1,381,522 research outputs found

    System Evolution Barriers and How to Overcome Them!

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    Creating complex systems from scratch is time consuming and costly, therefore a strategy often chosen by companies is to evolve existing systems. Yet evolving a system is also complicated. Complex systems are usually the result of multidisciplinary teams, therefore it is essential to understand barriers those teams face when evolving a system.\ud From the research carried at Philips Healthcare MRI, we have identified that main evolution barriers employees face are; managing system complexity,communication across disciplines and departments, finding the necessary system information, lack of system overview, and ineffective knowledge sharing. Those barriers were identified as the root cause of many development problems and bad decisions.\ud To overcome those barriers, and therefore enhance the evolution process, effective reuse of knowledge is essential. This knowledge must be presented in a fashion that can be understood by a broad set of stakeholders. In this paper system evolution barriers and a method to effectively deal with them, based on the creation of A3 Architecture Overviews, is presented

    Collaborative Environments. Considerations Concerning Some Collaborative Systems

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    It is obvious, that all collaborative environments (workgroups, communities of practice, collaborative enterprises) are based on knowledge and between collaboration and knowledge management there is a strong interdependence. The evolution of information systems in these collaborative environments led to the sudden necessity to adopt, for maintaining the virtual activities and processes, the latest technologies/systems, which are capable to support integrated collaboration in business services. In these environments, portal-based IT platforms will integrate multi-agent collaborative systems, collaborative tools, different enterprise applications and other useful information systems.collaboration, collaborative environments, knowledge management, collaborative systems, portals, knowledge portals, agile development of portals

    Considerations for a design and operations knowledge support system for Space Station Freedom

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    Engineering and operations of modern engineered systems depend critically upon detailed design and operations knowledge that is accurate and authoritative. A design and operations knowledge support system (DOKSS) is a modern computer-based information system providing knowledge about the creation, evolution, and growth of an engineered system. The purpose of a DOKSS is to provide convenient and effective access to this multifaceted information. The complexity of Space Station Freedom's (SSF's) systems, elements, interfaces, and organizations makes convenient access to design knowledge especially important, when compared to simpler systems. The life cycle length, being 30 or more years, adds a new dimension to space operations, maintenance, and evolution. Provided here is a review and discussion of design knowledge support systems to be delivered and operated as a critical part of the engineered system. A concept of a DOKSS for Space Station Freedom (SSF) is presented. This is followed by a detailed discussion of a DOKSS for the Lyndon B. Johnson Space Center and Work Package-2 portions of SSF

    Knowledge-based vision and simple visual machines

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    The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong

    Knowledge-based Economic Development as a Unifying Vision in a Post-awakening Arab World

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    This article traces the evolution of knowledge-based economic development in the Arab World. In pursuing this objective, many countries in the region have made large state-driven human capital investments with the goals of job creation, economic integration, economic diversification, environmental sustainability, and social development. An assessment of the effectiveness of Arab investments in human capital shows marginal progress towards knowledge-based development over the last decade. A disconnect between the skills developed in Arab skills formation systems and those required by private sector employers relegates Arab businesses to contesting lower-skilled, non-knowledge intensive industries which has stalled knowledge-based development in the region.Arab World; Middle East; skills formation; knowledge economy; competitiveness; skills development policy; economic development

    The Emergence of Scaling in Sequence-based Physical Models of Protein Evolution

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    It has recently been discovered that many biological systems, when represented as graphs, exhibit a scale-free topology. One such system is the set of structural relationships among protein domains. The scale-free nature of this and other systems has previously been explained using network growth models that, while motivated by biological processes, do not explicitly consider the underlying physics or biology. In the present work we explore a sequence-based model for the evolution protein structures and demonstrate that this model is able to recapitulate the scale-free nature observed in graphs of real protein structures. We find that this model also reproduces other statistical feature of the protein domain graph. This represents, to our knowledge, the first such microscopic, physics-based evolutionary model for a scale-free network of biological importance and as such has strong implications for our understanding of the evolution of protein structures and of other biological networks.Comment: 20 pages (including figures), 4 figures, to be submitted to PNA
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