Small Projects: A Method for improving Learning.

Ideas concerning problem-based learning (PBL) developed after running different experiences in different Spanish Universities, are discussed. The driver for introducing PBL has been the requirement for studying Mathematics by the Engineering students. A methodology hybrid of problem-based learning for Mathematics in Engineering studies is proposed. The model is a combination of formal lectures, practical and laboratory sessions with autonomous small projects.

An approach to stakeholders involvement in the preparedness for nuclear and radiological emergency response & recovery in Spain

The elaboration of a generic decision-making strategy to address the evolution of an emergency situation, from the stages of response to recovery, and including a planning stage, can facilitate timely, effective and consistent decision making by the response organisations at every level within the emergency management structure and between countries, helping to ensure optimal protection of health, environment, and society. The degree of involvement of stakeholders in this process is a key strategic element for strengthening the local preparedness and response and can help a successful countermeasures strategy. A significant progress was made with the multi-national European project EURANOS (2004-2009) which brought together best practice, knowledge and technology to enhance the preparedness for Europe's response to any radiation emergency and long term contamination. The subsequent establishment of a European Technology Platform and the recent launch of the research project NERIS-TP ("Towards a self sustaining European Technology Platform (NERIS-TP) on Preparedness for Nuclear and Radiological Emergency Response and Recovery") are aimed to continue with the remaining tasks for gaining appropriate levels of emergency preparedness at local level in most European countries. One of the objectives of the NERIS-TP project is: Strengthen the preparedness at the local/national level by setting up dedicated fora and developing new tools or adapting the tools developed within the EURANOS projects (such as the governance framework for preparedness, the handbooks on countermeasures, the RODOS system, and the MOIRA DSS for long term contamination in catchments) to meet the needs of local communities. CIEMAT and UPM in close interaction with the Nuclear Safety Council will explore, within this project, the use and application in Spain of such technical tools, including other national tools and information and communication strategies to foster cooperation between local, national and international stakeholders. The aim is identify and involve relevant stakeholders in emergency preparedness to improve the development and implementation of appropriate protection strategies as part of the consequence management and the transition to recovery. In this paper, an overview of the "state of the art" on this area in Spain and the methodology and work Plan proposed by the Spanish group within the project NERIS to grow the stakeholder involvement in the preparedness to emergency response and recovery is presented.

Algoritmos de agrupación para flujos de datos en entornos centralizados y distribuidos

