Community arts are potentially valuable tools in building community and regenerating distressed neighbourhoods. Community-based art organizations exist in most major cities across North America and abroad. These groups are concerned with social and environmental community issues (e.g., youth poverty, sustainability, racism) and use art as a medium for social change through community empowerment and personal development. Many of these organizations operate on limited funding and are required to complete program evaluations to demonstrate the merit of their programs. While some program evaluation literature touches on the role of arts-based research methods, very little focuses specifically on using these methods with community-based art organizations—particularly organizations with programming intended for children and youth. This Major Degree Project seeks to address this gap and explore the role of creative, arts-based evaluation methods for community-based art organizations’ program evaluation.

Infrastructure services are of fundamental importance to a country's economic and social well-being; however, decisions about such investments are complex, involving multiple actors, high levels of uncertainty and creating a multi-decadal lock-in to the choices made. Methodologies to assist with such decisions ('appraisal' methodologies) have developed to consider many of these complexities, yet they remain sector specific, ignoring interdependencies between infrastructure networks. Such methodologies thereby ignore the opportunities or vulnerabilities derived from these inter-relationships, simply assuming that cross-sectoral services will be provided, and ignoring the constraints created for future development. Furthermore, the siloed methodologies make calculation of the total system effects impossible, undermining strategic plans and obscuring any need (or ability to) prioritise across sectors. The work herein aimed to develop and demonstrate a strategic approach, capable of providing a more complete valuation of infrastructure investments by taking the interdependencies between the networks into account. In so doing, it examined the hypothesis that use of such a methodology could help deliver more robust outcomes. The work is founded on development of a common, cross-sector appraisal methodology: fifteen common, monetised infrastructure performance metrics, developed by reviewing the strategic priorities of infrastructure and the existing sectoral cost benefit analyses. This was integrated with best practice from portfolio, pathway and real option approaches to create a longer term, system focused analysis of the decision space. Testing the framework through a case study (the Thames Hub Vision), chosen specifically for its high number of sectors, diversity of impacts and magnitude of its interdependencies, it examines the information gained by the increased methodological complexity. The results demonstrate that current appraisal methods are indeed incomplete, with interdependencies creating additional value and the opportunity for increased robustness. Indeed, ignored system effects are found to be sufficient to reverse the result of the analysis and future effects enabled by the investments are found to be up to an order of magnitude greater than the direct impacts recorded by current appraisals. Furthermore, the response and sensitivity to uncertainty is shown to be affected by consideration of the system effects, both directly, through their application to multiple assets and indirectly, through interaction with the wider investment landscape. The proposed approach captures these values and relationships, allowing more informed decisions to be made. In addition, a decision support tool is developed providing the means to identify which opportunities stakeholders wish to maintain, how these can be created and which variables must be tracked to ensure the opportunities remain valid. The work therefore promotes a more active, strategic approach to infrastructure investment, allowing translation between national targets, regional stakeholder values and sector-specific technical requirements, to create a more holistic plan for a country's infrastructure networks.

