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Global warming has more than doubled the likelihood of extreme weather events, e.g. the 2003 European heat wave, the growing intensity of rain and snow in the Northern Hemisphere, and the increasing risk of flooding in the United Kingdom. It has also induced an increasing number of deadly tropical cyclones with a continuing trend. Many individual meteorological dynamic simulations and statistical models are available for forecasting hurricanes but they neither forecast well hurricane intensity nor produce clear-cut consensus. We develop a novel hurricane forecasting model by straddling two seemingly unrelated disciplines — physical science and finance — based on the well known price discovery function of trading in financial markets. Traders of hurricane derivative contracts employ all available forecasting models, public or proprietary, to forecast hurricanes in order to make their pricing and trading decisions. By using transactional price changes of these contracts that continuously clear the market supply and demand as the predictor, and with calibration to extract the embedded hurricane information by developing hurricane futures and futures option pricing models, one can gain a forward-looking market-consensus forecast out of all of the individual forecasting models employed. Our model can forecast when a hurricane will make landfall, how destructive it will be, and how this destructive power will evolve from inception to landing. While the NHC (National Hurricane Center) blends 50 plus individual forecasting results for its consensus model forecasts using a subjective approach, our aggregate is market-based. Believing their proprietary forecasts are sufficiently different from our market-based forecasts, traders could also examine the discrepancy for a potential trading opportunity using hurricane derivatives. We also provide a real case analysis of Hurricane Irene in 2011 using our methodology.
Asymmetric PCRAM structure with the upper contact opening at an offset to the bottom contact opening allowed us to improve the thermal distribution within the phase change layer and lower the reset current to 50% that of a conventional symmetrical structure. In terms of endurance, asymmetric cell lasted for 1.1 × 108 cycles which is at least 10X higher than the conventional symmetrical cell. These results were based on Ge2Sb2Te5 as the phase change material.
In this paper, we used nitrogen doped Ge2Sb2Te5  instead and the thickness of this phase change layer was 100 nm. During the sputtering of Ge2Sb2Te5, the Argon gas flow rate was fixed at 15 sccm while nitrogen flow rates of 0, 3, 4.5 and 6 sccm were introduced each time. Thus N2/Ar gas ratio of 0, 0.2, 0.3 and 0.4 were obtained respectively. After fabrication, the cell endurance of Asymmetric PCRAM cells incorporating Ge2Sb2Te5 doped with varying concentrations of nitrogen was tested. During testing, the PCRAM was repeatedly Reset/Set and the resistances of the two states were recorded at every 100k cycles. The cell was considered to be functioning well when its Reset/Set resistance ratio was greater than 10. From experiments, N-doped asymmetric cell with N2/Ar gas ratio of 0.2 lasted 2.4 × 1010 cycles which is 1000 times that of a conventional symmetrical PCRAM cells. The N2 doping concentration was also shown to be optimized when the N2/Ar gas ratio was fixed at 0.2. Higher doping concentrations with N2/Ar gas ratio of 0.3 and 0.4 decreased the cell endurance to 8.8 × 108 and 7.6 × 108 cycles respectively. Excessive doped nitrogen atoms might have degraded the phase change material, causing breakdown to occur sooner.
N-doped conventional symmetrical PCRAM was also fabricated and its overwrite cycles were measured only up to 1.2 × 109. With better thermal confinement, asymmetric PCRAM has proved to be better in endurance too. The above results were based on asymmetric PCRAM cells with 1 µm offset.
 H. Horii et al, “A Novel Cell Technology Using N-doped GeSbTe Films For Phase Change RAM”, p. 177-178, VLSI Tech. 2003
This paper tests the Kraus-Litzenberger (1976) three-moment capital asset pricing model using Hansen's (1982) generalized method-of-moments (GMM). The GMM approach does not impose strong distributional assumptions on the asset returns. This is an interesting issue since there is no obvious multivariate distribution for returns that also exhibits co-skewness. Using monthly stock returns to test the model, there is some evidence that systematic skewness is priced.
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