2,774 research outputs found

    ECONOMIC IMPACTS OF THE FINANCIAL CRISIS ON THE KOREAN FARM AND NON-FARM SECTORS

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    The objective of this study is to construct a macroeconomic model emphasizing agriculture and analyze the economic impacts of the financial crisis on the Korean farm and non-farm sectors. The simulation results show that financial shocks have great impacts on general economy and change the resource allocation within and between farm and non-farm sectors.Financial Crisis, Macroeconomic Model, Agricultural Finance, Research Methods/ Statistical Methods,

    Theoretical and Experimental Study of the Biefeld Brown Effect

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    By applying high voltage to an asymmetric capacitor, a thrust is created in the direction from the cathode to anode electrodes of the capacitor. Because of the high voltages, the anode ionizes the dielectric medium (air) and creates an “ion wind” by repelling the positively charged ions toward the cathode. This thrust is a result of the law of conservation of momentum. The application to this thrust has been observed in popular media as a levitating device (craft) but its full applications are still unknown and limited to lightweight crafts. In order to uncover its potential applications, this Honors Thesis built 47 lightweight crafts and tested them with an Ion Power Supply (GR8) that ranged its voltage from 20 kV to 30 kV. From the 47 crafts, the Quadrangle, Q2, which is a 30 x 30 cm2 square shaped craft, was the ideal craft. A variable payload measured to observe the relationship between the current supplied by the Power Supply and the weight of the craft

    The G1 cyclin Cln3p regulates vacuole homeostasis through phosphorylation of a scaffold protein, Bem1p, in Saccharomyces cerevisiae

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    How proliferating cells maintain the copy number and overall size of their organelles is not clear. In the budding yeast Saccharomyces cerevisiae the G1 cyclins Cln1,2,3p control initiation of cell division by regulating the activity of the cyclin-dependent kinase (Cdk) Cdc28p. We show that Cln3p controls vacuolar (lysosomal) biogenesis and segregation. First, loss of Cln3p, but not Cln1p or Cln2p, resulted in vacuolar fragmentation. Although the vacuoles of cln3ÃÂ cells were fragmented, together they occupied a large space, which accounted for a significant fraction of the overall cell size increase in cln3ÃÂ cells. Second, cytosol prepared from cells lacking Cln3p had reduced vacuolar homotypic fusion activity in cell-free assays. Third, vacuolar segregation was perturbed in cln3ÃÂ cells. Our findings reveal a novel role for a eukaryotic G1 cyclin in cytoplasmic organelle biogenesis and segregation. Furthermore we show that the scaffold protein Bem1p, a critical regulator of Cdc42p activity, is a downstream effector of Cln3p/Cdc28p complex. The Cdc42p GTPase is known to be required for vacuole fusion. Our results suggest that Ser72 on Bem1p is phosphorylated by Cdc28p in a Cln3p-dependent manner to promote vacuole fusion. Replacing Ser72 with Asp, to mimic phosphorylation at an optimal Cdkconsensus site located in the first SH3 domain of Bem1p, suppressed vacuolar fragmentation in cells lacking Cln3p. Using in vivo and in vitro assays, we found that Cln3p was unable to promote vacuole fusion in the absence of Bem1p or in the presence of a non-phosphorylatable Bem1p-Ser72Ala mutant. Furthermore, activation of Cdc42p also suppressed vacuolar fragmentation in the absence of Cln3p. Our results provide a mechanism that links cyclin-dependent kinase activity with vacuole fusion through Bem1p and the Cdc42p GTPase cycle

    Strangeness-driven Exploration in Multi-Agent Reinforcement Learning

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    Efficient exploration strategy is one of essential issues in cooperative multi-agent reinforcement learning (MARL) algorithms requiring complex coordination. In this study, we introduce a new exploration method with the strangeness that can be easily incorporated into any centralized training and decentralized execution (CTDE)-based MARL algorithms. The strangeness refers to the degree of unfamiliarity of the observations that an agent visits. In order to give the observation strangeness a global perspective, it is also augmented with the the degree of unfamiliarity of the visited entire state. The exploration bonus is obtained from the strangeness and the proposed exploration method is not much affected by stochastic transitions commonly observed in MARL tasks. To prevent a high exploration bonus from making the MARL training insensitive to extrinsic rewards, we also propose a separate action-value function trained by both extrinsic reward and exploration bonus, on which a behavioral policy to generate transitions is designed based. It makes the CTDE-based MARL algorithms more stable when they are used with an exploration method. Through a comparative evaluation in didactic examples and the StarCraft Multi-Agent Challenge, we show that the proposed exploration method achieves significant performance improvement in the CTDE-based MARL algorithms.Comment: 9 pages, 7 figure
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