Los avances en el hardware permiten disponer de grandes volúmenes de datos, surgiendo aplicaciones que deben suministrar información en tiempo cuasi-real, la monitorización de pacientes, ej., el seguimiento sanitario de las conducciones de agua, etc. Las necesidades de estas aplicaciones hacen emerger el modelo de flujo de datos (data streaming) frente al modelo almacenar-para-despuésprocesar (store-then-process). Mientras que en el modelo store-then-process, los datos son almacenados para ser posteriormente consultados; en los sistemas de streaming, los datos son procesados a su llegada al sistema, produciendo respuestas continuas sin llegar a almacenarse. Esta nueva visión impone desafíos para el procesamiento de datos al vuelo: 1) las respuestas deben producirse de manera continua cada vez que nuevos datos llegan al sistema; 2) los datos son accedidos solo una vez y, generalmente, no son almacenados en su totalidad; y 3) el tiempo de procesamiento por dato para producir una respuesta debe ser bajo. Aunque existen dos modelos para el cómputo de respuestas continuas, el modelo evolutivo y el de ventana deslizante; éste segundo se ajusta mejor en ciertas aplicaciones al considerar únicamente los datos recibidos más recientemente, en lugar de todo el histórico de datos. En los últimos años, la minería de datos en streaming se ha centrado en el modelo evolutivo. Mientras que, en el modelo de ventana deslizante, el trabajo presentado es más reducido ya que estos algoritmos no sólo deben de ser incrementales si no que deben borrar la información que caduca por el deslizamiento de la ventana manteniendo los anteriores tres desafíos. Una de las tareas fundamentales en minería de datos es la búsqueda de agrupaciones donde, dado un conjunto de datos, el objetivo es encontrar grupos representativos, de manera que se tenga una descripción sintética del conjunto. Estas agrupaciones son fundamentales en aplicaciones como la detección de intrusos en la red o la segmentación de clientes en el marketing y la publicidad. Debido a las cantidades masivas de datos que deben procesarse en este tipo de aplicaciones (millones de eventos por segundo), las soluciones centralizadas puede ser incapaz de hacer frente a las restricciones de tiempo de procesamiento, por lo que deben recurrir a descartar datos durante los picos de carga. Para evitar esta perdida de datos, se impone el procesamiento distribuido de streams, en concreto, los algoritmos de agrupamiento deben ser adaptados para este tipo de entornos, en los que los datos están distribuidos. En streaming, la investigación no solo se centra en el diseño para tareas generales, como la agrupación, sino también en la búsqueda de nuevos enfoques que se adapten mejor a escenarios particulares. Como ejemplo, un mecanismo de agrupación ad-hoc resulta ser más adecuado para la defensa contra la denegación de servicio distribuida (Distributed Denial of Services, DDoS) que el problema tradicional de k-medias. En esta tesis se pretende contribuir en el problema agrupamiento en streaming tanto en entornos centralizados y distribuidos. Hemos diseñado un algoritmo centralizado de clustering mostrando las capacidades para descubrir agrupaciones de alta calidad en bajo tiempo frente a otras soluciones del estado del arte, en una amplia evaluación. Además, se ha trabajado sobre una estructura que reduce notablemente el espacio de memoria necesario, controlando, en todo momento, el error de los cómputos. Nuestro trabajo también proporciona dos protocolos de distribución del cómputo de agrupaciones. Se han analizado dos características fundamentales: el impacto sobre la calidad del clustering al realizar el cómputo distribuido y las condiciones necesarias para la reducción del tiempo de procesamiento frente a la solución centralizada. Finalmente, hemos desarrollado un entorno para la detección de ataques DDoS basado en agrupaciones. En este último caso, se ha caracterizado el tipo de ataques detectados y se ha desarrollado una evaluación sobre la eficiencia y eficacia de la mitigación del impacto del ataque. ABSTRACT Advances in hardware allow to collect huge volumes of data emerging applications that must provide information in near-real time, e.g., patient monitoring, health monitoring of water pipes, etc. The data streaming model emerges to comply with these applications overcoming the traditional store-then-process model. With the store-then-process model, data is stored before being consulted; while, in streaming, data are processed on the fly producing continuous responses. The challenges of streaming for processing data on the fly are the following: 1) responses must be produced continuously whenever new data arrives in the system; 2) data is accessed only once and is generally not maintained in its entirety, and 3) data processing time to produce a response should be low. Two models exist to compute continuous responses: the evolving model and the sliding window model; the latter fits best with applications must be computed over the most recently data rather than all the previous data. In recent years, research in the context of data stream mining has focused mainly on the evolving model. In the sliding window model, the work presented is smaller since these algorithms must be incremental and they must delete the information which expires when the window slides. Clustering is one of the fundamental techniques of data mining and is used to analyze data sets in order to find representative groups that provide a concise description of the data being processed. Clustering is critical in applications such as network intrusion detection or customer segmentation in marketing and advertising. Due to the huge amount of data that must be processed by such applications (up to millions of events per second), centralized solutions are usually unable to cope with timing restrictions and recur to shedding techniques where data is discarded during load peaks. To avoid discarding of data, processing of streams (such as clustering) must be distributed and adapted to environments where information is distributed. In streaming, research does not only focus on designing for general tasks, such as clustering, but also in finding new approaches that fit bests with particular scenarios. As an example, an ad-hoc grouping mechanism turns out to be more adequate than k-means for defense against Distributed Denial of Service (DDoS). This thesis contributes to the data stream mining clustering technique both for centralized and distributed environments. We present a centralized clustering algorithm showing capabilities to discover clusters of high quality in low time and we provide a comparison with existing state of the art solutions. We have worked on a data structure that significantly reduces memory requirements while controlling the error of the clusters statistics. We also provide two distributed clustering protocols. We focus on the analysis of two key features: the impact on the clustering quality when computation is distributed and the requirements for reducing the processing time compared to the centralized solution. Finally, with respect to ad-hoc grouping techniques, we have developed a DDoS detection framework based on clustering.We have characterized the attacks detected and we have evaluated the efficiency and effectiveness of mitigating the attack impact.

Guidance for the development of accessibility evaluation tools following the Unified Software Development Process

Automated and semi-automated accessibility evaluation tools are key to streamline the process of accessibility assessment, and ultimately ensure that software products, contents, and services meet accessibility requirements. Different evaluation tools may better fit different needs and concerns, accounting for a variety of corporate and external policies, content types, invocation methods, deployment contexts, exploitation models, intended audiences and goals; and the specific overall process where they are introduced. This has led to the proliferation of many evaluation tools tailored to specific contexts. However, tool creators, who may be not familiar with the realm of accessibility and may be part of a larger project, lack any systematic guidance when facing the implementation of accessibility evaluation functionalities. Herein we present a systematic approach to the development of accessibility evaluation tools, leveraging the different artifacts and activities of a standardized development process model (the Unified Software Development Process), and providing templates of these artifacts tailored to accessibility evaluation tools. The work presented specially considers the work in progress in this area by the W3C/WAI Evaluation and Report Working Group (ERT WG)

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Changes in the governance system for higher education in Peru to meet the challenges of the 21st century

