Variability and uncertainty in Swedish exposure factors for use in quantitative exposure assessments.

Science.gov (United States)

Filipsson, Monika; Öberg, Tomas; Bergbäck, Bo

2011-01-01

Information of exposure factors used in quantitative risk assessments has previously been compiled and reported for U.S. and European populations. However, due to the advancement of science and knowledge, these reports are in continuous need of updating with new data. Equally important is the change over time of many exposure factors related to both physiological characteristics and human behavior. Body weight, skin surface, time use, and dietary habits are some of the most obvious examples covered here. A wealth of data is available from literature not primarily gathered for the purpose of risk assessment. Here we review a number of key exposure factors and compare these factors between northern Europe--here represented by Sweden--and the United States. Many previous compilations of exposure factor data focus on interindividual variability and variability between sexes and age groups, while uncertainty is mainly dealt with in a qualitative way. In this article variability is assessed along with uncertainty. As estimates of central tendency and interindividual variability, mean, standard deviation, skewness, kurtosis, and multiple percentiles were calculated, while uncertainty was characterized using 95% confidence intervals for these parameters. The presented statistics are appropriate for use in deterministic analyses using point estimates for each input parameter as well as in probabilistic assessments. © 2010 Society for Risk Analysis.

Statistical analysis of nuclear power plant pump failure rate variability: some preliminary results

International Nuclear Information System (INIS)

Martz, H.F.; Whiteman, D.E.

1984-02-01

In-Plant Reliability Data System (IPRDS) pump failure data on over 60 selected pumps in four nuclear power plants are statistically analyzed using the Failure Rate Analysis Code (FRAC). A major purpose of the analysis is to determine which environmental, system, and operating factors adequately explain the variability in the failure data. Catastrophic, degraded, and incipient failure severity categories are considered for both demand-related and time-dependent failures. For catastrophic demand-related pump failures, the variability is explained by the following factors listed in their order of importance: system application, pump driver, operating mode, reactor type, pump type, and unidentified plant-specific influences. Quantitative failure rate adjustments are provided for the effects of these factors. In the case of catastrophic time-dependent pump failures, the failure rate variability is explained by three factors: reactor type, pump driver, and unidentified plant-specific influences. Finally, point and confidence interval failure rate estimates are provided for each selected pump by considering the influential factors. Both types of estimates represent an improvement over the estimates computed exclusively from the data on each pump

Analysis of Design Variables of Annular Linear Induction Electromagnetic Pump using an MHD Model

Energy Technology Data Exchange (ETDEWEB)

Kwak, Jae Sik; Kim, Hee Reyoung [Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of)

2015-05-15

The generated force is affected by lots of factors including electrical input, hydrodynamic flow, geometrical shape, and so on. These factors, which are the design variables of an ALIP, should be suitably analyzed to optimally design an ALIP. Analysis on the developed pressure and efficiency of the ALIP according to the change of design variables is required for the ALIP satisfying requirements. In this study, the design variables of the ALIP are analyzed by using ideal MHD analysis model. Electromagnetic force and efficiency are derived by analyzing the main design variables such as pump core length, inner core diameter, flow gap and turns of coils. The developed pressure and efficiency of the ALIP were derived and analyzed on the change of the main variables such as pump core length, inner core diameter, flow gap, and turns of coils of the ALIP.

The Recoverability of P-Technique Factor Analysis

Science.gov (United States)

Molenaar, Peter C. M.; Nesselroade, John R.

2009-01-01

It seems that just when we are about to lay P-technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables--dynamic factor models--it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even…

Evaluating the underlying factors behind variable rate debt.

Science.gov (United States)

McCue, Michael J; Kim, Tae Hyun Tanny

2007-01-01

Recent trends show a greater usage of variable rate debt among health care bond issues. In 2004, 63.4% of the total health care bonds issued were variable rate compared with 30.6% in 1995 (Fitch Ratings, 2005). The purpose of this study is to gain a better understanding of the underlying factors, credit spread, issue characteristics, and issuer factors behind why hospitals and health system borrowers select variable rate debt compared with fixed rate debt. From 2000 to 2004, this study sampled 230 newly issued tax-exempt bonds issued by acute care hospitals and health care systems that included both variable and fixed rate debt issues. Using a logistic regression model, hospitals with variable rate debt issues were assigned a value of 1, whereas hospitals with fixed rate debt issues were assigned a value of 0. This study found a positive association between bond insurance and variable rate debt and a negative association between callable feature and variable rate debt. Facilities located in certificate-of-need states that possessed higher case mix acuity, earned higher profit margins, generated higher debt service coverage, and held less debt were more likely to issue variable rate debt. Overall, hospital managers and board members of hospitals possessing a strong financial performance have an interest in utilizing variable rate debt to lower their cost of capital. In addition, this outcome may also reflect that investment bankers are doing a better job in educating senior hospital management about the interest rate savings benefit of variable rate compared with fixed rate debt.

Using variable combination population analysis for variable selection in multivariate calibration.

Science.gov (United States)

Yun, Yong-Huan; Wang, Wei-Ting; Deng, Bai-Chuan; Lai, Guang-Bi; Liu, Xin-bo; Ren, Da-Bing; Liang, Yi-Zeng; Fan, Wei; Xu, Qing-Song

2015-03-03

Variable (wavelength or feature) selection techniques have become a critical step for the analysis of datasets with high number of variables and relatively few samples. In this study, a novel variable selection strategy, variable combination population analysis (VCPA), was proposed. This strategy consists of two crucial procedures. First, the exponentially decreasing function (EDF), which is the simple and effective principle of 'survival of the fittest' from Darwin's natural evolution theory, is employed to determine the number of variables to keep and continuously shrink the variable space. Second, in each EDF run, binary matrix sampling (BMS) strategy that gives each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, model population analysis (MPA) is employed to find the variable subsets with the lower root mean squares error of cross validation (RMSECV). The frequency of each variable appearing in the best 10% sub-models is computed. The higher the frequency is, the more important the variable is. The performance of the proposed procedure was investigated using three real NIR datasets. The results indicate that VCPA is a good variable selection strategy when compared with four high performing variable selection methods: genetic algorithm-partial least squares (GA-PLS), Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). The MATLAB source code of VCPA is available for academic research on the website: http://www.mathworks.com/matlabcentral/fileexchange/authors/498750. Copyright © 2015 Elsevier B.V. All rights reserved.

Partitioning the variability of fasting plasma glucose levels in pedigrees. Genetic and environmental factors.

Science.gov (United States)

Boehnke, M; Moll, P P; Kottke, B A; Weidman, W H

1987-04-01

Fasting plasma glucose measurements made in 1972-1977 on normoglycemic individuals in three-generation Caucasian pedigrees from Rochester, Minnesota were analyzed. The authors determined the contributions of polygenic loci and environmental factors to fasting plasma glucose variability in these pedigrees. To that end, fasting plasma glucose measurements were normalized by an inverse normal scores transformation and then regressed separately for males and females on measured concomitants including age, body mass index (weight/height2), season of measurement, sex hormone use, and diuretic use. The authors found that 27.7% of the variability in normalized fasting plasma glucose in these pedigrees is explained by these measured concomitants. Subsequent variance components analysis suggested that unmeasured polygenic loci and unmeasured shared environmental factors together account for at least an additional 36.7% of the variability in normalized fasting plasma glucose, with genes alone accounting for at least 27.3%. These results are consistent with the known familiality of diabetes, for which fasting plasma glucose level is an important predictor. Further, these familial factors provide an explanation for at least half the variability in normalized fasting plasma glucose which remains after regression on known concomitants.

Factor Economic Analysis at Forestry Enterprises

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M.Yu. Chik

2018-03-01

Full Text Available The article studies the importance of economic analysis according to the results of research of scientific works of domestic and foreign scientists. The calculation of the influence of factors on the change in the cost of harvesting timber products by cost items has been performed. The results of the calculation of the influence of factors on the change of costs on 1 UAH are determined using the full cost of sold products. The variable and fixed costs and their distribution are allocated that influences the calculation of the impact of factors on cost changes on 1 UAH of sold products. The paper singles out the general results of calculating the influence of factors on cost changes on 1 UAH of sold products. According to the results of the analysis, the list of reserves for reducing the cost of production at forest enterprises was proposed. The main sources of reserves for reducing the prime cost of forest products at forest enterprises are investigated based on the conducted factor analysis.

Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

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Renfu Jia

2016-01-01

Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

Science.gov (United States)

Szabo, J.K.; Fedriani, E.M.; Segovia-Gonzalez, M. M.; Astheimer, L.B.; Hooper, M.J.

2010-01-01

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.

Analysis of Economic Factors Affecting Stock Market

OpenAIRE

Xie, Linyin

2010-01-01

This dissertation concentrates on analysis of economic factors affecting Chinese stock market through examining relationship between stock market index and economic factors. Six economic variables are examined: industrial production, money supply 1, money supply 2, exchange rate, long-term government bond yield and real estate total value. Stock market comprises fixed interest stocks and equities shares. In this dissertation, stock market is restricted to equity market. The stock price in thi...

Common Factor Analysis Versus Principal Component Analysis: Choice for Symptom Cluster Research

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Hee-Ju Kim, PhD, RN

2008-03-01

Conclusion: If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research, CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

An inter-battery factor analysis of the comrey personality scales and the 16 personality factor questionnaire

OpenAIRE

Gideon P. de Bruin

2000-01-01

The scores of 700 Afrikaans-speaking university students on the Comrey Personality Scales and the 16 Personality Factor Questionnaire were subjected to an inter-battery factor analysis. This technique uses only the correlations between two sets of variables and reveals only the factors that they have in common. Three of the Big Five personality factors were revealed, namely Extroversion, Neuroticism and Conscientiousness. However, the Conscientiousness factor contained a relatively strong uns...

The cross wavelet analysis of dengue fever variability influenced by meteorological conditions

Science.gov (United States)

Lin, Yuan-Chien; Yu, Hwa-Lung; Lee, Chieh-Han

2015-04-01

The multiyear variation of meteorological conditions induced by climate change causes the changing diffusion pattern of infectious disease and serious epidemic situation. Among them, dengue fever is one of the most serious vector-borne diseases distributed in tropical and sub-tropical regions. Dengue virus is transmitted by several species of mosquito and causing lots amount of human deaths every year around the world. The objective of this study is to investigate the impact of meteorological variables to the temporal variation of dengue fever epidemic in southern Taiwan. Several extreme and average indices of meteorological variables, i.e. temperature and humidity, were used for this analysis, including averaged, maximum and minimum temperature, and average rainfall, maximum 1-hr rainfall, and maximum 24-hr rainfall. This study plans to identify and quantify the nonlinear relationship of meteorological variables and dengue fever epidemic, finding the non-stationary time-frequency relationship and phase lag effects of those time series from 1998-2011 by using cross wavelet method. Results show that meteorological variables all have a significant time-frequency correlation region to dengue fever epidemic in frequency about one year (52 weeks). The associated phases can range from 0 to 90 degrees (0-13 weeks lag from meteorological factors to dengue incidences). Keywords: dengue fever, cross wavelet analysis, meteorological factor

Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

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Isabel Gallego-Alvarez

2014-11-01

Full Text Available The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.

Factors that Affect Poverty Areas in North Sumatera Using Discriminant Analysis

Science.gov (United States)

Nasution, D. H.; Bangun, P.; Sitepu, H. R.

2018-04-01

In Indonesia, especially North Sumatera, the problem of poverty is one of the fundamental problems that become the focus of government both central and local government. Although the poverty rate decreased but the fact is there are many people who are poor. Poverty happens covers several aspects such as education, health, demographics, and also structural and cultural. This research will discuss about several factors such as population density, Unemployment Rate, GDP per capita ADHK, ADHB GDP per capita, economic growth and life expectancy that affect poverty in Indonesia. To determine the factors that most influence and differentiate the level of poverty of the Regency/City North Sumatra used discriminant analysis method. Discriminant analysis is one multivariate analysis technique are used to classify the data into a group based on the dependent variable and independent variable. Using discriminant analysis, it is evident that the factor affecting poverty is Unemployment Rate.

An Instrumental Variable Probit (IVP Analysis on Depressed Mood in Korea: The Impact of Gender Differences and Other Socio-Economic Factors

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Lara Gitto

2015-08-01

Full Text Available Background Depression is a mental health state whose frequency has been increasing in modern societies. It imposes a great burden, because of the strong impact on people’s quality of life and happiness. Depression can be reliably diagnosed and treated in primary care: if more people could get effective treatments earlier, the costs related to depression would be reversed. The aim of this study was to examine the influence of socio-economic factors and gender on depressed mood, focusing on Korea. In fact, in spite of the great amount of empirical studies carried out for other countries, few epidemiological studies have examined the socio-economic determinants of depression in Korea and they were either limited to samples of employed women or did not control for individual health status. Moreover, as the likely data endogeneity (i.e. the possibility of correlation between the dependent variable and the error term as a result of autocorrelation or simultaneity, such as, in this case, the depressed mood due to health factors that, in turn might be caused by depression, might bias the results, the present study proposes an empirical approach, based on instrumental variables, to deal with this problem. Methods Data for the year 2008 from the Korea National Health and Nutrition Examination Survey (KNHANES were employed. About seven thousands of people (N= 6,751, of which 43% were males and 57% females, aged from 19 to 75 years old, were included in the sample considered in the analysis. In order to take into account the possible endogeneity of some explanatory variables, two Instrumental Variables Probit (IVP regressions were estimated; the variables for which instrumental equations were estimated were related to the participation of women to the workforce and to good health, as reported by people in the sample. Explanatory variables were related to age, gender, family factors (such as the number of family members and marital status and socio

An Instrumental Variable Probit (IVP) analysis on depressed mood in Korea: the impact of gender differences and other socio-economic factors.

Science.gov (United States)

Gitto, Lara; Noh, Yong-Hwan; Andrés, Antonio Rodríguez

2015-04-16

Depression is a mental health state whose frequency has been increasing in modern societies. It imposes a great burden, because of the strong impact on people's quality of life and happiness. Depression can be reliably diagnosed and treated in primary care: if more people could get effective treatments earlier, the costs related to depression would be reversed. The aim of this study was to examine the influence of socio-economic factors and gender on depressed mood, focusing on Korea. In fact, in spite of the great amount of empirical studies carried out for other countries, few epidemiological studies have examined the socio-economic determinants of depression in Korea and they were either limited to samples of employed women or did not control for individual health status. Moreover, as the likely data endogeneity (i.e. the possibility of correlation between the dependent variable and the error term as a result of autocorrelation or simultaneity, such as, in this case, the depressed mood due to health factors that, in turn might be caused by depression), might bias the results, the present study proposes an empirical approach, based on instrumental variables, to deal with this problem. Data for the year 2008 from the Korea National Health and Nutrition Examination Survey (KNHANES) were employed. About seven thousands of people (N= 6,751, of which 43% were males and 57% females), aged from 19 to 75 years old, were included in the sample considered in the analysis. In order to take into account the possible endogeneity of some explanatory variables, two Instrumental Variables Probit (IVP) regressions were estimated; the variables for which instrumental equations were estimated were related to the participation of women to the workforce and to good health, as reported by people in the sample. Explanatory variables were related to age, gender, family factors (such as the number of family members and marital status) and socio-economic factors (such as education

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

Science.gov (United States)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Analysis of source regions and meteorological factors for the variability of spring PM10 concentrations in Seoul, Korea

Science.gov (United States)

Lee, Jangho; Kim, Kwang-Yul

2018-02-01

CSEOF analysis is applied for the springtime (March, April, May) daily PM10 concentrations measured at 23 Ministry of Environment stations in Seoul, Korea for the period of 2003-2012. Six meteorological variables at 12 pressure levels are also acquired from the ERA Interim reanalysis datasets. CSEOF analysis is conducted for each meteorological variable over East Asia. Regression analysis is conducted in CSEOF space between the PM10 concentrations and individual meteorological variables to identify associated atmospheric conditions for each CSEOF mode. By adding the regressed loading vectors with the mean meteorological fields, the daily atmospheric conditions are obtained for the first five CSEOF modes. Then, HYSPLIT model is run with the atmospheric conditions for each CSEOF mode in order to back trace the air parcels and dust reaching Seoul. The K-means clustering algorithm is applied to identify major source regions for each CSEOF mode of the PM10 concentrations in Seoul. Three main source regions identified based on the mean fields are: (1) northern Taklamakan Desert (NTD), (2) Gobi Desert and (GD), and (3) East China industrial area (ECI). The main source regions for the mean meteorological fields are consistent with those of previous study; 41% of the source locations are located in GD followed by ECI (37%) and NTD (21%). Back trajectory calculations based on CSEOF analysis of meteorological variables identify distinct source characteristics associated with each CSEOF mode and greatly facilitate the interpretation of the PM10 variability in Seoul in terms of transportation route and meteorological conditions including the source area.

Meta-Analysis of the Effects of Foods and Derived Products Containing Ellagitannins and Anthocyanins on Cardiometabolic Biomarkers: Analysis of Factors Influencing Variability of the Individual Responses

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María-Teresa García-Conesa

2018-02-01

Full Text Available Understanding interindividual variability in response to dietary polyphenols remains essential to elucidate their effects on cardiometabolic disease development. A meta-analysis of 128 randomized clinical trials was conducted to investigate the effects of berries and red grapes/wine as sources of anthocyanins and of nuts and pomegranate as sources of ellagitannins on a range of cardiometabolic risk biomarkers. The potential influence of various demographic and lifestyle factors on the variability in the response to these products were explored. Both anthocyanin- and ellagitannin-containing products reduced total-cholesterol with nuts and berries yielding more significant effects than pomegranate and grapes. Blood pressure was significantly reduced by the two main sources of anthocyanins, berries and red grapes/wine, whereas waist circumference, LDL-cholesterol, triglycerides, and glucose were most significantly lowered by the ellagitannin-products, particularly nuts. Additionally, we found an indication of a small increase in HDL-cholesterol most significant with nuts and, in flow-mediated dilation by nuts and berries. Most of these effects were detected in obese/overweight people but we found limited or non-evidence in normoweight individuals or of the influence of sex or smoking status. The effects of other factors, i.e., habitual diet, health status or country where the study was conducted, were inconsistent and require further investigation.

Meta-Analysis of the Effects of Foods and Derived Products Containing Ellagitannins and Anthocyanins on Cardiometabolic Biomarkers: Analysis of Factors Influencing Variability of the Individual Responses

Science.gov (United States)

Chambers, Karen; Andrés-Lacueva, Cristina; Konic Ristic, Aleksandra; Hollands, Wendy J.; Kroon, Paul A.; Rodríguez-Mateos, Ana; Istas, Geoffrey; Kontogiorgis, Christos A.; Morand, Christine; Espín, Juan Carlos

2018-01-01

Understanding interindividual variability in response to dietary polyphenols remains essential to elucidate their effects on cardiometabolic disease development. A meta-analysis of 128 randomized clinical trials was conducted to investigate the effects of berries and red grapes/wine as sources of anthocyanins and of nuts and pomegranate as sources of ellagitannins on a range of cardiometabolic risk biomarkers. The potential influence of various demographic and lifestyle factors on the variability in the response to these products were explored. Both anthocyanin- and ellagitannin-containing products reduced total-cholesterol with nuts and berries yielding more significant effects than pomegranate and grapes. Blood pressure was significantly reduced by the two main sources of anthocyanins, berries and red grapes/wine, whereas waist circumference, LDL-cholesterol, triglycerides, and glucose were most significantly lowered by the ellagitannin-products, particularly nuts. Additionally, we found an indication of a small increase in HDL-cholesterol most significant with nuts and, in flow-mediated dilation by nuts and berries. Most of these effects were detected in obese/overweight people but we found limited or non-evidence in normoweight individuals or of the influence of sex or smoking status. The effects of other factors, i.e., habitual diet, health status or country where the study was conducted, were inconsistent and require further investigation. PMID:29495642

Análisis del fracaso empresarial por sectores: factores diferenciadores = Cross-industry analysis of business failure: differential factors

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María Jesús Mures Quintana

2012-12-01

Full Text Available El objetivo de este trabajo se centra en el análisis del fracaso empresarial por sectores, a fin de identificar los factores explicativos y predictivos de este fenómeno que son diferentes en tres de los principales sectores que se distinguen en toda economía: industria, construcción y servicios. Para cada uno de estos sectores, seguimos el mismo procedimiento. En primer lugar, aplicamos un análisis de componentes principales con el que identificamos los factores explicativos del fracaso empresarial en los tres sectores. A continuación, consideramos dichos factores como variables independientes en un análisis discriminante, que aplicamos para predecir el fracaso de una muestra de empresas, utilizando no sólo información financiera en forma de ratios, sino también otras variables no financieras relativas a las empresas, así como información externa a las mismas que refleja las condiciones macroeconómicas bajo las que desarrollan su actividad. This paper focuses on a cross-industry analysis of business failure, in order to identify the explanatory and predictor factors of this event that are different in three of the main industries in every economy: manufacturing, building and service. For each one of these industries, the same procedure is followed. First, a principal components analysis is applied in order to identify the explanatory factors of business failure in the three industries. Next, these factors are considered as independent variables in a discriminant analysis, so as to predict the firms’ failure, using not only financial information expressed by ratios, but also other non-financial variables related to the firms, as well as external information that reflects macroeconomic conditions under which they develop their activity.

Comparison of Two- and Three-Dimensional Methods for Analysis of Trunk Kinematic Variables in the Golf Swing.

