16,940 research outputs found

    Survey of the literature on innovation and economic performance

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    Despite very strong differences in their treatment of technological change in economic theory, both the neoclassical and the more Schumpetarian (and evolutionary) economic approaches often assume that market selection rewards the most innovative firms. However, despite such strong assumptions, empirical evidence on whether innovative firms perform better than non-innovative firms remains inconclusive. If innovators do not grow more, does this imply that market selection fails? And does the different impact of innovation on industrial performance (measured by firm growth and profitability) and financial performance (measured by market value and stock returns) signal differences in how industrial and financial markets react to firm level efforts around innovation? This discussion paper reviews the literature on the interaction between innovation and economic/financial performance, and outlines the way that work within FINNOV Work Package 2 (SELECTION), Co-Evolution of Industry Dynamics and Financial Dynamics, will contribute to better understanding this interaction

    Identification of appropriate temporal scales of dominant low flow indicators in the Main River, Germany

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    Models incorporating the appropriate temporal scales of dominant indicators for low flows are assumed to perform better than models with arbitrary selected temporal scales. In this paper, we investigate appropriate temporal scales of dominant low flow indicators: precipitation (P), evapotranspiration (ET) and the standardized groundwater storage index (G). This analysis is done in the context of low flow forecasting with a lead time of 14 days in the Main River, a tributary of the Rhine River, located in Germany. Correlation coefficients (i.e. Pearson, Kendall and Spearman) are used to reveal the appropriate temporal scales of dominant low flow indicators at different time lags between low flows and indicators and different support scales of indicators. The results are presented for lag values and support scales, which result in correlation coefficients between low flows and dominant indicators falling into the maximum 10% percentile range. P has a maximum Spearman correlation coefficient (ρ) of 0.38 (p = 0.95) at a support scale of 336 days and a lag of zero days. ET has a maximum ρ of –0.60 (p = 0.95) at a support scale of 280 days and a lag of 56 days and G has a maximum ρ of 0.69 (p = 0.95) at a support scale of 7 days and a lag of 3 days. The identified appropriate support scales and lags can be used for low flow forecasting with a lead time of 14 days

    The Effects of Selective and Indiscriminate Repression on the 2013 Gezi Park Nonviolent Resistance Campaign

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    We investigate the differential effects of selective and indiscriminate repression on the rate of protest actions during the nonviolent resistance campaign in Gezi Park, Turkey, in 2013. After deriving theoretical expectations about how and why these forms of repression will influence protest actions, we test them with protest event data that were collected from a major local newspaper and subsequently validated through a comparison with two other independent Twitter datasets. Utilizing a Poisson autoregressive estimation model, we find that selective repression, as measured by the number of arrested activists who were detained while they were not demonstrating, decreased the rate of protest actions. Meanwhile, indiscriminate repression, as measured by the frequency of the government’s use of lethal and nonlethal violence against protesters during demonstrations, increased the rate of protest actions. Our findings support prior research on the influence of indiscriminate repression on backfire outcomes. They also provide evidence for the impact of selective repression on movement demobilization through the removal of opposition activists. Finally, the targeted arrest strategy of selective repression that was employed in the Gezi campaign has implications for the feasibility of the strategic incapacitation model of protest policing

    A Novel Design Approach to X-Band Minkowski Reflectarray Antennas using the Full-Wave EM Simulation-based Complete Neural Model with a Hybrid GA-NM Algorithm

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    In this work, a novel multi-objective design optimization procedure is presented for the Minkowski Reflectarray RAs using a complete 3-D CST Microwave Studio MWS-based Multilayer Perceptron Neural Network MLP NN model including the substrate constant εr with a hybrid Genetic GA and Nelder-Mead NM algorithm. The MLP NN model provides an accurate and fast model and establishes the reflection phase of a unit Minkowski RA element as a continuous function within the input domain including the substrate 1 ≤ εr ≤ 6; 0.5mm ≤ h ≤ 3mm in the frequency between 8GHz ≤ f ≤ 12GHz. This design procedure enables a designer to obtain not only the most optimum Minkowski RA design all throughout the X- band, at the same time the optimum Minkowski RAs on the selected substrates. Moreover a design of a fully optimized X-band 15×15 Minkowski RA antenna is given as a worked example with together the tolerance analysis and its performance is also compared with those of the optimized RAs on the selected traditional substrates. Finally it may be concluded that the presented robust and systematic multi-objective design procedure is conveniently applied to the Microstrip Reflectarray RAs constructed from the advanced patches
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