Experimental Activity on the Tubular SOFC CHP100 kWe Field Unit in Italy: Factor Significance, Effects and Regression Model Analysis

2006 ◽  
Author(s):  
M. Cali` ◽  
G. Orsello ◽  
M. Santarelli ◽  
P. Leone
Keyword(s):  
Regression Models ◽  
Thermal Power ◽  
Sensitivity Coefficient ◽  
Factorial Analysis ◽  
Screening Tests ◽  
Combined Effects ◽  
Test Plan ◽  
Utilization Factor ◽  
Independent Variables ◽  
Field Unit

The CHP 100 kWe tubular SOFC plant built by Siemens is operating at the Gas Turbine Technologies (GTT) in Turin. The generator started up on the June 2005 and produces electric and thermal power used in the factory. A first period of the activity was needed to lead the operation of the SOFC plant at the nominal condition and to reach the complete conditioning of the SOFC tubular fuel cells. Then, in order to characterize the operation of the generator and of the cogenerative system a first experimental campaign was designed by using the factorial analysis. With these methods, the effect of some independent variables (factors) on the plant operation is analyzed in form of screening tests: the analysis allows one to estimate the significance of the main and combined effects of each considered factor through an analysis of variance (ANOVA) on the experimental data. Moreover, the test plan has been designed by using a simple 2k factorial and a 2k factorial with spherical central composite design (CCD); these approaches allow to obtain respectively first-order and second-order regression models for some chosen dependent variables (i.e. DC and AC electric power, generator voltage, recovered heat etc...). The test plan has been performed at the fixed generator current of 500 Amps with two repetitions for each designed treatment. The factorial analysis has been applied considering two factors (22 factorial analysis): setup temperature of the generator and fuel utilization factor. First, the significance of the main and combined effects of the two considered factor has been evaluated. Then, the obtained data have been analyzed by using the Response Surface Methodology analysis (RSM). Finally, the regression models have been obtained for every dependent variable analyzed, with an outline of the sensitivity coefficient linking the dependent and the independent variables.

Prédiction de la composition corporelle post-mortem de cerfs de Virginie (Odocoileus virginianus) à partir de mesures morphologiques et massiques

1997 ◽  
Vol 75 (11) ◽  
pp. 1790-1795 ◽  
Author(s):  
Chantal Bois ◽  
Michel Crête ◽  
Jean Huot ◽  
Jean-Pierre Quellet
Keyword(s):  
Odocoileus Virginianus ◽  
Regression Models ◽  
Carcass Composition ◽  
Chemical Analyses ◽  
Linear Regression Models ◽  
Mass Measurements ◽  
Composition Corporelle ◽  
Independent Variables ◽  
Multiple Linear Regression Models ◽  
Field Stations

Morphologic and mass measurements were taken on 24 complete white-tailed deer (Odocoileus virginianus) carcasses of varying ages and both sexes in southern Quebec. Each carcass was divided into three parts (skin, viscera, rest) to determine water, protein, fat, and ash content by chemical analyses. Fat content of carcasses varied between 0.8 and 17.4%. Multiple linear regression models were selected to predict carcass composition from morphologic and mass measurements. Two situations were considered: measurements taken at the laboratory on whole animals and measurements taken at field stations on eviscerated carcasses provided by hunters. All selected models can be applied to any deer without taking into account age or sex; they include 1 – 4 independent variables. For whole animals, adjusted R2 of models varied between 0.99 (water) and 0.89 (ash); models developed for field stations were less precise, the lowest R2 values being 0.82 and 0.73 for ash and fat, respectively. These models can be useful for research and management purposes.

scholarly journals Sonographic Cervical Shortening after Labor Induction is a Predictor of Vaginal Delivery

2016 ◽  
Vol 38 (12) ◽  
pp. 585-588 ◽  
Author(s):  
Ugo Indraccolo ◽  
Gennaro Scutiero ◽  
Pantaleo Greco
Keyword(s):  
Logistic Regression ◽  
Vaginal Delivery ◽  
Regression Models ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Cervical Length ◽  
Labor Induction ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Sonographic Evaluation

