112 resultados para simultaneous volatility


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Magnetic resonance imaging (MRI) offers the opportunity to study biological tissues and processes in a non-disruptive manner. The technique shows promise for the study of the load-bearing performance (consolidation) of articular cartilage and changes in articular cartilage accompanying osteoarthritis. Consolidation of articular cartilage involves the recording of two transient characteristics: the change over time of strain and the hydrostatic excess pore pressure (HEPP). MRI study of cartilage consolidation under mechanical load is limited by difficulties in measuring the HEPP in the presence of the strong magnetic fields associated with the MRI technique. Here we describe the use of MRI to image and characterize bovine articular cartilage deforming under load in an MRI compatible consolidometer while monitoring pressure with a Fabry-Perot interferometer-based fiber-optic pressure transducer.

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Multiple copies of expression cassettes driven by the Trichoderma reesei xylanase 2 (xyn2) and cellobiohydrolase 2 (cbh2) promoters were introduced into the recombinant T. reesei EC-21 generated to express a thermostable Dictyoglomus thermophilum xylanase (XynB) under the egl2 promoter for further improvement of the enzyme yield. The transformants were screened based on increased XynB activity only. Multiple promoter transformant MPP-4 expressing the xynB gene under all the three promoters was found to be the highest producer of XynB, giving a 65% increase in yield compared to the parental single-promoter recombinant EC-21. The multiple-promoter transformant strains harboured six to nine copies of the xynB gene. Amongst the three promoters, egl2 seemed to have the strongest effect on XynB expression. The shotgun approach we used proved to be effective for rapid enhancement of protein expression using three promoters active at the near-neutral pH of the cultivation medium.

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We investigated memories of room-sized spatial layouts learned by sequentially or simultaneously viewing objects from a stationary position. In three experiments, sequential viewing (one or two objects at a time) yielded subsequent memory performance that was equivalent or superior to simultaneous viewing of all objects, even though sequential viewing lacked direct access to the entire layout. This finding was replicated by replacing sequential viewing with directed viewing in which all objects were presented simultaneously and participants’ attention was externally focused on each object sequentially, indicating that the advantage of sequential viewing over simultaneous viewing may have originated from focal attention to individual object locations. These results suggest that memory representation of object-to-object relations can be constructed efficiently by encoding each object location separately, when those locations are defined within a single spatial reference system. These findings highlight the importance of considering object presentation procedures when studying spatial learning mechanisms.

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Multiscale numerical modeling of the species balance and transport in the ionized gas phase and on the nanostructured solid surface complemented by the heat exchange model is used to demonstrate the possibility of minimizing the Gibbs-Thompson effect in low-temperature, low-pressure chemically active plasma-assisted growth of uniform arrays of very thin Si nanowires, impossible otherwise. It is shown that plasma-specific effects drastically shorten and decrease the dispersion of the incubation times for the nucleation of nanowires on non-uniform Au catalyst nanoparticle arrays. The fast nucleation makes it possible to avoid a common problem of small catalyst nanoparticle burying by amorphous silicon. These results explain a multitude of experimental observations on chemically active plasma-assisted Si nanowire growth and can be used for the synthesis of a range of inorganic nanowires for environmental, biomedical, energy conversion, and optoelectronic applications.

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It is shown that the simultaneous saturation of Ni nanoparticles used as catalyst for vertically aligned carbon nanotube and nanocone arrays can be improved in low-temperature plasma- or ion-assisted processes compared with neutral gas-based routes. The results of hybrid multiscale numerical simulations of the catalyst nanoarrays (particle sizes of 2 and 10 nm) saturation with carbon show the possibility of reducing the difference in catalyst incubation times for smallest and largest catalyst particles by up to a factor of 2. This approach is generic and provides process conditions for simultaneous nucleation and growth of uniform arrays of vertically aligned nanostructures. © 2008 American Institute of Physics.

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The present invention relates to genetically modified cells that are capable of optimal transgene expression by co-expressing a silencing suppressor whilst at the same time are also capable of silencing a gene, such as a naturally occurring gene of the cell. The present invention also relates to methods of producing the modified cells, as well as relates to processes for obtaining a genetically modified cell with a desired property.

