801 resultados para omitted neighborhood attributes


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The term “business transformation” is a buzzword, often used to signify fundamental changes undergone by organisations. Despite numerous works in enterprise transformation, IT-enabled business transformation and organizational transformation, there appears to be a lack of consensus on what actually constitutes a business transformation as opposed to other types of redesign or organisational improvement projects. Consequently, knowledge about which elements of a business system that are impacted by such an endeavour is largely inconsistent, and partially conflicting. We present a business transformation typology that considers 18 attributes pertaining to the transforming organisation and the transformation initiative. To explore our typology, we analysed 10 published case studies and classified them along two dimensions – one ranging from marginal to fundamental changes, and another on internal and external visibility. Our literature review reveals how the terminology has been misused, and we provide some directions to provide more clarity around transformation phenomena in IS research.

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longitudinal study of data modelling across grades 1-3. The activity engaged children in designing, implementing, and analysing a survey about their new playground. Data modelling involves investigations of meaningful phenomena, deciding what is worthy of attention (identifying complex attributes), and then progressing to organising, structuring, visualising, and representing data. The core components of data modelling addressed here are children’s structuring and representing of data, with a focus on their display of metarepresentational competence (diSessa, 2004). Such competence includes students’ abilities to invent or design a variety of new representations, explain their creations, understand the role they play, and critique and compare the adequacy of representations. Reported here are the ways in which the children structured and represented their data, the metarepresentational competence displayed, and links between their metarepresentational competence and conceptual competence.

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In defining the contemporary role of the specialist nurse it is necessary to challenge the concept of nursing as merely a combination of skills and knowledge. Nursing must be demonstrated and defined in the context of client care and include the broader notions of professional development and competence. This qualitative study sought to identify the competency standards for nurse specialists in critical care and to articulate the differences between entry-to-practice standards and the advanced practice of specialist nurses. Over 800 hours of specialist critical care nursing practice were observed and grouped into 'domains' or major themes of specialist practice using a constant comparison qualitative technique. These domains were further refined to describe attributes of the registered nurses which resulted in effective and/or superior performance (competency standards) and to provide examples of performance (performance criteria) which met the defined standard. Constant comparison of the emerging domains, competency standards and performance criteria to observations of specialist critical care practice, ensured the results provided a true reflection of the specialist nursing role. Data analysis resulted in 20 competency standards grouped into six domains: professional practice, reflective practice, enabling, clinical problem solving, teamwork, and leadership. Each of these domains is comprised of between two and seven competency standards. Each standard is further divided into component parts or 'elements' and the elements are illustrated with performance criteria. The competency standards are currently being used in several Australian critical care educational programmes and are the foundation for an emerging critical care credentialling process. They have been viewed with interest by a variety of non-critical care specialty groups and may form a common precursor from which further specialist nursing practice assessment will evolve.

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Cameron, Verhoeven and Court have noted that many screen producers do not see their tertiary education as being beneficial to their careers. We hypothesise that Universities have traditionally not trained students in producing skills because of the division of labour between Faculties of Art and Faculties of Business; and because their focus on art rather than entertainment has downplayed the importance of producing. This article presents a SOTL (Scholarship of Teaching and Learning) whole-of-program evaluation of a new cross-Faculty Bachelor of Entertainment Industries at QUT, devoted to providing students with graduate attributes for producing including creative skills (understanding story, the aesthetics of entertainment, etc), business skills (business models, finance, marketing, etc) and legal skills (contracts, copyright, etc). Stakeholder evaluations suggest that entertainment producers are highly supportive of this new course.

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This paper presents a novel technique for segmenting an audio stream into homogeneous regions according to speaker identities, background noise, music, environmental and channel conditions. Audio segmentation is useful in audio diarization systems, which aim to annotate an input audio stream with information that attributes temporal regions of the audio into their specific sources. The segmentation method introduced in this paper is performed using the Generalized Likelihood Ratio (GLR), computed between two adjacent sliding windows over preprocessed speech. This approach is inspired by the popular segmentation method proposed by the pioneering work of Chen and Gopalakrishnan, using the Bayesian Information Criterion (BIC) with an expanding search window. This paper will aim to identify and address the shortcomings associated with such an approach. The result obtained by the proposed segmentation strategy is evaluated on the 2002 Rich Transcription (RT-02) Evaluation dataset, and a miss rate of 19.47% and a false alarm rate of 16.94% is achieved at the optimal threshold.