Science.gov (United States)

Smith, Aimée C; Roberts, Jonathan R; Wallace, Eric S; Kong, Pui; Forrester, Stephanie E

2016-02-01

Two-dimensional methods have been used to compute trunk kinematic variables (flexion/extension, lateral bend, axial rotation) and X-factor (difference in axial rotation between trunk and pelvis) during the golf swing. Recent X-factor studies advocated three-dimensional (3D) analysis due to the errors associated with two-dimensional (2D) methods, but this has not been investigated for all trunk kinematic variables. The purpose of this study was to compare trunk kinematic variables and X-factor calculated by 2D and 3D methods to examine how different approaches influenced their profiles during the swing. Trunk kinematic variables and X-factor were calculated for golfers from vectors projected onto the global laboratory planes and from 3D segment angles. Trunk kinematic variable profiles were similar in shape; however, there were statistically significant differences in trunk flexion (-6.5 ± 3.6°) at top of backswing and trunk right-side lateral bend (8.7 ± 2.9°) at impact. Differences between 2D and 3D X-factor (approximately 16°) could largely be explained by projection errors introduced to the 2D analysis through flexion and lateral bend of the trunk and pelvis segments. The results support the need to use a 3D method for kinematic data calculation to accurately analyze the golf swing.

ZnO crystals obtained by electrodeposition: Statistical analysis of most important process variables

International Nuclear Information System (INIS)

Cembrero, Jesus; Busquets-Mataix, David

2009-01-01

In this paper a comparative study by means of a statistical analysis of the main process variables affecting ZnO crystal electrodeposition is presented. ZnO crystals were deposited on two different substrates, silicon wafer and indium tin oxide. The control variables were substrate types, electrolyte concentration, temperature, exposition time and current density. The morphologies of the different substrates were observed using scanning electron microscopy. The percentage of substrate area covered by ZnO deposit was calculated by computational image analysis. The design of the applied experiments was based on a two-level factorial analysis involving a series of 32 experiments and an analysis of variance. Statistical results reveal that variables exerting a significant influence on the area covered by ZnO deposit are electrolyte concentration, substrate type and time of deposition, together with a combined two-factor interaction between temperature and current density. However, morphology is also influenced by surface roughness of the substrates

Variations of Histone Modification Patterns: Contributions of Inter-plant Variability and Technical Factors

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Sylva Brabencová

2017-12-01

Full Text Available Inter-individual variability of conspecific plants is governed by differences in their genetically determined growth and development traits, environmental conditions, and adaptive responses under epigenetic control involving histone post-translational modifications. The apparent variability in histone modifications among plants might be increased by technical variation introduced in sample processing during epigenetic analyses. Thus, to detect true variations in epigenetic histone patterns associated with given factors, the basal variability among samples that is not associated with them must be estimated. To improve knowledge of relative contribution of biological and technical variation, mass spectrometry was used to examine histone modification patterns (acetylation and methylation among Arabidopsis thaliana plants of ecotypes Columbia 0 (Col-0 and Wassilewskija (Ws homogenized by two techniques (grinding in a cryomill or with a mortar and pestle. We found little difference in histone modification profiles between the ecotypes. However, in comparison of the biological and technical components of variability, we found consistently higher inter-individual variability in histone mark levels among Ws plants than among Col-0 plants (grown from seeds collected either from single plants or sets of plants. Thus, more replicates of Ws would be needed for rigorous analysis of epigenetic marks. Regarding technical variability, the cryomill introduced detectably more heterogeneity in the data than the mortar and pestle treatment, but mass spectrometric analyses had minor apparent effects. Our study shows that it is essential to consider inter-sample variance and estimate suitable numbers of biological replicates for statistical analysis for each studied organism when investigating changes in epigenetic histone profiles.

Variations of Histone Modification Patterns: Contributions of Inter-plant Variability and Technical Factors.

Science.gov (United States)

Brabencová, Sylva; Ihnatová, Ivana; Potěšil, David; Fojtová, Miloslava; Fajkus, Jiří; Zdráhal, Zbyněk; Lochmanová, Gabriela

2017-01-01

Inter-individual variability of conspecific plants is governed by differences in their genetically determined growth and development traits, environmental conditions, and adaptive responses under epigenetic control involving histone post-translational modifications. The apparent variability in histone modifications among plants might be increased by technical variation introduced in sample processing during epigenetic analyses. Thus, to detect true variations in epigenetic histone patterns associated with given factors, the basal variability among samples that is not associated with them must be estimated. To improve knowledge of relative contribution of biological and technical variation, mass spectrometry was used to examine histone modification patterns (acetylation and methylation) among Arabidopsis thaliana plants of ecotypes Columbia 0 (Col-0) and Wassilewskija (Ws) homogenized by two techniques (grinding in a cryomill or with a mortar and pestle). We found little difference in histone modification profiles between the ecotypes. However, in comparison of the biological and technical components of variability, we found consistently higher inter-individual variability in histone mark levels among Ws plants than among Col-0 plants (grown from seeds collected either from single plants or sets of plants). Thus, more replicates of Ws would be needed for rigorous analysis of epigenetic marks. Regarding technical variability, the cryomill introduced detectably more heterogeneity in the data than the mortar and pestle treatment, but mass spectrometric analyses had minor apparent effects. Our study shows that it is essential to consider inter-sample variance and estimate suitable numbers of biological replicates for statistical analysis for each studied organism when investigating changes in epigenetic histone profiles.

Sensitivity analysis on uncertainty variables affecting the NPP's LUEC with probabilistic approach

International Nuclear Information System (INIS)

Nuryanti; Akhmad Hidayatno; Erlinda Muslim

2013-01-01

One thing that is quite crucial to be reviewed prior to any investment decision on the nuclear power plant (NPP) project is the calculation of project economic, including calculation of Levelized Unit Electricity Cost (LUEC). Infrastructure projects such as NPP’s project are vulnerable to a number of uncertainty variables. Information on the uncertainty variables which makes LUEC’s value quite sensitive due to the changes of them is necessary in order the cost overrun can be avoided. Therefore this study aimed to do the sensitivity analysis on variables that affect LUEC with probabilistic approaches. This analysis was done by using Monte Carlo technique that simulate the relationship between the uncertainty variables and visible impact on LUEC. The sensitivity analysis result shows the significant changes on LUEC value of AP1000 and OPR due to the sensitivity of investment cost and capacity factors. While LUEC changes due to sensitivity of U 3 O 8 ’s price looks not quite significant. (author)

Decision making model design for antivirus software selection using Factor Analysis and Analytical Hierarchy Process

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Nurhayati Ai

2018-01-01

Full Text Available Virus spread increase significantly through the internet in 2017. One of the protection method is using antivirus software. The wide variety of antivirus software in the market tends to creating confusion among consumer. Selecting the right antivirus according to their needs has become difficult. This is the reason we conduct our research. We formulate a decision making model for antivirus software consumer. The model is constructed by using factor analysis and AHP method. First we spread questionnaires to consumer, then from those questionnaires we identified 16 variables that needs to be considered on selecting antivirus software. This 16 variables then divided into 5 factors by using factor analysis method in SPSS software. These five factors are security, performance, internal, time and capacity. To rank those factors we spread questionnaires to 6 IT expert then the data is analyzed using AHP method. The result is that performance factors gained the highest rank from all of the other factors. Thus, consumer can select antivirus software by judging the variables in the performance factors. Those variables are software loading speed, user friendly, no excessive memory use, thorough scanning, and scanning virus fast and accurately.

Joint effects of climate variability and socioecological factors on dengue transmission: epidemiological evidence.

Science.gov (United States)

Akter, Rokeya; Hu, Wenbiao; Naish, Suchithra; Banu, Shahera; Tong, Shilu

2017-06-01

To assess the epidemiological evidence on the joint effects of climate variability and socioecological factors on dengue transmission. Following PRISMA guidelines, a detailed literature search was conducted in PubMed, Web of Science and Scopus. Peer-reviewed, freely available and full-text articles, considering both climate and socioecological factors in relation to dengue, published in English from January 1993 to October 2015 were included in this review. Twenty studies have met the inclusion criteria and assessed the impact of both climatic and socioecological factors on dengue dynamics. Among those, four studies have further investigated the relative importance of climate variability and socioecological factors on dengue transmission. A few studies also developed predictive models including both climatic and socioecological factors. Due to insufficient data, methodological issues and contextual variability of the studies, it is hard to draw conclusion on the joint effects of climate variability and socioecological factors on dengue transmission. Future research should take into account socioecological factors in combination with climate variables for a better understanding of the complex nature of dengue transmission as well as for improving the predictive capability of dengue forecasting models, to develop effective and reliable early warning systems. © 2017 John Wiley & Sons Ltd.

Text mining factor analysis (TFA) in green tea patent data

Science.gov (United States)

Rahmawati, Sela; Suprijadi, Jadi; Zulhanif

2017-03-01

Factor analysis has become one of the most widely used multivariate statistical procedures in applied research endeavors across a multitude of domains. There are two main types of analyses based on factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Both EFA and CFA aim to observed relationships among a group of indicators with a latent variable, but they differ fundamentally, a priori and restrictions made to the factor model. This method will be applied to patent data technology sector green tea to determine the development technology of green tea in the world. Patent analysis is useful in identifying the future technological trends in a specific field of technology. Database patent are obtained from agency European Patent Organization (EPO). In this paper, CFA model will be applied to the nominal data, which obtain from the presence absence matrix. While doing processing, analysis CFA for nominal data analysis was based on Tetrachoric matrix. Meanwhile, EFA model will be applied on a title from sector technology dominant. Title will be pre-processing first using text mining analysis.

Multivariate factor analysis of Girgentana goat milk composition

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Pietro Giaccone

2010-01-01

Full Text Available The interpretation of the several variables that contribute to defining milk quality is difficult due to the high degree of  correlation among them. In this case, one of the best methods of statistical processing is factor analysis, which belongs  to the multivariate groups; for our study this particular statistical approach was employed.  A total of 1485 individual goat milk samples from 117 Girgentana goats, were collected fortnightly from January to July,  and analysed for physical and chemical composition, and clotting properties. Milk pH and tritable acidity were within the  normal range for fresh goat milk. Morning milk yield resulted 704 ± 323 g with 3.93 ± 1.23% and 3.48±0.38% for fat  and protein percentages, respectively. The milk urea content was 43.70 ± 8.28 mg/dl. The clotting ability of Girgentana  milk was quite good, with a renneting time equal to 16.96 ± 3.08 minutes, a rate of curd formation of 2.01 ± 1.63 min-  utes and a curd firmness of 25.08 ± 7.67 millimetres.  Factor analysis was performed by applying axis orthogonal rotation (rotation type VARIMAX; the analysis grouped the  milk components into three latent or common factors. The first, which explained 51.2% of the total covariance, was  defined as “slow milks”, because it was linked to r and pH. The second latent factor, which explained 36.2% of the total  covariance, was defined as “milk yield”, because it is positively correlated to the morning milk yield and to the urea con-  tent, whilst negatively correlated to the fat percentage. The third latent factor, which explained 12.6% of the total covari-  ance, was defined as “curd firmness,” because it is linked to protein percentage, a30 and titatrable acidity. With the aim  of evaluating the influence of environmental effects (stage of kidding, parity and type of kidding, factor scores were anal-  ysed with the mixed linear model. Results showed significant effects of the season of

Structural Analysis of Correlated Factors: Lessons from the Verbal-Performance Dichotomy of the Wechsler Scales.

Science.gov (United States)

Macmann, Gregg M.; Barnett, David W.

1994-01-01

Describes exploratory and confirmatory analyses of verbal-performance procedures to illustrate concepts and procedures for analysis of correlated factors. Argues that, based on convergent and discriminant validity criteria, factors should have higher correlations with variables that they purport to measure than with other variables. Discusses…

Characterization factors for terrestrial acidification at the global scale: a systematic analysis of spatial variability and uncertainty.

Science.gov (United States)

Roy, Pierre-Olivier; Azevedo, Ligia B; Margni, Manuele; van Zelm, Rosalie; Deschênes, Louise; Huijbregts, Mark A J

2014-12-01

Characterization factors (CFs) are used in life cycle assessment (LCA) to quantify the potential impact per unit of emission. CFs are obtained from a characterization model which assess the environmental mechanisms along the cause-effect chain linking an emission to its potential damage on a given area of protection, such as loss in ecosystem quality. Up to now, CFs for acidifying emissions did not cover the global scale and were only representative of their characterization model geographical scope. Consequently, current LCA practices implicitly assume that all emissions from a global supply chain occur within the continent referring to the characterization method geographical scope. This paper provides worldwide 2°×2.5° spatially-explicit CFs, representing the change in relative loss of terrestrial vascular plant species due to an emission change of nitrogen oxides (NOx), ammonia (NH3) and sulfur dioxide (SO2). We found that spatial variability in the CFs is much larger compared to statistical uncertainty (six orders of magnitude vs. two orders of magnitude). Spatial variability is mainly caused by the atmospheric fate factor and soil sensitivity factor, while the ecological effect factor is the dominant contributor to the statistical uncertainty. The CFs provided in our study allow the worldwide spatially explicit evaluation of life cycle impacts related to acidifying emissions. This opens the door to evaluate regional life cycle emissions of different products in a global economy. Copyright © 2014 Elsevier B.V. All rights reserved.

Variability in wheat: factors affecting its nutritional value

NARCIS (Netherlands)

Gutierrez del Alamo Oms, A.; Verstegen, M.W.A.; Hartog, den L.A.; Villamide, M.J.

2008-01-01

Wheat is a common raw material used to provide energy in broiler diets. Its apparent metabolisable energy and its influence on broiler performance varies between wheat samples. Reasons for that variability can be classified as intrinsic (variety, chemical composition) and extrinsic factors (growing

Harmonic and complex analysis in several variables

CERN Document Server

Krantz, Steven G

2017-01-01

Authored by a ranking authority in harmonic analysis of several complex variables, this book embodies a state-of-the-art entrée at the intersection of two important fields of research: complex analysis and harmonic analysis. Written with the graduate student in mind, it is assumed that the reader has familiarity with the basics of complex analysis of one and several complex variables as well as with real and functional analysis. The monograph is largely self-contained and develops the harmonic analysis of several complex variables from the first principles. The text includes copious examples, explanations, an exhaustive bibliography for further reading, and figures that illustrate the geometric nature of the subject. Each chapter ends with an exercise set. Additionally, each chapter begins with a prologue, introducing the reader to the subject matter that follows; capsules presented in each section give perspective and a spirited launch to the segment; preludes help put ideas into context. Mathematicians and...

The Analysis of Factors Influencing Effectivenes of Property Taxes in Karanganyar Regency

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Endang Brotojoyo

2018-03-01

Full Text Available The purpose of this study was to test empirically Effect of Compensation, Motivation and External Factors To Performance Officer With Property Taxes Voting in the District Effectiveness Matesih Karanganyar. The analysis technique used is using validity and reliability test, linearity test, regression analysis, path analysis, t test, F test, test the coefficient of determination and correlation analysis. Compensation Hypothesis Test Results significantly influence the effectiveness of tax collection. Motivation significantly influences the effectiveness of tax collection. External factors do not significant effect on effectiveness of tax collection. Compensation significant effect on the performance of Officers. Motivation significant effect on the performance of the Property Taxes polling clerk. External factors do not significant effect on the performance of Officers. Effectiveness of tax collection clerk significant effects on performance. F test results can be concluded jointly variable compensation, motivation, and external factors affecting the effectiveness of tax collection performance. The R2 total of 0,974 means that the performance of the Property Taxes in the district polling officer Matesih Karanganyar explained by the variable compensation, motivation, external factors and the effectiveness of tax collection amounted to 97.4%. The results of path analysis showed that the effective compensation and motivation through a direct path, while external factors are not effective for direct and indirect pathways.

Interplay Among Psychopathologic Variables, Personal Resources, Context-Related Factors, and Real-life Functioning in Individuals With Schizophrenia: A Network Analysis.

Science.gov (United States)

Galderisi, Silvana; Rucci, Paola; Kirkpatrick, Brian; Mucci, Armida; Gibertoni, Dino; Rocca, Paola; Rossi, Alessandro; Bertolino, Alessandro; Strauss, Gregory P; Aguglia, Eugenio; Bellomo, Antonello; Murri, Martino Belvederi; Bucci, Paola; Carpiniello, Bernardo; Comparelli, Anna; Cuomo, Alessandro; De Berardis, Domenico; Dell'Osso, Liliana; Di Fabio, Fabio; Gelao, Barbara; Marchesi, Carlo; Monteleone, Palmiero; Montemagni, Cristiana; Orsenigo, Giulia; Pacitti, Francesca; Roncone, Rita; Santonastaso, Paolo; Siracusano, Alberto; Vignapiano, Annarita; Vita, Antonio; Zeppegno, Patrizia; Maj, Mario

2018-04-01

Enhanced understanding of factors associated with symptomatic and functional recovery is instrumental to designing personalized treatment plans for people with schizophrenia. To date, this is the first study using network analysis to investigate the associations among cognitive, psychopathologic, and psychosocial variables in a large sample of community-dwelling individuals with schizophrenia. To assess the interplay among psychopathologic variables, cognitive dysfunctions, functional capacity, personal resources, perceived stigma, and real-life functioning in individuals with schizophrenia, using a data-driven approach. This multicenter, cross-sectional study involved 26 university psychiatric clinics and/or mental health departments. A total of 921 community-dwelling individuals with a DSM-IV diagnosis of schizophrenia who were stabilized on antipsychotic treatment were recruited from those consecutively presenting to the outpatient units of the sites between March 1, 2012, and September 30, 2013. Statistical analysis was conducted between July 1 and September 30, 2017. Measures covered psychopathologic variables, neurocognition, social cognition, functional capacity, real-life functioning, resilience, perceived stigma, incentives, and service engagement. Of 740 patients (221 women and 519 men; mean [SD] age, 40.0 [10.9] years) with complete data on the 27 study measures, 163 (22.0%) were remitted (with a score of mild or better on 8 core symptoms). The network analysis showed that functional capacity and everyday life skills were the most central and highly interconnected nodes in the network. Psychopathologic variables split in 2 domains, with positive symptoms being one of the most peripheral and least connected nodes. Functional capacity bridged cognition with everyday life skills; the everyday life skills node was connected to disorganization and expressive deficits. Interpersonal relationships and work skills were connected to avolition; the interpersonal

ANALYSIS OF THE FACTORS AFFECTING THE AVERAGE

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Carmen BOGHEAN

2013-12-01

Full Text Available Productivity in agriculture most relevantly and concisely expresses the economic efficiency of using the factors of production. Labour productivity is affected by a considerable number of variables (including the relationship system and interdependence between factors, which differ in each economic sector and influence it, giving rise to a series of technical, economic and organizational idiosyncrasies. The purpose of this paper is to analyse the underlying factors of the average work productivity in agriculture, forestry and fishing. The analysis will take into account the data concerning the economically active population and the gross added value in agriculture, forestry and fishing in Romania during 2008-2011. The distribution of the average work productivity per factors affecting it is conducted by means of the u-substitution method.

Deterministic factor analysis: methods of integro-differentiation of non-integral order

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Valentina V. Tarasova

2016-12-01

Full Text Available Objective to summarize the methods of deterministic factor economic analysis namely the differential calculus and the integral method. nbsp Methods mathematical methods for integrodifferentiation of nonintegral order the theory of derivatives and integrals of fractional nonintegral order. Results the basic concepts are formulated and the new methods are developed that take into account the memory and nonlocality effects in the quantitative description of the influence of individual factors on the change in the effective economic indicator. Two methods are proposed for integrodifferentiation of nonintegral order for the deterministic factor analysis of economic processes with memory and nonlocality. It is shown that the method of integrodifferentiation of nonintegral order can give more accurate results compared with standard methods method of differentiation using the first order derivatives and the integral method using the integration of the first order for a wide class of functions describing effective economic indicators. Scientific novelty the new methods of deterministic factor analysis are proposed the method of differential calculus of nonintegral order and the integral method of nonintegral order. Practical significance the basic concepts and formulas of the article can be used in scientific and analytical activity for factor analysis of economic processes. The proposed method for integrodifferentiation of nonintegral order extends the capabilities of the determined factorial economic analysis. The new quantitative method of deterministic factor analysis may become the beginning of quantitative studies of economic agents behavior with memory hereditarity and spatial nonlocality. The proposed methods of deterministic factor analysis can be used in the study of economic processes which follow the exponential law in which the indicators endogenous variables are power functions of the factors exogenous variables including the processes

Assessing Heterogeneity for Factor Analysis Model with Continuous and Ordinal Outcomes

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Ye-Mao Xia

2016-01-01

Full Text Available Factor analysis models with continuous and ordinal responses are a useful tool for assessing relations between the latent variables and mixed observed responses. These models have been successfully applied to many different fields, including behavioral, educational, and social-psychological sciences. However, within the Bayesian analysis framework, most developments are constrained within parametric families, of which the particular distributions are specified for the parameters of interest. This leads to difficulty in dealing with outliers and/or distribution deviations. In this paper, we propose a Bayesian semiparametric modeling for factor analysis model with continuous and ordinal variables. A truncated stick-breaking prior is used to model the distributions of the intercept and/or covariance structural parameters. Bayesian posterior analysis is carried out through the simulation-based method. Blocked Gibbs sampler is implemented to draw observations from the complicated posterior. For model selection, the logarithm of pseudomarginal likelihood is developed to compare the competing models. Empirical results are presented to illustrate the application of the methodology.