Submitted by Marcela Pádua Silva (marcelapaduas@hotmail.com) on 2018-04-25T17:47:40Z No. of bitstreams: 1 Dissertação de mestrado_Marcela Pádua Silva_UNESP_Repositório.pdf: 13412752 bytes, checksum: aee4115dd6a534fd9be005e039b894b2 (MD5) / Approved for entry into archive by Maria Marlene Zaniboni null (zaniboni@bauru.unesp.br) on 2018-04-25T19:41:13Z (GMT) No. of bitstreams: 1 silva_mp_me_bauru.pdf: 13412752 bytes, checksum: aee4115dd6a534fd9be005e039b894b2 (MD5) / Made available in DSpace on 2018-04-25T19:41:13Z (GMT). No. of bitstreams: 1 silva_mp_me_bauru.pdf: 13412752 bytes, checksum: aee4115dd6a534fd9be005e039b894b2 (MD5) Previous issue date: 2018-03-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O planejamento da infraestrutura verde urbana tem se tornado cada vez mais essencial para combater os efeitos adversos da urbanização, contribuir para a saúde física e mental dos seus habitantes e favorecer uma melhor qualidade vida urbana. Faz parte dessa infraestrutura a arborização de ruas e avenidas, conhecida como arborização viária, que em geral é feita sem planejamento e, por isso, gera os mais diversos conflitos com outros importantes sistemas urbanos. Um dos problemas recorrentes é a falta de normas que garantam à diversidade arbórea, o sombreamento adequado, a manutenção e incentivo a flora e fauna locais. Nesse contexto, essa pesquisa visa contribuir com a proposição de um método de avaliação da qualidade da arborização viária, a partir de parâmetros espaciais e ambientais. Esse método é validado em um estudo de caso em um bairro de Bauru, cidade do centro oeste paulista. Os resultados indicam que a área analisada possui uma arborização viária parcialmente adequada, cujos principais problemas são: a composição de espécies, que possui uma variedade bem menor que a recomendada, o que facilita na propagação de pragas e doenças; a altura média da primeira bifurcação abaixo da recomendada pelas normas de acessibilidade, o que é causa da grande porcentagem de árvores de pequeno porte; e a média para a área livre de pavimentação, que apresentou também bem abaixo do encontrado na literatura, o que prejudica o desenvolvimento do indivíduo arbóreo. O método proposto é efetivo na identificação de problemas e pode auxiliar na caracterização da área e na tomada de decisões para planejamentos futuros. / Urban green infrastructure planning has become increasingly essential in order to combat the adverse effects of urbanization, contribute to the physical and mental health of its inhabitants and favor a better quality urban life. The urban infrastructure has as its part the afforestation of streets and avenues, known as road forestation, which is usually done without planning and, therefore, generates the most diverse conflicts with other important urban systems. One of the recurring problems is a lack of standards to ensure tree diversity, adequate shading, maintenance and encouragement of local flora and fauna. In this context, this research aims to contribute to the proposition of a method to evaluate the quality of road arborization, based on spatial and environmental parameters. This method is validated in a case study in a neighborhood of Bauru, in the midwest of state of São Paulo. The results indicate that the area under study has a partially adequate afforestation. Three main problems have been identified for an area: a species composition, which has a much smaller variety than a recommendation, which facilitates the spread of pests and diseases; the average height of the first bifurcation was below that recommended by the standards of accessibility, which is the cause of the large percentage of small trees; and the average for the paving free area was also well below in the literature, which impairs the development of the arboreal individual. The method was effective in the identification of problems, which may help in characterizing the area and making decisions for future planning.

No description available.

