909 resultados para Knowledge Process
Resumo:
The subject of management is renowned for its addiction to fads and fashions. Project Management is no exception. The issue of interest for this paper is the establishment of the 'College of Complex Project Managers' and their 'competency standard for complex project managers.' Both have generated significant interest in the Project Management community, and like any other human endeavour they should be subject to critical evaluation. The results of this evaluation show significant flaws in the definition of complex in this case, the process by which the College and its standard have emerged, and the content of the standard. However, there is a significant case for a portfolio of research that extends the existing bodies of knowledge into large-scale complicated (or major) projects that would be owned by the relevant practitioner communities, rather than focused on one organization. Research questions are proposed that would commence this stream of activity towards an intelligent synthesis of what is required to manage in both complicated and truly complex environments.
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Over the last decade, ambitious claims have been made in the management literature about the contribution of emotional intelligence to success and performance. Writers in this genre have predicted that individuals with high emotional intelligence perform better in all aspects of management. This paper outlines the development of a new emotional intelligence measure, the Workgroup Emotional Intelligence Profile, Version 3 (WEIP-3), which was designed specifically to profile the emotional intelligence of individuals in work teams. We applied the scale in a study of the link between emotional intelligence and two measures of team performance: team process effectiveness and team goal focus. The results suggest that the average level of emotional intelligence of team members, as measured by the WEIP-3, is reflected in the initial performance of teams. In our study, low emotional intelligence teams initially performed at a lower level than the high emotional intelligence teams. Over time, however, teams with low average emotional intelligence raised their performance to match that of teams with high emotional intelligence.
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Quasi-birth-and-death (QBD) processes with infinite “phase spaces” can exhibit unusual and interesting behavior. One of the simplest examples of such a process is the two-node tandem Jackson network, with the “phase” giving the state of the first queue and the “level” giving the state of the second queue. In this paper, we undertake an extensive analysis of the properties of this QBD. In particular, we investigate the spectral properties of Neuts’s R-matrix and show that the decay rate of the stationary distribution of the “level” process is not always equal to the convergence norm of R. In fact, we show that we can obtain any decay rate from a certain range by controlling only the transition structure at level zero, which is independent of R. We also consider the sequence of tandem queues that is constructed by restricting the waiting room of the first queue to some finite capacity, and then allowing this capacity to increase to infinity. We show that the decay rates for the finite truncations converge to a value, which is not necessarily the decay rate in the infinite waiting room case. Finally, we show that the probability that the process hits level n before level 0 given that it starts in level 1 decays at a rate which is not necessarily the same as the decay rate for the stationary distribution.
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Multiple sclerosis and idiopathic dilated cardiomyopathy are two conditions in which an autoimmune process is implicated in the pathogenesis. There is evidence to support clustering of autoimmune diseases in patients with multiple sclerosis and their families. To our knowledge, this is the first report of idiopathic dilated cardiomyopathy occurring in a patient with multiple sclerosis.
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We consider a branching model, which we call the collision branching process (CBP), that accounts for the effect of collisions, or interactions, between particles or individuals. We establish that there is a unique CBP, and derive necessary and sufficient conditions for it to be nonexplosive. We review results on extinction probabilities, and obtain explicit expressions for the probability of explosion and the expected hitting times. The upwardly skip-free case is studied in some detail.
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Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).
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The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.
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Recent efforts in the characterization of air-water flows properties have included some clustering process analysis. A cluster of bubbles is defined as a group of two or more bubbles, with a distinct separation from other bubbles before and after the cluster. The present paper compares the results of clustering processes two hydraulic structures. That is, a large-size dropshaft and a hydraulic jump in a rectangular horizontal channel. The comparison highlighted some significant differences in clustering production and structures. Both dropshaft and hydraulic jump flows are complex turbulent shear flows, and some clustering index may provide some measure of the bubble-turbulence interactions and associated energy dissipation.