Capital Cost Optimization for Prefabrication: A Factor Analysis Evaluation Model

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Hong Xue

2018-01-01

Full Text Available High capital cost is a significant hindrance to the promotion of prefabrication. In order to optimize cost management and reduce capital cost, this study aims to explore the latent factors and factor analysis evaluation model. Semi-structured interviews were conducted to explore potential variables and then questionnaire survey was employed to collect professionals’ views on their effects. After data collection, exploratory factor analysis was adopted to explore the latent factors. Seven latent factors were identified, including “Management Index”, “Construction Dissipation Index”, “Productivity Index”, “Design Efficiency Index”, “Transport Dissipation Index”, “Material increment Index” and “Depreciation amortization Index”. With these latent factors, a factor analysis evaluation model (FAEM, divided into factor analysis model (FAM and comprehensive evaluation model (CEM, was established. The FAM was used to explore the effect of observed variables on the high capital cost of prefabrication, while the CEM was used to evaluate comprehensive cost management level on prefabrication projects. Case studies were conducted to verify the models. The results revealed that collaborative management had a positive effect on capital cost of prefabrication. Material increment costs and labor costs had significant impacts on production cost. This study demonstrated the potential of on-site management and standardization design to reduce capital cost. Hence, collaborative management is necessary for cost management of prefabrication. Innovation and detailed design were needed to improve cost performance. The new form of precast component factories can be explored to reduce transportation cost. Meanwhile, targeted strategies can be adopted for different prefabrication projects. The findings optimized the capital cost and improved the cost performance through providing an evaluation and optimization model, which helps managers to

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

Science.gov (United States)

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

An SPSSR -Menu for Ordinal Factor Analysis

Directory of Open Access Journals (Sweden)

Mario Basto

2012-01-01

Full Text Available Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calculations. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It offers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper offers an SPSS dialog written in theR programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

Design of ideal cascades of gas centrifuges with variable separation factors

International Nuclear Information System (INIS)

Olander, D.R.

1976-01-01

A method of designing ideal cascades in which the separation factor varies with stage number is presented and applied to centrifuges as separating units. The centrifuge is characterized by a performance function, which gives the separative power, optimized with respect to all internal variables, as a function of cut and throughput. For centrifuges with certain types of performance functions, variable-α ideal cascades can provide a product at a lower cost than the conventional ideal cascade in which the separation factor is independent of stage number

Impact of socio-demographic factors on the mitigating actions for climate change: a path analysis with mediating effects of attitudinal variables.

Science.gov (United States)

Masud, Muhammad Mehedi; Akhatr, Rulia; Nasrin, Shamima; Adamu, Ibrahim Mohammed

2017-12-01

Socio-demographic factors play a significant role in increasing the individual's climate change awareness and in setting a favorable individual attitude towards its mitigation. To better understand how the adversative effects of climate change can be mitigated, this study attempts to investigate the impact of socio-demographic factors on the mitigating actions of the individuals (MAOI) on climate change. Qualitative data were collected from a face-to-face survey of 360 respondents in the Kuala Lumpur region of Malaysia through a close-ended questionnaire. Analysis was conducted on the mediating effects of attitudinal variables through the path model by using the SEM. Findings indicate that the socio-demographic factors such as gender, age, education, income, and ethnicity can greatly influence the individual's awareness, attitude, risk perception, and knowledge of climate change issues. The results drawn from this study also revealed that the attitudinal factors act as a mediating effect between the socio-demographic factors and the MAOI, thereby, indicating that both the socio-demographic factors and the attitudinal factors have significant effects on the MAOI towards climate change. The outcome of this study can help policy makers and other private organizations to decide on the appropriate actions to take in managing climate change effects. These actions which encompass improving basic climate change education and making the public more aware of the local dimensions of climate change are important for harnessing public engagement and support that can also stimulate climate change awareness and promote mitigating actions to n protect the environment from the impact of climate change.

Analysis on factors affecting consumers decision on purchasing simple-type houses

Science.gov (United States)

Rumintang, A.; Sholichin, I.

2018-01-01

In line with the increase of the population and the need of comfortable houses, as affected by modernization era, the house demand is getting higher. Hence, conducting a research on consumers need and want in buying a house should be seriously attempted to succeed marketing activity. Using an analysis consumers’ behavior, the researcher will know few affecting factors related to consumers’ satisfaction in buying a house. Among other, the factors in question include: house price, house condition, facilities, location and accessability. The sample of this research was drawn from the residents of Graha Asri Housing, Taman Bulang Permai, and Sukodono Permai. Based on the analysis and discussion, some conclusions are made as follow: the factors and variables affecting the consumers’ decision on each choice of house is different and also the same variables on three sources of data include housing atmosphere, cleaning service, ease of access to shopping center, health clinics or hospitals, tourism spot, schools, and the bus station.

Influence of environmental factors on birth weight variability of ...

African Journals Online (AJOL)

Administrator

2011-05-30

May 30, 2011 ... significant (P < 0.05). Type of birth also had effect on the body weight of lambs at birth in both Pirot and ... Key words: Environmental factors, birth weight variability, indigenous sheep. ... breeding plans to improve production.

WHY DO SOME NATIONS SUCCEED AND OTHERS FAIL IN INTERNATIONAL COMPETITION? FACTOR ANALYSIS AND CLUSTER ANALYSIS AT EUROPEAN LEVEL

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Popa Ion

2015-07-01

Full Text Available As stated by Michael Porter (1998: 57, 'this is perhaps the most frequently asked economic question of our times.' However, a widely accepted answer is still missing. The aim of this paper is not to provide the BIG answer for such a BIG question, but rather to provide a different perspective on the competitiveness at the national level. In this respect, we followed a two step procedure, called “tandem analysis”. (OECD, 2008. First we employed a Factor Analysis in order to reveal the underlying factors of the initial dataset followed by a Cluster Analysis which aims classifying the 35 countries according to the main characteristics of competitiveness resulting from Factor Analysis. The findings revealed that clustering the 35 states after the first two factors: Smart Growth and Market Development, which recovers almost 76% of common variability of the twelve original variables, are highlighted four clusters as well as a series of useful information in order to analyze the characteristics of the four clusters and discussions on them.

Analysis models for variables associated with breastfeeding duration

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Edson Theodoro dos S. Neto

2013-09-01

Full Text Available OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78% children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages. RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55 and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1 increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3 and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5. However, protective factors (maternal age and family income differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

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Hesse Morten

2005-05-01

Full Text Available Abstract Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133, and (b a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

Variability of African Farming Systems from Phenological Analysis of NDVI Time Series

Science.gov (United States)

Vrieling, Anton; deBeurs, K. M.; Brown, Molly E.

2011-01-01

Food security exists when people have access to sufficient, safe and nutritious food at all times to meet their dietary needs. The natural resource base is one of the many factors affecting food security. Its variability and decline creates problems for local food production. In this study we characterize for sub-Saharan Africa vegetation phenology and assess variability and trends of phenological indicators based on NDVI time series from 1982 to 2006. We focus on cumulated NDVI over the season (cumNDVI) which is a proxy for net primary productivity. Results are aggregated at the level of major farming systems, while determining also spatial variability within farming systems. High temporal variability of cumNDVI occurs in semiarid and subhumid regions. The results show a large area of positive cumNDVI trends between Senegal and South Sudan. These correspond to positive CRU rainfall trends found and relate to recovery after the 1980's droughts. We find significant negative cumNDVI trends near the south-coast of West Africa (Guinea coast) and in Tanzania. For each farming system, causes of change and variability are discussed based on available literature (Appendix A). Although food security comprises more than the local natural resource base, our results can perform an input for food security analysis by identifying zones of high variability or downward trends. Farming systems are found to be a useful level of analysis. Diversity and trends found within farming system boundaries underline that farming systems are dynamic.

Factor analysis for imperfect maintenance planning at nuclear power plants by cognitive task analysis

International Nuclear Information System (INIS)

Takagawa, Kenichi; Iida, Hiroyasu

2011-01-01

Imperfect maintenance planning was frequently identified in domestic nuclear power plants. To prevent such an event, we analyzed causal factors in maintenance planning stages and showed the directionality of countermeasures in this study. There is a pragmatic limit in finding the causal factors from the items based on report descriptions. Therefore, the idea of the systemic accident model, which is used to monitor the performance variability in normal circumstances, is taken as a new concept instead of investigating negative factors. As an actual method for analyzing usual activities, cognitive task analysis (CTA) was applied. Persons who experienced various maintenance activities at one electric power company were interviewed about sources related to decision making during maintenance planning, and then usual factors affecting planning were extracted as performance variability factors. The tendency of domestic events was analyzed using the classification item of those factors, and the directionality of countermeasures was shown. The following are critical for preventing imperfect maintenance planning: the persons in charge should fully understand the situation of the equipment for which they are responsible in the work planning and maintenance evaluation stages, and they should definitely understand, for example, the maintenance bases of that equipment. (author)

The influence of biological and technical factors on quantitative analysis of amyloid PET: Points to consider and recommendations for controlling variability in longitudinal data.

Science.gov (United States)

Schmidt, Mark E; Chiao, Ping; Klein, Gregory; Matthews, Dawn; Thurfjell, Lennart; Cole, Patricia E; Margolin, Richard; Landau, Susan; Foster, Norman L; Mason, N Scott; De Santi, Susan; Suhy, Joyce; Koeppe, Robert A; Jagust, William

2015-09-01

In vivo imaging of amyloid burden with positron emission tomography (PET) provides a means for studying the pathophysiology of Alzheimer's and related diseases. Measurement of subtle changes in amyloid burden requires quantitative analysis of image data. Reliable quantitative analysis of amyloid PET scans acquired at multiple sites and over time requires rigorous standardization of acquisition protocols, subject management, tracer administration, image quality control, and image processing and analysis methods. We review critical points in the acquisition and analysis of amyloid PET, identify ways in which technical factors can contribute to measurement variability, and suggest methods for mitigating these sources of noise. Improved quantitative accuracy could reduce the sample size necessary to detect intervention effects when amyloid PET is used as a treatment end point and allow more reliable interpretation of change in amyloid burden and its relationship to clinical course. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Study on the factors affecting the quality of public bus transportation service in Bali Province using factor analysis

Science.gov (United States)

Susilawati, M.; Nilakusmawati, D. P. E.

2017-06-01

The volume of mobility flows are increasing day by day and the condition of the number of people using private transport modes contribute to traffic congestion. With the limited capacity of the road, one of the alternatives solution to reduce congestion is to optimize the use of public transport. The purposes of this study are to determine the factors that influence user’s satisfaction on the quality of public bus transportation service and determine variables that became identifier on the dominant factor affecting user’s satisfaction. The study was conducted for the public bus transportation between districts in the province of Bali, which is among the eight regencies and one municipality, using a questionnaire as a data collection instrument. Service variables determinant of user’s satisfaction in this study, described in 25 questions, which were analyzed using factor analysis. The results showed there were six factors that explain the satisfaction of users of public transport in Bali, with a total diversity of data that can be parsed by 61.436%. These factors are: Safety and comfort, Responsiveness, Capacity, Tangible, Safety, Reliability. The dominant factor affecting public transport user satisfaction is the safety and comfort, with the most influential variable is feeling concerned about the personal safety of users when on the bus.

Standardizing effect size from linear regression models with log-transformed variables for meta-analysis.

Science.gov (United States)

Rodríguez-Barranco, Miguel; Tobías, Aurelio; Redondo, Daniel; Molina-Portillo, Elena; Sánchez, María José

2017-03-17

Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed. In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese. The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables.

A Sociological Analysis on the Effective factors in Tends to Hijab

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Mahmood Sharepour

2012-12-01

Full Text Available From a sociological perspective. Hjjab is formed in the context of social relations in which the framework. women's issues with cultural. social. political. Economic and religious factors. spiritual. personality and behavior that is different from the paradigms and perspectives is worthy. The purpose of this study is social factors associated with the tendency of female students to wear veil. The statistical population of female students studying at the University make up the number in 1390 was equal to 13,000. 560 of whom have a multi-stage cluster sampling method was selected to the questionnaire with reliability 0.74 responded Results of multivariable regression analysis showed that the most Important variable influencing the direction and lends 10 veil has been the attitudes 10 feminism variable. Other variables affecting status, lifestyle and location. Analytical model explained only 33% of the factors affecting be as two conflicting tendencies. as a moderate 10 strong in university student.

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

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Austin Stanforth

2016-09-01

Full Text Available Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS and Tropical Rainfall Measuring Mission (TRMM satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations.

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

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Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao

2015-01-01

Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of

An analysis of factors associated with influenza, pneumoccocal, Tdap, and herpes zoster vaccine uptake in the US adult population and corresponding inter-state variability.

Science.gov (United States)

La, Elizabeth M; Trantham, Laurel; Kurosky, Samantha K; Odom, Dawn; Aris, Emmanuel; Hogea, Cosmina

2018-02-01

Despite longstanding recommendations for routine vaccination against influenza; pneumococcal; tetanus, diphtheria, acellular pertussis (Tdap); and herpes zoster (HZ) among the United States general adult population, vaccine uptake remains low. Understanding factors that influence adult vaccination and coverage variability beyond the national level are important steps toward developing targeted strategies for increasing vaccination coverage. A retrospective analysis was conducted using data from the Behavioral Risk Factor Surveillance System (2011-2014). Multivariable logistic regression modeling was employed to identify individual factors associated with vaccination (socio-demographics, health status, healthcare utilization, state of residence) and generate adjusted vaccination coverage and compliance estimates nationally and by state. Results indicated that multiple characteristics were consistently associated with a higher likelihood of vaccination across all four vaccines, including female sex, increased educational attainment, and annual household income. Model-adjusted vaccination coverage estimates varied widely by state, with inter-state variability for the most recent year of data as follows: influenza (aged ≥18 years) 30.2-49.5%; pneumococcal (aged ≥65 years) 64.0-74.7%; Tdap (aged ≥18 years) 18.7-46.6%; and HZ (aged ≥60 years) 21.3-42.9%. Model-adjusted compliance with age-appropriate recommendations across vaccines was low and also varied by state: influenza+Tdap (aged 18-59 years) 7.9-24.7%; influenza+Tdap+HZ (aged 60-64 years) 4.1-14.4%; and influenza+Tdap+HZ+pneumococcal (aged ≥65 years) 3.0-18.3%. In summary, after adjusting for individual characteristics associated with vaccination, substantial heterogeneity across states remained, suggesting that other local factors (e.g. state policies) may be impacting adult vaccines uptake. Further research is needed to understand such factors, focusing on differences between states with high versus

Climate Variability, Social and Environmental Factors, and Ross River Virus Transmission: Research Development and Future Research Needs

Science.gov (United States)

Tong, Shilu; Dale, Pat; Nicholls, Neville; Mackenzie, John S.; Wolff, Rodney; McMichael, Anthony J.

2008-01-01

Background Arbovirus diseases have emerged as a global public health concern. However, the impact of climatic, social, and environmental variability on the transmission of arbovirus diseases remains to be determined. Objective Our goal for this study was to provide an overview of research development and future research directions about the interrelationship between climate variability, social and environmental factors, and the transmission of Ross River virus (RRV), the most common and widespread arbovirus disease in Australia. Methods We conducted a systematic literature search on climatic, social, and environmental factors and RRV disease. Potentially relevant studies were identified from a series of electronic searches. Results The body of evidence revealed that the transmission cycles of RRV disease appear to be sensitive to climate and tidal variability. Rainfall, temperature, and high tides were among major determinants of the transmission of RRV disease at the macro level. However, the nature and magnitude of the interrelationship between climate variability, mosquito density, and the transmission of RRV disease varied with geographic area and socioenvironmental condition. Projected anthropogenic global climatic change may result in an increase in RRV infections, and the key determinants of RRV transmission we have identified here may be useful in the development of an early warning system. Conclusions The analysis indicates that there is a complex relationship between climate variability, social and environmental factors, and RRV transmission. Different strategies may be needed for the control and prevention of RRV disease at different levels. These research findings could be used as an additional tool to support decision making in disease control/surveillance and risk management. PMID:19079707

ANALYSIS OF FACTORS WHICH AFFECTING THE ECONOMIC GROWTH

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Suparna Wijaya

2017-03-01

Full Text Available High economic growth and sustainable process are main conditions for sustainability of economic country development. They are also become measures of the success of the country's economy. Factors which tested in this study are economic and non-economic factors which impacting economic development. This study has a goal to explain the factors that influence on macroeconomic Indonesia. It used linear regression modeling approach. The analysis result showed that Tax Amnesty, Exchange Rate, Inflation, and interest rate, they jointly can bring effect which amounted to 77.6% on economic growth whereas the remaining 22.4% is the influenced by other variables which not observed in this study. Keywords: tax amnesty, exchange rates, inflation, SBI and economic growth

Power calculator for instrumental variable analysis in pharmacoepidemiology.

Science.gov (United States)

Walker, Venexia M; Davies, Neil M; Windmeijer, Frank; Burgess, Stephen; Martin, Richard M

2017-10-01

Instrumental variable analysis, for example with physicians' prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research. The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others. The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists. © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association

Lifestyle factors and socioeconomic variables associated with abdominal obesity in Brazilian adolescents.

Science.gov (United States)

Moraes, Augusto César Ferreira de; Falcão, Mário Cícero

2013-01-01

Lifestyle variables have a key role in the development of abdominal obesity (AO). The objective of this study was to identify lifestyle factors and socioeconomic variables associated with AO in adolescents. This study carried out a school-based survey in the Brazilian city of Maringá in Paraná. The representative sample was of 991 adolescents (54.5% girls) from both public and private high schools selected through multi-stage random sampling. AO was classified according to waist circumference value. The independent variables studied were: gender, age, socioeconomic level, parental and household characteristics, smoking, alcohol use, physical inactivity, sedentary behaviour and nutrition-related habits. Poisson regression was used with robust variance adjustment to analyse the associations. The analysis was stratified by sexes. The prevalence of AO was 32.7% (girls = 36.3%, boys = 28.4%). In girls, excessive intake of fried foods was inversely associated with AO and excessive consumption of soda was positively associated. In boys, the results demonstrated a negative association with excessive consumption of sweets and soda. It is concluded that the prevalence of AO among adolescents was higher in both sexes. AO is associated with different eating habits in females and males and these relationships are mediated by familial contexts.

Review and classification of variability analysis techniques with clinical applications

Science.gov (United States)

2011-01-01

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis. PMID:21985357

Review and classification of variability analysis techniques with clinical applications.

Science.gov (United States)

Bravi, Andrea; Longtin, André; Seely, Andrew J E

2011-10-10

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.

Nonalcoholic steatohepatitis in precision medicine: Unraveling the factors that contribute to individual variability.

Science.gov (United States)

Clarke, John D; Cherrington, Nathan J

2015-07-01

There are numerous factors in individual variability that make the development and implementation of precision medicine a challenge in the clinic. One of the main goals of precision medicine is to identify the correct dose for each individual in order to maximize therapeutic effect and minimize the occurrence of adverse drug reactions. Many promising advances have been made in identifying and understanding how factors such as genetic polymorphisms can influence drug pharmacokinetics (PK) and contribute to variable drug response (VDR), but it is clear that there remain many unidentified variables. Underlying liver diseases such as nonalcoholic steatohepatitis (NASH) alter absorption, distribution, metabolism, and excretion (ADME) processes and must be considered in the implementation of precision medicine. There is still a profound need for clinical investigation into how NASH-associated changes in ADME mediators, such as metabolism enzymes and transporters, affect the pharmacokinetics of individual drugs known to rely on these pathways for elimination. This review summarizes the key PK factors in individual variability and VDR and highlights NASH as an essential underlying factor that must be considered as the development of precision medicine advances. A multifactorial approach to precision medicine that considers the combination of two or more risk factors (e.g. genetics and NASH) will be required in our effort to provide a new era of benefit for patients. Copyright © 2015 Elsevier Inc. All rights reserved.

The use of cognitive ability measures as explanatory variables in regression analysis.

Science.gov (United States)

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2012-12-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.

24-Hour Blood Pressure Variability Assessed by Average Real Variability: A Systematic Review and Meta-Analysis.

Science.gov (United States)

Mena, Luis J; Felix, Vanessa G; Melgarejo, Jesus D; Maestre, Gladys E

2017-10-19

Although 24-hour blood pressure (BP) variability (BPV) is predictive of cardiovascular outcomes independent of absolute BP levels, it is not regularly assessed in clinical practice. One possible limitation to routine BPV assessment is the lack of standardized methods for accurately estimating 24-hour BPV. We conducted a systematic review to assess the predictive power of reported BPV indexes to address appropriate quantification of 24-hour BPV, including the average real variability (ARV) index. Studies chosen for review were those that presented data for 24-hour BPV in adults from meta-analysis, longitudinal or cross-sectional design, and examined BPV in terms of the following issues: (1) methods used to calculate and evaluate ARV; (2) assessment of 24-hour BPV determined using noninvasive ambulatory BP monitoring; (3) multivariate analysis adjusted for covariates, including some measure of BP; (4) association of 24-hour BPV with subclinical organ damage; and (5) the predictive value of 24-hour BPV on target organ damage and rate of cardiovascular events. Of the 19 assessed studies, 17 reported significant associations between high ARV and the presence and progression of subclinical organ damage, as well as the incidence of hard end points, such as cardiovascular events. In all these cases, ARV remained a significant independent predictor ( P <0.05) after adjustment for BP and other clinical factors. In addition, increased ARV in systolic BP was associated with risk of all cardiovascular events (hazard ratio, 1.18; 95% confidence interval, 1.09-1.27). Only 2 cross-sectional studies did not find that high ARV was a significant risk factor. Current evidence suggests that ARV index adds significant prognostic information to 24-hour ambulatory BP monitoring and is a useful approach for studying the clinical value of BPV. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Chromophoric dissolved organic matter (CDOM) variability in Barataria Basin using excitation-emission matrix (EEM) fluorescence and parallel factor analysis (PARAFAC).

