, multi-level inverters with proper PWM strategy, better generator design to minimize effective capacitive couplings in shaft voltage, active common-mode filter, reducing dc-link voltage and increasing modulation index. These methods have been verified with mathematical analysis and simulations.

Geographical information systems : an effective planning and decision-making platform for community health coalitions in Australia

The development of locally-based healthcare initiatives, such as community health coalitions that focus on capacity building programs and multi-faceted responses to long-term health problems, have become an increasingly important part of the public health landscape. As a result of their complexity and the level of investment, it has become necessary to develop innovative ways to help manage these new healthcare approaches. Geographical Information Systems (GIS) have been suggested as one of the innovative approaches that will allow community health coalitions to better manage and plan their activities. The focus of this paper is to provide a commentary on the use of GIS as a tool for community coalitions and discuss some of the potential benefits and issues surrounding the development of these tools.

Personalising teacher professional development : strategies enabling effective learning for educators of 21st century students

In the Knowledge Society, new demands are placed on teachers as they strive to empower young people to be global citizens, ready for the 21st century. Systemic shifts need to be made, however, to build capacity across the workforce to practise new ways of teaching and learning, including the personalisation of teacher professional development. This article argues new strategies and approaches for effective adult learning, including an individualised focus, context-based learning and an empowerment of teachers to develop their own personal learning networks. This article concludes with an analysis of the challenges facing professional development leaders in moving towards personalised teacher learning.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.