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We test the predictive ability of investor sentiment on the return and volatility at the aggregate market level in the U.S., four largest European countries and three Asia-Pacific countries. We find that in the U.S., France and Italy periods of high consumer confidence levels are followed by low market returns. In Japan both the level and the change in consumer confidence boost the market return in the next month. Further, shifts in sentiment significantly move conditional volatility in most of the countries, and in Italy such impacts lead to an increase in returns by 4.7% in the next month.

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This paper presents a novel method to rank map hypotheses by the quality of localization they afford. The highest ranked hypothesis at any moment becomes the active representation that is used to guide the robot to its goal location. A single static representation is insufficient for navigation in dynamic environments where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners. In our approach we simultaneously rank multiple map hypotheses by the influence that localization in each of them has on locally accurate odometry. This is done online for the current locally accurate window by formulating a factor graph of odometry relaxed by localization constraints. Comparison of the resulting perturbed odometry of each hypothesis with the original odometry yields a score that can be used to rank map hypotheses by their utility. We deploy the proposed approach on a real robot navigating a structurally noisy office environment. The configuration of the environment is physically altered outside the robots sensory horizon during navigation tasks to demonstrate the proposed approach of hypothesis selection.

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This paper employs a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011. Understanding the price behavior of commodity prices and the volatility transmission mechanism between these markets and the stock exchanges are crucial for each participant, including governments, traders, portfolio managers, consumers, and producers. For return and volatility spillover, the results show significant transmission among the S&P 500 and commodity markets. The past shocks and volatility of the S&P 500 strongly influenced the oil and gold markets. This study finds that the highest conditional correlations are between the S&P 500 and gold index and the S&P 500 and WTI index. We also analyze the optimal weights and hedge ratios for commodities/S&P 500 portfolio holdings using the estimates for each index. Overall, our findings illustrate several important implications for portfolio hedgers for making optimal portfolio allocations, engaging in risk management and forecasting future volatility in equity and commodity markets. © 2013 Elsevier B.V.

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Trade flows of commodities are generally affected by the principles of comparative advantage in a free trade. However, trade flows might be enhanced or distorted not only by various government interventions, but also by exchange rate fluctuations among others. This study applies a commodity-specific gravity model to selected vegetable trade flows among Organization for Economic Co-operation and Development (OECD) countries to determine the effects of exchange rate uncertainty on the trade flows. Using the data from 1996 to 2002, the results show that, while the exchange rate uncertainty significantly reduces trade in the majority of commodity flows, there is evidence that both short- and long-term volatility have positive effect on trade flows of specific commodities. This study also tests the regional preferential trade agreements such as the North American Free Trade Agreement (NAFTA), the Asia-Pacific Economic Cooperation (APEC) and the EU, and their different effects on commodities.

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For robots operating in outdoor environments, a number of factors, including weather, time of day, rough terrain, high speeds, and hardware limitations, make performing vision-based simultaneous localization and mapping with current techniques infeasible due to factors such as image blur and/or underexposure, especially on smaller platforms and low-cost hardware. In this paper, we present novel visual place-recognition and odometry techniques that address the challenges posed by low lighting, perceptual change, and low-cost cameras. Our primary contribution is a novel two-step algorithm that combines fast low-resolution whole image matching with a higher-resolution patch-verification step, as well as image saliency methods that simultaneously improve performance and decrease computing time. The algorithms are demonstrated using consumer cameras mounted on a small vehicle in a mixed urban and vegetated environment and a car traversing highway and suburban streets, at different times of day and night and in various weather conditions. The algorithms achieve reliable mapping over the course of a day, both when incrementally incorporating new visual scenes from different times of day into an existing map, and when using a static map comprising visual scenes captured at only one point in time. Using the two-step place-recognition process, we demonstrate for the first time single-image, error-free place recognition at recall rates above 50% across a day-night dataset without prior training or utilization of image sequences. This place-recognition performance enables topologically correct mapping across day-night cycles.