Science.gov (United States)

Singh, Shatrughan; D'Sa, Eurico J; Swenson, Erick M

2010-07-15

Chromophoric dissolved organic matter (CDOM) variability in Barataria Basin, Louisiana, USA,was examined by excitation emission matrix (EEM) fluorescence combined with parallel factor analysis (PARAFAC). CDOM optical properties of absorption and fluorescence at 355nm along an axial transect (36 stations) during March, April, and May 2008 showed an increasing trend from the marine end member to the upper basin with mean CDOM absorption of 11.06 + or - 5.01, 10.05 + or - 4.23, 11.67 + or - 6.03 (m(-)(1)) and fluorescence 0.80 + or - 0.37, 0.78 + or - 0.39, 0.75 + or - 0.51 (RU), respectively. PARAFAC analysis identified two terrestrial humic-like (component 1 and 2), one non-humic like (component 3), and one soil derived humic acid like (component 4) components. The spatial variation of the components showed an increasing trend from station 1 (near the mouth of basin) to station 36 (end member of bay; upper basin). Deviations from this increasing trend were observed at a bayou channel with very high chlorophyll-a concentrations especially for component 3 in May 2008 that suggested autochthonous production of CDOM. The variability of components with salinity indicated conservative mixing along the middle part of the transect. Component 1 and 4 were found to be relatively constant, while components 2 and 3 revealed an inverse relationship for the sampling period. Total organic carbon showed increasing trend for each of the components. An increase in humification and a decrease in fluorescence indices along the transect indicated an increase in terrestrial derived organic matter and reduced microbial activity from lower to upper basin. The use of these indices along with PARAFAC results improved dissolved organic matter characterization in the Barataria Basin. Copyright 2010 Elsevier B.V. All rights reserved.

Analysis of Factors Affecting Inflation in Indonesia: an Islamic Perspective

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Elis Ratna Wulan

2015-04-01

Full Text Available This study aims to determine the factors affecting inflation. The research is descriptive quantitative in nature. The data used are reported exchange rates, interest rates, money supply and inflation during 2008-2012. The research data was analyzed using multiple linear regression analysis. The results showed in the year 2008-2012 the condition of each variable are (1 the rate of inflation has a negative trend, (2 the interest rate has a negative trend, (3 the money supply has a positive trend, (4 the value of exchange rate has a positive trend. The test results by using multiple linear regression analysis result that variable interest rates, the money supply and the exchange rate of the rupiah significant effect on the rate of inflation.

Influence of environmental factors on birth weight variability of ...

African Journals Online (AJOL)

The present investigation was carried out to study the influence of environmental factors on the birth weight variability of two breeds of sheep. Animals used in this research were taken from the Pirot and Svrljig indigenous sheep breeds. The data were collected from 1999 to 2009 and were analyzed to determine the effect of ...

PATH ANALYSIS OF RECORDING SYSTEM INNOVATION FACTORS AFFECTING ADOPTION OF GOAT FARMERS

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S. Okkyla

2014-09-01

Full Text Available The objective of this study was to evaluate the path analysis of recording system innovation factorsaffecting adoption of goat farmers. This study was conducted from January to February 2014 inPringapus District, Semarang Regency by using survey method. For determining the location, this studyused purposive sampling method. The amount of respondents were determined by quota samplingmethod. Total respondents randomly chosed were 146 farmers. The data were descriptively andquantitatively analyzed by using path analysis of statistical package for the social science (SPSS 16.Independent variables in this study were internal factor, motivation, innovation characteristics,information source, and dependent variable was adoption. Analysis of linear regression showed thatthere was no significant effect of internal factor on adoption, so that it was important to use the trimmingmethod in path analysis. The result of path analysis showed that the influence of motivation, innovationcharacteristics and information source on adoption were 0.168; 0.720 and 0.09, respectively. Innovationcharacteristics were the greatest effect on adoption. In conclusion, by improving innovationcharacteristics of respondent through motivation and information source may significantly increase theadoption of recording system in goat farmers.

Scalable conditional induction variables (CIV) analysis

KAUST Repository

Oancea, Cosmin E.

2015-02-01

Subscripts using induction variables that cannot be expressed as a formula in terms of the enclosing-loop indices appear in the low-level implementation of common programming abstractions such as Alter, or stack operations and pose significant challenges to automatic parallelization. Because the complexity of such induction variables is often due to their conditional evaluation across the iteration space of loops we name them Conditional Induction Variables (CIV). This paper presents a flow-sensitive technique that summarizes both such CIV-based and affine subscripts to program level, using the same representation. Our technique requires no modifications of our dependence tests, which is agnostic to the original shape of the subscripts, and is more powerful than previously reported dependence tests that rely on the pairwise disambiguation of read-write references. We have implemented the CIV analysis in our parallelizing compiler and evaluated its impact on five Fortran benchmarks. We have found that that there are many important loops using CIV subscripts and that our analysis can lead to their scalable parallelization. This in turn has led to the parallelization of the benchmark programs they appear in.

Seismic analysis response factors and design margins of piping systems

International Nuclear Information System (INIS)

Shieh, L.C.; Tsai, N.C.; Yang, M.S.; Wong, W.L.

1985-01-01

The objective of the simplified methods project of the Seismic Safety Margins Research Program is to develop a simplified seismic risk methodology for general use. The goal is to reduce seismic PRA costs to roughly 60 man-months over a 6 to 8 month period, without compromising the quality of the product. To achieve the goal, it is necessary to simplify the calculational procedure of the seismic response. The response factor approach serves this purpose. The response factor relates the median level response to the design data. Through a literature survey, we identified the various seismic analysis methods adopted in the U.S. nuclear industry for the piping system. A series of seismic response calculations was performed. The response factors and their variabilities for each method of analysis were computed. A sensitivity study of the effect of piping damping, in-structure response spectra envelop method, and analysis method was conducted. In addition, design margins, which relate the best-estimate response to the design data, are also presented

Elimination of chromatographic and mass spectrometric problems in GC-MS analysis of Lavender essential oil by multivariate curve resolution techniques: Improving the peak purity assessment by variable size moving window-evolving factor analysis.

Science.gov (United States)

Jalali-Heravi, Mehdi; Moazeni-Pourasil, Roudabeh Sadat; Sereshti, Hassan

2015-03-01

In analysis of complex natural matrices by gas chromatography-mass spectrometry (GC-MS), many disturbing factors such as baseline drift, spectral background, homoscedastic and heteroscedastic noise, peak shape deformation (non-Gaussian peaks), low S/N ratio and co-elution (overlapped and/or embedded peaks) lead the researchers to handle them to serve time, money and experimental efforts. This study aimed to improve the GC-MS analysis of complex natural matrices utilizing multivariate curve resolution (MCR) methods. In addition, to assess the peak purity of the two-dimensional data, a method called variable size moving window-evolving factor analysis (VSMW-EFA) is introduced and examined. The proposed methodology was applied to the GC-MS analysis of Iranian Lavender essential oil, which resulted in extending the number of identified constituents from 56 to 143 components. It was found that the most abundant constituents of the Iranian Lavender essential oil are α-pinene (16.51%), camphor (10.20%), 1,8-cineole (9.50%), bornyl acetate (8.11%) and camphene (6.50%). This indicates that the Iranian type Lavender contains a relatively high percentage of α-pinene. Comparison of different types of Lavender essential oils showed the composition similarity between Iranian and Italian (Sardinia Island) Lavenders. Published by Elsevier B.V.

Assessing factors related to waist circumference and obesity: application of a latent variable model.

Science.gov (United States)

Dalvand, Sahar; Koohpayehzadeh, Jalil; Karimlou, Masoud; Asgari, Fereshteh; Rafei, Ali; Seifi, Behjat; Niksima, Seyed Hassan; Bakhshi, Enayatollah

2015-01-01

Because the use of BMI (Body Mass Index) alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome) and obesity (binary outcome) among Iranian adults. Data included 18,990 Iranian individuals aged 20-65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variable model, we estimated the relation of two correlated responses (waist circumference and obesity) with independent variables including age, gender, PR (Place of Residence), PA (physical activity), smoking status, SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), CHOL (cholesterol), FBG (Fasting Blood Glucose), diabetes, and FHD (family history of diabetes). All variables were related to both obesity and waist circumference (WC). Older age, female sex, being an urban resident, physical inactivity, nonsmoking, hypertension, hypercholesterolemia, hyperglycemia, diabetes, and having family history of diabetes were significant risk factors that increased WC and obesity. Findings from this study of Iranian adult settings offer more insights into factors associated with high WC and high prevalence of obesity in this population.

Assessing Factors Related to Waist Circumference and Obesity: Application of a Latent Variable Model

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Sahar Dalvand

2015-01-01

Full Text Available Background. Because the use of BMI (Body Mass Index alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome and obesity (binary outcome among Iranian adults. Methods. Data included 18,990 Iranian individuals aged 20–65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variable model, we estimated the relation of two correlated responses (waist circumference and obesity with independent variables including age, gender, PR (Place of Residence, PA (physical activity, smoking status, SBP (Systolic Blood Pressure, DBP (Diastolic Blood Pressure, CHOL (cholesterol, FBG (Fasting Blood Glucose, diabetes, and FHD (family history of diabetes. Results. All variables were related to both obesity and waist circumference (WC. Older age, female sex, being an urban resident, physical inactivity, nonsmoking, hypertension, hypercholesterolemia, hyperglycemia, diabetes, and having family history of diabetes were significant risk factors that increased WC and obesity. Conclusions. Findings from this study of Iranian adult settings offer more insights into factors associated with high WC and high prevalence of obesity in this population.

Causes of liver failure and impact analysis of prognostic risk factors

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WU Xiaoqing

2013-04-01

Full Text Available ObjectiveTo perform a retrospective analysis of patients with liver failure to investigate the causative factors and related risk factors that may affect patient prognosis. MethodsThe clinical, demographic, and laboratory data of 79 consecutive patients diagnosed with liver failure and treated at our hospital between January 2010 and January 2012 (58 males and 21 females; age range: 16-74 years old were collected from the medical records. To identify risk factors of liver failure, the patient variables were assessed by Student’s t-test (continuous variables or Chi-squared test (categorical variables. Multivariate logistic regression analysis was used to investigate the relation between patient outcome and independent risk factors. ResultsThe 79 cases of liver failure were grouped according to disease severity: acute liver failure (n=6; 5 died, subacute liver failure (n=35; 19 died, and chronic liver failure (n=38; 28 died. The overall rate of death was 66%. The majority of cases (81% were related to hepatitis B virus infection. While the three groups of liver failure severity did not show significant differences in sex, mean age, occupation, presence of potassium disorder, total bilirubin (TBil or total cholesterol (CHO at admission, or lowest recorded level of CHO during hospitalization, there were significant intergroup differences in highest recorded TBil level, prothrombin activity (PTA at admission, and highest and lowest recorded PTA, and highest recorded level of CHO. Five independent risk factors were identified: the highest recorded TBil level during hospitalization, presence of infection, hepatorenal syndrome, gastrointestinal bleeding, and hepatic encephalopathy. ConclusionThe major cause of liver failure in this cohort of patients was hepatitis infection, and common biomarkers of liver function, such as TBil, CHO and PTA, may indicate patients with poor prognosis despite clinical intervention. Complications should be addressed as

Robust cluster analysis and variable selection

CERN Document Server

Ritter, Gunter

2014-01-01

Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of bot

[Quantitative analysis of drug expenditures variability in dermatology units].

Science.gov (United States)

Moreno-Ramírez, David; Ferrándiz, Lara; Ramírez-Soto, Gabriel; Muñoyerro, M Dolores

2013-01-01

Variability in adjusted drug expenditures among clinical departments raises the possibility of difficult access to certain therapies at the time that avoidable expenditures may also exist. Nevertheless, drug expenditures are not usually applied to clinical practice variability analysis. To identify and quantify variability in drug expenditures in comparable dermatology department of the Servicio Andaluz de Salud. Comparative economic analysis regarding the drug expenditures adjusted to population and health care production in 18 dermatology departments of the Servicio Andaluz de Salud. The 2012 cost and production data (homogeneous production units -HPU-)were provided by Inforcoan, the cost accounting information system of the Servicio Andaluz de Salud. The observed drug expenditure ratio ranged from 0.97?/inh to 8.90?/inh and from 208.45?/HPU to 1,471.95?/ HPU. The Pearson correlation between drug expenditure and population was 0.25 and 0.35 for the correlation between expenditure and homogeneous production (p=0.32 and p=0,15, respectively), both Pearson coefficients confirming the lack of correlation and arelevant degree of variability in drug expenditures. The quantitative analysis of variability performed through Pearson correlation has confirmed the existence of drug expenditure variability among comparable dermatology departments. Copyright © 2013 SEFH. Published by AULA MEDICA. All rights reserved.

Spacesuit Glove-Induced Hand Trauma and Analysis of Potentially Related Risk Variables

Science.gov (United States)

Charvat, Chacqueline M.; Norcross, Jason; Reid, Christopher R.; McFarland, Shane M.

2015-01-01

Injuries to the hands are common among astronauts who train for extravehicular activity (EVA). When the gloves are pressurized, they restrict movement and create pressure points during tasks, sometimes resulting in pain, muscle fatigue, abrasions, and occasionally more severe injuries such as onycholysis. Glove injuries, both anecdotal and recorded, have been reported during EVA training and flight persistently through NASA's history regardless of mission or glove model. Theories as to causation such as glove-hand fit are common but often lacking in supporting evidence. Previous statistical analysis has evaluated onycholysis in the context of crew anthropometry only. The purpose of this study was to analyze all injuries (as documented in the medical records) and available risk factor variables with the goal to determine engineering and operational controls that may reduce hand injuries due to the EVA glove in the future. A literature review and data mining study were conducted between 2012 and 2014. This study included 179 US NASA crew who trained or completed an EVA between 1981 and 2010 (crossing both Shuttle and ISS eras) and wore either the 4000 Series or Phase VI glove during Extravehicular Mobility Unit (EMU) spacesuit EVA training and flight. All injuries recorded in medical records were analyzed in their association to candidate risk factor variables. Those risk factor variables included demographic characteristics, hand anthropometry, glove fit characteristics, and training/EVA characteristics. Utilizing literature, medical records and anecdotal causation comments recorded in crewmember injury data, investigators were able to identify several risk factors associated with increased risk of glove related injuries. Prime among them were smaller hand anthropometry, duration of individual suited exposures, and improper glove-hand fit as calculated by the difference in the anthropometry middle finger length compared to the baseline EVA glove middle finger length.

Application of factor analysis in identification of dominant hydrogeochemical processes of some nitrogenous groundwater of Serbia

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Stojković Jana

2013-01-01

Full Text Available Multivariate statistical analyses are used for reducing large datasets to a smaller number of variables, which explain main hydrogeochemical processes that control water geochemistry. Factor analysis (FA allows discovering intercorrelations inside the data matrix and grouping of similar variables, i.e. chemical parameters. In this way new variables are extracted, which are called factors, and each factor is explained by some hydrogeochemical process. Applying FA to a dataset that consists of 15 chemical parameters measured on 40 groundwater samples from Serbia, four factors were extracted, which explain 73.9% of total variance in the analyzed dataset. Interpretation of obtained factors indicated several hydrogeochemical processes: the impact of sea water intrusions and volatiles in previous geological periods, solutes diffusion from the marine clay, cation exchange and dissolution of carbonate and silicate minerals. [Projekat Ministarstva nauke Republike Srbije, br. 43004

Effective Analysis of C Programs by Rewriting Variability

DEFF Research Database (Denmark)

Iosif-Lazar, Alexandru Florin; Melo, Jean; Dimovski, Aleksandar

2017-01-01

and effective analysis and verification of real-world C program families. Importance. We report some interesting variability-related bugs that we discovered using various state-of-the-art single-program C verification tools, such as Frama-C, Clang, LLBMC.......Context. Variability-intensive programs (program families) appear in many application areas and for many reasons today. Different family members, called variants, are derived by switching statically configurable options (features) on and off, while reuse of the common code is maximized. Inquiry....... Verification of program families is challenging since the number of variants is exponential in the number of features. Existing single-program analysis and verification tools cannot be applied directly to program families, and designing and implementing the corresponding variability-aware versions is tedious...

Real analysis series, functions of several variables, and applications

CERN Document Server

Laczkovich, Miklós

2017-01-01

This book develops the theory of multivariable analysis, building on the single variable foundations established in the companion volume, Real Analysis: Foundations and Functions of One Variable. Together, these volumes form the first English edition of the popular Hungarian original, Valós Analízis I & II, based on courses taught by the authors at Eötvös Loránd University, Hungary, for more than 30 years. Numerous exercises are included throughout, offering ample opportunities to master topics by progressing from routine to difficult problems. Hints or solutions to many of the more challenging exercises make this book ideal for independent study, or further reading. Intended as a sequel to a course in single variable analysis, this book builds upon and expands these ideas into higher dimensions. The modular organization makes this text adaptable for either a semester or year-long introductory course. Topics include: differentiation and integration of functions of several variables; infinite numerica...

A meta analysis of the variability in firm performance attributable to human resource variables

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Lloyd Kapondoro

2015-01-01

Full Text Available The contribution of Human Resource Management (HRM practices to organisation-wide performance is a critical aspect of the Human Resource (HR value proposition. The purpose of the study was to describe the strength of HRM practices and systems in influencing overall organisational performance. While research has concluded that there is a significant positive relationship between HRM practices or systems and an organisation’s market performance, the strength of this relationship has relatively not received much analysis in order to explain the degree to which HRM practices explain variance in firm performance. The study undertook a meta-analysis of published researches in international journals. The study established that HRM variables accounted for an average of 31% of the variability in firm performance. Cohen’s f2 calculated for this study as a meta effect size calculation yielded an average of 0.681, implying that HRM variables account for 68% of variability in firm performance. A one sample Kolmogorov-Smirnov test showed that the distribution of R2 is not normal. A major managerial implication of this study is that effective HRM practices have a significant business case. The study provides, quantitatively, the average variability in firm success that HRM accounts for.

Bayesian analysis of factors associated with fibromyalgia syndrome subjects

Science.gov (United States)

Jayawardana, Veroni; Mondal, Sumona; Russek, Leslie

2015-01-01

Factors contributing to movement-related fear were assessed by Russek, et al. 2014 for subjects with Fibromyalgia (FM) based on the collected data by a national internet survey of community-based individuals. The study focused on the variables, Activities-Specific Balance Confidence scale (ABC), Primary Care Post-Traumatic Stress Disorder screen (PC-PTSD), Tampa Scale of Kinesiophobia (TSK), a Joint Hypermobility Syndrome screen (JHS), Vertigo Symptom Scale (VSS-SF), Obsessive-Compulsive Personality Disorder (OCPD), Pain, work status and physical activity dependent from the "Revised Fibromyalgia Impact Questionnaire" (FIQR). The study presented in this paper revisits same data with a Bayesian analysis where appropriate priors were introduced for variables selected in the Russek's paper.

Genetic distance estimates and variable factors distinguishing between goat Kacang, Muara and Samosir

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Hamdan; Saputra, H.; Mirwandhono, E.; Hasnudi; Sembiring, I.; Umar, S.; Ginting, N.; Alwiyah

2018-02-01

The purpose of this research was to look the genetic distance and factors distinguishing variable betwen types of goats in North Sumatera. This research have been conducted in PayaBakung, Hamparan Perak and Klambir Lima village, Deli Serdang district, Batu Binumbun, Aritonang, HutaGinjang village, Muarasubdistrict, North Tapanuli district and ParbabaDolok, Siopat Sosor, Sinabulan village, Ronggur Nihuta Pangururan village, Sitonggi-tonggi village in the subdistrict RonggurNihuta, Samosir district of the month of July 2016. The data was analyzed using descriptive, discriminants, canonical, Principal Component Analysis, Distance genetic and Tree Phylogenetic. The result showed that the nearest genetic distance goat found in Kacang and Samosir (1.973), and the farthest genetic distnace find in Samosir and Muara (8.671). The variables made it difference was goat race Base Rim Horn (0.856) and Long Horn (0.878). Genetic distance values most far between Muaragoat with Samosir goat was (8.671). The conclude that the crossing superior result, must be cross between two goat types with value genetics most distance. It will have a better chance heterosis in cross result.

Scalable conditional induction variables (CIV) analysis

DEFF Research Database (Denmark)

Oancea, Cosmin Eugen; Rauchwerger, Lawrence

2015-01-01

parallelizing compiler and evaluated its impact on five Fortran benchmarks. We have found that that there are many important loops using CIV subscripts and that our analysis can lead to their scalable parallelization. This in turn has led to the parallelization of the benchmark programs they appear in.......Subscripts using induction variables that cannot be expressed as a formula in terms of the enclosing-loop indices appear in the low-level implementation of common programming abstractions such as filter, or stack operations and pose significant challenges to automatic parallelization. Because...... the complexity of such induction variables is often due to their conditional evaluation across the iteration space of loops we name them Conditional Induction Variables (CIV). This paper presents a flow-sensitive technique that summarizes both such CIV-based and affine subscripts to program level, using the same...

