929 resultados para Community Networks


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Rural support organisations have emerged since agricultural restructuring of the 1980s. The paper draws on research from the UK and Canada to suggest that the support in both countries is derived from a patrilineal culture that still dominates family farming. The paper begins by outlining a conceptual basis for arguing that such a culture can be understood as comprising of male and female relational gender identities capable of explaining farm relationships, farm survival and adjustment strategies and community engagement. These components, it is argued, are facets of the patrilineal farming culture which must be understood if its impacts on all its members are to be appropriately comprehended and supported.

The paper has two key aims, therefore. Firstly it suggests that a more nuanced understanding of farming „culture? which is persistently patrilineal in nature is now required which is capable of addressing the realities of farming individuals?
lives as they perceive them. The conceptualisation of such a culture is informed
by drawing on insights from gender theory, agricultural geography and rural studies. This conceptual discussion provides the context for the paper?s second aim which is to demonstrate how rural support in both the UK and Canada is derived „from? and is influenced by such a patrilineal culture . Findings are
presented identifying five key themes from this conceptualization which influence the support of such organisations. Thus, it is suggested, that the nature of rural support can be better understood and the appropriateness of the support interrogated when such conceptualization is taken on board.

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The Dementia Care Networks Study investigated 4 community-based, not-for-profit dementia care networks in Ontario, Canada. Investigators examined whether sociodemographic and health characteristics, type of support network, and amount of service use among care recipients and caregivers (n = 267 dyads) were associated with experiences with care processes. The process-of-care constructs were: family physicians' awareness of services; experiences with health care workers, and assessment and placement activities. The findings highlighted that family physicians' understanding of dementia and their ability to work with the dyad to become aware of and accept services, was an important component in the dyad's satisfaction. If caregivers received home support and the care recipients received emotional support from their social support network, they were more likely to be satisfied with their experiences with health care workers. In summary, increased awareness and provision of services were associated with more positive perceptions of network effectiveness. © 2007 Sage Publications.

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Community support services (CSSs) have been developed in Canada and other Western nations to enable persons coping with health or social issues to continue to live in the community. This study addresses the extent to which awareness of CSSs is structured by the social determinants of health. In a telephone interview conducted in February-March 2006, 1152 community-dwelling older adults (response rate 12.4%) from Hamilton, Ontario, Canada were made to read a series of four vignettes and were asked whether they were able to identify a CSS they may turn to in that situation. Across the four vignettes, 40% of participants did name a CSS as a possible source of assistance. Logistic regression was used to determine factors related to awareness of CSSs. Respondents most likely to have awareness of CSS include the middle-aged and higher-income groups. Being knowledgeable about where to look for information about CSSs, having social support and being a member of a club or voluntary organisations are also significant predictors of awareness of CSSs. Study results suggest that efforts be made to improve the level of awareness and access to CSSs among older adults by targeting their social networks as well as their health and social care providers. © 2011 Blackwell Publishing Ltd.

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The extent to which climate change might diminish the efficacy of protected areas is one of the most pressing conservation questions. Many projections suggest that climate-driven species distribution shifts will leave protected areas impoverished and species inadequately protected while other evidence suggests that intact ecosystems within protected areas will be resilient to change. Here, we tackle this problem empirically. We show how recent changes in distribution of 139 Tanzanian savannah bird species are linked to climate change, protected area status and land degradation. We provide the first evidence of climate-driven range shifts for an African bird community. Our results suggest that the continued maintenance of existing protected areas is an appropriate conservation response to the challenge of climate and environmental change.

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The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

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In distributed networks, it is often useful for the nodes to be aware of dense subgraphs, e.g., such a dense subgraph could reveal dense substructures in otherwise sparse graphs (e.g. the World Wide Web or social networks); these might reveal community clusters or dense regions for possibly maintaining good communication infrastructure. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs that the member nodes are aware of. The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that (2 + e)-approximate the densest subgraph and (3 + e)-approximate the at-least-k-densest subgraph (for a given parameter k). Our algorithms work for a wide range of parameter values and run in O(D log n) time. Further, a special case of our results also gives the first fully decentralized approximation algorithms for densest and at-least-k-densest subgraph problems for static distributed graphs. © 2012 Springer-Verlag.