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Business process design is primarily driven by process improvement objectives. However, the role of control objectives stemming from regulations and standards is becoming increasingly important for businesses in light of recent events that led to some of the largest scandals in corporate history. As organizations strive to meet compliance agendas, there is an evident need to provide systematic approaches that assist in the understanding of the interplay between (often conflicting) business and control objectives during business process design. In this paper, our objective is twofold. We will firstly present a research agenda in the space of business process compliance, identifying major technical and organizational challenges. We then tackle a part of the overall problem space, which deals with the effective modeling of control objectives and subsequently their propagation onto business process models. Control objective modeling is proposed through a specialized modal logic based on normative systems theory, and the visualization of control objectives on business process models is achieved procedurally. The proposed approach is demonstrated in the context of a purchase-to-pay scenario.
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Historically, business process design has been driven by business objectives, specifically process improvement. However this cannot come at the price of control objectives which stem from various legislative, standard and business partnership sources. Ensuring the compliance to regulations and industrial standards is an increasingly important issue in the design of business processes. In this paper, we advocate that control objectives should be addressed at an early stage, i.e., design time, so as to minimize the problems of runtime compliance checking and consequent violations and penalties. To this aim, we propose supporting mechanisms for business process designers. This paper specifically presents a support method which allows the process designer to quantitatively measure the compliance degree of a given process model against a set of control objectives. This will allow process designers to comparatively assess the compliance degree of their design as well as be better informed on the cost of non-compliance.
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Knowledge is a product of human social systems and, therefore, the foundations of the knowledge-based economy are social and cultural. Communication is central to knowledge creation and diffusion, and Public Policy in Knowledge-Based Economies highlights specific social and cultural conditions that can enhance the communication, use and creation of knowledge in a society.The purpose of this book is to illustrate how these social and cultural conditions are identified and analysed through new conceptual frameworks. Such frameworks are necessary to penetrate the surface features of knowledge-based economies - science and technology - and disclose what drives such economies.This book will provide policymakers, analysts and academics with the fundamental tools needed for the development of policy in this little understood and emerging area.
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The nature of an experiment involving 204 residents is outlined and the results are reported and analysed. Two consecutive surveys of the respondents provide data about their stated knowledge of 23 wildlife species present in tropical Australia, most of which exclusively occur there. In addition, these surveys provide data about the willingness of respondents to pay for the conservation of those species belonging to three taxa; reptiles, mammals, and birds. Thus it is possible to compare the respondents’ stated knowledge of the species with their willingness to pay for their conservation, and to draw relevant inferences from this. From the initial survey and these associations, interesting relationships can be observed between those variables (knowledge and willingness to pay). The second survey was completed after the respondents’ knowledge of the species was experimentally increased and became more balanced. This is shown to result in increased dispersion (greater discrimination) in willingness to contribute to conservation of the different species in the set of wildlife species considered. Both theoretical and policy conclusions are drawn from the results.
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This case study concentrates on the extent of knowledge of members of the Australian public of Australia’s tropical bird species and their willingness to pay for their conservation. In order to place this issue in context, it first provides background information on the status of Australian bird species, focusing attention on species that occur in tropical Australia. Then, using survey results, this study considers the hypothesis that the public’s relative support for the conservation of different bird species depends on its comparative knowledge of their existence and status. Based on experimental results from a sample of residents of Brisbane, Queensland (Australia), it is found that their knowledge of bird species that occur exclusively in the Australian tropics (including tropical Queensland) is very poor compared to those that also occur in the Brisbane area and are relatively common. Experimental results indicate that when respondents in the sample had an option to allocate $1,000 between ten bird species listed in the survey, it resulted in a greater allocation of funds to the better known and more common species than when they were provided with balanced information about all the selected species. With balanced information the average allocation to bird species confined mostly to the Australian tropics, particularly those threatened or endangered, increased. The general consequences of this for policies for the conservation of birds are discussed.