Historical Data and Trends

To validate the FTS pressure drop model, data was compiled from 9 different historical studies where FTS data was available. Data was taken from Ingmanson et al. (1961), Armour and Cannon (1968), Blatt (1970b), Castle (1972), Burge et al. (1973), Burge and Blackmon (1973b), Cady (1973,1977), and Ludewig et al. (1974). Data reported in Amneus 

To compare performance across all LAD screens, and to determine fitting parameters a and p, Equation (3.33) is cast into a non-dimensional form. The resulting equation is in terms of a modified Reynolds (Re) number based on characteristic length of friction factor f, and the equation relating the two, which are defined as  

FIGURE 3.11 Friction Factor versus Screen Reynolds Number for Dutch Twill Weaves. 

FIGURE 3.12 Friction Factor versus Screen Reynoids Number for All Other Weaves. 

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The risk being analyzed in a TSVA is an expression of the likehhood that a defined threat will exploit a specific vulnerabihty of a particnlar target or combination of targets to cause a given set of consequences along a transportation route. Since historical data and trends (available for accidents) do not directly apply to intentional acts, a secnrity risk analysis is usually performed using qualitative techniques. The expected outcome is a qualitative estimate of risk that can be used as a basis for determining which security issues may require additional protective countermeasures. 

Scheduling changes. This is difficrrlt to correct in cyclical businesses, but with good historical data and trending, reasonable consistency that minimizes supplier interruptions can be reached. 

Post-transfer the key risks are routinely re-assessed. By performing appropriate statistically based analysis, the receiving unit can continue to monitor the data trends versus historical data and re-affirm that the method continues to provide satisfactory and reproducible performance. 

Kousa et al. [20] classified exposure models as statistical, mathematical and mathematical-stochastic models. Statistical models are based on the historical data and capture the past statistical trend of pollutants [21]. The mathematical modelling, also called deterministic modelling, involves application of emission inventories, combined with air quality and population activity modelling. The stochastic approach attempts to include a treatment of the inherent uncertainties of the model [22],... 

In general, policy analysts use two structural types of models to make forecasts of future activity levels econometric and engineering or process models. Econometric models use historic data and relationships to estimate future trends in variables of interest. Engineering or process models use the physical relationships of production processes (i.e., the relationship between inputs to a production process and its outputs) to predict levels of the dependent variables. In general, the use of econometric modeling techniques to forecast activity levels provides a better long-term (beyond 20 years) trend because it relies on long-term, historical... 

A study is considered valid if the results obtained with positive and negative controls are consistent with the laboratory s historical data and with the literature. Statistical analysis is usually applied to compare treated and negative control groups. Both pairwise and linear trend tests can be used. Because of the low background and Poisson distribution, data transformation (e.g., log transformation) is sometimes needed before using tests applicable to normally distributed data. Otherwise, nonparametric analyses should be preferred. 

The decision to introduce periodic revalidation should be based essentially on a review of historical data, i.e., data generated during in-process and finished product testing after the latest validation, aimed at verifying that the process is under control. During the review of such historical data, any trend in the data collected should be evaluated. 

Compared to ordinary time series models, the Multiplicative Seasonal Model needs more historical data, and the Multiplicative Seasonal Model can be applied to a wider field because data in daily life always have an obvious trend and seasonal features. Therefore, the Multiplicative Seasonal Model can well solve such problems that involve some issues about forecasting, and as well as reach a high precision. The model in this paper, ARIMA (4,1,1)(1,1,1) well matches the monthly changing number of national coal mine accidents. Moreover, the more historical data, the more accurate the forecasted result is. AH above, the Multiplicative Seasonal Model is a practical tool for us to forecast or to apply in many other fields. 

A static method assumes that the estimates of level, trend, and seasonality within the systematic component do not vary as new demand is observed. In this case, we estimate each of these parameters based on historical data and then use the same values for aU future forecasts. In this section, we discuss a static forecasting method for use when demand has a trend as well as a seasonal component. We assume that the systematic component of demand is mixed that is. 

Fashion apparel and other seasonal goods follow a seasonal pattern of danand. Thus, collaborative planning in these categories has a horizon of a single season and is performed at seasonal intervals. Given the seasonal nature, forecasts rely less on historical data and more on collaborative interpretation of industry trends, macroeconomic factors, and customer tastes. In this form of collaboration, the trading partners develop an assortment plan jointly. The output is a planned purchase order at the style/color/size level. The planned order is shared electronically in advance... 

Historical data on the indicator. Existing information on the statistical variation, bias, and other interpretational attributes of potential biological indicators should be examined and considered in the design of a sampling program for assessing trends in mercury bioaccumulation. 

Historical operating data is retained in the computer memory. Averages and trends can be displayed, for plant investigation and trouble shooting. 

Another product area appeared in the 1980s called building automation systems (BAS). These systems included historical data, trend logging and fire and security functions in addition to conventional energy management functions. 

The risk probability is assessed based on input from analysts at transferring site, experience of receiving site with methods and products, historical method performance, e.g. method/product process capability data, stability trends, OOS history, etc. (Raska et al., 2010). 

A collection of scenarios is generated that best captures the trend of raw material prices of the different types of crude oil and the sales prices of the saleable refining products for a representative period of time based on available historical data. A probability ps, with index s denoting the sth scenario, is assigned to each scenario to reflect the likelihood of each scenario being realized with ps = 1. 

Historical control data can also be obtained from other laboratories such as CROs, some of which have a long history of reproductive and developmental toxicity testing and have large multispecies historical control databases that can span decades, and are therefore very useful for observing long term trends. This type of data can be especially informative when looking for occurrences of rare malformations in controls. Charles River Laboratories (www.criver.com) is one CRO that has made its historical data for reproductive and developmental toxicity publicly available. Historical control data for the CD rat and the New Zealand White rabbit is also available in the literature (8, 9, 13-15). Other laboratories may provide data upon request. 

One important aspect of the quality systems approach is the ongoing collection and analysis of quality data to continuously evaluate quality system effectiveness. Historical data, process knowledge, and risk analysis methods can be applied to identify specific data requirements. Trending and other data analysis methods can allow identification of actual and potential sources of nonconformity so that appropriate corrective and preventive actions can be taken in accordance with established change control procedures. 

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