734 resultados para Hybrid professionalism


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Two semiconducting hybrid gallium selenides, [Ga6Se9(C6H14N2)4][H2O] (1) and [C6H14N2][Ga4Se6(C6H14N2)2] (2), were prepared using a solvothermal method in the pres-ence of 1,2-diaminocyclohexane (1,2-DACH). Both materials consist of neutral inorganic layers, in which 1,2-DACH is co-valently bonded to gallium. In (1), the organic amine acts as a monodentate and a bidentate ligand, while in (2) bidentate and uncoordinated 1,2-DACH molecules coexist.

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We have succeeded in the preparation of electrospun fibers of polystyrene incorporating a metallo-organic polymer of [Fe (II) (4-octadecyl-1,2,4-triazole)3(ClO4)2]n. The obtained fibers have diameters in the range 2–4 µm and show the characteristic spin-crossover transition associated with the metallo-organic polymer. The structure of both, polystyrene and the metallo-organic polymer, in the fibers was also studied.

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Increasing attention has been paid in both public administration and organizational theory to understanding how physicians assume a ‘hybrid’ role as they take onmanagerial responsibilities. Limited theoretical attention has been devoted to the processes involved in negotiating, developing, and maintaining such a role.We draw on identity theory, using a qualitative, five-year longitudinal case study, to explore how hybrid physician–managers in the English National Health Service and the organizations they are situated in achieve this. We highlight the importance of saliency – how central an identity is to an individual’s values and beliefs – in managing new identities.We found three differing responses to taking on a hybrid physician–manager role, with identity emerging as a mitigating factor for negotiating potentially conflicting roles. We discuss the implications for existing theory and practice in the management of public organizations and identify an agenda for further research.

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We systematically compare the performance of ETKF-4DVAR, 4DVAR-BEN and 4DENVAR with respect to two traditional methods (4DVAR and ETKF) and an ensemble transform Kalman smoother (ETKS) on the Lorenz 1963 model. We specifically investigated this performance with increasing nonlinearity and using a quasi-static variational assimilation algorithm as a comparison. Using the analysis root mean square error (RMSE) as a metric, these methods have been compared considering (1) assimilation window length and observation interval size and (2) ensemble size to investigate the influence of hybrid background error covariance matrices and nonlinearity on the performance of the methods. For short assimilation windows with close to linear dynamics, it has been shown that all hybrid methods show an improvement in RMSE compared to the traditional methods. For long assimilation window lengths in which nonlinear dynamics are substantial, the variational framework can have diffculties fnding the global minimum of the cost function, so we explore a quasi-static variational assimilation (QSVA) framework. Of the hybrid methods, it is seen that under certain parameters, hybrid methods which do not use a climatological background error covariance do not need QSVA to perform accurately. Generally, results show that the ETKS and hybrid methods that do not use a climatological background error covariance matrix with QSVA outperform all other methods due to the full flow dependency of the background error covariance matrix which also allows for the most nonlinearity.

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Background: P300 and steady-state visual evoked potential(SSVEP) approaches have been widely used for brain–computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored. New method: The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength. Result: The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm. Comparison with existing method: A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm.Allthe paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification. Conclusions: The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.

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There is growing recognition that hybrid organizations can play a critical role in tackling intractable global sustainable development challenges. At the same time, acute social, environmental, and economic challenges are opening up “opportunity” spaces for hybrids. Different institutional contexts are also leading to variable hybrid forms linked to the focus of their mission and their profit-oriented status. This article presents a process for identifying, mapping, and building impact indicators based on a study of 20 hybrid organizations in Sub-Saharan Africa.

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Tetrahedral chalcogenide clusters, with their well-defined molecular structures and interesting properties, are attractive building blocks for hybrid materials, in which porosity may be integrated with electronic or optical properties. Linkage of tetrahedral clusters often occurs through bridging chalcogenolate ligands, and results in extended structures of inorganic connectivity. However, linkage of tetrahedral clusters via organic ligands is also possible and a number of coordination polymers have already been reported. Recent advances on the synthesis and crystal structures of extended hybrid structures based on tetrahedral clusters are described here.