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This paper describes in detail our Security-Critical Program Analyser (SCPA). SCPA is used to assess the security of a given program based on its design or source code with regard to data flow-based metrics. Furthermore, it allows software developers to generate a UML-like class diagram of their program and annotate its confidential classes, methods and attributes. SCPA is also capable of producing Java source code for the generated design of a given program. This source code can then be compiled and the resulting Java bytecode program can be used by the tool to assess the program's overall security based on our security metrics.

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This paper begins by identifying the key attributes for future STEM teachers. Then based on a review of the literature, a framework for informing reforms to pre-service teacher education programs to facilitate the development of these attributes in future STEM teachers is presented and discussed. This framework consists of a set of three principles together with eight strategies for the operationalization of these principles. During the discussion, the implications for the structure and implementation of future pre-service STEM teacher education programs are explored.

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Large-scale international comparative studies and cross-ethnic studies have revealed that Chinese students, whether living in China or overseas, consistently outperform their counterparts in mathematics achievement. These studies tended to explain this result from psychological, educational, or cultural perspectives. However, there is scant sociological investigation addressing Chinese students’ better mathematics achievement. Drawing on Bourdieu’s sociological theory, this study conceptualises Chinese Australians’ “Chineseness” by the notion of ‘habitus’ and considers this “Chineseness” generating but not determinating mechanism that underpins Chinese Australians’ mathematics learning. Two hundred and thirty complete responses from Chinese Australian participants were collected by an online questionnaire. Simple regression model statistically significantly well predicted mathematics achievement by “Chineseness” (F = 141.90, R = .62, t = 11.91, p < .001). Taking account of “Chineseness” as a sociological mechanism for Chinese Australians’ mathematics learning, this study complements psychological and educational impacts on better mathematics achievement of Chinese students revealed by previous studies. This study also challenges the cultural superiority discourse that attributes better mathematics achievement of Chinese students to cultural factors.

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Readily accepted knowledge regarding crash causation is consistently omitted from efforts to model and subsequently understand motor vehicle crash occurrence and their contributing factors. For instance, distracted and impaired driving accounts for a significant proportion of crash occurrence, yet is rarely modeled explicitly. In addition, spatially allocated influences such as local law enforcement efforts, proximity to bars and schools, and roadside chronic distractions (advertising, pedestrians, etc.) play a role in contributing to crash occurrence and yet are routinely absent from crash models. By and large, these well-established omitted effects are simply assumed to contribute to model error, with predominant focus on modeling the engineering and operational effects of transportation facilities (e.g. AADT, number of lanes, speed limits, width of lanes, etc.) The typical analytical approach—with a variety of statistical enhancements—has been to model crashes that occur at system locations as negative binomial (NB) distributed events that arise from a singular, underlying crash generating process. These models and their statistical kin dominate the literature; however, it is argued in this paper that these models fail to capture the underlying complexity of motor vehicle crash causes, and thus thwart deeper insights regarding crash causation and prevention. This paper first describes hypothetical scenarios that collectively illustrate why current models mislead highway safety researchers and engineers. It is argued that current model shortcomings are significant, and will lead to poor decision-making. Exploiting our current state of knowledge of crash causation, crash counts are postulated to arise from three processes: observed network features, unobserved spatial effects, and ‘apparent’ random influences that reflect largely behavioral influences of drivers. It is argued; furthermore, that these three processes in theory can be modeled separately to gain deeper insight into crash causes, and that the model represents a more realistic depiction of reality than the state of practice NB regression. An admittedly imperfect empirical model that mixes three independent crash occurrence processes is shown to outperform the classical NB model. The questioning of current modeling assumptions and implications of the latent mixture model to current practice are the most important contributions of this paper, with an initial but rather vulnerable attempt to model the latent mixtures as a secondary contribution.