Objective Analyzing if the sonographic evaluation of the cervix (cervical shortening) is a prognostic marker for vaginal delivery. Methods Women who underwent labor induction by using dinoprostone were enrolled. Before the induction and three hours after it, the cervical length was measured by ultrasonography to obtain the cervical shortening. The cervical shortening was introduced in logistic regression models among independent variables and for calculating receiver operating characteristic (ROC) curves. Results Each centimeter in the cervical shortening increases the odds of vaginal delivery in 24.4% within 6 hours; in 16.1% within 24 hours; and in 10.5% within 48 hours. The best predictions for vaginal delivery are achieved for births within 6 and 24 hours, while the cervical shortening poorly predicts vaginal delivery within 48 hours. Conclusion The greater the cervical shortening 3 hours after labor induction, the higher the likelihood of vaginal delivery within 6, 24 and 48 hours.

Spatial Distribution of Snow Storage and Snowmelt Dynamics in Central Yenisei Siberia

2021 ◽  
pp. 82-92
Author(s):  
I. V. Danilova ◽  
◽  
A. A. Onuchin ◽  
◽  
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Sea Level ◽  
Thermal Regime ◽  
Regression Models ◽  
Weather Station ◽  
Digital Maps ◽  
Independent Variables ◽  
Snow Storage

In this paper the spatial distribution of water reserves in the snow cover and the dynamics of snow cover melting due to the peculiarity of the thermal regime were analyzed for the central part of Yenisei Siberia. To create digital maps of water reserves in the snow cover, regression models were developed. The geographic coordinates, elevation above sea level and the distance from the orographic boundaries were used as independent variables in regression models. Based on the created maps, the dynamics of snow cover melting was obtained in the study area, taking into account the thermal regime at a key weather station.

scholarly journals Enzyme optimization to reduce the viscosity of pitanga (Eugenia uniflora L.) juice

2016 ◽  
Vol 19 (0) ◽  
Author(s):  
Ricardo Schmitz Ongaratto ◽  
Luiz Antonio Viotto
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Incubation Time ◽  
Regression Models ◽  
Incubation Temperature ◽  
Correlation Coefficients ◽  
Enzyme Concentration ◽  
Optimum Conditions ◽  
Activity Time ◽  
Independent Variables

Summary The aim of this work was to separately evaluate the effects of pectinase and cellulase on the viscosity of pitanga juice, and determine the optimum conditions for their use employing response surface methodology. The independent variables were pectinase concentration (0-2.0 mg.g–1) and cellulase concentration (0-1.0 mg.g–1), activity time (10-110 min) and incubation temperature (23.2-56.8 °C). The use of pectinase and cellulase reduced the viscosity by about 15% and 25%, respectively. The results showed that enzyme concentration was the most important factor followed by activity time, and for the application of cellulase the incubation temperature had a significant effect too. The regression models showed correlation coefficients (R2) near to 0.90. The pectinase application conditions that led to the lowest viscosity were: concentration of 1.7 mg.g–1, incubation temperature of 37.6 °C and incubation time of 80 minutes, while for cellulase the values were: concentration of 1.0 mg.g-1, temperature range of 25 °C to 35 °C and incubation time of 110 minutes.

scholarly journals Can Cut-Off-Values for Tumor Size or Patient Age in Breast Ultrasound Reduce Unnecessary Biopsies or is it all About Bi-rads?– A Retrospective Analysis of 763 Biopsied T1-Sized Lesions

2017 ◽  
Vol 03 (03) ◽  
pp. E94-E98 ◽  
Author(s):  
Laura Holzer-Fruehwald ◽  
Matthias Meissnitzer ◽  
Michael Weber ◽  
Stephan Holzer ◽  
Klaus Hergan ◽  
...  
Keyword(s):  
Regression Models ◽  
Breast Lesion ◽  
Binary Logistic Regression ◽  
Lesion Size ◽  
Breast Lesions ◽  
Predictive Values ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Oval Shape ◽  
Histological Result

