Bioretention Cell Design for Full Scale Project in Grove, Oklahoma

Sensitivity analysis of bioretention cell design elements can provide a theoretical basis for the design and construction of a bioretention cell. This study uses the storm management model (SWMM) and the bioretention infiltration RECARGA to generate runoff and outflow time series for calculation of hydrologic performance metrics. The hydrologic performance metrics include: the overflow ratio, groundwater recharge ratio, ponding time and underdrain flow ratio. The FAST method is chose to analyze sensitivity of design elements for two types of bioretention cell, one without underdrain and the other with underdrain. The results show that the surface area is the most sensitivity to most the hydrologic metrics for both types of bioretention, while the planting soil depth and the gravel depth are the two least sensitive elements. The saturated infiltration rates of planting soil and native soil are another two sensitive elements for bioretention cells without underdrain, but the saturated infiltration rate of planting soil and underdrain size are another two sensitive design elements for bioretention cells with underdrain.Keywords: Global sensitivity analysis; bioretention cell; design elements; FAST

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Contrast and Resolution in Alfresco Microscopy and Thick Specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100096102 ◽

1972 ◽

Vol 30 ◽

pp. 570-571

Author(s):

J. R. Sellar ◽

J. M. Cowley

Keyword(s):

Solid Material ◽

Cell Design ◽

Current Interest ◽

High Voltage Electron Microscopy ◽

Voltage Electron ◽

Transmission Electron ◽

Environmental Cell ◽

Scanning Transmission ◽

Conventional Transmission Electron Microscope ◽

Transmission Microscope

Current interest in high voltage electron microscopy, especially in the scanning mode, has prompted the development of a method for determining the contrast and resolution of images of specimens in controlled-atmosphere stages or open to the air, hydrated biological specimens being a good example. Such a method would be of use in the prediction of microscope performance and in the subsequent optimization of environmental cell design for given circumstances of accelerating voltage, cell gas pressure and constitution, and desired resolution.Fig. 1 depicts the alfresco cell of a focussed scanning transmission microscope with a layer of gas L (and possibly a thin window W) between the objective O and specimen T. Using the principle of reciprocity, it may be considered optically equivalent to a conventional transmission electron microscope, if the beams were reversed. The layer of gas or solid material after the specimen in the STEM or before the specimen in TEM has no great effect on resolution or contrast and so is ignored here.

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Relation of Statistically Significant, Abnormal, and Typical WAIS-R VIQ-PIQ Discrepancies to Full Scale IQs

European Journal of Psychological Assessment ◽

10.1027//1015-5759.16.2.107 ◽

2000 ◽

Vol 16 (2) ◽

pp. 107-114 ◽

Cited By ~ 3

Author(s):

Louis M. Hsu ◽

Judy Hayman ◽

Judith Koch ◽

Debbie Mandell

Keyword(s):

Graphical Method ◽

The United States ◽

Full Scale ◽

True Score ◽

Absolute Value ◽

Normative Population ◽

The Us ◽

The Mean ◽

And Performance ◽

Quadratic Models

Summary: In the United States' normative population for the WAIS-R, differences (Ds) between persons' verbal and performance IQs (VIQs and PIQs) tend to increase with an increase in full scale IQs (FSIQs). This suggests that norm-referenced interpretations of Ds should take FSIQs into account. Two new graphs are presented to facilitate this type of interpretation. One of these graphs estimates the mean of absolute values of D (called typical D) at each FSIQ level of the US normative population. The other graph estimates the absolute value of D that is exceeded only 5% of the time (called abnormal D) at each FSIQ level of this population. A graph for the identification of conventional “statistically significant Ds” (also called “reliable Ds”) is also presented. A reliable D is defined in the context of classical true score theory as an absolute D that is unlikely (p < .05) to be exceeded by a person whose true VIQ and PIQ are equal. As conventionally defined reliable Ds do not depend on the FSIQ. The graphs of typical and abnormal Ds are based on quadratic models of the relation of sizes of Ds to FSIQs. These models are generalizations of models described in Hsu (1996) . The new graphical method of identifying Abnormal Ds is compared to the conventional Payne-Jones method of identifying these Ds. Implications of the three juxtaposed graphs for the interpretation of VIQ-PIQ differences are discussed.

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A Modification of the Payne-Jones Method of Identifying Abnormal Differences in WISC-R Performance and Verbal IQ's

European Journal of Psychological Assessment ◽

10.1027/1015-5759.12.1.27 ◽

1996 ◽

Vol 12 (1) ◽

pp. 27-32 ◽

Cited By ~ 1

Author(s):

Louis M. Hsu

Keyword(s):

Full Scale ◽

True Score ◽

Verbal Iq ◽

Score Difference ◽

The Difference ◽

And Performance

The difference (D) between a person's Verbal IQ (VIQ) and Performance IQ (PIQ) has for some time been considered clinically meaningful ( Kaufman, 1976 , 1979 ; Matarazzo, 1990 , 1991 ; Matarazzo & Herman, 1985 ; Sattler, 1982 ; Wechsler, 1984 ). Particularly useful is information about the degree to which a difference (D) between scores is “abnormal” (i.e., deviant in a standardization group) as opposed to simply “reliable” (i.e., indicative of a true score difference) ( Mittenberg, Thompson, & Schwartz, 1991 ; Silverstein, 1981 ; Payne & Jones, 1957 ). Payne and Jones (1957) proposed a formula to identify “abnormal” differences, which has been used extensively in the literature, and which has generally yielded good approximations to empirically determined “abnormal” differences ( Silverstein, 1985 ; Matarazzo & Herman, 1985 ). However applications of this formula have not taken into account the dependence (demonstrated by Kaufman, 1976 , 1979 , and Matarazzo & Herman, 1985 ) of Ds on Full Scale IQs (FSIQs). This has led to overestimation of “abnormality” of Ds of high FSIQ children, and underestimation of “abnormality” of Ds of low FSIQ children. This article presents a formula for identification of abnormal WISC-R Ds, which overcomes these problems, by explicitly taking into account the dependence of Ds on FSIQs.

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