Environmental, morphological, and productive characterization of Sardinian goats and use of latent explanatory factors for population analysis.

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Vacca, G M; Paschino, P; Dettori, M L; Bergamaschi, M; Cipolat-Gotet, C; Bittante, G; Pazzola, M

2016-09-01

Dairy goat farming is practiced worldwide, within a range of different farming systems. Here we investigated the effects of environmental factors and morphology on milk traits of the Sardinian goat population. Sardinian goats are currently reared in Sardinia (Italy) in a low-input context, similar to many goat farming systems, especially in developing countries. Milk and morphological traits from 1,050 Sardinian goats from 42 farms were recorded. We observed a high variability regarding morphological traits, such as coat color, ear length and direction, horn presence, and udder shape. Such variability derived partly from the unplanned repeated crossbreeding of the native Sardinian goats with exotic breeds, especially Maltese goats. The farms located in the mountains were characterized by the traditional farming system and the lowest percentage of crossbred goats. Explanatory factors analysis was used to summarize the interrelated measured milk variables. The explanatory factor related to fat, protein, and energy content of milk (the "Quality" latent variable) explained about 30% of the variance of the whole data set of measured milk traits followed by the "Hygiene" (19%), "Production" (19%), and "Acidity" (11%) factors. The "Quality" and "Hygiene" factors were not affected by any of the farm classification items, whereas "Production" and "Acidity" were affected only by altitude and size of herds, respectively, indicating the adaptation of the local goat population to different environmental conditions. The use of latent explanatory factor analysis allowed us to clearly explain the large variability of milk traits, revealing that the Sardinian goat population cannot be divided into subpopulations based on milk attitude The factors, properly integrated with genetic data, may be useful tools in future selection programs.

Analyzing Mathematics Beliefs of Pre-Service Teachers Using Confirmatory Factor Analysis

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Mazlini Adnan

2011-12-01

Full Text Available Mathematics beliefs play an important role in enhancing the quality and the effectiveness of teaching and learning. This study analyzes the mathematics beliefs of 317 pre-service teachers from six Higher Education Institutions (HEIs (Government Public Universities who were randomly selected to participate in this study. Questionnaires consisting of twenty three items were given to the respondents during the data collection process. The validation of the items was done by using confirmatory factor analysis (CFA. In order to obtain a model fit for the measurement model of mathematics beliefs, several fit index tests such as CMINDF, GFI, AGFI, IFI, NFI, CFI, TLI and RMSEA were used. Constructivist beliefs and traditional beliefs were identified as the contributing factors in the model. The analysis also revealed that mathematics beliefs consist of structures of two hidden variables. The correlation between the two variables (constructivist beliefs and traditional beliefs is at a moderate level. Hence, pre-service teachers should be able to recognize their type of mathematics beliefs in order to become effective mathematics teachers.

Assessing Factors Related to Waist Circumference and Obesity: Application of a Latent Variable Model

OpenAIRE

Dalvand, Sahar; Koohpayehzadeh, Jalil; Karimlou, Masoud; Asgari, Fereshteh; Rafei, Ali; Seifi, Behjat; Niksima, Seyed Hassan; Bakhshi, Enayatollah

2015-01-01

Background. Because the use of BMI (Body Mass Index) alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome) and obesity (binary outcome) among Iranian adults. Methods. Data included 18,990 Iranian individuals aged 20–65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variabl...

Absence of prognostic value of nuclear shape factor analysis in colorectal carcinoma: relevance of interobserver and intraobserver variability.

Science.gov (United States)

Di Fabio, Francesco; Shrier, Ian; Bégin, Louis R; Gordon, Philip H

2008-12-01

Several retrospective studies, including our previous investigation, have shown a prognostic value of nuclear shape factor in colorectal carcinomas. This prospective study was designed to assess the reliability of nuclear shape factor determined by nuclear morphometry and to confirm its prognostic value. Ninety-eight patients who underwent colorectal carcinoma resection were prospectively enrolled. Measurement of nuclear shape factor was performed by using a computer-based image analysis system. Nuclear shape factor was defined as the degree of circularity of the nucleus (1.0 for a perfect circle and values by American Joint Committee on Cancer stage were: 0.73 (0.07) in Stage I, 0.74 (0.06) in Stage II, and 0.75 (0.05) in Stage III carcinomas (P = 0.78, ANOVA). The intraobserver agreement was poor for observer A (r = 0.28) and practically nonexistent for observer B (r = -0.004, Pearson correlation). The intraclass coefficient for interobserver agreement was practically nonexistent. No significant association between nuclear shape factor and ten-year survival was found. Our prospective results, as opposed to our previous retrospective results, suggest that the reliability for nuclear shape factor morphometric analysis is very poor. We failed to confirm a prognostic value for nuclear shape factor in colorectal carcinoma.

Identifying Factors Causing Variability in Greenhouse Gas (GHG) Fluxes in a Polygonal Tundra Landscape

Science.gov (United States)

Arora, B.; Wainwright, H. M.; Vaughn, L. S.; Curtis, J. B.; Torn, M. S.; Dafflon, B.; Hubbard, S. S.

2017-12-01

Greenhouse gas (GHG) flux variations in Arctic tundra environments are important to understand because of the vast amount of soil carbon stored in these regions and the potential of these regions to convert from a global carbon sink to a source under warmer conditions. Multiple factors potentially contribute to GHG flux variations observed in these environments, including snowmelt timing, growing season length, active layer thickness, water table variations, and temperature fluctuations. The objectives of this study are to investigate temporal variability in CO2 and CH4 fluxes at Barrow, AK over three successive growing seasons (2012-14) and to determine the factors influencing this variability using a novel entropy-based classification scheme. We analyzed soil, vegetation, and climate parameters as well as GHG fluxes at multiple locations within low-, flat- and high-centered polygons at Barrow, AK as part of the Next Generation Ecosystem Experiment (NGEE) Arctic project. Entropy results indicate that different environmental factors govern variability in GHG fluxes under different spatiotemporal settings. In particular, flat-centered polygons are more likely to become significant sources of CO2 during warm and dry years as opposed to high-centered polygons that contribute considerably to CO2 emissions during cold and wet years. In contrast, the highest CH4 emissions were always associated with low-centered polygons. Temporal variability in CO2 fluxes was primarily associated with factors affecting soil temperature and/or vegetation dynamics during early and late season periods. Temporal variability in CH4 fluxes was primarily associated with changes in vegetation cover and its covariability with primary controls such as seasonal thaw—rather than direct response to changes in soil moisture. Overall, entropy results document which factors became important under different spatiotemporal settings, thus providing clues concerning the manner in which ecosystem

Predictor variables of happiness and its connection with risk and protective factors for health

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Maite eGaraigordobil

2015-08-01

Full Text Available Great thinkers, philosophers, scientists, and artists from History have often been concerned about one of the most important elements of life: happiness. The study had four goals: 1 To analyze possible differences in feelings of happiness as a function of sex and age; 2 To explore the relations of happiness with risk factors (psychopathological symptoms, behavior problems and protective factors (self-concept-self-esteem, cooperative behavior, social skills for health; 3 To identify predictor variables of happiness; and 4 To explore whether self-esteem mediates the relationship between happiness and psychopathological symptoms. The sample comprised 286 adolescents (14-16 years old. The study used a descriptive, correlational, and cross-sectional methodology. Seven assessment instruments were administered. The ANOVAs confirm that there are no sex differences, but happiness decreases as age increases. Pearson coefficients show that adolescents with more feelings of happiness had fewer psychopathological symptoms (somatization, obsession-compulsion, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism…, fewer behavioral problems (school-academic, antisocial behavior, shyness-withdrawal, psychopathological, psychosomatic, high social adaptation, high self-concept/self-esteem, many cooperative behaviors, many appropriate social skills, and few negative social skills (inappropriate assertiveness, impulsiveness, jealousy-withdrawal. Multiple regression analysis identified five variables predicting happiness: high self-concept, few symptoms of depression, many cooperative behaviors, high self-esteem, and low psychoticism. Results showed a partial mediational effect of self-esteem in the relation between happiness and psychopathological symptoms. The discussion focuses on the importance of implementing programs to promote feelings of happiness, as well as protective factors for health (self

Predictor variables of happiness and its connection with risk and protective factors for health

Science.gov (United States)

Garaigordobil, Maite

2015-01-01

Great thinkers, philosophers, scientists, and artists from History have often been concerned about one of the most important elements of life: happiness. The study had four goals: (1) To analyze possible differences in feelings of happiness as a function of sex and age; (2) To explore the relations of happiness with risk factors (psychopathological symptoms, behavior problems) and protective factors (self-concept-self-esteem, cooperative behavior, social skills) for health; (3) To identify predictor variables of happiness; and (4) To explore whether self-esteem mediates the relationship between happiness and psychopathological symptoms. The sample comprised 286 adolescents (14–16 years old). The study used a descriptive, correlational, and cross-sectional methodology. Seven assessment instruments were administered. The ANOVAs confirm that there are no sex differences, but happiness decreases as age increases. Pearson coefficients show that adolescents with more feelings of happiness had fewer psychopathological symptoms (somatization, obsession–compulsion, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism…), fewer behavioral problems (school-academic, antisocial behavior, shyness-withdrawal, psychopathological, psychosomatic), high social adaptation, high self-concept/self-esteem, many cooperative behaviors, many appropriate social skills, and few negative social skills (inappropriate assertiveness, impulsiveness, jealousy-withdrawal). Multiple regression analysis identified five variables predicting happiness: high self-concept, few symptoms of depression, many cooperative behaviors, high self-esteem, and low psychoticism. Results showed a partial mediational effect of self-esteem in the relation between happiness and psychopathological symptoms. The discussion focuses on the importance of implementing programs to promote feelings of happiness, as well as protective factors for health (self

Phenotypic factor analysis of psychopathology reveals a new body-related transdiagnostic factor.

Science.gov (United States)

Pezzoli, Patrizia; Antfolk, Jan; Santtila, Pekka

2017-01-01

Comorbidity challenges the notion of mental disorders as discrete categories. An increasing body of literature shows that symptoms cut across traditional diagnostic boundaries and interact in shaping the latent structure of psychopathology. Using exploratory and confirmatory factor analysis, we reveal the latent sources of covariation among nine measures of psychopathological functioning in a population-based sample of 13024 Finnish twins and their siblings. By implementing unidimensional, multidimensional, second-order, and bifactor models, we illustrate the relationships between observed variables, specific, and general latent factors. We also provide the first investigation to date of measurement invariance of the bifactor model of psychopathology across gender and age groups. Our main result is the identification of a distinct "Body" factor, alongside the previously identified Internalizing and Externalizing factors. We also report relevant cross-disorder associations, especially between body-related psychopathology and trait anger, as well as substantial sex and age differences in observed and latent means. The findings expand the meta-structure of psychopathology, with implications for empirical and clinical practice, and demonstrate shared mechanisms underlying attitudes towards nutrition, self-image, sexuality and anger, with gender- and age-specific features.

Canonical correlation analysis of infant's size at birth and maternal factors: a study in rural northwest Bangladesh.

Science.gov (United States)

Kabir, Alamgir; Merrill, Rebecca D; Shamim, Abu Ahmed; Klemn, Rolf D W; Labrique, Alain B; Christian, Parul; West, Keith P; Nasser, Mohammed

2014-01-01

This analysis was conducted to explore the association between 5 birth size measurements (weight, length and head, chest and mid-upper arm [MUAC] circumferences) as dependent variables and 10 maternal factors as independent variables using canonical correlation analysis (CCA). CCA considers simultaneously sets of dependent and independent variables and, thus, generates a substantially reduced type 1 error. Data were from women delivering a singleton live birth (n = 14,506) while participating in a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural Bangladesh. The first canonical correlation was 0.42 (P<0.001), demonstrating a moderate positive correlation mainly between the 5 birth size measurements and 5 maternal factors (preterm delivery, early pregnancy MUAC, infant sex, age and parity). A significant interaction between infant sex and preterm delivery on birth size was also revealed from the score plot. Thirteen percent of birth size variability was explained by the composite score of the maternal factors (Redundancy, RY/X = 0.131). Given an ability to accommodate numerous relationships and reduce complexities of multiple comparisons, CCA identified the 5 maternal variables able to predict birth size in this rural Bangladesh setting. CCA may offer an efficient, practical and inclusive approach to assessing the association between two sets of variables, addressing the innate complexity of interactions.

Canonical correlation analysis of infant's size at birth and maternal factors: a study in rural northwest Bangladesh.

Directory of Open Access Journals (Sweden)

Alamgir Kabir

Full Text Available This analysis was conducted to explore the association between 5 birth size measurements (weight, length and head, chest and mid-upper arm [MUAC] circumferences as dependent variables and 10 maternal factors as independent variables using canonical correlation analysis (CCA. CCA considers simultaneously sets of dependent and independent variables and, thus, generates a substantially reduced type 1 error. Data were from women delivering a singleton live birth (n = 14,506 while participating in a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural Bangladesh. The first canonical correlation was 0.42 (P<0.001, demonstrating a moderate positive correlation mainly between the 5 birth size measurements and 5 maternal factors (preterm delivery, early pregnancy MUAC, infant sex, age and parity. A significant interaction between infant sex and preterm delivery on birth size was also revealed from the score plot. Thirteen percent of birth size variability was explained by the composite score of the maternal factors (Redundancy, RY/X = 0.131. Given an ability to accommodate numerous relationships and reduce complexities of multiple comparisons, CCA identified the 5 maternal variables able to predict birth size in this rural Bangladesh setting. CCA may offer an efficient, practical and inclusive approach to assessing the association between two sets of variables, addressing the innate complexity of interactions.

Levey-Jennings Analysis Uncovers Unsuspected Causes of Immunohistochemistry Stain Variability.

Science.gov (United States)

Vani, Kodela; Sompuram, Seshi R; Naber, Stephen P; Goldsmith, Jeffrey D; Fulton, Regan; Bogen, Steven A

Almost all clinical laboratory tests use objective, quantitative measures of quality control (QC), incorporating Levey-Jennings analysis and Westgard rules. Clinical immunohistochemistry (IHC) testing, in contrast, relies on subjective, qualitative QC review. The consequences of using Levey-Jennings analysis for QC assessment in clinical IHC testing are not known. To investigate this question, we conducted a 1- to 2-month pilot test wherein the QC for either human epidermal growth factor receptor 2 (HER-2) or progesterone receptor (PR) in 3 clinical IHC laboratories was quantified and analyzed with Levey-Jennings graphs. Moreover, conventional tissue controls were supplemented with a new QC comprised of HER-2 or PR peptide antigens coupled onto 8 μm glass beads. At institution 1, this more stringent analysis identified a decrease in the HER-2 tissue control that had escaped notice by subjective evaluation. The decrement was due to heterogeneity in the tissue control itself. At institution 2, we identified a 1-day sudden drop in the PR tissue control, also undetected by subjective evaluation, due to counterstain variability. At institution 3, a QC shift was identified, but only with 1 of 2 controls mounted on each slide. The QC shift was due to use of the instrument's selective reagent drop zones dispense feature. None of these events affected patient diagnoses. These case examples illustrate that subjective QC evaluation of tissue controls can detect gross assay failure but not subtle changes. The fact that QC issues arose from each site, and in only a pilot study, suggests that immunohistochemical stain variability may be an underappreciated problem.

Exploring factors that influence work analysis data: A meta-analysis of design choices, purposes, and organizational context.

Science.gov (United States)

DuVernet, Amy M; Dierdorff, Erich C; Wilson, Mark A

2015-09-01

Work analysis is fundamental to designing effective human resource systems. The current investigation extends previous research by identifying the differential effects of common design decisions, purposes, and organizational contexts on the data generated by work analyses. The effects of 19 distinct factors that span choices of descriptor, collection method, rating scale, and data source, as well as project purpose and organizational features, are explored. Meta-analytic results cumulated from 205 articles indicate that many of these variables hold significant consequences for work analysis data. Factors pertaining to descriptor choice, collection method, rating scale, and the purpose for conducting the work analysis each showed strong associations with work analysis data. The source of the work analysis information and organizational context in which it was conducted displayed fewer relationships. Findings can be used to inform choices work analysts make about methodology and postcollection evaluations of work analysis information. (c) 2015 APA, all rights reserved).

Pre-analytical and analytical factors influencing Alzheimer's disease cerebrospinal fluid biomarker variability.

Science.gov (United States)

Fourier, Anthony; Portelius, Erik; Zetterberg, Henrik; Blennow, Kaj; Quadrio, Isabelle; Perret-Liaudet, Armand

2015-09-20

A panel of cerebrospinal fluid (CSF) biomarkers including total Tau (t-Tau), phosphorylated Tau protein at residue 181 (p-Tau) and β-amyloid peptides (Aβ42 and Aβ40), is frequently used as an aid in Alzheimer's disease (AD) diagnosis for young patients with cognitive impairment, for predicting prodromal AD in mild cognitive impairment (MCI) subjects, for AD discrimination in atypical clinical phenotypes and for inclusion/exclusion and stratification of patients in clinical trials. Due to variability in absolute levels between laboratories, there is no consensus on medical cut-off value for the CSF AD signature. Thus, for full implementation of this core AD biomarker panel in clinical routine, this issue has to be solved. Variability can be explained both by pre-analytical and analytical factors. For example, the plastic tubes used for CSF collection and storage, the lack of reference material and the variability of the analytical protocols were identified as important sources of variability. The aim of this review is to highlight these pre-analytical and analytical factors and describe efforts done to counteract them in order to establish cut-off values for core CSF AD biomarkers. This review will give the current state of recommendations. Copyright © 2015. Published by Elsevier B.V.

Risk and protective factors of internet addiction: a meta-analysis of empirical studies in Korea.

Science.gov (United States)

Koo, Hoon Jung; Kwon, Jung-Hye

2014-11-01

A meta-analysis of empirical studies performed in Korea was conducted to systematically investigate the associations between the indices of Internet addiction (IA) and psychosocial variables. Systematic literature searches were carried out using the Korean Studies Information Service System, Research Information Sharing Service, Science Direct, Google Scholar, and references in review articles. The key words were Internet addiction, (Internet) game addiction, and pathological, problematic, and excessive Internet use. Only original research papers using Korean samples published from 1999 to 2012 and officially reviewed by peers were included for analysis. Ninety-five studies meeting the inclusion criteria were identified. The magnitude of the overall effect size of the intrapersonal variables associated with internet addiction was significantly higher than that of interpersonal variables. Specifically, IA demonstrated a medium to strong association with "escape from self" and "self-identity" as self-related variables. "Attention problem", "self-control", and "emotional regulation" as control and regulation-relation variables; "addiction and absorption traits" as temperament variables; "anger" and "aggression" as emotion and mood and variables; "negative stress coping" as coping variables were also associated with comparably larger effect sizes. Contrary to our expectation, the magnitude of the correlations between relational ability and quality, parental relationships and family functionality, and IA were found to be small. The strength of the association between IA and the risk and protective factors was found to be higher in younger age groups. The findings highlight a need for closer examination of psychosocial factors, especially intrapersonal variables when assessing high-risk individuals and designing intervention strategies for both general IA and Internet game addiction.

Variability, Predictability, and Race Factors Affecting Performance in Elite Biathlon.

Science.gov (United States)

Skattebo, Øyvind; Losnegard, Thomas

2018-03-01

To investigate variability, predictability, and smallest worthwhile performance enhancement in elite biathlon sprint events. In addition, the effects of race factors on performance were assessed. Data from 2005 to 2015 including >10,000 and >1000 observations for each sex for all athletes and annual top-10 athletes, respectively, were included. Generalized linear mixed models were constructed based on total race time, skiing time, shooting time, and proportions of targets hit. Within-athlete race-to-race variability was expressed as coefficient of variation of performance times and standard deviation (SD) in proportion units (%) of targets hit. The models were adjusted for random and fixed effects of subject identity, season, event identity, and race factors. The within-athlete variability was independent of sex and performance standard of athletes: 2.5-3.2% for total race time, 1.5-1.8% for skiing time, and 11-15% for shooting times. The SD of the proportion of hits was ∼10% in both shootings combined (meaning ±1 hit in 10 shots). The predictability in total race time was very high to extremely high for all athletes (ICC .78-.84) but trivial for top-10 athletes (ICC .05). Race times during World Championships and Olympics were ∼2-3% faster than in World Cups. Moreover, race time increased by ∼2% per 1000 m of altitude, by ∼5% per 1% of gradient, by 1-2% per 1 m/s of wind speed, and by ∼2-4% on soft vs hard tracks. Researchers and practitioners should focus on strategies that improve biathletes' performance by at least 0.8-0.9%, corresponding to the smallest worthwhile enhancement (0.3 × within-athlete variability).

Within and between Individual Variability of Exposure to Work-Related Musculoskeletal Disorder Risk Factors

Directory of Open Access Journals (Sweden)

Mohsen Zare

2018-05-01

Full Text Available Industrial companies indicate a tendency to eliminate variations in operator strategies, particularly following implementation of the lean principle. Companies believe when the operators perform the same prescribed tasks, they have to execute them in the same manner (completing the same gestures and being exposed to the same risk factors. They attempt to achieve better product quality by standardizing and reducing operational leeway. However, operators adjust and modify ways of performing tasks to balance between their abilities and the requirements of the job. This study aims to investigate the variability of exposure to physical risk factors within and between operators when executing the same prescribed tasks. The Ergonomic Standard method was used to evaluate two workstations. Seven operators were observed thirty times between repeated cycle times at those workstations. The results revealed the variability of exposure to risk factors between and within operators in the repeated execution of the same tasks. Individual characteristics and operators’ strategies might generate the variability of exposure to risk factors that may be an opportunity to reduce the risks of work-related musculoskeletal disorders (WR-MSDs. However, sometimes operators’ strategies may cause overexposure to risk factors; operators most often adopt such strategies to undertake their tasks while reducing the workload.

