955 resultados para Embedded computer systems
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Recently honeycomb meshes have been considered as alternative candidates for interconnection networks in parallel and distributed computer systems. This paper presents a solution to one of the open problems about honeycomb meshes—the so-called three disjoint path problem. The problem requires minimizing the length of the longest of any three disjoint paths between 3-degree nodes. This solution provides information on the re-routing of traffic along the network in the presence of faults.
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Monitoring multiple myeloma patients for relapse requires sensitive methods to measure minimal residual disease and to establish a more precise prognosis. The present study aimed to standardize a real-time quantitative polymerase chain reaction (PCR) test for the IgH gene with a JH consensus self-quenched fluorescence reverse primer and a VDJH or DJH allele-specific sense primer (self-quenched PCR). This method was compared with allele-specific real-time quantitative PCR test for the IgH gene using a TaqMan probe and a JH consensus primer (TaqMan PCR). We studied nine multiple myeloma patients from the Spanish group treated with the MM2000 therapeutic protocol. Self-quenched PCR demonstrated sensitivity of >or=10(-4) or 16 genomes in most cases, efficiency was 1.71 to 2.14, and intra-assay and interassay reproducibilities were 1.18 and 0.75%, respectively. Sensitivity, efficiency, and residual disease detection were similar with both PCR methods. TaqMan PCR failed in one case because of a mutation in the JH primer binding site, and self-quenched PCR worked well in this case. In conclusion, self-quenched PCR is a sensitive and reproducible method for quantifying residual disease in multiple myeloma patients; it yields similar results to TaqMan PCR and may be more effective than the latter when somatic mutations are present in the JH intronic primer binding site.
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Objective: To determine what, how, for whom, why, and in what circumstances educational interventions to improve the delivery of nutrition care by doctors and other healthcare professionals work?
Design: Realist synthesis following a published protocol and reported following Realist and Meta-narrative Evidence Synthesis: Evolving Standards (RAMESES) guidelines. A multidisciplinary team searched Medline, CINAHL, ERIC, EMBASE, PsyINFO, Sociological Abstracts, Web of Science, Google Scholar, and Science Direct for published and unpublished (grey) literature. The team identified studies with varied designs; appraised their ability to answer the review question; identified relationships between contexts, mechanisms, and outcomes (CMOs); and entered them into a spreadsheet configured for the purpose. The final synthesis identified commonalities across CMO configurations.
Results: Over half of the 46 studies from which we extracted data originated from the US. Interventions that improved the delivery of nutrition care improved skills and attitudes rather than just knowledge; provided opportunities for superiors to model nutrition care; removed barriers to nutrition care in health systems; provided participants with local, practically relevant tools and messages; and incorporated non-traditional, innovative teaching strategies. Operating in contexts where student and qualified healthcare professionals provided nutrition care in both developed and developing countries, these interventions yielded health outcomes by triggering a range of mechanisms, which included: feeling competent; feeling confident and comfortable; having greater self-efficacy; being less inhibited by barriers in healthcare systems; and feeling that nutrition care was accepted and recognised.
Conclusion: These findings show how important it is to move education for nutrition care beyond the simple acquisition of knowledge. They show how educational interventions embedded within systems of healthcare can improve patients’ health by helping health students and professionals to appreciate the importance of delivering nutrition care and feel competent to deliver it.
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Collaboration in the public sector is imperative to achieve e-government objectives such as improved efficiency and effectiveness of public administration and improved quality of public services. Collaboration across organizational and institutional boundaries requires public organizations to share e-government systems and services through for instance, interoperable information technology and processes. Demands on public organizations to become more open also require that public organizations adopt new collaborative approaches for inviting and engaging citizens in governmental activities. E-government related collaboration in the public sector is challenging, however, and collaboration initiatives often fail. Public organizations need to learn how to collaborate since forms of e-government collaboration and expected outcomes are mostly unknown. How public organizations can collaborate and the expected outcomes are thus investigated in this thesis by studying multiple collaboration cases on the acquisition and implementation of a particular e-government investment (digital archive). This thesis also investigates how e-government collaboration can be facilitated through artifacts. It is done through a case study, where objects that cross boundaries between collaborating communities in the public sector are studied, and by designing a configurable process model integrating several processes for social services. By using design science, this thesis also investigates how an m-government solution that facilitates collaboration between citizens and public organizations can be designed. The thesis contributes to literature through describing five different modes of interorganizational collaboration in the public sector and the expected benefits from each mode. It also contributes with an instantiation of a configurable process model supporting three open social e-services and with evidence of how it can facilitate collaboration. This thesis further describes how boundary objects facilitate collaboration between different communities in an open government design initiative. It contributes with a designed mobile government solution, thereby providing proof of concept and initial design implications for enabling collaboration with citizens through citizen sourcing (outsourcing a governmental activity to citizens through an open call). This thesis also identifies research streams within e-government collaboration research through a literature review and the thesis contributions are related to the identified research streams. This thesis gives directions for future research by suggesting that future research should focus further on understanding e-government collaboration and how information and communication technology can facilitate collaboration in the public sector. It is suggested that further research should investigate m-government solutions to form design theories. Future research should also examine how value can be co-created in e-government collaboration.