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Experimental evidence suggests that nitric oxide (NO) and hydrogen sulfide (H2S) signaling pathways are intimately intertwined, with mutual attenuation or potentiation of biological responses in the cardiovascular system and elsewhere. The chemical basis of this interaction is elusive. Moreover, polysulfides recently emerged as potential mediators of H2S/sulfide signaling, but their biosynthesis and relationship to NO remain enigmatic. We sought to characterize the nature, chemical biology, and bioactivity of key reaction products formed in the NO/sulfide system. At physiological pH, we find that NO and sulfide form a network of cascading chemical reactions that generate radical intermediates as well as anionic and uncharged solutes, with accumulation of three major products: nitrosopersulfide (SSNO−), polysulfides, and dinitrososulfite N-nitrosohydroxylamine-N-sulfonate (SULFI/NO), each with a distinct chemical biology and in vitro and in vivo bioactivity. SSNO− is resistant to thiols and cyanolysis, efficiently donates both sulfane sulfur and NO, and potently lowers blood pressure. Polysulfides are both intermediates and products of SSNO− synthesis/decomposition, and they also decrease blood pressure and enhance arterial compliance. SULFI/NO is a weak combined NO/nitroxyl donor that releases mainly N2O on decomposition; although it affects blood pressure only mildly, it markedly increases cardiac contractility, and formation of its precursor sulfite likely contributes to NO scavenging. Our results unveil an unexpectedly rich network of coupled chemical reactions between NO and H2S/sulfide, suggesting that the bioactivity of either transmitter is governed by concomitant formation of polysulfides and anionic S/N-hybrid species. This conceptual framework would seem to offer ample opportunities for the modulation of fundamental biological processes governed by redox switching and sulfur trafficking.

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The crystal structures of gallium sulfides prepared under solvothermal conditions, using 4-picoline as a solvent, are described. These materials contain [Ga10S16(NC6H7)4]2− clusters, in which the terminal S2− anions have been replaced by covalently bonded 4-picoline molecules. Whilst these phases contain isolated supertetrahedral clusters separated by organic moieties, linkage of such clusters via organic ligands is possible under suitable reaction conditions. These organically-functionalised supertetrahedra could therefore be used to design novel Metal-Organic frameworks (MOFs) in which the normally-encountered metal centers are replaced by supertetrahedral clusters.

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The synthesis and characterisation of a hybrid supertetrahedral nanocluster, [Ga10S16(NC7H9)4]2−, in which the terminal S2− anions have been replaced by covalently bonded amine molecules, is described.

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This study aimed at evaluating the effect of increasing organic loading rates and of enzyme pretreatment on the stability and efficiency of a hybrid upflow anaerobic sludge blanket reactor (UASBh) treating dairy effluent. The UASBh was submitted to the following average organic loading rates (OLR) 0.98 Kg.m(-3).d(-1), 4.58 Kg.m(-3).d(-1), 8.89 Kg.m(-3).d(-1) and 15.73 Kg.m(-3).d(-1), and with the higher value, the reactor was fed with effluent with and without an enzymatic pretreatment to hydrolyze fats. The hydraulic detention time was 24 h, and the temperature was 30 +/- 2 degrees C. The reactor was equipped with a superior foam bed and showed good efficiency and stability until an OLR of 8.89 Kg.m(-3).d(-1). The foam bed was efficient for solid retention and residual volatile acid concentration consumption. The enzymatic pretreatment did not contribute to the process stability, propitiating loss in both biomass and system efficiency. Specific methanogenic activity tests indicated the presence of inhibition after the sludge had been submitted to the pretreated effluent It was concluded that continuous exposure to the hydrolysis products or to the enzyme caused a dramatic drop in the efficiency and stability of the process, and the single exposure of the biomass to this condition did not inhibit methane formation. (C) 2011 Elsevier B.V. All rights reserved.

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The Alagoas Curassow Mitu mitu is considered extinct in the wild. Since 1979, two females and a male caught in the wild have bred successfully in captivity, and, in 1990, hybridizations between M. mitu and Razor-billed Mitu M. tuberosum were performed. By June 2008, there were around 130 living birds in two different aviaries. We sequenced two regions of the mitochondrial DNA of both captive stocks of Alagoas Curassows. We unequivocally identified hybrids that have haplotype typical of M. tuberosum. However, unless the original studbook can be recovered there is no confident way to discriminate ""pure"" M. mitu birds for breeding and reintroduction purposes. Allied with morphological data gathered in an independent study, we suggest that conservation actions need to focus on specimens with diagnostic phenotypic characters of M. mitu, and avoid birds with mitochondria, genetic contribution of M. tuberosum. Although we have detected low levels of genetic variability among captive birds, the steady increase of the captive population suggests that inbreeding depression and hybridization are not a reproductive hindrance. Reintroduction of some of these potential hybrid birds in the original area of occurrence of the Alagoas Curassow may be the only hope to fill in the ecological niche left vacant. An educational program involving local communities to conserve future reintroduction of curassows and their restored habitat is highly recommended. Accepted 12 November 2009.

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Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

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There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.