Logistic Regression and Path Analysis Method to Analyze Factors influencing Students’ Achievement

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Noeryanti, N.; Suryowati, K.; Setyawan, Y.; Aulia, R. R.

2018-04-01

Students' academic achievement cannot be separated from the influence of two factors namely internal and external factors. The first factors of the student (internal factors) consist of intelligence (X1), health (X2), interest (X3), and motivation of students (X4). The external factors consist of family environment (X5), school environment (X6), and society environment (X7). The objects of this research are eighth grade students of the school year 2016/2017 at SMPN 1 Jiwan Madiun sampled by using simple random sampling. Primary data are obtained by distributing questionnaires. The method used in this study is binary logistic regression analysis that aims to identify internal and external factors that affect student’s achievement and how the trends of them. Path Analysis was used to determine the factors that influence directly, indirectly or totally on student’s achievement. Based on the results of binary logistic regression, variables that affect student’s achievement are interest and motivation. And based on the results obtained by path analysis, factors that have a direct impact on student’s achievement are students’ interest (59%) and students’ motivation (27%). While the factors that have indirect influences on students’ achievement, are family environment (97%) and school environment (37).

Which factors enhance positive drug reimbursement recommendation in Scotland? A retrospective analysis 2006-2013.

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Charokopou, Mata; Majer, Istvan M; Raad, Johan de; Broekhuizen, Stefan; Postma, Maarten; Heeg, Bart

2015-03-01

To identify the factors that influence the Scottish Medicines Consortium (SMC) in deciding whether to accept pharmaceutical technologies for use within the Scottish health care system. A database of SMC submissions between 2006 and 2013 was created, containing a range of clinical, economic, and other factors extracted from published health technology assessment reports. A binomial outcome variable was used, defined as the decision to "accept for use" or "not recommend" a technology. Univariate and multivariate analyses were conducted to assess the impact by means of odds ratios (ORs) of the submitted evidence on the recommendation decision. Out of 463 applications, 265 were accepted for use (57%) and 198 (43%) were not recommended for use within National Health Service Scotland. Univariate analyses showed that 13 variables significantly affected the SMC decision. Of these 13 variables, 7 variables were shown to have a meaningful impact in the multivariate analysis. Four of these concerned the outcome of cost-effectiveness analyses; the fact that a submission was supported by a cost-minimization analysis was the strongest positive variable (OR = 10.30) and a submission showing a product not being cost-effective (i.e., incremental cost-effectiveness ratio above £30,000/quality-adjusted life-year gained) was the strongest negative predictor (OR = 0.47). The other variables concerned whether the submission was related to a product indicated for a nervous system disease (OR = 0.41), whether it was indicated for nonchronic use (OR = 1.66), and whether the submission was performed by a big company (OR = 2.83). This study demonstrated that the outcome of cost-effectiveness analyses is an important factor affecting the SMC's reimbursement recommendation decision. Copyright © 2015. Published by Elsevier Inc.

A Retrospective Analysis of Factors Affecting Early Stoma Complications.

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Koc, Umit; Karaman, Kerem; Gomceli, Ismail; Dalgic, Tahsin; Ozer, Ilter; Ulas, Murat; Ercan, Metin; Bostanci, Erdal; Akoglu, Musa

2017-01-01

Despite advances in surgical techniques and products for stoma care, stoma-related complications are still common. A retrospective analysis was performed of the medical records of 462 consecutive patients (295 [63.9%] female, 167 [36.1 %] male, mean age 55.5 ± 15.1 years, mean body mass index [BMI] 25.1 ± 5.2) who had undergone stoma creation at the Gastroenterological Surgery Clinic of Turkiye Yuksek İhtisas Teaching and Research Hospital between January 2008 and December 2012 to examine the incidence of early (ie, within 30 days after surgery) stoma complications and identify potential risk factors. Variables abstracted included gender, age, and BMI; existence of malignant disease; comorbidities (diabetes mellitus, hypertension, coronary artery disease, chronic respiratory disease); use of neoadjuvant chemoradiotherapy; permanent or temporary stoma; type of stoma (loop/end stoma); stoma localization; and the use of preoperative marking of the stoma site. Data were entered and analyzed using statistical software. Descriptive statistics, chi-squared, and Mann-Whitney U tests were used to describe and analyze all variables, and logistic regression analysis was used to determine independent risk factors for stoma complications. Ostomy-related complications developed in 131 patients (28.4%) Of these, superficial mucocutaneous separation was the most frequent complication (90 patients, 19.5%), followed by stoma retraction (15 patients, 3.2%). In univariate analysis, malignant disease (P = .025), creation of a colostomy (P = .002), and left lower quadrant stoma location (P toma complication. Only stoma location was an independent risk factor for the development of a stoma complication (P = .044). The rate of stoma complications was not significantly different between patients who underwent nonemergent surgery (30% in patients preoperatively sited versus 28.4% not sited) and patients who underwent emergency surgery (27.1%). Early stoma complication rates were higher

Atmospheric forcing of decadal Baltic Sea level variability in the last 200 years. A statistical analysis

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Huenicke, B. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Kuestenforschung

2008-11-06

This study aims at the estimation of the impact of different atmospheric factors on the past sealevel variations (up to 200 years) in the Baltic Sea by statistically analysing the relationship between Baltic Sea level records and observational and proxy-based reconstructed climatic data sets. The focus lies on the identification and possible quantification of the contribution of sealevel pressure (wind), air-temperature and precipitation to the low-frequency (decadal and multi-decadal) variability of Baltic Sea level. It is known that the wind forcing is the main factor explaining average Baltic Sea level variability at inter-annual to decadal timescales, especially in wintertime. In this thesis it is statistically estimated to what extent other regional climate factors contribute to the spatially heterogeneous Baltic Sea level variations around the isostatic trend at multi-decadal timescales. Although the statistical analysis cannot be completely conclusive, as the potential climate drivers are all statistically interrelated to some degree, the results indicate that precipitation should be taken into account as an explanatory variable for sea-level variations. On the one hand it has been detected that the amplitude of the annual cycle of Baltic Sea level has increased throughout the 20th century and precipitation seems to be the only factor among those analysed (wind through SLP field, barometric effect, temperature and precipitation) that can account for this evolution. On the other hand, precipitation increases the ability to hindcast inter-annual variations of sea level in some regions and seasons, especially in the Southern Baltic in summertime. The mechanism by which precipitation exerts its influence on Baltic Sea level is not ascertained in this statistical analysis due to the lack of long salinity time series. This result, however, represents a working hypothesis that can be confirmed or disproved by long simulations of the Baltic Sea system - ocean

The Effect of Birth Weight on Academic Performance: Instrumental Variable Analysis.

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Lin, Shi Lin; Leung, Gabriel Matthew; Schooling, C Mary

2017-05-01

Observationally, lower birth weight is usually associated with poorer academic performance; whether this association is causal or the result of confounding is unknown. To investigate this question, we obtained an effect estimate, which can have a causal interpretation under specific assumptions, of birth weight on educational attainment using instrumental variable analysis based on single nucleotide polymorphisms determining birth weight combined with results from the Social Science Genetic Association Consortium study of 126,559 Caucasians. We similarly obtained an estimate of the effect of birth weight on academic performance in 4,067 adolescents from Hong Kong's (Chinese) Children of 1997 birth cohort (1997-2016), using twin status as an instrumental variable. Birth weight was not associated with years of schooling (per 100-g increase in birth weight, -0.006 years, 95% confidence interval (CI): -0.02, 0.01) or college completion (odds ratio = 1.00, 95% CI: 0.96, 1.03). Birth weight was also unrelated to academic performance in adolescents (per 100-g increase in birth weight, -0.004 grade, 95% CI: -0.04, 0.04) using instrumental variable analysis, although conventional regression gave a small positive association (0.02 higher grade, 95% CI: 0.01, 0.03). Observed associations of birth weight with academic performance may not be causal, suggesting that interventions should focus on the contextual factors generating this correlation. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Determining The Factors Affecting Fruit Hardness of Different Peach Types with Meta Analysis

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Hande Küçükönder

2014-09-01

Full Text Available The aim of this study is to determine the factor effective in determining the hardness of Caterina, Suidring, Royal Glory and Tirrenia peach types using meta analysis. In the study, the impact force (Fi and the contact time (tc were detected and the impulse values (I that are expressed as independent variable in the area under the curve were calculated in the measurements performed using the technique of a low-mass lateral impactor multiplicated with peach. Using the theory of elasticity, the independent variables were determined as Fmax (maximum impact force, contact time (tmax, Fmax/tmax, 1/tmax, 1/tmax2,5, Fmax/tmax 1.25 and Fmax2.5 parameters. The correlation coefficient values showing the relationship between these parameters and the dependent variable Magness-Taylor force (MT were calculated and were combined with meta-analysis by using the Hunter-Schmid and Fisher’s Z methods. The Cohen’s classification criterion was used in evaluating the resulting mean effect size (combined correlation value and in determining its direction. As a result of the meta-analysis, the mean effect size according to Hunter-Schmid method was found 0.436 (0.371-0.497 positively directed in 95% confidence interval, while it was found 0.468 (0.390-0.545 according to Fisher’s Z method. The effect sizes in both methods were determined “mid-level” according to the Cohen’s classification. When the significance level of the studies was analyzed with the Z test, all of the ones that taken into the meta analysis has been found statistically significant. As a result of the meta analysis in this study evaluating the relationship of peach types with the fruit hardness, the mean effect size has been found to reach “strong level”. Consequently, “maximum shock acceleration” was found to be a more effective factor comparing to the other factors in determining the the fruit hardness according to the results of meta analysis applied in both methods.

Individual participant data meta-analysis of prognostic factor studies: state of the art?

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Abo-Zaid Ghada

2012-04-01

Full Text Available Abstract Background Prognostic factors are associated with the risk of a subsequent outcome in people with a given disease or health condition. Meta-analysis using individual participant data (IPD, where the raw data are synthesised from multiple studies, has been championed as the gold-standard for synthesising prognostic factor studies. We assessed the feasibility and conduct of this approach. Methods A systematic review to identify published IPD meta-analyses of prognostic factors studies, followed by detailed assessment of a random sample of 20 articles published from 2006. Six of these 20 articles were from the IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in traumatic brain injury collaboration, for which additional information was also used from simultaneously published companion papers. Results Forty-eight published IPD meta-analyses of prognostic factors were identified up to March 2009. Only three were published before 2000 but thereafter a median of four articles exist per year, with traumatic brain injury the most active research field. Availability of IPD offered many advantages, such as checking modelling assumptions; analysing variables on their continuous scale with the possibility of assessing for non-linear relationships; and obtaining results adjusted for other variables. However, researchers also faced many challenges, such as large cost and time required to obtain and clean IPD; unavailable IPD for some studies; different sets of prognostic factors in each study; and variability in study methods of measurement. The IMPACT initiative is a leading example, and had generally strong design, methodological and statistical standards. Elsewhere, standards are not always as high and improvements in the conduct of IPD meta-analyses of prognostic factor studies are often needed; in particular, continuous variables are often categorised without reason; publication bias and availability bias are rarely

Latent variable models an introduction to factor, path, and structural equation analysis

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Loehlin, John C

2004-01-01

This fourth edition introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. The book is intended for advanced students and researchers in the areas of social, educational, clinical, ind

Confirmatory factor analysis of posttraumatic stress symptoms in sexually harassed women.

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Palmieri, Patrick A; Fitzgerald, Louise F

2005-12-01

Posttraumatic stress disorder (PTSD) factor analytic research to date has not provided a clear consensus on the structure of posttraumatic stress symptoms. Seven hypothesized factor structures were evaluated using confirmatory factor analysis of the Posttraumatic Stress Disorder Checklist, a paper-and-pencil measure of posttraumatic stress symptom severity, in a sample of 1,218 women who experienced a broad range of workplace sexual harassment. The model specifying correlated re-experiencing, effortful avoidance, emotional numbing, and hyperarousal factors provided the best fit to the data. Virtually no support was obtained for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV; American Psychiatric Association, 1994) three-factor model of re-experiencing, avoidance, and hyperarousal factors. Different patterns of correlations with external variables were found for the avoidance and emotional numbing factors, providing further validation of the supported model.

Frequency spectrum analysis of finger photoplethysmographic waveform variability during haemodialysis.

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Javed, Faizan; Middleton, Paul M; Malouf, Philip; Chan, Gregory S H; Savkin, Andrey V; Lovell, Nigel H; Steel, Elizabeth; Mackie, James

2010-09-01

This study investigates the peripheral circulatory and autonomic response to volume withdrawal in haemodialysis based on spectral analysis of photoplethysmographic waveform variability (PPGV). Frequency spectrum analysis was performed on the baseline and pulse amplitude variabilities of the finger infrared photoplethysmographic (PPG) waveform and on heart rate variability extracted from the ECG signal collected from 18 kidney failure patients undergoing haemodialysis. Spectral powers were calculated from the low frequency (LF, 0.04-0.145 Hz) and high frequency (HF, 0.145-0.45 Hz) bands. In eight stable fluid overloaded patients (fluid removal of >2 L) not on alpha blockers, progressive reduction in relative blood volume during haemodialysis resulted in significant increase in LF and HF powers of PPG baseline and amplitude variability (P analysis of finger PPGV may provide valuable information on the autonomic vascular response to blood volume reduction in haemodialysis, and can be potentially utilized as a non-invasive tool for assessing peripheral circulatory control during routine dialysis procedure.

Factor Analysis and Modelling for Rapid Quality Assessment of Croatian Wheat Cultivars with Different Gluten Characteristics

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Želimir Kurtanjek

2008-01-01

Full Text Available Factor analysis and multivariate chemometric modelling for rapid assessment of baking quality of wheat cultivars from Slavonia region, Croatia, have been applied. The cultivars Žitarka, Kata, Monika, Ana, Demetra, Divana and Sana were grown under controlled conditions at the experimental field of Agricultural Institute Osijek during three years (2000–2002. Their quality properties were evaluated by 45 different chemical, physical and biochemical variables. The measured variables were grouped as: indirect quality parameters (6, farinographic parameters (7, extensographic parameters (5, baking test parameters (2 and reversed phase-high performance liquid chromatography (RP-HPLC of gluten proteins (25. The aim of this study is to establish minimal number (three, i.e. principal factors, among the 45 variables and to derive multivariate linear regression models for their use in simple and fast prediction of wheat properties. Selection of the principal factors based on the principal component analysis (PCA has been applied. The first three main factors of the analysis include: total glutenins (TGT, total ω-gliadins (Tω- and the ratio of dough resistance/extensibility (R/Ext. These factors account for 76.45 % of the total variance. Linear regression models gave average regression coefficients (R evaluated for the parameter groups: indirect quality R=0.91, baking test R=0.63, farinographic R=0.78, extensographic R=0.95 and RP-HPLC of gluten data R=0.90. Errors in the model predictions were evaluated by the 95 % significance intervals of the calibration lines. Practical applications of the models for rapid quality assessment and laboratory experiment planning were emphasized.

Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

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Liu, Yan; Salvendy, Gavriel

2009-05-01

This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

Organizational variables on nurses' job performance in Turkey: nursing assessments.

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Top, Mehmet

2013-01-01

The purpose of this study was to describe the influence of organizational variables on hospital staff nurses' job performance as reported by staff nurses in two cities in Turkey. Hospital ownership status, employment status were examined for their effect on this influence. The reported influence of organizational variables on job performance was measured by a questionnaire developed for this study. Nurses were asked to evaluate the influence of 28 organizational variables on their job performance using a five-point Likert-type scale (1- Never effective, 5- Very effective). The study used comparative and descriptive study design. The staff nurses who were included in this study were 831 hospital staff nurses. Descriptive statistics, frequencies, t-test, ANOVA and factor analysis were used for data analysis. The study showed the relative importance of the 28 organizational variables in influencing nurses' job performance. Nurses in this study reported that workload and technological support are the most influential organizational variables on their job performance. Factor analysis yielded a five-factor model that explained 53.99% of total variance. Administratively controllable influence job organizational variables influence job performance of nurses in different magnitude.

Profile and Risk Factor Analysis of Unintentional Injuries in Children.

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Bhamkar, Rahul; Seth, Bageshree; Setia, Maninder Singh

2016-10-01

To study the profile and various risk factors associated with unintentional injuries in children. The study is a cross sectional analysis of data collected from 351 children presenting with unintentional injury to a tertiary care hospital in Navi Mumbai, India. Data were collected about variables based on Haddon Phase Factor Matrix - host, environment and agent factors. Proportions for categorical variables across various groups were compared using Chi square test or Fisher's exact test. Logistic regression model was used to evaluate the factors. Falls (36 %) were the most common injuries followed by bites (23 %). Majority of children were school going children (38 %) followed by preschool children (29 %). Forty-seven percent were from lower socioeconomic class. Commonest place of injury was home (48 %) and the commonest time was evening (49 %). Though there was male predominance in injuries, the difference across gender did not vary significantly (p = 0.15). Poisonings were significantly more common in infants and toddlers and in rural population (p risk of bites compared to urban (p Profile of injuries varies widely as per the variations in agent, host and environmental factors. Socio-environmental, economic conditions and infancy-toddler age groups are predisposing risk factors for bites and poisoning. Although rural areas and lower socioeconomic class population are more vulnerable to serious types of injuries, they still lack essential basic medical care.

Factor analysis of the Hamilton Depression Rating Scale in Parkinson's disease.

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Broen, M P G; Moonen, A J H; Kuijf, M L; Dujardin, K; Marsh, L; Richard, I H; Starkstein, S E; Martinez-Martin, P; Leentjens, A F G

2015-02-01

Several studies have validated the Hamilton Depression Rating Scale (HAMD) in patients with Parkinson's disease (PD), and reported adequate reliability and construct validity. However, the factorial validity of the HAMD has not yet been investigated. The aim of our analysis was to explore the factor structure of the HAMD in a large sample of PD patients. A principal component analysis of the 17-item HAMD was performed on data of 341 PD patients, available from a previous cross sectional study on anxiety. An eigenvalue ≥1 was used to determine the number of factors. Factor loadings ≥0.4 in combination with oblique rotations were used to identify which variables made up the factors. Kaiser-Meyer-Olkin measure (KMO), Cronbach's alpha, Bartlett's test, communality, percentage of non-redundant residuals and the component correlation matrix were computed to assess factor validity. KMO verified the sample's adequacy for factor analysis and Cronbach's alpha indicated a good internal consistency of the total scale. Six factors had eigenvalues ≥1 and together explained 59.19% of the variance. The number of items per factor varied from 1 to 6. Inter-item correlations within each component were low. There was a high percentage of non-redundant residuals and low communality. This analysis demonstrates that the factorial validity of the HAMD in PD is unsatisfactory. This implies that the scale is not appropriate for studying specific symptom domains of depression based on factorial structure in a PD population. Copyright © 2014 Elsevier Ltd. All rights reserved.

An Exploratory Factor Analysis of the Sexual Orientation Counselor Competency Scale: Examining the Variable of Experience

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Ali, Shainna; Lambie, Glenn; Bloom, Zachary D.

2017-01-01

The Sexual Orientation Counselor Competency Scale (SOCCS), developed by Bidell in 2005, measures counselors' levels of skills, awareness, and knowledge in assisting lesbian, gay, or bisexual (LGB) clients. In an effort to gain an increased understanding of the construct validity of the SOCCS, researchers performed an exploratory factor analysis on…

Rocky Mountain spotted fever in Georgia, 1961-75: analysis of social and environmental factors affecting occurrence.

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Newhouse, V F; Choi, K; Holman, R C; Thacker, S B; D'Angelo, L J; Smith, J D

1986-01-01

For the period of 1961 through 1975, 10 geographic and sociologic variables in each of the 159 counties of Georgia were analyzed to determine how they were correlated with the occurrence of Rocky Mountain spotted fever (RMSF). Combinations of variables were transformed into a smaller number of factors using principal-component analysis. Based upon the relative values of these factors, geographic areas of similarity were delineated by cluster analysis. It was found by use of these analyses that the counties of the State formed four similarity clusters, which we called south, central, lower north and upper north. When the incidence of RMSF was subsequently calculated for each of these regions of similarity, the regions had differing RMSF incidence; low in the south and upper north, moderate in the central, and high in the lower north. The four similarity clusters agreed closely with the incidence of RMSF when both were plotted on a map. Thus, when analyzed simultaneously, the 10 variables selected could be used to predict the occurrence of RMSF. The most important variables were those of climate and geography. Of secondary, but still major importance, were the changes over the 15-year period in variables associated with humans and their environmental alterations. Detailed examination of these factors has permitted quantitative evaluation of the simultaneous impacts of the geographic and sociologic variables on the occurrence of RMSF in Georgia. These analyses could be updated to reflect changes in the relevant variables and tested as a means of identifying new high risk areas for RMSF in the State. More generally, this method might be adapted to clarify our understanding of the relative importance of individual variables in the ecology of other diseases or environmental health problems. PMID:3090609

Determining the Number of Factors in P-Technique Factor Analysis

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Lo, Lawrence L.; Molenaar, Peter C. M.; Rovine, Michael

2017-01-01

Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A…

A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis

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An Gie Yong

2013-10-01

Full Text Available The following paper discusses exploratory factor analysis and gives an overview of the statistical technique and how it is used in various research designs and applications. A basic outline of how the technique works and its criteria, including its main assumptions are discussed as well as when it should be used. Mathematical theories are explored to enlighten students on how exploratory factor analysis works, an example of how to run an exploratory factor analysis on SPSS is given, and finally a section on how to write up the results is provided. This will allow readers to develop a better understanding of when to employ factor analysis and how to interpret the tables and graphs in the output.

