10 resultados para Network Structures

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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By switching the level of analysis and aggregating data from the micro-level of individual cases to the macro-level, quantitative data can be analysed within a more case-based approach. This paper presents such an approach in two steps: In a first step, it discusses the combination of Social Network Analysis (SNA) and Qualitative Comparative Analysis (QCA) in a sequential mixed-methods research design. In such a design, quantitative social network data on individual cases and their relations at the micro-level are used to describe the structure of the network that these cases constitute at the macro-level. Different network structures can then be compared by QCA. This strategy allows adding an element of potential causal explanation to SNA, while SNA-indicators allow for a systematic description of the cases to be compared by QCA. Because mixing methods can be a promising, but also a risky endeavour, the methodological part also discusses the possibility that underlying assumptions of both methods could clash. In a second step, the research design presented beforehand is applied to an empirical study of policy network structures in Swiss politics. Through a comparison of 11 policy networks, causal paths that lead to a conflictual or consensual policy network structure are identified and discussed. The analysis reveals that different theoretical factors matter and that multiple conjunctural causation is at work. Based on both the methodological discussion and the empirical application, it appears that a combination of SNA and QCA can represent a helpful methodological design for social science research and a possibility of using quantitative data with a more case-based approach.

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Porcine reproductive and respiratory syndrome virus (PRRSV) is wide-spread in pig populations globally. In many regions of Europe with intensive pig production and high herd densities, the virus is endemic and can cause disease and production losses. This fuels discussion about the feasibility and sustainability of virus elimination from larger geographic regions. The implementation of a program aiming at virus elimination for areas with high pig density is unprecedented and its potential success is unknown. The objective of this work was to approach pig population data with a simple method that could support assessing the feasibility of a sustainable regional PRRSV elimination. Based on known risk factors such as pig herd structure and neighborhood conditions, an index characterizing individual herds' potential for endemic virus circulation and reinfection was designed. This index was subsequently used to compare data of all pig herds in two regions with different pig- and herd-densities in Lower Saxony (North-West Germany) where PRRSV is endemic. Distribution of the indexed herds was displayed using GIS. Clusters of high herd index densities forming potential risk hot spots were identified which could represent key target areas for surveillance and biosecurity measures under a control program aimed at virus elimination. In an additional step, for the study region with the higher pig density (2463 pigs/km(2) farmland), the potential distribution of PRRSV-free and non-free herds during the implementation of a national control program aiming at national virus elimination was modeled. Complex herd and trade network structures suggest that PRRSV elimination in regions with intensive pig farming like that of middle Europe would have to involve legal regulation and be accompanied by important trade and animal movement restrictions. The proposed methodology of risk index mapping could be adapted to areas varying in size, herd structure and density. Interpreted in the regional context, this could help to classify the density of risk and to accordingly target resources and measures for elimination.

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The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.

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The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.

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PURPOSE: There is a need for valid and reliable short scales that can be used to assess social networks and social supports and to screen for social isolation in older persons. DESIGN AND METHODS: The present study is a cross-national and cross-cultural evaluation of the performance of an abbreviated version of the Lubben Social Network Scale (LSNS-6), which was used to screen for social isolation among community-dwelling older adult populations in three European countries. Based on the concept of lack of redundancy of social ties we defined clinical cut-points of the LSNS-6 for identifying persons deemed at risk for social isolation. RESULTS: Among all three samples, the LSNS-6 and two subscales (Family and Friends) demonstrated high levels of internal consistency, stable factor structures, and high correlations with criterion variables. The proposed clinical cut-points showed good convergent validity, and classified 20% of the respondents in Hamburg, 11% of those in Solothurn (Switzerland), and 15% of those in London as at risk for social isolation. IMPLICATIONS: We conclude that abbreviated scales such as the LSNS-6 should be considered for inclusion in practice protocols of gerontological practitioners. Screening older persons based on the LSNS-6 provides quantitative information on their family and friendship ties, and identifies persons at increased risk for social isolation who might benefit from in-depth assessment and targeted interventions.

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Three new coordination polymers [M(Pht)(1-MeIm)2]n (where M=Cu (1), Zn (2), Co (3); Pht2−=dianion of o-phthalic acid; 1-MeIm=1-methylimidazole) and two compounds [M(1-MeIm)6](HPht)2 · 2H2O (M=Co (4), Ni (5)) have been synthesized and characterized by X-ray crystallography. The structures of 1–3 (2 is isostructural to 3) consist of [M(1-MeIm)2] building units connected by 1,6-bridging phthalate ions to form infinite chains. In complex 1, each copper(II) center adopts a square coordination mode of N2O2 type by two O atoms from different phthalate ions and two N atoms of 1-MeIm, whereas in 3 two independent metal atoms are tetrahedrally (N2O2) coordinated to a pair of Pht ligands and a pair of 1-MeIm molecules. There are only van der Waals interactions between the chains in 1, while the three-dimensional network in 3 is assembled by C–H⋯O contacts. In contrast to polymers 1–3 the structures of 4 and 5 (complexes are also isostructural) are made up of the [M(1-MeIm)6]2+ cation, two hydrogen phthalate anions (HPht−) and two H2O solvate molecules. The coordination around each metal(II) atom is octahedral with six nitrogen atoms of 1-MeIm. Extended hydrogen bonding networks embracing the solvate water molecules and a phthalate residue as well as the weak C–H⋯O interactions stabilize the three-dimensional structures. Magnetic studies clearly show that the magnetic ions do not interact with each other. Furthermore, in compound 4 we have another example of a highly anisotropic Co2+ ion with a rhombic g-tensor and large zero-field-splitting. The complexes were also characterized by IR and 1H NMR spectroscopy, thermogravimetric analysis, and all data are discussed in the terms of known structures.

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Structural characteristics of social networks have been recognized as important factors of effective natural resource governance. However, network analyses of natural resource governance most often remain static, even though governance is an inherently dynamic process. In this article, we investigate the evolution of a social network of organizational actors involved in the governance of natural resources in a regional nature park project in Switzerland. We ask how the maturation of a governance network affects bonding social capital and centralization in the network. Applying separable temporal exponential random graph modeling (STERGM), we test two hypotheses based on the risk hypothesis by Berardo and Scholz (2010) in a longitudinal setting. Results show that network dynamics clearly follow the expected trend toward generating bonding social capital but do not imply a shift toward less hierarchical and more decentralized structures over time. We investigate how these structural processes may contribute to network effectiveness over time.

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Guidelines for a gender-fair use of the languages represented in the ITN LCG network were analyzed comparatively for specific criteria. All institutional or governmental guidelines aim at attenuating male-biased representations that are brought about by certain grammatical structures of the respective language. These guidelines primarily focus on the use of masculine forms as generics because they reduce the visibility of women in language. The comparison shows that guidelines for English, a language without grammatical gender, emphasize neutralization as a means of referring to both sexes. This differs from grammatical gender languages, such as German and Italian, in which feminine-masculine word-pairs are recommended in order to avoid the masculine bias. The guidelines all aim to promote the formulation of comprehensive and readable texts that are free of discrimination.