Tidewater (Norway)

The current study aimed to assess perinatal depression (PD) screening and treatment practices of obstetrical health care providers. Retrospective record reviews (n = 557) evaluated the PD screening, referral, and treatment practices at an Obstetrician/Gynecology practice. This study assessed the frequency of screening for PD, rates of elevated Edinburgh Postnatal Depression Scale (EPDS) scores, treatment recommendations, demographic correlates, and predictors of elevated EPDS scores. PD screening completion rates were: 60.1% (intake), 35% (glucola test), and 85.5% (6-week follow-up). Rates of clinically elevated EPDS scores were: 18.21% (intake), 17.43% (glucola test), and 13.00% (6-week follow-up). Correlates of clinically elevated EPDS scores at intake and 6-week follow-up were history of depression, history of anxiety, and young age. History of depression and anxiety were associated with an increased likelihood of having a clinically significant EPDS score at intake. Intake EPDS score and history of depression were associated with an increased likelihood of having a clinically significant EPDS score at 6-week follow-up. Obstetric/gynecology providers should screen for perinatal depression at every obstetrical appointment. It is important to thoroughly assess history of depression and anxiety. Education and training for health care providers and perinatal women may improve the mental health experience of perinatal women. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Expert systems (ES) are computer programs which make recommendations and draw conclusions from heuristic rules and from relationships derived out of human experience. A decision support system (DSS) is a computer-based management information system (MIS) linking one or more data bases to computer software for exploration and analysis of the data. Combined in a single system an ES and a DSS comprise an expert support system. A simple expert support system for use in fishery stock assessment and management is described. It demonstrates a number of practical benefits of the use of expert systems technology to implement and integrate some simple tasks, i.e., bioeconomic modelling, cost/benefit sampling analyses, and technical interpretation, namely: (1) the effects of different population structures and patterns of fishing on modelling results can be examined under the programmed guidance of the expert system; (2) the effects of different levels of sampling precision on modelling results can be similarly examined; (3) cost/benefit analyses of different sampling budgets can be examined; and (4) management advice specific to each sampling, surplus production (four different models), and net present value (NPV) case is given. An examination of the future of expert support systems in fishery stock assessment and management considers four possible levels of support, as follows: (1) to provide technical explanations, (2) to manage and catalog repetitive processes, (3) to guide “low-level decisions”, and (4) to contribute directly to strategic and policy decisions. Currently, movement in expert systems usage is toward so-called “embedded systems”, for example, an expert support system which comprises, in addition to computational models and databases, one or more expert systems. Problems related to the design and development of ESS for fishery stock assessment and management are challenging conceptual problems; the construction and programming of ESS are reasonably tractable tasks. Determining what an ESS will do is often the principle conceptual question. Expert support systems provide another tool for fishery stock assessment scientists and managers to increase their productivity.

Hybridization programs utilizing exotic soybean [Glycine max (L.) Merr.] germplasm might be more successful if selection systems were used to identify plant introductions (PIs) that combine well with adapted genotypes to produce high‐yielding breeding populations. The objectives of this study were to compare PIs selected for visual appearance vs. yield using a topcross procedure (TCP), compare the performance of PI parents with their progeny utilizing a TCP, to compare the performance of testers used in the topcross, and to identify PIs that combine well with adapted genotypes to produce high‐yielding progeny. Nine PIs selected for desirable visual appearance and 14 yield‐selected PIs were each crossed to four adapted lines in a TCP. The 92 F2 bulks were evaluated along with the 23 PIs and four adapted testers in three environments with two replications per environment. The F2 bulks produced from the yield‐selected PIs had a mean yield of 2669 kg/ha, which was significantly (P < 0.01) greater than the yield (2566 kg/ha) of the F2 bulks produced from the visually selected PIs. A positive correlation (r = 0.65; P < 0.01) between PI parent yield and PI group yield (mean of four F2 bulks with a common PI parent) demonstrated good association for yield between parent and progeny. Selecting PIs for yield, per se, appeared to be the more efficient method to identify desirable PIs for germplasm development programs. Our results indicate that the choice of a tester would not be critical if a TCP was utilized. The plant introductions PI 85469, PI 88306, and PI 88486 produced the three highest‐yielding PI groups and, therefore, are recommended for germplasm development programs in the mid‐Atlantic states. We suggest that these three PIs be evaluated in other geographic regions. The F2 bulks generally had acceptable lodging resistance, seed qualities, and seed weights.