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This paper examines approaches to the visualisation of ‘invisible’ communications networks. It situates network visualisation as a critical design exercise, and explores how community artists might use such a practice to develop telematic art projects – works that use communications networks as their medium. The paper’s hypotheses are grounded in the Australian community media arts field, but could be applied to other collaborative contexts.

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In many countries formal or informal palliative care networks (PCNs) have evolved to better integrate community-based services for individuals with a life-limiting illness. We conducted a cross-sectional survey using a customized tool to determine the perceptions of the processes of palliative care delivery reflective of horizontal integration from the perspective of nurses, physicians and allied health professionals working in a PCN, as well as to assess the utility of this tool. The process elements examined were part of a conceptual framework for evaluating integration of a system of care and centred on interprofessional collaboration. We used the Index of Interdisciplinary Collaboration (IIC) as a basis of measurement. The 86 respondents (85% response rate) placed high value on working collaboratively and most reported being part of an interprofessional team. The survey tool showed utility in identifying strengths and gaps in integration across the network and in detecting variability in some factors according to respondent agency affiliation and profession. Specifically, support for interprofessional communication and evaluative activities were viewed as insufficient. Impediments to these aspects of horizontal integration may be reflective of workload constraints, differences in agency operations or an absence of key structural features.


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The proposition of increased innovation in network applications and reduced cost for network operators has won over the networking world to the vision of Software-Defined Networking (SDN). With the excitement of holistic visibility across the network and the ability to program network devices, developers have rushed to present a range of new SDN-compliant hardware, software and services. However, amidst this frenzy of activity, one key element has only recently entered the debate: Network Security. In this article, security in SDN is surveyed presenting both the research community and industry advances in this area. The challenges to securing the network from the persistent attacker are discussed and the holistic approach to the security architecture that is required for SDN is described. Future research directions that will be key to providing network security in SDN are identified.

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Internet Tra c, Internet Applications, Internet Attacks, Tra c Pro ling, Multi-Scale Analysis abstract Nowadays, the Internet can be seen as an ever-changing platform where new and di erent types of services and applications are constantly emerging. In fact, many of the existing dominant applications, such as social networks, have appeared recently, being rapidly adopted by the user community. All these new applications required the implementation of novel communication protocols that present di erent network requirements, according to the service they deploy. All this diversity and novelty has lead to an increasing need of accurately pro ling Internet users, by mapping their tra c to the originating application, in order to improve many network management tasks such as resources optimization, network performance, service personalization and security. However, accurately mapping tra c to its originating application is a di cult task due to the inherent complexity of existing network protocols and to several restrictions that prevent the analysis of the contents of the generated tra c. In fact, many technologies, such as tra c encryption, are widely deployed to assure and protect the con dentiality and integrity of communications over the Internet. On the other hand, many legal constraints also forbid the analysis of the clients' tra c in order to protect their con dentiality and privacy. Consequently, novel tra c discrimination methodologies are necessary for an accurate tra c classi cation and user pro ling. This thesis proposes several identi cation methodologies for an accurate Internet tra c pro ling while coping with the di erent mentioned restrictions and with the existing encryption techniques. By analyzing the several frequency components present in the captured tra c and inferring the presence of the di erent network and user related events, the proposed approaches are able to create a pro le for each one of the analyzed Internet applications. The use of several probabilistic models will allow the accurate association of the analyzed tra c to the corresponding application. Several enhancements will also be proposed in order to allow the identi cation of hidden illicit patterns and the real-time classi cation of captured tra c. In addition, a new network management paradigm for wired and wireless networks will be proposed. The analysis of the layer 2 tra c metrics and the di erent frequency components that are present in the captured tra c allows an e cient user pro ling in terms of the used web-application. Finally, some usage scenarios for these methodologies will be presented and discussed.