Dispositional Variables and Work-Family Conflict: A Meta-Analysis

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Allen, Tammy D.; Johnson, Ryan C.; Saboe, Kristin N.; Cho, Eunae; Dumani, Soner; Evans, Sarah

2012-01-01

Meta-analysis was used to comprehensively summarize the relationship between dispositional variables and both directions of work-family conflict. The largest effects detected were those associated with negative affect, neuroticism, and self-efficacy; all were in expected directions. In general, negative trait-based variables (e.g., negative affect…

Risk and Protective Factors of Internet Addiction: A Meta-Analysis of Empirical Studies in Korea

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Koo, Hoon Jung

2014-01-01

Purpose A meta-analysis of empirical studies performed in Korea was conducted to systematically investigate the associations between the indices of Internet addiction (IA) and psychosocial variables. Materials and Methods Systematic literature searches were carried out using the Korean Studies Information Service System, Research Information Sharing Service, Science Direct, Google Scholar, and references in review articles. The key words were Internet addiction, (Internet) game addiction, and pathological, problematic, and excessive Internet use. Only original research papers using Korean samples published from 1999 to 2012 and officially reviewed by peers were included for analysis. Ninety-five studies meeting the inclusion criteria were identified. Results The magnitude of the overall effect size of the intrapersonal variables associated with internet addiction was significantly higher than that of interpersonal variables. Specifically, IA demonstrated a medium to strong association with "escape from self" and "self-identity" as self-related variables. "Attention problem", "self-control", and "emotional regulation" as control and regulation-relation variables; "addiction and absorption traits" as temperament variables; "anger" and "aggression" as emotion and mood and variables; "negative stress coping" as coping variables were also associated with comparably larger effect sizes. Contrary to our expectation, the magnitude of the correlations between relational ability and quality, parental relationships and family functionality, and IA were found to be small. The strength of the association between IA and the risk and protective factors was found to be higher in younger age groups. Conclusion The findings highlight a need for closer examination of psychosocial factors, especially intrapersonal variables when assessing high-risk individuals and designing intervention strategies for both general IA and Internet game addiction. PMID:25323910

Iterative Strain-Gage Balance Calibration Data Analysis for Extended Independent Variable Sets

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Ulbrich, Norbert Manfred

2011-01-01

A new method was developed that makes it possible to use an extended set of independent calibration variables for an iterative analysis of wind tunnel strain gage balance calibration data. The new method permits the application of the iterative analysis method whenever the total number of balance loads and other independent calibration variables is greater than the total number of measured strain gage outputs. Iteration equations used by the iterative analysis method have the limitation that the number of independent and dependent variables must match. The new method circumvents this limitation. It simply adds a missing dependent variable to the original data set by using an additional independent variable also as an additional dependent variable. Then, the desired solution of the regression analysis problem can be obtained that fits each gage output as a function of both the original and additional independent calibration variables. The final regression coefficients can be converted to data reduction matrix coefficients because the missing dependent variables were added to the data set without changing the regression analysis result for each gage output. Therefore, the new method still supports the application of the two load iteration equation choices that the iterative method traditionally uses for the prediction of balance loads during a wind tunnel test. An example is discussed in the paper that illustrates the application of the new method to a realistic simulation of temperature dependent calibration data set of a six component balance.

Attribute Analysis of Aridity Variability in North Xinjiang, China

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Yanfeng Wu

2016-01-01

Full Text Available Identifying the dominant meteorological factors affecting aridity variability can improve our understanding of climate change and its future trend in arid and semiarid regions. This study investigated the spatiotemporal aridity variability in North Xinjiang, China, from 1961 to 2013, based on the UNESCO aridity index (precipitation/potential evapotranspiration, and analyzed its association with meteorological factors. The results suggest that North Xinjiang is becoming more humid with an increasing trend in aridity index. Precipitation, temperature, and relative humidity have positive correlation with aridity, and evapotranspiration, sunshine hours, and wind speed have negative correlation with aridity. Wind speed and sunshine hours have a higher sensitivity and more contribution to aridity. This study provides an understanding of the effect of recent climate change on drought in northwest China.

Investigating product development strategy in beverage industry using factor analysis

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Naser Azad

2013-03-01

Full Text Available Selecting a product development strategy that is associated with the company's current service or product innovation, based on customers’ needs and changing environment, plays an important role in increasing demand, increasing market share, increasing sales and profits. Therefore, it is important to extract effective variables associated with product development to improve performance measurement of firms. This paper investigates important factors influencing product development strategies using factor analysis. The proposed model of this paper investigates 36 factors and, using factor analysis, we extract six most influential factors including information sharing, intelligence information, exposure strategy, differentiation, research and development strategy and market survey. The first strategy, partnership, includes five sub-factor including product development partnership, partnership with foreign firms, customers’ perception from competitors’ products, Customer involvement in product development, inter-agency coordination, customer-oriented approach to innovation and transmission of product development change where inter-agency coordination has been considered the most important factor. Internal strengths are the most influential factors impacting the second strategy, intelligence information. The third factor, introducing strategy, introducing strategy, includes four sub criteria and consumer buying behavior is the most influencing factor. Differentiation is the next important factor with five components where knowledge and expertise in product innovation is the most important one. Research and development strategy with four sub-criteria where reducing product development cycle plays the most influential factor and finally, market survey strategy is the last important factor with three factors and finding new market plays the most important role.

Measurement-Device Independency Analysis of Continuous-Variable Quantum Digital Signature

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Tao Shang

2018-04-01

Full Text Available With the practical implementation of continuous-variable quantum cryptographic protocols, security problems resulting from measurement-device loopholes are being given increasing attention. At present, research on measurement-device independency analysis is limited in quantum key distribution protocols, while there exist different security problems for different protocols. Considering the importance of quantum digital signature in quantum cryptography, in this paper, we attempt to analyze the measurement-device independency of continuous-variable quantum digital signature, especially continuous-variable quantum homomorphic signature. Firstly, we calculate the upper bound of the error rate of a protocol. If it is negligible on condition that all measurement devices are untrusted, the protocol is deemed to be measurement-device-independent. Then, we simplify the calculation by using the characteristics of continuous variables and prove the measurement-device independency of the protocol according to the calculation result. In addition, the proposed analysis method can be extended to other quantum cryptographic protocols besides continuous-variable quantum homomorphic signature.

Analysis of Dynamic Characteristics of Portal Frame with Variable Section

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Hao Jianing

2016-01-01

Full Text Available Combined with a portal frame design, by the use of finite element software ANSYS, the finite element model of single specimens of portal rigid frame and the overall portal rigid frame building are established. portal rigid frame’s beam and column is variable cross section. Through the modal analysis, comparative analysis of the frequency and vibration type of the radiolabeling specimens and finite element model of the whole, for the further development of variable cross-section portal rigid frame of earthquake and wind vibration analysis lay the foundation.

Topex-Poseidon analysis of sea level variability over the Atlantic Ocean

Science.gov (United States)

Catalan P-U, M.; Villares, P.; Catalan, M.; Gomez-Enri, J.

2003-04-01

The variability of sea level and surface geostrophic currents in Atlantic Ocean is investigated using 333 cycles of altimeter information obtained by TOPEX-POSEIDON satellite. After the improvements of orbit accuracy, the most important concern to studies of sea level variability from altimeter height data are related with the formalism used for modelling the altimetric measurement corrections. Presently, one of the main sources of potential error is the correction for atmospheric pressure loading, the so-called ‘inverse barometer effect’. As is well known, this correction is intended to adjust the sea surface elevation for the static effects of the downward force of the mass of the atmosphere on the sea surface, adjusted, in this oversimplified model in 1cm/mbar. The exact response of the sea surface to atmospheric pressure loading depends on the space and time scales of the pressure field and must be specially a concern at high latitudes where atmospheric pressure fluctuations are large due to the intensity of low pressure fields at these latitudes and the additional uncertainty in the model estimates of the local sea level pressure. To study these effects over the whole Atlantic Ocean we compute a linear regression adjustment and an Empirical Orthogonal Functions Decomposition (EOFD), between sea level variation without inverse barometer correction and the atmospheric pressure, in all the Topex-Poseidon cross points over the whole Atlantic, including both the Artic and Antarctic Oceans. We use the barometric factor computed from the linear regression to correct the satellite mean sea level variation, comparing the correlation with the pressure. Our results show an important improvement in the decorrelation between sea level and atmospheric pressure time series, compared with the use of Inverse Barometer model, at most of the satellite cross points. The complicated nature of sea level variability at high latitudes justify that EOFD analysis conclusions

Glycemic variability is an independent predictive factor for development of hepatic fibrosis in nonalcoholic fatty liver disease.

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Motoi Hashiba

Full Text Available Patients with nonalcoholic fatty liver disease (NAFLD and nonalcoholic steatohepatitis (NASH often have metabolic disorders including insulin resistance and type 2 diabetes mellitus (T2DM. We clarified the predictive factors in glucose metabolism for progression of hepatic fibrosis in patients with NAFLD by the 75-g oral glucose tolerance test (75gOGTT and a continuous glucose monitoring system (CGMS. One hundred sixty-nine patients (68 female and 101 male patients with biopsy-proven NAFLD with performance with 75gOGTT were enrolled and divided into four groups according to the stage of hepatic fibrosis (F0-3. The proportion of patients with T2DM significantly gradually increased, HbA1c and the homeostasis model assessment of insulin resistance were significantly elevated, and 1,5-anhydroglucitol (1,5-AG was remarkably decreased with the progression of fibrosis. In the 75gOGTT, both plasma glucose and insulin secretion were remarkably increased with the progression of fibrosis. The only factor significantly associated with advanced fibrosis was 1,5-AG (P = 0.008 as determined by multivariate logistic regression analysis. We next evaluated the changes in blood glucose during 24 hours by monitoring with the CGMS to confirm the relationship between glycemic variability and progression of fibrosis. Variability of median glucose, standard deviation of median glucose (P = 0.0022, maximum blood glucose (P = 0.0019, and ΔMin-max blood glucose (P = 0.0029 were remarkably higher in severe fibrosis than in mild fibrosis.Hyperinsulinemia and hyperglycemia, especially glycemic variability, are important predictive factors in glucose impairment for the progression of hepatic fibrosis in NAFLD.

Stepwise data envelopment analysis (DEA); choosing variables for measuring technical efficiency in Norwegian electricity distribution

International Nuclear Information System (INIS)

Kittelsen, S.A.C.

1993-04-01

Electric power distribution is an activity that in principle delivers a separate product to each customer. A specification of products for a utility as a whole leads potentially to a large number of product aspects including topographic and climatic conditions, and the level of disaggregation of factors and products may give the production and cost functions a high dimensionality. Some aggregation is therefore necessary. Non-parametric methods like Data Envelopment Analysis (DEA) have the advantage that they may give meaningful results when parametric methods would not have enough degrees of freedom, but will have related problems if the variables are collinear or are irrelevant. Although aggregate efficiency measures will not be much affected, rates of transformation will be corrupted and observations with extreme values may be measured as efficient by default. Little work has been done so far on the statistical properties of the non-parametric efficiency measure. This paper utilizes a suggestion by Rajiv Banker to measure the significance of the change in results when disaggregating or introducing an extra variable, and shows how one can let the data participate in deciding which variables should be included in the analysis. 32 refs., 7 figs., 4 tabs

An easy guide to factor analysis

CERN Document Server

Kline, Paul

2014-01-01

Factor analysis is a statistical technique widely used in psychology and the social sciences. With the advent of powerful computers, factor analysis and other multivariate methods are now available to many more people. An Easy Guide to Factor Analysis presents and explains factor analysis as clearly and simply as possible. The author, Paul Kline, carefully defines all statistical terms and demonstrates step-by-step how to work out a simple example of principal components analysis and rotation. He further explains other methods of factor analysis, including confirmatory and path analysis, a

A Generalized Stability Analysis of the AMOC in Earth System Models: Implication for Decadal Variability and Abrupt Climate Change

Energy Technology Data Exchange (ETDEWEB)

Fedorov, Alexey V. [Yale Univ., New Haven, CT (United States)

2015-01-14

The central goal of this research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) as related to climate variability and abrupt climate change within a hierarchy of climate models ranging from realistic ocean models to comprehensive Earth system models. Generalized Stability Analysis, a method that quantifies the transient and asymptotic growth of perturbations in the system, is one of the main approaches used throughout this project. The topics we have explored range from physical mechanisms that control AMOC variability to the factors that determine AMOC predictability in the Earth system models, to the stability and variability of the AMOC in past climates.

Evaluating measurement models in clinical research: covariance structure analysis of latent variable models of self-conception.

Science.gov (United States)

Hoyle, R H

1991-02-01

Indirect measures of psychological constructs are vital to clinical research. On occasion, however, the meaning of indirect measures of psychological constructs is obfuscated by statistical procedures that do not account for the complex relations between items and latent variables and among latent variables. Covariance structure analysis (CSA) is a statistical procedure for testing hypotheses about the relations among items that indirectly measure a psychological construct and relations among psychological constructs. This article introduces clinical researchers to the strengths and limitations of CSA as a statistical procedure for conceiving and testing structural hypotheses that are not tested adequately with other statistical procedures. The article is organized around two empirical examples that illustrate the use of CSA for evaluating measurement models with correlated error terms, higher-order factors, and measured and latent variables.

Liquidity indicator for the Croatian economy – Factor analysis approach

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Mirjana Čižmešija

2014-12-01

Full Text Available Croatian business surveys (BS are conducted in the manufacturing industry, retail trade and construction sector. In all of these sectors, manager´s assessments of liquidity are measured. The aim of the paper was to form a new composite liquidity indicator by including business survey liquidity measures from all three covered economic sectors in the Croatian economy mentioned above. In calculating the leading indicator, a factor analysis approach was used. However, this kind of indicator does not exist in a Croatia or in any other European economy. Furthermore, the issue of Croatian companies´ illiquidity is highly neglected in the literature. The empirical analysis consists of two parts. In the first part the new liquidity indicator was formed using factor analysis. One factor (representing the new liquidity indicator; LI was extracted out of the three liquidity variables in three economic sectors. This factor represents the new liquidity indicator. In the second part, econometric models were applied in order to investigate the forecasting properties of the new business survey liquidity indicator, when predicting the direction of changes in Croatian industrial production. The quarterly data used in the research covered the period from January 2000 to April 2013. Based on econometric analysis, it can be concluded that the LI is a leading indicator of Croatia’s industrial production with better forecasting properties then the standard liquidity indicators (formed in a manufacturing industry.

An analysis of the variables that provide a supply chain with sustainable competitiveness An analysis of the variables that provide a supply chain with sustainable competitiveness Análisis de las variables que proporcionan una competitividad sostenible de la cadena de suministro

Directory of Open Access Journals (Sweden)

José A.D. Machuca

2012-04-01

Full Text Available Purpose: An agile, adaptable and aligned (Triple A supply chain (SC would seem to be key to obtaining sustainable competitive advantages. Little previous research has been done into the topic, however, and there are even discrepancies on the conceptual level. For this reason this study aims to propose a reference framework to determine the dimensions and factors that define agility, adaptability and alignment in the SC and to facilitate both the evaluation of its state by managers and researchers with respect to these variables and also the development of empirical research that determines its impact on performance.Design/methodology: A systematic literature review was carried out of specialist Operations Management, Logistics, Management and Supply Chain Management journals using the ProQuest (Abi/Inform Global database. The articles retrieved were examined and those that were relevant for this study were selected. Using these, a qualitative analysis was done that led to the proposed goal being achieved. Findings: This study sets out the definitions, dimensions and factors of the three variables and groups them together for the first time, thus providing a solid conceptual frame. Although the number of articles that analyse one or other of the variables is growing, it is still low. Agility is the variable on which most research has been done, while adaptability is the least analysed.Research limitations/implications: A theoretical reference framework is proposed for the Triple A in the SC based on earlier studies which do not discuss the joint effect of three variables, as a result of which there is no tested theoretical base. The model will be analysed empirically in future research.Originality/value: The lack of papers on agility, adaptability and alignment in the supply chain and the lack of a consensus regarding the dimensions and factors to define them reveal a need for studies such as this. Normal 0 21 false false false Microsoft

Factors affecting public prejudice and social distance on mental illness: analysis of contextual effect by multi-level analysis.

Science.gov (United States)

Jang, Hyeongap; Lim, Jun-Tae; Oh, Juhwan; Lee, Seon-Young; Kim, Yong-Ik; Lee, Jin-Seok

2012-03-01

While there have been many quantitative studies on the public's attitude towards mental illnesses, it is hard to find quantitative study which focused on the contextual effect on the public's attitude. The purpose of this study was to identify factors that affect the public's beliefs and attitudes including contextual effects. We analyzed survey on the public's beliefs and attitudes towards mental illness in Korea with multi-level analysis. We analyzed the public's beliefs and attitudes in terms of prejudice as an intermediate outcome and social distance as a final outcome. Then, we focused on the associations of factors, which were individual and regional socio-economic factors, familiarity, and knowledge based on the comparison of the intermediate and final outcomes. Prejudice was not explained by regional variables but was only correlated with individual factors. Prejudice increased with age and decreased by high education level. However, social distance controlling for prejudice increased in females, in people with a high education level, and in regions with a high education level and a high proportion of the old. Therefore, social distance without controlling for prejudice increased in females, in the elderly, in highly educated people, and in regions with a high education and aged community. The result of the multi-level analysis for the regional variables suggests that social distance for mental illness are not only determined by individual factors but also influenced by the surroundings so that it could be tackled sufficiently with appropriate considering of the relevant regional context with individual characteristics.

Prognostic factors in pediatric pulmonary arterial hypertension: A systematic review and meta-analysis.

Science.gov (United States)

Ploegstra, Mark-Jan; Zijlstra, Willemijn M H; Douwes, Johannes M; Hillege, Hans L; Berger, Rolf M F

2015-04-01

Despite the introduction of targeted therapies in pediatric pulmonary arterial hypertension (PAH), prognosis remains poor. For the definition of treatment strategies and guidelines, there is a high need for an evidence-based recapitulation of prognostic factors. The aim of this study was to identify and evaluate prognostic factors in pediatric PAH by a systematic review of the literature and to summarize the prognostic value of currently reported prognostic factors using meta-analysis. Medline, EMBASE and Cochrane Library were searched on April 1st 2014 to identify original studies that described predictors of mortality or lung-transplantation exclusively in children with PAH. 1053 citations were identified, of which 25 were included for further analysis. Hazard ratios (HR) and 95% confidence intervals were extracted from the papers. For variables studied in at least three non-overlapping cohorts, a combined HR was calculated using random-effects meta-analysis. WHO functional class (WHO-FC, HR 2.7), (N-terminal pro-) brain natriuretic peptide ([NT-pro]BNP, HR 3.2), mean right atrial pressure (mRAP, HR 1.1), cardiac index (HR 0.7), indexed pulmonary vascular resistance (PVRi, HR 1.3) and acute vasodilator response (HR 0.3) were identified as significant prognostic factors (p ≤ 0.001). This systematic review combined with separate meta-analyses shows that WHO-FC, (NT-pro)BNP, mRAP, PVRi, cardiac index and acute vasodilator response are consistently reported prognostic factors for outcome in pediatric PAH. These variables are useful clinical tools to assess prognosis and should be incorporated in treatment strategies and guidelines for children with PAH. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Risk factors for persistent problems following acute whiplash injury: update of a systematic review and meta-analysis.