Abstract Aims and Objectives To assess whether it is possible to establish a size cut-off-value for sonographically visible breast lesions in a screening situation, under which it is justifiable to obviate a biopsy and to evaluate the grayscale characteristics of the identified lesions. Materials and Methods Images of sonographically visible and biopsied breast lesions of 684 patients were retrospectively reviewed and assessed for the following parameters: size, shape, margin, lesion boundary, vascularity, patient’s age, side of breast, histological result, and initial BI-RADS category. Statistical analyses (t-test for independent variables, ROC analyses, binary logistic regression models, cross-tabulations, positive/negative predictive values) were performed using IBM SPSS (Version 21.0). Results Of all 763 biopsied lesions, 223 (29.2%) showed a malignant histologic result, while 540 (70.8%) were benign. Although we did find a statistically significant correlation of malignancy and lesion size (p=0.031), it was not possible to define a cut-off value, under which it would be justifiable to obviate a biopsy in terms of sensitivity and specificity (AUC: 0.558) at any age. Lesions showing the characteristics of a round or oval shape, a sharp delineation and no echogenic rim (n=112) were benign with an NPV of 99.1%. Conclusion It is not possible to define a cut-off value for size or age, under which a biopsy of a sonographically visible breast lesion can be obviated in the screening situation. The combination of the 3 grayscale characteristics, shape (round or oval), margin (circumscribed) and no echogenic-rim sign, showed an NPV of 99.1%. Therefore, it seems appropriate to classify such lesions as BI-RADS 2.

Hollywood Rules

2017 ◽  
pp. 1-9
Author(s):  
Karl Schmedders ◽  
Charlotte Snyder ◽  
Ute Schaedel
Keyword(s):  
Confidence Intervals ◽  
Statistical Methods ◽  
Regression Models ◽  
Functional Form ◽  
Hedge Fund ◽  
Fund Manager ◽  
Wall Street ◽  
Independent Variables ◽  
Point Estimates ◽  
Hedge Fund Manager

Wall Street hedge fund manager Kim Meyer is considering investing in an SFA (slate financing arrangement) in Hollywood. Dave Griffith, a Hollywood producer, is pitching for the investment and has conducted a broad analysis of recent movie data to determine the important drivers of a movie’s success. In order to convince Meyer to invest in an SFA, Griffith must anticipate possible questions to maximize his persuasiveness.Students will analyze the factors driving a movie’s revenue using various statistical methods, including calculating point estimates, computing confidence intervals, conducting hypothesis tests, and developing regression models (in which they must both choose the relevant set of independent variables as well as determine an appropriate functional form for the regression equation). The case also requires the interpretation of the quantitative findings in the context of the application.

scholarly journals Factors Affecting Capacity Utilization of Thermal Power (Coal) Plants in India

2020 ◽  
Author(s):  
Alok Kumar Tripathi
Keyword(s):  
Power Plants ◽  
Thermal Power ◽  
Partial Least Square ◽  
The Other ◽  
Thermal Power Plants ◽  
Draft Report ◽  
Utilization Factor ◽  
Other Hand ◽  
Major Factors ◽  
Energy Shortage

As on 31.03.2020, 55.4 % (205135 MW) of total installed capacity (370106 MW) in India is through coal and lignite based power plants. These plants, set up by central, state and private utilities with substantial capital investment are facing consistently reducing Plant Utilization Factor (known as Plant Load Factor, PLF, in India). In the year 2019-20 the national average thermal power PLF stood at 55.4%, down from 78.6 % in 2007-08. On the other hand, the electricity demand is consistently rising in the country and there exists a peak and energy shortage at national level. In 2019-20 energy shortage was 0.7 % and peak shortage was 0.5 %. A disturbing paradox therefore exists here. On one hand, the country is power deficit, and on the other hand, a large amount of coal based affordable power, ready to be generated by thermal power generators, remains grossly unused. Looking into the fact that considerable investment has gone into developing these thermal power generation assets in the country, the falling PLF is a matter of concern for all the key stakeholders including the power producers, lenders, regulators and consumers. This paper identifies seven major factors that are affecting PLF of thermal power plants and then makes an attempt to project future scenario of PLF so that critical stakeholders can intervene through appropriate actions. Primary research with responses from power professionals has been used to find out the major factors. Future projection of PLF has been done using Partial Least Square (PLS) regression. Projection shows that in the Business As Usual case (Factors increasing at the current CAGR rate), the thermal power plants will face very low level of PLF (14.76 %) by 2024-25. This will mean that many plants will be shut down and many will run for only few hours in a day that too at very low loads. If the future generation mix is kept as indicated by Central Electricity Authority (CEA), a Govt. of India in its report (Draft report on optimal generation capacity mix for 2029-30- CEA- Govt of India) then the thermal power plant average PLF can sustain above 68 % until 2024-25. If followed, this path can be a breather for the thermal power plants.