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This thesis explores aesthetization in general and fashion in particular in digital technology design and how we can design digital technology to account for the extended influences of fashion. The thesis applies a combination of methods to explore the new design space at the intersection of fashion and technology. First, it contributes to theoretical understandings of aesthetization and fashion institutionalization that influence digital technology design. We show that there is an unstable aesthetization in mobile design and the increased aesthetization is closely related to the fashion industry. Fashion emerged through shared institutional activities, which are usually in the form of action nets in the design of digital devices. “Tech Fashion” is proposed to interpret such dynamic action nets of institutional arrangements that make digital technology fashionable and desirable. Second, through associative design research, we have designed and developed two prototypes that account for institutionalized fashion values, such as the concept “outfit-centric accessory.” We call for a more extensive collaboration between fashion design and interaction design.
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Réalisé en cotutelle avec l'École normale supérieure de Cachan – Université Paris-Saclay
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This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.
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Abstract not available
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Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee -- However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: (i) importance of mentors role, (ii) teamwork skills and (iii) remote training support -- For these reasons, a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training of users adopting different medical roles during the training sesión -- Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in the training session -- To provide successful training involving good collaborative performance, CNVSS should handle heterogeneity factors such as users’ machine capabilities and network conditions, among others -- Several systems for collaborative training of surgical procedures have been developed as research projects -- To the best of our knowledge none has focused on handling heterogeneity in CNVSS -- Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors -- Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative sesión -- In this document, the development of a context-aware architecture for collaborative networked virtual surgical simulators, in order to handle the heterogeneity involved in the collaboration session, is proposed -- To achieve this, the following main contributions are accomplished in this thesis: (i) Which and how infrastructure heterogeneity factors affect the collaboration of two users performing a virtual surgical procedure were determined and analyzed through a set of experiments involving users collaborating, (ii) a context-aware software architecture for a CNVSS was proposed and implemented -- The architecture handles heterogeneity factors affecting collaboration, applying various adaptation mechanisms and finally, (iii) A mechanism for handling heterogeneity factors involved in a CNVSS is described, implemented and validated in a set of testing scenarios
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Abstract. The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.
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The growing demand for large-scale virtualization environments, such as the ones used in cloud computing, has led to a need for efficient management of computing resources. RAM memory is the one of the most required resources in these environments, and is usually the main factor limiting the number of virtual machines that can run on the physical host. Recently, hypervisors have brought mechanisms for transparent memory sharing between virtual machines in order to reduce the total demand for system memory. These mechanisms “merge” similar pages detected in multiple virtual machines into the same physical memory, using a copy-on-write mechanism in a manner that is transparent to the guest systems. The objective of this study is to present an overview of these mechanisms and also evaluate their performance and effectiveness. The results of two popular hypervisors (VMware and KVM) using different guest operating systems (Linux and Windows) and different workloads (synthetic and real) are presented herein. The results show significant performance differences between hypervisors according to the guest system workloads and execution time.
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Starting in December 1982 the University of Nottingham decided to phototypeset almost all of its examination papers `in house' using the troff, tbl and eqn programs running under UNIX. This tutorial lecture highlights the features of the three programs with particular reference to their strengths and weaknesses in a production environment. The following issues are particularly addressed: Standards -- all three software packages require the embedding of commands and the invocation of pre-written macros, rather than `what you see is what you get'. This can help to enforce standards, in the absence of traditional compositor skills. Hardware and Software -- the requirements are analysed for an inexpensive preview facility and a low-level interface to the phototypesetter. Mathematical and Technical papers -- the fine-tuning of eqn to impose a standard house style. Staff skills and training -- systems of this kind do not require the operators to have had previous experience of phototypesetting. Of much greater importance is willingness and flexibility in learning how to use computer systems.
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The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we review the algorithms used, the development of the systems and the outcome of their implementation. We provide an introduction and analysis of the key developments within this field, in addition to making suggestions for future research.
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The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.