Machine learning (ML) has become ubiquitous in various disciplines and applications, serving as a powerful tool for developing predictive models to analyze diverse variables of interest. With the advent of the digital era, the proliferation of data has presented numerous opportunities for growth and expansion across various domains. However, along with these opportunities, there is a unique set of challenges that arises due to the dynamic and ever-changing nature of data. These challenges include concept drift, which refers to shifting data distributions over time, and other data-related issues that can be framed as learning problems. Traditional static models are inadequate in handling these issues, underscoring the need for novel approaches to enhance the performance robustness and reliability of ML models to effectively navigate the inherent non-stationarity in the online world. The field of concept drift is characterized by several intricate aspects that challenge learning algorithms, including the analysis of model performance, which requires evaluating and understanding how the ML model's predictive capability is affected by different problem settings. Additionally, determining the magnitude of drift necessary for change detection is an indispensable task, as it involves identifying substantial shifts in data distributions. Moreover, the integration of adaptive methodologies is essential for updating ML models in response to data dynamics, enabling them to maintain their effectiveness and reliability in evolving environments. In light of the significance and complexity of the topic, this dissertation offers a fresh perspective on the performance robustness and adaptivity of ML models in non-stationary environments. The main contributions of this research include exploring and organizing the literature, analyzing the performance of ML models in the presence of different types of drift, and proposing innovative methodologies for drift detection and adaptation that solve real-world problems. By addressing these challenges, this research paves the way for the development of more robust and adaptive ML solutions capable of thriving in dynamic and evolving data landscapes. / Machine learning (ML) is widely used in various disciplines as a powerful tool for developing predictive models to analyze diverse variables. In the digital era, the abundance of data has created growth opportunities, but it also brings challenges due to the dynamic nature of data. One of these challenges is concept drift, the shifting data distributions over time. Consequently, traditional static models are inadequate for handling these challenges in the online world. Concept drift, with its intricate aspects, presents a challenge for learning algorithms. Analyzing model performance and detecting substantial shifts in data distributions are crucial for integrating adaptive methodologies to update ML models in response to data dynamics, maintaining effectiveness and reliability in evolving environments. In this dissertation, a fresh perspective is offered on the robustness and adaptivity of ML models in non-stationary environments. This research explores and organizes existing literature, analyzes ML model performance in the presence of drift, and proposes innovative methodologies for detecting and adapting to drift in real-world problems. The aim is to develop more robust and adaptive ML solutions capable of thriving in dynamic and evolving data landscapes.

This study emerged from a desire to put to an end the crisis mentality surrounding the status of History as a subject in the secondary school. There appears to be consensus amongst didactitions and practitioners of the subject that the present malaise from which History teaching suffers derives from a number of complex sources. The study, however, takes as its point of departure the problem of the content orientated (product) syllabus which over-emphasises the acquisition of factual information and neglects the conceptual understanding (process) of the subject. Experience is providing the futility of teaching only content (information) to the modern adolescent. Therefore in order to resolve the problem the study focuses, inter alia, on concepts, structures and syllabuses. It is concluded that all subjects are based on conceptual structures which, in turn, have a direct bearing on the authentic education of pupils in general and conceptualization in particular. It is suggested, therefore, that historical content (product) can only have formative value if it is harnessed to facilitate conceptualization (process). Towards this end a History syllabus which embraces both the product and process approaches is advocated for implementation. In essence this means that the content of History is organised around concepts, that is, relevant concepts are chosen as themes around which the syllabus content is structured. Such an approach, it is believed, would not only help to develop universally valid generalizations but also facilitate the conceptualization process necessary for obtaining historical insight. A model, with examples, is presented as a suggestion for implementation in the classroom. Altenative proposals are also mentioned. If historical conceptualization is to be effevively realised in the classroom, then it becomes necessary to obtain a perspective on the learning-psychological processes involved in conceptualization. In this regard, specific theoris are highlighted to guide the History teacher in the classroom. Ti is finally hoped that the new approach suggested would assist teachers, at least to some extent, to resolve the problem of conceptualization in History teaching and thereby help to store the subject to its original position of respect in the school curriculum / Curriculum and Instructional Studies / D.Ed. (Didactics)

Approved for public release, distribution is unlimited / Configuration Management (CM) plays a vital role in the development of trusted computing systems. The Common Criteria (CC) provides a framework for performing Information Technology (IT) security evaluations of these systems and further emphasizes CM's role in the development and evaluation process by specifying a minimum set of CM qualities for each Evaluated Assurance Level (EAL). As an evaluation guide, the Common Methodology for Information Technology Security Evaluation, Part 2: Evaluation Methodology (CEM), recommends a set of minimum CM guidelines which can be used by evaluators in the performance of a CM evaluation at the lower Evaluated Assurance Levels. Evaluators and developers will quickly note the CEM's lack of recommended CM guidelines at the higher assurance levels. Thorough study of the listed references supports the hypothesis for this work: Configuration Management guidelines are useful in the evaluation of trusted computing systems. As an assurance mechanism, complete CM guidance helps users of high assurance products obtain a degree of confidence the system security requirements operate as intended and do not contain clandestine code. Complete CM guidance provides evaluators with a "completed assurance scale" and ensures only authorized changes were made to the TOE during development. Useful CM guidelines at the higher assurance levels (EAL5, 6, and 7) will help developers and evaluators ensure products meet the minimum requirements needed for high assurance systems. / Lieutenant, United States Navy