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The work presented in this Ph.D thesis was developed in the context of complex network theory, from a statistical physics standpoint. We examine two distinct problems in this research field, taking a special interest in their respective critical properties. In both cases, the emergence of criticality is driven by a local optimization dynamics. Firstly, a recently introduced class of percolation problems that attracted a significant amount of attention from the scientific community, and was quickly followed up by an abundance of other works. Percolation transitions were believed to be continuous, until, recently, an 'explosive' percolation problem was reported to undergo a discontinuous transition, in [93]. The system's evolution is driven by a metropolis-like algorithm, apparently producing a discontinuous jump on the giant component's size at the percolation threshold. This finding was subsequently supported by number of other experimental studies [96, 97, 98, 99, 100, 101]. However, in [1] we have proved that the explosive percolation transition is actually continuous. The discontinuity which was observed in the evolution of the giant component's relative size is explained by the unusual smallness of the corresponding critical exponent, combined with the finiteness of the systems considered in experiments. Therefore, the size of the jump vanishes as the system's size goes to infinity. Additionally, we provide the complete theoretical description of the critical properties for a generalized version of the explosive percolation model [2], as well as a method [3] for a precise calculation of percolation's critical properties from numerical data (useful when exact results are not available). Secondly, we study a network flow optimization model, where the dynamics consists of consecutive mergings and splittings of currents flowing in the network. The current conservation constraint does not impose any particular criterion for the split of current among channels outgoing nodes, allowing us to introduce an asymmetrical rule, observed in several real systems. We solved analytically the dynamic equations describing this model in the high and low current regimes. The solutions found are compared with numerical results, for the two regimes, showing an excellent agreement. Surprisingly, in the low current regime, this model exhibits some features usually associated with continuous phase transitions.

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Community involvement in the fields of town planning and urban regeneration includes a wide range of opportunities for residents and service users to engage with networks, partnerships and centres of power. Both the terminology and degree of the transfer of power to citizens varies in different policy areas and contexts but five core objectives can be identified. This article approaches the subject of community empowerment by exploring the theoretical literature; reviewing recent policy pronouncements relating to community involvement in England and by discussing a recent case study of an Urban II project in London. The conclusions suggest that community empowerment is always likely to be partial and contingent on local circumstances and the wider context.

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In this paper we address the real-time capabilities of P-NET, which is a multi-master fieldbus standard based on a virtual token passing scheme. We show how P-NET’s medium access control (MAC) protocol is able to guarantee a bounded access time to message requests. We then propose a model for implementing fixed prioritybased dispatching mechanisms at each master’s application level. In this way, we diminish the impact of the first-come-first-served (FCFS) policy that P-NET uses at the data link layer. The proposed model rises several issues well known within the real-time systems community: message release jitter; pre-run-time schedulability analysis in non pre-emptive contexts; non-independence of tasks at the application level. We identify these issues in the proposed model and show how results available for priority-based task dispatching can be adapted to encompass priority-based message dispatching in P-NET networks.

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Fractional order modeling of biological systems has received significant interest in the research community. Since the fractal geometry is characterized by a recurrent structure, the self-similar branching arrangement of the airways makes the respiratory system an ideal candidate for the application of fractional calculus theory. To demonstrate the link between the recurrence of the respiratory tree and the appearance of a fractional-order model, we develop an anatomically consistent representation of the respiratory system. This model is capable of simulating the mechanical properties of the lungs and we compare the model output with in vivo measurements of the respiratory input impedance collected in 20 healthy subjects. This paper provides further proof of the underlying fractal geometry of the human lungs, and the consequent appearance of constant-phase behavior in the total respiratory impedance.

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The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.