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Inferences consistent with “recognition-based” decision-making may be drawn for various reasons other than recognition alone. We demonstrate that, for 2-alternative forced-choice decision tasks, less-is-more effects (reduced performance with additional learning) are not restricted to recognition-based inference but can also be seen in circumstances where inference is knowledge-based but item knowledge is limited. One reason why such effects may not be observed more widely is the dependence of the effect on specific values for the validity of recognition and knowledge cues. We show that both recognition and knowledge validity may vary as a function of the number of items recognized. The implications of these findings for the special nature of recognition information, and for the investigation of recognition-based inference, are discussed

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Four hull-less barley samples were milled on a Buhler MLU 202 laboratory mill and individual and combined milling fractions were characterized. The best milling performance was obtained when the samples were conditioned to 14.3% moisture. Yields were 37-48% for straight-run flour, 47-56% for shorts, and 5-8% for bran. The beta-glucan contents of the straight-run white flours were 1.6-2.1%, of which approximate to49% was water-extractable. The arabinoxylan contents were 1.2-1.5%, of which approximate to17% was water-extractable. Shorts and bran fractions contained more beta-glucan (4.2-5.8% and 3.0-4.7%, respectively) and arabinoxylan (6.1-7.7% and 8.1-11.8%, respectively) than the white flours. For those fractions, beta-glucan extractability was high (58.5 and 52.3%, respectively), whereas arabinoxylan extractability was very low (approximate to6.5 and 2.0%, respectively). The straight-run white flours had low alpha-amylase, beta-glucanase, and endoxylanase activities. The highest alpha-amylase activity was found in the shorts fractions and the highest beta-glucanase and endoxylanase activities were generally found in the bran fractions. Endoxylanase inhibitor activities were low in the white flours and highest in the shorts fractions. High flavanoid, tocopherol, and tocotrienol contents were found in bran and shorts fractions.

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Inference on the basis of recognition alone is assumed to occur prior to accessing further information (Pachur & Hertwig, 2006). A counterintuitive result of this is the “less-is-more” effect: a drop in the accuracy with which choices are made as to which of two or more items scores highest on a given criterion as more items are learned (Frosch, Beaman & McCloy, 2007; Goldstein & Gigerenzer, 2002). In this paper, we show that less-is-more effects are not unique to recognition-based inference but can also be observed with a knowledge-based strategy provided two assumptions, limited information and differential access, are met. The LINDA model which embodies these assumptions is presented. Analysis of the less-is-more effects predicted by LINDA and by recognition-driven inference shows that these occur for similar reasons and casts doubt upon the “special” nature of recognition-based inference. Suggestions are made for empirical tests to compare knowledge-based and recognition-based less-is-more effects

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The Schiff base ligand, HL (2-[1-(3-methylamino-propylimino)-ethyl]-phenol), the 1:1 condensation product of 2-hydroxy acetophenone and N-methyl-1,3-diaminopropane, has been synthesized and characterized by X-ray crystallography as the perchlorate salt [H2L]ClO4 (1). The structure consists of discrete [H2L](+) cations and perchlorate anions. Two dinuclear Ni-II complexes, [Ni2L2(NO2)(2)] (2), [Ni2L2(NO3)(2)] (3) have been synthesized using this ligand and characterized by single crystal X-ray analyses. Complexes 2 and 3 are centrosymmetric dimers in which the Ni-II ions are in distorted fac- and mer-octahedral environments, respectively, bridged by two mu(2)-phenolate ions of deprotonated ligand, L. The plane of the phenyl rings and the Ni2O2 basal plane are nearly coplanar in 2 but almost perpendicular in 3. We have studied and explained this different behavior using high level DFT calculations (RI-BP86/def2-TZVP level of theory). The conformation observed in 3, which is energetically less favorable, is stabilized via intermolecular non-covalent interactions. Under the excitation of ultraviolet light, characteristic fluorescence of compound 1 was observed; by comparison fluorescence intensity decreases in case of compound 3 and completely quenched in compound 2.

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Impact Assessments (IAs) were introduced at the EU level under the rhetorical facade of ‘better regulation’. The actual aim was to improve not only the quality but also the reputation of EU regulation before stakeholders. However, evidence brought forward by a number of evaluations pointed out that IAs are yet to achieve acceptable quality standards. The paper offers an overview of different disciplinary approaches for looking at IAs. It suggests that risk regulation encompasses the theoretical foundations to help understand the role of IAs in the EU decisionmaking process. The analysis of 60 early days preliminary IAs provides empirical evidence regarding policy alternatives, methodology of consultation and use of quantitative techniques. Findings suggest that dawn period IAs were used mainly to provide some empirical evidence for regulatory intervention in front of stakeholders. The paper concludes with assumptions about the future role of IAs at EU level.