The field of nonprofit management education is nascent and little of the research has extended into the area of leadership as a requisite competency for nonprofit leaders. Likewise, the research on leadership has not been widely extended to the nonprofit sector. Prior research suggests a broad range of competencies are necessary to lead in the dynamic, complex environment of nonprofits, the exercise of which differs from that in the for-profit sector. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

Made available in DSpace on 2016-03-22T17:26:55Z (GMT). No. of bitstreams: 1 glenio.pdf: 746737 bytes, checksum: db1de13a2c397a85d2c2e723089feb97 (MD5) Previous issue date: 2013-03-12 / This work aims to conduct an analysis of valuation methodology currently used for the Program System Open University of Brazil, the Brazilian Government policy for expansion of education in distance mode, given its complexity, presents difficulties in its implementation and execution. This necessitates the further reflection on their management and evaluation, especially in the face of increasing demands and demands that society has imposed the educational system. Through literature research and documentation on the subject, if sought to recover the theoretical references and political context in which the system is built and UAB, leaving these benchmarks, identify and understand the characteristics and limitations of the current evaluation system of this public policy, the to contribute to the construction of an evaluation model that is capable of dealing with the complexity and uniqueness of this program / O presente trabalho tem por objetivo realizar uma análise da metodologia de avaliação atualmente utilizada para o Programa Sistema Universidade Aberta do Brasil, política pública do Governo Brasileiro para expansão do ensino na modalidade a distância que, dada sua complexidade, apresenta dificuldades na sua implantação e execução. Isto torna necessário o aprofundamento da reflexão sobre sua gestão e avaliação, especialmente em face das crescentes demandas e cobranças que a sociedade vem impondo ao sistema educacional. Por meio de pesquisa bibliográfica e documental sobre o tema, se buscou recuperar as referências teóricas e o contexto político no qual se construiu o Sistema UAB e, partindo destes referenciais, compreender e identificar as características e limitações do atual sistema de avaliação desta política pública, de modo a contribuir para a construção de um modelo de avaliação que seja capaz de dar conta da complexidade e singularidade deste programa

<p>This thesis is devoted to understanding and counteracting the primary contributing factor in traffic crashes: inattention. Foremost, it demonstrates the fundamental importance of proactive gaze in the road centre area for action guidance in driving. Inattention is explained with regard to two visual functions (vision-for-action and vision-for-identification), three forms of attentional selection (action-driven-, stimulus-driven-, and goal-directed attention), and two forms of prediction influences (extrapolation-based- and decision-based prediction influences). In Study I an automated eye-movement analysis method was developed for a purpose-built eye-tracking sensor, and was successfully validated. This analysis method was further developed, and several new measures of gaze concentration to the road centre area were created. Study II demonstrated that a sharp decrease in the amount of road centre viewing time is accompanied by a dramatic spatial concentration towards the road centre area in returning gaze during visual tasks. During cognitive tasks, a spatial gaze concentration to road centre is also evident; however contrary to visual tasks, road centre viewing time is increased because the eyes are not directed towards an object within the vehicle. Study III found that gaze concentration measures are highly sensitive to driving task demands as well as to visual and auditory in-vehicle tasks. Gaze concentration to the road centre area was found as driving task complexity increased, as shown in differences between rural curved- and straight sections, between rural and motorway road types, and between simulator and field motorways. Further, when task duration was held constant and the in-vehicle visual task became more difficult, drivers looked less at the road centre area ahead, and looked at the display more often, for longer periods, and for more varied durations. In closing, it is shown how this knowledge can be applied to create in-vehicle attention support functions that counteract the effects of inattention.</p>