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In order to drive sustainable financial profitability, service firms make significant investments in creating service environments that consumers will prefer over the environments of their competitors. To date, servicescape research is over-focused on understanding consumers’ emotional and physiological responses to servicescape attributes, rather than taking a holistic view of how consumers cognitively interpret servicescapes. This thesis argues that consumers will cognitively ascribe symbolic meanings to servicescapes and then evaluate if those meanings are congruent with their sense of Self in order to form a preference for a servicescape. Consequently, this thesis takes a Self Theory approach to servicescape symbolism to address the following broad research question: How do ascribed symbolic meanings influence servicescape preference? Using a three-study, mixed-method approach, this thesis investigates the symbolic meanings consumers ascribe to servicescapes and empirically tests whether the joint effects of congruence between consumer Self and the symbolic meanings ascribed to servicescapes influence consumers’ servicescape preference. First, Study One identifies the symbolic meanings ascribed to salient servicescape attributes using a combination of repertory tests and laddering techniques within 19 semi-structured individual depth interviews. Study Two modifies an existing scale to create a symbolic servicescape meaning scale in order to measure the symbolic meanings ascribed to servicescapes. Finally, Study Three utilises the Self-Congruity Model to empirically examine the joint effects of consumer Self and servicescape on consumers’ preference for servicescapes. Using polynomial regression with response surface analysis, 14 joint effect models demonstrate that both Self-Servicescape incongruity and congruity influence consumers’ preference for servicescapes. Combined, the findings of three studies suggest that the symbolic meanings ascribed to servicescapes and their (in)congruities with consumers’ sense of self can be used to predict consumers’ preferences for servicescapes. These findings have several key theoretical and practical contributions to services marketing.

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Research relating to mentoring has burgeoned in the last two decades and as might be expected there is immense variation in the nature and rigour of this research. For some, mentoring seems to be a panacea for many societal problems as the studies have focussed on juvenile crime, teenage pregnancy, academic performance, drug usage, school dropout rates, teacher attributes, parental relationship, heightened self-confidence, general “at risk” children and issues of gender, ethnicity, socio economic status and equity. Rather than a panacea, it would be more accurate to suggest that in specific circumstances mentoring has the potential to be associated with beneficial outcomes.

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What psychological function does brand loyalty serve? Drawing on Katz’s (1960) Functional Theory of Attitudes, we propose that there are four functions (or motivational antecedents) of loyalty: utilitarian, knowledge, value-expressive and ego-defensive. We discuss how each function relates to the three dimensions of loyalty (i.e. emotional, cognitive, and behavioural loyalty). Then this conceptualisation of brand loyalty is explored using four consumer focus groups. These exploratory results demonstrate that the application of a functional approach to brand loyalty yields insights which have not been apparent in previous research. More specifically, this paper notes insights in relation to brand loyalty from a consumer’s perspective, including the notion that the ego-defensive function is an orientation around what others think and feel. This creates the possibilities for future research into brand loyalty via social network analysis, in order to better understand how the thoughts of others affect consumers’ loyalty attributes. --------------------------------------------------------------------------------

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Power system restoration after a large area outage involves many factors, and the procedure is usually very complicated. A decision-making support system could then be developed so as to find the optimal black-start strategy. In order to evaluate candidate black-start strategies, some indices, usually both qualitative and quantitative, are employed. However, it may not be possible to directly synthesize these indices, and different extents of interactions may exist among these indices. In the existing black-start decision-making methods, qualitative and quantitative indices cannot be well synthesized, and the interactions among different indices are not taken into account. The vague set, an extended version of the well-developed fuzzy set, could be employed to deal with decision-making problems with interacting attributes. Given this background, the vague set is first employed in this work to represent the indices for facilitating the comparisons among them. Then, a concept of the vague-valued fuzzy measure is presented, and on that basis a mathematical model for black-start decision-making developed. Compared with the existing methods, the proposed method could deal with the interactions among indices and more reasonably represent the fuzzy information. Finally, an actual power system is served for demonstrating the basic features of the developed model and method.

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With the advent of digital media and online information resources, public libraries as physical destinations for information access are being increasingly challenged. As a response, many libraries follow the trend of removing bookshelves in order to provide more floorspace for social interaction and collaboration. Such spaces follow a Commons 2.0 model: they are designed to support collaborative work and social learning. The acquisition of skills and knowledge is facilitated as a result of being surrounded by and interacting with a community of likeminded others. Based on the results of a case study on a Commons 2.0 library space, this paper describes several issues of collaboration and social learning in public library settings. Acknowledging the significance of the architectural characteristics of the physical space, we discuss opportunities for ambient media to better reflect the social attributes of the library as a place; i.e. amplify the sense of other co-present library visitors and provide opportunities for shared encounters and conversations, which would remain invisible otherwise. We present the design of a user check-in system for improving the library as a physical destination for social learning, sharing, and inspiration for and by the community.

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Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com.