scholarly journals Regional regression models of percentile flows for the contiguous US: Expert versus data-driven independent variable selection

2016 ◽  
Author(s):  
Geoffrey Fouad ◽  
André Skupin ◽  
Christina L. Tague
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Predictive Performance ◽  
Data Driven ◽  
Mean Annual Precipitation ◽  
Expert Assessment ◽  
Independent Variables ◽  
Regional Regression ◽  
Data Driven Approach ◽  
Small Set

Abstract. Percentile flows are statistics derived from the flow duration curve (FDC) that describe the flow equaled or exceeded for a given percent of time. These statistics provide important information for managing rivers, but are often unavailable since most basins are ungauged. A common approach for predicting percentile flows is to deploy regional regression models based on gauged percentile flows and related independent variables derived from physical and climatic data. The first step of this process identifies groups of basins through a cluster analysis of the independent variables, followed by the development of a regression model for each group. This entire process hinges on the independent variables selected to summarize the physical and climatic state of basins. Distributed physical and climatic datasets now exist for the contiguous United States (US). However, it remains unclear how to best represent these data for the development of regional regression models. The study presented here developed regional regression models for the contiguous US, and evaluated the effect of different approaches for selecting the initial set of independent variables on the predictive performance of the regional regression models. An expert assessment of the dominant controls on the FDC was used to identify a small set of independent variables likely related to percentile flows. A data-driven approach was also applied to evaluate two larger sets of variables that consist of either (1) the averages of data for each basin or (2) both the averages and statistical distribution of basin data distributed in space and time. The small set of variables from the expert assessment of the FDC and two larger sets of variables for the data-driven approach were each applied for a regional regression procedure. Differences in predictive performance were evaluated using 184 validation basins withheld from regression model development. The small set of independent variables selected through expert assessment produced similar, if not better, performance than the two larger sets of variables. A parsimonious set of variables only consisted of mean annual precipitation, potential evapotranspiration, and baseflow index. Additional variables in the two larger sets of variables added little to no predictive information. Regional regression models based on the parsimonious set of variables were developed using 734 calibration basins, and were converted into a tool for predicting 13 percentile flows in the contiguous US. Supplementary Material for this paper includes an R graphical user interface for predicting the percentile flows of basins within the range of conditions used to calibrate the regression models. The equations and performance statistics of the models are also supplied in tabular form.

Evaluating the Statistical Significance of Models Developed by Stepwise Regression

1983 ◽  
Vol 20 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Shelby H. McIntyre ◽  
David B. Montgomery ◽  
V. Srinivasan ◽  
Barton A. Weitz
Keyword(s):  
Monte Carlo ◽  
Null Hypothesis ◽  
Regression Models ◽  
Stepwise Regression ◽  
Statistical Significance ◽  
Selection Procedure ◽  
Coefficient Of Determination ◽  
Forward Selection ◽  
Monte Carlo Simulation Study ◽  
Independent Variables

Information for evaluating the statistical significance of stepwise regression models developed with a forward selection procedure is presented. Cumulative distributions of the adjusted coefficient of determination ([Formula: see text]) under the null hypothesis of no relationship between the dependent variable and m potential independent variables are derived from a Monté Carlo simulation study. The study design included sample sizes of 25, 50, and 100, available independent variables of 10, 20, and 40, and three criteria for including variables in the regression model. The results reveal that the biases involved in testing statistical significance by two well-known rules are very large, thus demonstrating the desirability of using the Monté Carlo cumulative [Formula: see text] distributions developed by the authors. Although the results were derived under the assumption of uncorrelated predictors, the authors show that the results continue to be useful for the correlated predictor case.

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