The competition in markets, the distribution of limited resources based on productivity and performance, and the efficient management of universities are changing the criteria of trust and legitimacy of the educational system in Peru. Universities are perceived more as institutions of the public sector, while the services they offer must rather contribute to the modernization of the emerging society and the knowledge economy. Higher Educations reforms - initiated in the 1980s - have been inspired by the successful university organizations that have managed to change their governance and addressed to transform certain bureaucratic institutions into organizations capable of playing active role in this global competition for resources and best talent. Within this context, Peruvian universities are facing two major challenges: adapting themselves to new global perspectives and being able to develop a better response to society demands, needs and expectations. This article proposes a model of governance system for higher education in Peru that gives a comprehensive solution to these challenges, allowing dealing with the problems of universities for their development and inclusion within the global trends. For this purpose, a holistic and qualitative methodologic approach was developed, considering an integrated method which considered educational reality as a whole, understanding its facts, components and elements that affects its outcomes. It is proposed to define a policy for university education in Peru that permeates society, by changing the planning model from a social reform model to a policy analysis model, where the Peruvian State acts as sole responsible for responding to the demanding society as its legal representative complemented with some external and independent bodies that define the basis of best practice, as it is being done in many university models worldwide.

A SEGMENTAÇÃO DE PÚBLICOS NA COMUNICAÇÃO EMPRESARIAL: UMA AVALIAÇÃO CRÍTICA

A dissertação tem como base a importância do entendimento a respeito dos relacionamentos organizacionais para uma abordagem segmentada dos públicos na comunicação empresarial. A partir de uma reflexão teórica sobre o assunto e da observação de práticas atuais de mercado, foram estabelecidos parâmetros que contribuem para uma conceituação mais precisa dos interlocutores das corporações, no sentido de prover suas demandas informacionais. Tanto na análise das obras consultadas quanto na avaliação dos resultados da pesquisa com empresas de tradição na área de comunicação, demonstrou-se que há lacunas importantes a serem preenchidas. Entes elas, a inexistência de mecanismos que possam aferir com maior precisão as expectativas dos vários segmentos de público em relação à comunicação das empresas, em uma via de mão-dupla, bem como a falta de canais de comunicação regulares com determinados grupos, notadamente no âmbito externo. As análises apontam para a adoção de um sistema de gestão do conhecimento focado nos públicos como elemento fundamental para a eficácia dos processos comunicacionais.(AU)

A SEGMENTAÇÃO DE PÚBLICOS NA COMUNICAÇÃO EMPRESARIAL: UMA AVALIAÇÃO CRÍTICA

A dissertação tem como base a importância do entendimento a respeito dos relacionamentos organizacionais para uma abordagem segmentada dos públicos na comunicação empresarial. A partir de uma reflexão teórica sobre o assunto e da observação de práticas atuais de mercado, foram estabelecidos parâmetros que contribuem para uma conceituação mais precisa dos interlocutores das corporações, no sentido de prover suas demandas informacionais. Tanto na análise das obras consultadas quanto na avaliação dos resultados da pesquisa com empresas de tradição na área de comunicação, demonstrou-se que há lacunas importantes a serem preenchidas. Entes elas, a inexistência de mecanismos que possam aferir com maior precisão as expectativas dos vários segmentos de público em relação à comunicação das empresas, em uma via de mão-dupla, bem como a falta de canais de comunicação regulares com determinados grupos, notadamente no âmbito externo. As análises apontam para a adoção de um sistema de gestão do conhecimento focado nos públicos como elemento fundamental para a eficácia dos processos comunicacionais.(AU)

Utilizing Evidence-Based Psychology Practice in the University of Denver's Professional Psychology Center: A Proposal

As acceptance of the Evidence-based Psychology Practice (EBPP) model continues to grow (Pagoto, Spring, Coups, Mulvaney, Coutu, & Ozakinci, 2007), it seems pertinent to explore how this model can be applied in different settings. This topic is timely as practitioners in the field are being held ever more accountable for the efficacy of the treatments they employ (Pagoto et al., 2007). Increased scrutiny has resulted in a need to integrate research into practice in order to ensure continued relevance in the ever-changing realm of American health care (Luebbe, Radcliffe, Callands, Green & Thorn, 2007; Collins, Leffingwell & Belar, 2007; Chwalisz, 2003). This paper explores how the requirements set forth by the American Psychological Association Presidential Task Force on Evidence-Based Practice (2006) can be implemented at the University of Denver's (DU) Professional Psychology Center (PPC), a training clinic for students enrolled in the Psy.D. program at DU's Graduate School of Professional Psychology (GSPP). In doing so, the methods employed by Collins et al. (2007) at Oklahoma State University (OSU) are used as a template and modified to accommodate differences between these two institutions.

