35 resultados para Episodic future thoughts


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Purpose: The purpose of this paper is to analyse the impact of business exits on future dimensions of entrepreneurial activity at the macroeconomic level. Design/methodology/approach: This research uses the Global Entrepreneurship Monitor (GEM) data for 41 countries and the Generalized Method of Moments (GMM) to carry out the analysis. The paper differentiates the effect of the two components of total entrepreneurial activity, and the two motivations for it – opportunity and necessity entrepreneurship. Findings: The results presented here show a positive and significant effect of the coefficient associated with exits in all models. This means that the levels of entrepreneurial activity exceed business exits. The robustness of the models are tested, including other variables such as the fear of failure, the Gross Domestic Product, role models, entrepreneurial skills and the unemployment variables. The main hypothesis which stated that at national level business exits imply greater rates of opportunity-driven entrepreneurship is corroborated. Originality/value: One would expect that unemployment rates would imply higher levels of necessity entrepreneurship. However, results show that unemployment rates do in fact favour opportunity entrepreneurship levels. This could be due to those government policies that are aimed at promoting entrepreneurship through the capitalization of unemployment to be totally invested in a new start-up. To the best of our knowledge, this is the first panel data study to link previous exit rates to future dimensions of entrepreneurial activity. Keywords: Entrepreneurship, business exits, social values, industrial organization Paper type: Research paper

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Everyday tasks seldom involve isolate actions but sequences of them. We can see whether previous actions influence the current one by exploring the response time to controlled sequences of stimuli. Specifically, depending on the response-stimulus temporal interval (RSI), different mechanisms have been proposed to explain sequential effects in two-choice serial response tasks. Whereas an automatic facilitation mechanism is thought to produce a benefit for response repetitions at short RSIs, subjective expectancies are considered to replace the automatic facilitation at longer RSIs, producing a cost-benefit pattern: repetitions are faster after other repetitions but they are slower after alternations. However, there is not direct evidence showing the impact of subjective expectancies on sequential effects. By using a fixed sequence, the results of the reported experiment showed that the repetition effect was enhanced in participants who acquired complete knowledge of the order. Nevertheless, a similar cost-benefit pattern was observed in all participants and in all learning blocks. Therefore, results of the experiment suggest that sequential effects, including the cost-benefit pattern, are the consequence of automatic mechanisms which operate independently of (and simultaneously with) explicit knowledge of the sequence or other subjective expectancies.

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Real-time predictions are an indispensable requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. The combination of predicting the state of the network and the evaluation of different traffic management strategies in the short term future allows system managers to anticipate the effects of traffic control strategies ahead of time in order to mitigate the effect of congestion. This paper presents the current framework of decision support systems for traffic management based on short and medium-term predictions and includes some reflections on their likely evolution, based on current scientific research and the evolution of the availability of new types of data and their associated methodologies.

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Organic food products are highly susceptible to fraud. Currently, administrative controls are conducted to detect fraud, but having an analytical tool able to verify the organic identity of food would be very supportive. The state-of-the-art in food authentication relies on fingerprinting approaches that find characteristic analytical patterns to unequivocally identify authentic products. While wide research on authentication has been conducted for other commodities, the authentication of organic chicken products is still in its infancy. Challenges include finding fingerprints to discriminate organic from conventional products, and recruiting sample sets that cover natural variability. Future research might be oriented towards developing new authentication models for organic feed, eggs and chicken meat, keeping models updated and implementing them into regulations. Meanwhile, these models might be very supportive to the administrative controls directing inspections towards suspicious fraudulent samples.

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The high sensitivity and excellent timing accuracy of Geiger mode avalanche photodiodes makes them ideal sensors as pixel detectors for particle tracking in high energy physics experiments to be performed in future linear colliders. Nevertheless, it is well known that these sensors suffer from dark counts and afterpulsing noise, which induce false hits (indistinguishable from event detection) as well as an increase of the necessary area of the readout system. In this work, we present a comparison between APDs fabricated in a high voltage 0.35 µm and a high integration 0.13 µm commercially available CMOS technologies that has been performed to determine which of them best fits the particle collider requirements. In addition, a readout circuit that allows low noise operation is introduced. Experimental characterization of the proposed pixel is also presented in this work.