Reynolds and Mach number simulation of Apollo and Gemini re-entry and comparison with flight

Reynolds and Mach numbers simulation of Apollo and Gemini reentry compared with flight dat

Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

This paper addresses aggregation in integer autoregressive moving average (INARMA) models. Although aggregation in continuous-valued time series has been widely discussed, the same is not true for integer-valued time series. Forecast horizon aggregation is addressed in this paper. It is shown that the overlapping forecast horizon aggregation of an INARMA process results in an INARMA process. The conditional expected value of the aggregated process is also derived for use in forecasting. A simulation experiment is conducted to assess the accuracy of the forecasts produced by the aggregation method and to compare it to the accuracy of cumulative h-step ahead forecasts over the forecasting horizon. The results of an empirical analysis are also provided

CCM2 Molecular Signaling Pathway

Cerebral cavernous malformations (CCM) are a central nervous system vascular malformation often responsible for hemorrhagic strokes. Molecular genetic studies have identified three genes (CCMl KRITI, CCM2 Malcavernin, and CCM3 PDCDIO) and four possible loci responsible for the pathogenesis of these lesions. CCMl functions through integrin signaling and regulation of RACl activity and may be involved in the MAPK and JNK signaling cascades. We hypothesized that CCM2 likely functioned through the same pathways and that CCM3 expression is regulated by these stress-induced signaling cascades. We showed that CCM2 likely functions through the MAPK pathway as the mouse homolog, osmosensing scaffold protein for MEKK3 (OSM), has been shown to interact in the p38 mitogen activated protein kinase (p38 MAPK) signaling pathway regulated by RAC1. We confirmed that CCMl and CCM2 signal as a complex since co-immunoprecipitation indicates joint expression. We also characterized the role of CCM3 in the MAPK pathway by identifying interacting serine threonine kinases (STK) and KIAA0826 based on yeast two-hybrid data. This was further examined through immunohistochemical analysis showing CCM3 is expressed in a variety of human organs especially arterial vascular endothelium in a similar pattern to CCM2. The yeast two-hybrid data supports current theories that there is a link between CCM pathogenesis and the ERK-MAPK cascade. These findings correlate with previous studies and further elucidate the signaling pathways involved in CCM pathogenesis which may in turn be helpful in future therapeutic advances

Milk production in Finnsheep and Romanov breeds

The Finn, Romanov and several U.S. sheep breeds were machine milked to obtain information on several measures of milk production and milk composition. Data were obtained over at wo year to four year period utilizing 146 purebred ewe records and 165 crossbred ewe records. The level of milk production for a 130 day lactation period for the breeds evaluated was generally low compared to traditional European dairy breeds. The least-squares overall mean for milk yield was 68.8 liters. The Suffolk (80.5 liters) and the Rambouillet (75.3 liters) were superior. The Targhee, Dorset and Lincoln breeds followed in order for milk yield. The Finn (64.0 liters) and Romanov (39.1 liters) were lowest. Finn sired crossbred ewes had the highest level of milk production (84.4 liters) in a four breed diallel mating design of Finn, Dorset, Lincoln and Rambouillet breeds. A value of 15.4 % was found for heterosis in milk production

Water leakage forecasting: The application of a modified fuzzy evolving algorithm

This paper investigates the use of evolving fuzzy algorithms in forecasting. An evolving Takagi-Sugeno (eTS) algorithm, which is based on a recursive version of the subtractive algorithm is considered. It groups data into several clusters based on Euclidean distance between the relevant independent variables. The Mod eTS algorithm, which incorporates a modified dynamic update of cluster radii while accommodating new available data is proposed. The created clusters serve as a base for fuzzy If-Then rules with Gaussian membership functions which are defined using the cluster centres and have linear functions in the consequent i.e., Then parts of rules. The parameters of the linear functions are calculated using a weighted version of the Recursive Least Squares algorithm. The proposed algorithm is applied to a leakage forecasting problem faced by one of the leading UK water supplying companies. Using the real world data provided by the company the forecasting results obtained from the proposed modified eTS algorithm, Mod eTS, are compared to the standard eTS algorithm, exTS, eTS+ and fuzzy C-means clustering algorithm and some standard statistical forecasting methods. Different measures of forecasting accuracy are used. The results show higher accuracy achieved by applying the algorithm proposed compared to other fuzzy clustering algorithms and statistical methods. Similar results are obtained when comparing with other fuzzy evolving algorithms with dynamic cluster radii. Furthermore the algorithm generates typically a smaller number of clusters than standard fuzzy forecasting methods which leads to more transparent forecasting models

Formation of seasonal groups and application of seasonal indices

Estimating seasonal variations in demand is a challenging task faced by many organisations. There may be many stock-keeping units (SKUs) to forecast, but often data histories are short, with very few complete seasonal cycles. It has been suggested in the literature that group seasonal indices (GSI) methods should be used to take advantage of information on similar SKUs. This paper addresses two research questions: (1) how should groups be formed in order to use the GSI methods? and (2) when should the GSI methods and the individual seasonal indices (ISI) method be used? Theoretical results are presented, showing that seasonal grouping and forecasting may be unified, based on a Mean Square Error criterion, and K-means clustering. A heuristic K-means method is presented, which is competitive with the Average Linkage method. It offers a viable alternative to a company’s own grouping method or may be used with confidence if a company lacks a grouping method. The paper gives empirical findings that confirm earlier theoretical results that greater accuracy may be obtained by employing a rule that assigns the GSI method to some SKUs and the ISI method to the remainder