Portfolio Entrepreneurship in Family Firms: A Resource-based Perspective

The phenomenon of portfolio entrepreneurship has attracted considerable scholarly attention and is particularly relevant in the family fi rm context. However, there is a lack of knowledge of the process through which portfolio entrepreneurship develops in family firms. We address this gap by analyzing four in-depth, longitudinal family firm case studies from Europe and Latin America. Using a resource-based perspective, we identify six distinct resource categories that are relevant to the portfolio entrepreneurship process. Furthermore, we reveal that their importance varies across time. Our resulting resource-based process model of portfolio entrepreneurship in family firms makes valuable contributions to both theory and practice.

Getting the most from a site visit : a guide to assist schools visiting a Leadership and Learning Site for Reading Best Practices /

Includes a list of the Reading Best Practice Sites in Illinois and a list of the possible teaching strategies that are appropriate with each of the fourteen Best Practices.

Oral Health Experiences of Social Workers Serving Children in the Foster Care System: Assessing the Feasibility of a Social Worker-Driven Oral Health Intervention

Thesis (Master's)--University of Washington, 2016-06

On minimal models of process systems

Minimal representations are known to have no redundant elements, and are therefore of great importance. Based on the notions of performance and size indices and measures for process systems, the paper proposes conditions for a process model being minimal in a set of functionally equivalent models with respect to a size norm. Generalized versions of known procedures to obtain minimal process models for a given modelling goal, model reduction based on sensitivity analysis and incremental model building are proposed and discussed. The notions and procedures are illustrated and compared on a simple example, that of a simple nonlinear fermentation process with different modelling goals and on a case study of a heat exchanger modelling. (C) 2004 Elsevier Ltd. All rights reserved.

Modelling of two-stage anaerobic digestion using the IWA Anaerobic Digestion Model No. 1 (ADM1)

The aim of the study presented was to implement a process model to simulate the dynamic behaviour of a pilot-scale process for anaerobic two-stage digestion of sewage sludge. The model implemented was initiated to support experimental investigations of the anaerobic two-stage digestion process. The model concept implemented in the simulation software package MATLAB(TM)/Simulink(R) is a derivative of the IWA Anaerobic Digestion Model No.1 (ADM1) that has been developed by the IWA task group for mathematical modelling of anaerobic processes. In the present study the original model concept has been adapted and applied to replicate a two-stage digestion process. Testing procedures, including balance checks and 'benchmarking' tests were carried out to verify the accuracy of the implementation. These combined measures ensured a faultless model implementation without numerical inconsistencies. Parameters for both, the thermophilic and the mesophilic process stage, have been estimated successfully using data from lab-scale experiments described in literature. Due to the high number of parameters in the structured model, it was necessary to develop a customised procedure that limited the range of parameters to be estimated. The accuracy of the optimised parameter sets has been assessed against experimental data from pilot-scale experiments. Under these conditions, the model predicted reasonably well the dynamic behaviour of a two-stage digestion process in pilot scale. (C) 2004 Elsevier Ltd. All rights reserved.

Development and evaluation of a youth mental health community awareness campaign - The Compass Strategy

Background: Early detection and treatment of mental disorders in adolescents and young adults can lead to better health outcomes. Mental health literacy is a key to early recognition and help seeking. Whilst a number of population health initiatives have attempted to improve mental health literacy, none to date have specifically targeted young people nor have they applied the rigorous standards of population health models now accepted as best practice in other health areas. This paper describes the outcomes from the application of a health promotion model to the development, implementation and evaluation of a community awareness campaign designed to improve mental health literacy and early help seeking amongst young people. Method: The Compass Strategy was implemented in the western metropolitan Melbourne and Barwon regions of Victoria, Australia. The Precede-Proceed Model guided the population assessment, campaign strategy development and evaluation. The campaign included the use of multimedia, a website, and an information telephone service. Multiple levels of evaluation were conducted. This included a cross-sectional telephone survey of mental health literacy undertaken before and after 14 months of the campaign using a quasi-experimental design. Randomly selected independent samples of 600 young people aged 12 - 25 years from the experimental region and another 600 from a comparison region were interviewed at each time point. A series of binary logistic regression analyses were used to measure the association between a range of campaign outcome variables and the predictor variables of region and time. Results: The program was judged to have an impact on the following variables, as indicated by significant region-by-time interaction effects ( p < 0.05): awareness of mental health campaigns, self-identified depression, help for depression sought in the previous year, correct estimate of prevalence of mental health problems, increased awareness of suicide risk, and a reduction in perceived barriers to help seeking. These effects may be underestimated because media distribution error resulted in a small amount of print material leaking into the comparison region. Conclusion: We believe this is the first study to apply the rigorous standards of a health promotion model including the use of a control region to a mental health population intervention. The program achieved many of its aims despite the relatively short duration and moderate intensity of the campaign.