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Pesticide risk indicators provide simple support in the assessment of environmental and health risks from pesticide use, and can therefore inform policies to foster a sustainable interaction of agriculture with the environment. For their relative simplicity, indicators may be particularly useful under conditions of limited data availability and resources, such as in Less Developed Countries (LDCs). However, indicator complexity can vary significantly, in particular between those that rely on an exposure–toxicity ratio (ETR) and those that do not. In addition, pesticide risk indicators are usually developed for Western contexts, which might cause incorrect estimation in LDCs. This study investigated the appropriateness of seven pesticide risk indicators for use in LDCs, with reference to smallholding agriculture in Colombia. Seven farm-level indicators, among which 3 relied on an ETR (POCER, EPRIP, PIRI) and 4 on a non-ETR approach (EIQ, PestScreen, OHRI, Dosemeci et al., 2002), were calculated and then compared by means of the Spearman rank correlation test. Indicators were also compared with respect to key indicator characteristics, i.e. user friendliness and ability to represent the system under study. The comparison of the indicators in terms of the total environmental risk suggests that the indicators not relying on an ETR approach cannot be used as a reliable proxy for more complex, i.e. ETR, indicators. ETR indicators, when user-friendly, show a comparative advantage over non-ETR in best combining the need for a relatively simple tool to be used in contexts of limited data availability and resources, and for a reliable estimation of environmental risk. Non-ETR indicators remain useful and accessible tools to discriminate between different pesticides prior to application. Concerning the human health risk, simple algorithms seem more appropriate for assessing human health risk in LDCs. However, further research on health risk indicators and their validation under LDC conditions is needed.

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The relevance of regional policy for less favoured regions (LFRs) reveals itself when policy-makers must reconcile competitiveness with social cohesion through the adaptation of competition or innovation policies. The vast literature in this area generally builds on an overarching concept of ‘social capital’ as the necessary relational infrastructure for collective action diversification and policy integration, in a context much influenced by a dynamic of industrial change and a necessary balance between the creation and diffusion of ‘knowledge’ through learning. This relational infrastructure or ‘social capital’ is centred on people’s willingness to cooperate and ‘envision’ futures as a result of “social organization, such as networks, norms and trust that facilitate action and cooperation for mutual benefit” (Putnam, 1993: 35). Advocates of this interpretation of ‘social capital’ have adopted the ‘new growth’ thinking behind ‘systems of innovation’ and ‘competence building’, arguing that networks have the potential to make both public administration and markets more effective as well as ‘learning’ trajectories more inclusive of the development of society as a whole. This essay aims to better understand the role of ‘social capital’ in the production and reproduction of uneven regional development patterns, and to critically assess the limits of a ‘systems concept’ and an institution-centred approach to comparative studies of regional innovation. These aims are discussed in light of the following two assertions: i) learning behaviour, from an economic point of view, has its determinants, and ii) the positive economic outcomes of ‘social capital’ cannot be taken as a given. It is suggested that an agent-centred approach to comparative research best addresses the ‘learning’ determinants and the consequences of social networks on regional development patterns. A brief discussion of the current debate on innovation surveys has been provided to illustrate this point.

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Does the political regime of a country influence its involvement in international trade? A theoretical model that predicts that autocracies trade less than democracies is developed, and the predictions of the model are tested empirically using a panel of more than 130 countries for 1962–2000. In contrast to the existing literature, data on the regime type of individual countries are used rather than information about the congruence of the regime type of pairs of trading countries. In line with the model, autocracies are found to import substantially less than democracies, even after controlling for official trade policies. This finding is very stable and does not depend on a particular setup or estimation technique.

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Vine-growing in the Less-Favoured Areas of Greece is facing multiple challenges that might lead to its abandonment. In an attempt to maintain rural populations, Rural Development Schemes have been created that offer the opportunity to rural households to maintain or expand their farming businesses including vine-growing. This paper stems from a study that used data from a cross-sectional survey of 204 farmers to investigate how farming systems and farmers’ perception of corruption, amongst other socio-economic factors, affected their decisions to continue vine-growing through participation in Rural Development Schemes, in three remote Less-Favoured Areas of Greece. The Theory of Planned Behaviour was used to frame the research problem with the assumption being that an individual’s intention to participate in a Scheme is based on their prior beliefs about it. Data from the survey were reduced and simplified by the use of non-linear principal component analysis. The ensuing variables were used in selectivity corrected ordered probit models to reveal farmers’ attitudes towards viticulture and rural development. It was found that economic factors, perceived corruption and farmers’ attitudes were significant determinants on whether to participate in the Schemes. The research findings highlight the important role of perceived corruption and the need for policies that facilitate farmers’ access to decision making centres.

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Periocular recognition has recently become an active topic in biometrics. Typically it uses 2D image data of the periocular region. This paper is the first description of combining 3D shape structure with 2D texture. A simple and effective technique using iterative closest point (ICP) was applied for 3D periocular region matching. It proved its strength for relatively unconstrained eye region capture, and does not require any training. Local binary patterns (LBP) were applied for 2D image based periocular matching. The two modalities were combined at the score-level. This approach was evaluated using the Bosphorus 3D face database, which contains large variations in facial expressions, head poses and occlusions. The rank-1 accuracy achieved from the 3D data (80%) was better than that for 2D (58%), and the best accuracy (83%) was achieved by fusing the two types of data. This suggests that significant improvements to periocular recognition systems could be achieved using the 3D structure information that is now available from small and inexpensive sensors.