Abstract A knowledge of rock properties is required to predict the size and performance of a hydrocarbon reservoir. The usual methods of establishing rock properties are through analysis of core samples and electric log data. Each method provides a large number of data points from minute segments of the reservoir. Assuming the sampling procedure has provided an unbiased representative set of data, a statistical approach can be applied to the analysis to obtain a characteristic set of representative parameters. Several techniques are available for resolving the collected data into a set of porosity and permeability values. Some methods often require considerable time or more than a layman's knowledge of statistics. A procedure is proposed which emphasis Pearson's system for defining a distribution type and other auxiliary statistical parameters to ascertain the most representative measure for a set of data. This procedure is readily adaptable to computer programming which provides rigorous statistical results in a form that can be analyzed with limited supplemental knowledge. The method minimizes the analysis time required to resolve the data into a meaningful form and is more thorough than a purely empirical approach dependent upon graphic illustrations. Results are presented so that vertical and areal variations in porosities and permeabilities are readily discernible. The method provides for determining effective permeability, or permeability cutoff, resulting in a minimum distortion in the used data and guidelines for considering the adequacy of the sample sizes. Introduction The use Of core analysis to determine representative values for permeability, porosity and fluid saturation is common. There are several methods for determining a numerical representation for these parameters, all of which employ statistical tools to a greater or lesser degree of sophistication. The purpose of this article is to show how the use of more sophisticated statistical methods can accelerate and facilitate the selection of these reservoir measures. The procedure provides the engineer a better opportunity to concentrate on constructing a more accurate representation of the reservoir configuration. Use of an electronic computer is necessary for the full utilization of this procedure. The computer program should generate the frequency tables, statistical measures and Pearson's constants for defining the distribution, thus allowing the selection of the most representative measure. Emperical Statistics The tedium of manually analyzing core data can be eliminated by using a data processing program to resolve the data into a frequency table allowing analysis to proceed immediately. A common method of analysis relies on graphic illustrations for determining the type of distribution and the selection of a representative measure; however, such a method has a tendency to oversimplify and distort because it is intended to be applied to symmetrical distributions. A major consideration in using a computer to generate the frequency tables is determination of the interval size. It is common in the preparation of these tables to employ preset data intervals. If a variable interval is to he used, it is necessary that the data be arrayed for selection of the appropriate interval. Results provided from a preset interval do not always afford the best frequency table and may influence a graphic analysis. Fig. l is a presentation of data having a limited range. Histogram A is this data using 2-percent intervals and covering only four intervals. Histogram B is the same data presented in 1-percent intervals. It is apparent that the 1-percent intervals give a better presentation of the data. Additionally, linearly shifting the intervals can even more appropriately illustrate the distribution characteristics. Fig. 2 is the same data shifted about the mean while retaining the size of the interval. Permeability should generally be transformed to a logarithmic scale for better analysis. Selection of a proper log base is important because the base affects the log data as the interval size affects an arithmetic scale. The graphic method requires that the data can be approximated by a straight line when plotted on probability paper. Certain core data will result in distributions which do not adapt themselves to this procedure. Fig. 3 illustrates both a symmetrical and an asymmetrical distribution. It is difficult to approximate an asymmetrical distribution (Curve A) by a straight line.

Introduction The composite gas compressibility graph was constructed by combining the data from the three graphs originally presented by Standing and Katz. In its original form, the following method was used to solve for compressibility factors.Graphically determine the pseudo-critical pressure and temperature for the gas in question.Calculate the pseudo-reduced pressure and temperature.Graphically solve for the compressibility (Z) factor. By combining this original data, it was possible to construct a single composite graph which eliminates all intermediate calculations when solving for compressibility factors. Basic Data and Equations Fig. 1 is a plot of the pseudo-critical temperature vs specific gravity of natural gases, as originally presented by Standing and Katz. For a given specific gravity, the actual temperature in degrees Fahrenheit is related to the reduced temperature as follows. or .............................(1) Fig. 1 also includes a plot of pseudo-critical pressure vs gas gravity. For all practical purposes, this can be considered a straight line defined by .............................(2) and the reduced pressure is, by definition, ............................(3) or ............................(4) Construction of Composite Compressibility Chart The basic premise on which the chart was constructed is that, for any given pseudo-reduced pressure and temperature, all gases have the same compressibility (Z) factor regardless of gas gravity. To satisfy this condition, a basic graph was constructed with the left-hand portion being a group of straight-line plots of actual temperature vs pseudo-reduced temperature for various gas gravities ranging from 0.56 to 1.00 in 0.02 increments. The right-hand portion was then constructed as three straight-line plots of actual pressure vs pseudo-reduced pressure for gas gravities of 0.60, 0.70 and 1.00. Using Standing and Katz's "Compressibility of Natural Gases" graph, the basic graph was then completed by plotting and connecting equal values of Z, using a 0.70 gravity as a base. JPT P. 1081^

American Journal of Agricultural EconomicsVolume 42, Issue 4 p. 910-920 Note Tolerance Interval for Multiple Regression John Y. Lu, John Y. Lu Tidewater Oil CompanySearch for more papers by this author John Y. Lu, John Y. Lu Tidewater Oil CompanySearch for more papers by this author First published: 01 November 1960 https://doi.org/10.2307/1235126Citations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume42, Issue4November 1960Pages 910-920 RelatedInformation