Science.gov (United States)

Walton, David M; Macdermid, Joy C; Giorgianni, Anthony A; Mascarenhas, Joanna C; West, Stephen C; Zammit, Caroline A

2013-02-01

Systematic review and meta-analysis. To update a previous review and meta-analysis on risk factors for persistent problems following whiplash secondary to a motor vehicle accident. Prognosis in whiplash-associated disorder (WAD) has become an active area of research, perhaps owing to the difficulty of treating chronic problems. A previously published review and meta-analysis of prognostic factors included primary sources up to May 2007. Since that time, more research has become available, and an update to that original review is warranted. A systematic search of international databases was conducted, with rigorous inclusion criteria focusing on studies published between May 2007 and May 2012. Articles were scored, and data were extracted and pooled to estimate the odds ratio for any factor that had at least 3 independent data points in the literature. Four new cohorts (n = 1121) were identified. In combination with findings of a previous review, 12 variables were found to be significant predictors of poor outcome following whiplash, 9 of which were new (n = 2) or revised (n = 7) as a result of additional data. The significant variables included high baseline pain intensity (greater than 5.5/10), report of headache at inception, less than postsecondary education, no seatbelt in use during the accident, report of low back pain at inception, high Neck Disability Index score (greater than 14.5/50), preinjury neck pain, report of neck pain at inception (regardless of intensity), high catastrophizing, female sex, WAD grade 2 or 3, and WAD grade 3 alone. Those variables robust to publication bias included high pain intensity, female sex, report of headache at inception, less than postsecondary education, high Neck Disability Index score, and WAD grade 2 or 3. Three existing variables (preaccident history of headache, rear-end collision, older age) and 1 additional novel variable (collision severity) were refined or added in this updated review but showed no significant

The discriminatory analysis about factors correlative with the early hypothyroidism after 131I therapy for hyperthyroidism

International Nuclear Information System (INIS)

Xiong Lingjing; Liang Changhua; Deng Haoyu; Li Xinhui; Hu Shuo

2002-01-01

Objective: To explore the factors correlative with the early hypothyroidism after 131 I therapy for Graves' hyperthyroidism so as to cure it and decrease the early hypothyroidism occurring and prevent it from becoming irreversible hypothyroidism. Methods: Logistic regression discriminatory analysis by introducing multiple factors from group data and forward stepwise selection of 11 independent variables of 240 hyperthyroidism patients from clinical data and 1 dependent variable from follow-up data after 131 I therapy was conducted. Univariate analysis of each observed factor was performed, too. Results: (1)The results of multivariate analysis showed that the age of patients, the weight of thyroid, the suffering situation, the curve of 131 I absorption rate and the giving 131 I dosage/g thyroid tissue were correlated to early hypothyroidism. The results of univariate analysis showed that the weight of thyroid, the highest absorption of 131 I, the total treatment dosage of 131 I were correlated to early hypothyroidism. (2) The logistic regression equation was statistically significant. (3) The positive and negative predicting accuracy of the early hypothyroidism occurring was 64.08 %, 78.83 %, respectively, the overall predicting accuracy was 72.50%. Conclusions: The dosage of 131 I for treatment of hyperthyroid is the key factor according to the five correlative factors which are relating to the early hypothyroidism and the discriminatory classification. Enhanced follow-up and in time supplement of thyroid hormone are important measures for preventing the early hypothyroidism from becoming irreversible hypothyroidism

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

Science.gov (United States)

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

El Análisis de Covarianza como Mecanismo de Control de Factores de Confusión / Analysis of Covariance as a Methodology to Control Confounding Variables

Directory of Open Access Journals (Sweden)

Correa Londoño Guillermo

2013-08-01

Full Text Available Resumen. Parte de la variabilidad total en un estudio experimental puede explicarse por factores que son asignados y/o controlados por el investigador y que son de interés primario para este. Asimismo, los experimentos suelen involucrar factores que a pesar de su carácter secundario también afectan la respuesta. El mecanismo más comúnmente usado para controlar el efecto de factores secundarios es el bloqueo. Existen, sin embargo, situaciones en las que la fuente de variación secundaria solamente se reconoce tras haberse iniciado el experimento y/o en las que sus niveles no configuran categorías que permitan agrupar unidades experimentales homogéneas; en tales casos, podría considerarse la utilización de covariables para satisfacer los mismos objetivos que el bloqueo. Para aplicar una adecuada corrección mediante análisis de covarianza deben satisfacerse dos condiciones: la viabilidad y la pertinencia. La viabilidad se refiere a la posibilidad de explicar parte de la variabilidad de la respuesta en función de la covariable, mediante un modelo de regresión. La pertinencia tiene que ver con la adecuación de la corrección aplicada, considerando que al eliminar el efecto de la covariable no se arrastre parte del efecto de los tratamientos. La viabilidad suele evaluarse con apoyo de algún programa estadístico; la pertinencia, por su parte, exige una aproximación conceptual. / Abstract. Some portion of the total variability in an experimental study can be explained by factors that are controlled and/or assigned by the researcher, and that are of his primary interest. Likewise, experiments usually involve factors that, despite their ancillary nature, also affect the response. Blocking is the most widely used mechanism to control the effect of ancillary factors. There are, however, situations in which the secondary source of variation is recognized only after the experiment has been started and/or in which its levels don’t allow to

Analysis of Dynamic Characteristics of Portal Frame with Variable Section

OpenAIRE

Hao Jianing

2016-01-01

Combined with a portal frame design, by the use of finite element software ANSYS, the finite element model of single specimens of portal rigid frame and the overall portal rigid frame building are established. portal rigid frame’s beam and column is variable cross section. Through the modal analysis, comparative analysis of the frequency and vibration type of the radiolabeling specimens and finite element model of the whole, for the further development of variable cross-section portal rigid f...

Variables and risk factors associated with child abuse in daycare settings.

Science.gov (United States)

Schumacher, R B; Carlson, R S

1999-09-01

This article was developed to identify the variables associated with abuse of children in daycare centers and homes, and to specify risk factors to guide professionals and parents. The literature regarding child abuse (physical [PA], sexual [SA], and ritual [RA]) was reviewed, with emphasis on identification of variables associated with victims, perpetrators, and settings. Three factors increased the complexity of the review: (1) differences in definition and categorization complicated study comparison; (2) emotional tone affected some reviewers' definitions, methodology, and conclusions; and (3) some aspects of child abuse in daycare homes and centers have not been well researched. PA most frequently occurred in the form of over discipline, was a response to prior conflict with the child, and may have been inadvertently supported by parental permission for corporal punishment. Although SA occurred less frequently in centers than in homes, effects on the victim seemed worse in centers. SA often included PA. A Satanic overtone was frequently associated with RA, and RA coupled with SA was most devastating. Unfortunately, effects were not temporary. Males predominated the perpetrator profile. Multiple perpetrator abuse was worse (e.g., severity of intrusion). Failure of center staff to report suspicion of abuse by fellow staff or parents was cited as a worry by several researchers. Although research regarding abuse in daycare settings is sparse, one cannot wait for more or better research in order to identify risk factors. Based on literature reviewed, the authors provide risk factors for faculty, caregivers, parents, children, and professionals.

Risk Factors for Child Malnutrition in Bangladesh: A Multilevel Analysis of a Nationwide Population-Based Survey.

Science.gov (United States)

Chowdhury, Mohammad Rocky Khan; Rahman, Mohammad Shafiur; Khan, Mohammad Mubarak Hossain; Mondal, Mohammad Nazrul Islam; Rahman, Mohammad Mosiur; Billah, Baki

2016-05-01

To identify the prevalence and risk factors of child malnutrition in Bangladesh. Data was extracted from the Bangladesh Demographic Health Survey (2011). The outcome measures were stunting, wasting, and underweight. χ(2) analysis was performed to find the association of outcome variables with selected factors. Multilevel logistic regression models with a random intercept at each of the household and community levels were used to identify the risk factors of stunting, wasting, and underweight. From the 2011 survey, 7568 children less than 5 years of age were included in the current analysis. The overall prevalence of stunting, wasting, and underweight was 41.3% (95% CI 39.0-42.9). The χ(2) test and multilevel logistic regression analysis showed that the variables age, sex, mother's body mass index, mother's educational status, father's educational status, place of residence, socioeconomic status, community status, religion, region of residence, and food security are significant factors of child malnutrition. Children with poor socioeconomic and community status were at higher risk of malnutrition. Children from food insecure families were more likely to be malnourished. Significant community- and household-level variations were found. The prevalence of child malnutrition is still high in Bangladesh, and the risk was assessed at several multilevel factors. Therefore, prevention of malnutrition should be given top priority as a major public health intervention. Copyright © 2016 Elsevier Inc. All rights reserved.

Clustering of metabolic and cardiovascular risk factors in the polycystic ovary syndrome: a principal component analysis.

Science.gov (United States)

Stuckey, Bronwyn G A; Opie, Nicole; Cussons, Andrea J; Watts, Gerald F; Burke, Valerie

2014-08-01

Polycystic ovary syndrome (PCOS) is a prevalent condition with heterogeneity of clinical features and cardiovascular risk factors that implies multiple aetiological factors and possible outcomes. To reduce a set of correlated variables to a smaller number of uncorrelated and interpretable factors that may delineate subgroups within PCOS or suggest pathogenetic mechanisms. We used principal component analysis (PCA) to examine the endocrine and cardiometabolic variables associated with PCOS defined by the National Institutes of Health (NIH) criteria. Data were retrieved from the database of a single clinical endocrinologist. We included women with PCOS (N = 378) who were not taking the oral contraceptive pill or other sex hormones, lipid lowering medication, metformin or other medication that could influence the variables of interest. PCA was performed retaining those factors with eigenvalues of at least 1.0. Varimax rotation was used to produce interpretable factors. We identified three principal components. In component 1, the dominant variables were homeostatic model assessment (HOMA) index, body mass index (BMI), high density lipoprotein (HDL) cholesterol and sex hormone binding globulin (SHBG); in component 2, systolic blood pressure, low density lipoprotein (LDL) cholesterol and triglycerides; in component 3, total testosterone and LH/FSH ratio. These components explained 37%, 13% and 11% of the variance in the PCOS cohort respectively. Multiple correlated variables from patients with PCOS can be reduced to three uncorrelated components characterised by insulin resistance, dyslipidaemia/hypertension or hyperandrogenaemia. Clustering of risk factors is consistent with different pathogenetic pathways within PCOS and/or differing cardiometabolic outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Exploratory Factor Analysis With Small Samples and Missing Data.

Science.gov (United States)

McNeish, Daniel

2017-01-01

Exploratory factor analysis (EFA) is an extremely popular method for determining the underlying factor structure for a set of variables. Due to its exploratory nature, EFA is notorious for being conducted with small sample sizes, and recent reviews of psychological research have reported that between 40% and 60% of applied studies have 200 or fewer observations. Recent methodological studies have addressed small size requirements for EFA models; however, these models have only considered complete data, which are the exception rather than the rule in psychology. Furthermore, the extant literature on missing data techniques with small samples is scant, and nearly all existing studies focus on topics that are not of primary interest to EFA models. Therefore, this article presents a simulation to assess the performance of various missing data techniques for EFA models with both small samples and missing data. Results show that deletion methods do not extract the proper number of factors and estimate the factor loadings with severe bias, even when data are missing completely at random. Predictive mean matching is the best method overall when considering extracting the correct number of factors and estimating factor loadings without bias, although 2-stage estimation was a close second.

Real Exchange Rate Variability: An Empirical Analysis of the Developing Countries Case

OpenAIRE

Sebastian Edwards

1986-01-01

The purpose of this paper is to investigate the potential role of monetary and real factors in explaining real exchange rate variability in developing countries. For this purpose two indexes of real effective exchange rate variability that measure short-term and long-term variability were constructed for 30 countries. The results obtained, using a generalized least squares procedures on cross section data, indicate that real exchange rate variability has been affected both by real and monetar...

Psychobiological Factors Affecting Cortisol Variability in Human-Dog Dyads.

Directory of Open Access Journals (Sweden)

Iris Schöberl

Full Text Available Stress responses within dyads are modulated by interactions such as mutual emotional support and conflict. We investigated dyadic psychobiological factors influencing intra-individual cortisol variability in response to different challenging situations by testing 132 owners and their dogs in a laboratory setting. Salivary cortisol was measured and questionnaires were used to assess owner and dog personality as well as owners' social attitudes towards the dog and towards other humans. We calculated the individual coefficient of variance of cortisol (iCV = sd/mean*100 over the different test situations as a parameter representing individual variability of cortisol concentration. We hypothesized that high cortisol variability indicates efficient and adaptive coping and a balanced individual and dyadic social performance. Female owners of male dogs had lower iCV than all other owner gender-dog sex combinations (F = 14.194, p<0.001, whereas owner Agreeableness (NEO-FFI scaled positively with owner iCV (F = 4.981, p = 0.028. Dogs of owners high in Neuroticism (NEO-FFI and of owners who were insecure-ambivalently attached to their dogs (FERT, had low iCV (F = 4.290, p = 0.041 and F = 5.948, p = 0.016, as had dogs of owners with human-directed separation anxiety (RSQ or dogs of owners with a strong desire of independence (RSQ (F = 7.661, p = 0.007 and F = 9.192, p = 0.003. We suggest that both owner and dog social characteristics influence dyadic cortisol variability, with the human partner being more influential than the dog. Our results support systemic approaches (i.e. considering the social context in science and in counselling.

Latent variable modeling%建立隐性变量模型

Institute of Scientific and Technical Information of China (English)

蔡力

2012-01-01

@@ A latent variable model, as the name suggests,is a statistical model that contains latent, that is, unobserved, variables.Their roots go back to Spearman's 1904 seminal work[1] on factor analysis,which is arguably the first well-articulated latent variable model to be widely used in psychology, mental health research, and allied disciplines.Because of the association of factor analysis with early studies of human intelligence, the fact that key variables in a statistical model are, on occasion, unobserved has been a point of lingering contention and controversy.The reader is assured, however, that a latent variable,defined in the broadest manner, is no more mysterious than an error term in a normal theory linear regression model or a random effect in a mixed model.

Genetic analysis of glucosinolate variability in broccoli florets using genome-anchored single nucleotide polymorphisms.

Science.gov (United States)

Brown, Allan F; Yousef, Gad G; Reid, Robert W; Chebrolu, Kranthi K; Thomas, Aswathy; Krueger, Christopher; Jeffery, Elizabeth; Jackson, Eric; Juvik, John A

2015-07-01

The identification of genetic factors influencing the accumulation of individual glucosinolates in broccoli florets provides novel insight into the regulation of glucosinolate levels in Brassica vegetables and will accelerate the development of vegetables with glucosinolate profiles tailored to promote human health. Quantitative trait loci analysis of glucosinolate (GSL) variability was conducted with a B. oleracea (broccoli) mapping population, saturated with single nucleotide polymorphism markers from a high-density array designed for rapeseed (Brassica napus). In 4 years of analysis, 14 QTLs were associated with the accumulation of aliphatic, indolic, or aromatic GSLs in floret tissue. The accumulation of 3-carbon aliphatic GSLs (2-propenyl and 3-methylsulfinylpropyl) was primarily associated with a single QTL on C05, but common regulation of 4-carbon aliphatic GSLs was not observed. A single locus on C09, associated with up to 40 % of the phenotypic variability of 2-hydroxy-3-butenyl GSL over multiple years, was not associated with the variability of precursor compounds. Similarly, QTLs on C02, C04, and C09 were associated with 4-methylsulfinylbutyl GSL concentration over multiple years but were not significantly associated with downstream compounds. Genome-specific SNP markers were used to identify candidate genes that co-localized to marker intervals and previously sequenced Brassica oleracea BAC clones containing known GSL genes (GSL-ALK, GSL-PRO, and GSL-ELONG) were aligned to the genomic sequence, providing support that at least three of our 14 QTLs likely correspond to previously identified GSL loci. The results demonstrate that previously identified loci do not fully explain GSL variation in broccoli. The identification of additional genetic factors influencing the accumulation of GSL in broccoli florets provides novel insight into the regulation of GSL levels in Brassicaceae and will accelerate development of vegetables with modified or enhanced GSL

Conserved variable analysis of the marine boundary layer and air

Indian Academy of Sciences (India)

The present study is based on the observed features of the MBL (Marine Boundary Layer) during the Bay of Bengal and Monsoon Experiment (BOBMEX) - Pilot phase. Conserved Variable Analysis (CVA) of the conserved variables such as potential temperature, virtual potential temperature, equivalent potential temperature ...

Sensitivity Analysis of Weather Variables on Offsite Consequence Analysis Tools in South Korea and the United States

Directory of Open Access Journals (Sweden)

Min-Uk Kim

2018-05-01

Full Text Available We studied sensitive weather variables for consequence analysis, in the case of chemical leaks on the user side of offsite consequence analysis (OCA tools. We used OCA tools Korea Offsite Risk Assessment (KORA and Areal Location of Hazardous Atmospheres (ALOHA in South Korea and the United States, respectively. The chemicals used for this analysis were 28% ammonia (NH3, 35% hydrogen chloride (HCl, 50% hydrofluoric acid (HF, and 69% nitric acid (HNO3. The accident scenarios were based on leakage accidents in storage tanks. The weather variables were air temperature, wind speed, humidity, and atmospheric stability. Sensitivity analysis was performed using the Statistical Package for the Social Sciences (SPSS program for dummy regression analysis. Sensitivity analysis showed that impact distance was not sensitive to humidity. Impact distance was most sensitive to atmospheric stability, and was also more sensitive to air temperature than wind speed, according to both the KORA and ALOHA tools. Moreover, the weather variables were more sensitive in rural conditions than in urban conditions, with the ALOHA tool being more influenced by weather variables than the KORA tool. Therefore, if using the ALOHA tool instead of the KORA tool in rural conditions, users should be careful not to cause any differences in impact distance due to input errors of weather variables, with the most sensitive one being atmospheric stability.

Sensitivity Analysis of Weather Variables on Offsite Consequence Analysis Tools in South Korea and the United States.

Science.gov (United States)

Kim, Min-Uk; Moon, Kyong Whan; Sohn, Jong-Ryeul; Byeon, Sang-Hoon

2018-05-18

We studied sensitive weather variables for consequence analysis, in the case of chemical leaks on the user side of offsite consequence analysis (OCA) tools. We used OCA tools Korea Offsite Risk Assessment (KORA) and Areal Location of Hazardous Atmospheres (ALOHA) in South Korea and the United States, respectively. The chemicals used for this analysis were 28% ammonia (NH₃), 35% hydrogen chloride (HCl), 50% hydrofluoric acid (HF), and 69% nitric acid (HNO₃). The accident scenarios were based on leakage accidents in storage tanks. The weather variables were air temperature, wind speed, humidity, and atmospheric stability. Sensitivity analysis was performed using the Statistical Package for the Social Sciences (SPSS) program for dummy regression analysis. Sensitivity analysis showed that impact distance was not sensitive to humidity. Impact distance was most sensitive to atmospheric stability, and was also more sensitive to air temperature than wind speed, according to both the KORA and ALOHA tools. Moreover, the weather variables were more sensitive in rural conditions than in urban conditions, with the ALOHA tool being more influenced by weather variables than the KORA tool. Therefore, if using the ALOHA tool instead of the KORA tool in rural conditions, users should be careful not to cause any differences in impact distance due to input errors of weather variables, with the most sensitive one being atmospheric stability.

Quantitative effects of composting state variables on C/N ratio through GA-aided multivariate analysis

International Nuclear Information System (INIS)

Sun Wei; Huang, Guo H.; Zeng Guangming; Qin Xiaosheng; Yu Hui

2011-01-01

It is widely known that variation of the C/N ratio is dependent on many state variables during composting processes. This study attempted to develop a genetic algorithm aided stepwise cluster analysis (GASCA) method to describe the nonlinear relationships between the selected state variables and the C/N ratio in food waste composting. The experimental data from six bench-scale composting reactors were used to demonstrate the applicability of GASCA. Within the GASCA framework, GA searched optimal sets of both specified state variables and SCA's internal parameters; SCA established statistical nonlinear relationships between state variables and the C/N ratio; to avoid unnecessary and time-consuming calculation, a proxy table was introduced to save around 70% computational efforts. The obtained GASCA cluster trees had smaller sizes and higher prediction accuracy than the conventional SCA trees. Based on the optimal GASCA tree, the effects of the GA-selected state variables on the C/N ratio were ranged in a descending order as: NH 4 + -N concentration > Moisture content > Ash Content > Mean Temperature > Mesophilic bacteria biomass. Such a rank implied that the variation of ammonium nitrogen concentration, the associated temperature and the moisture conditions, the total loss of both organic matters and available mineral constituents, and the mesophilic bacteria activity, were critical factors affecting the C/N ratio during the investigated food waste composting. This first application of GASCA to composting modelling indicated that more direct search algorithms could be coupled with SCA or other multivariate analysis methods to analyze complicated relationships during composting and many other environmental processes. - Research Highlights: → A genetic algorithm aided stepwise cluster analysis method in food waste composting. → Nonlinear relationships between the selected state variables and the C/N ratio. → Introduced proxy tables save around 70% computational

Quantitative effects of composting state variables on C/N ratio through GA-aided multivariate analysis

Energy Technology Data Exchange (ETDEWEB)

Sun Wei [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); Huang, Guo H., E-mail: huangg@iseis.org [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); MOE Key Laboratory of Regional Energy Systems Optimization, Sino-Canada Energy and Environmental Research Academy, North China Electric Power University, Beijing, 102206 (China); Zeng Guangming [MOE Key Laboratory of Environmental Biology and Pollution Control, College of Environmental Science and Engineering, Hunan University, Changsha, Hunan, 410082 (China); Qin Xiaosheng [School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 (Singapore); Yu Hui [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada)

2011-03-01

It is widely known that variation of the C/N ratio is dependent on many state variables during composting processes. This study attempted to develop a genetic algorithm aided stepwise cluster analysis (GASCA) method to describe the nonlinear relationships between the selected state variables and the C/N ratio in food waste composting. The experimental data from six bench-scale composting reactors were used to demonstrate the applicability of GASCA. Within the GASCA framework, GA searched optimal sets of both specified state variables and SCA's internal parameters; SCA established statistical nonlinear relationships between state variables and the C/N ratio; to avoid unnecessary and time-consuming calculation, a proxy table was introduced to save around 70% computational efforts. The obtained GASCA cluster trees had smaller sizes and higher prediction accuracy than the conventional SCA trees. Based on the optimal GASCA tree, the effects of the GA-selected state variables on the C/N ratio were ranged in a descending order as: NH{sub 4}{sup +}-N concentration > Moisture content > Ash Content > Mean Temperature > Mesophilic bacteria biomass. Such a rank implied that the variation of ammonium nitrogen concentration, the associated temperature and the moisture conditions, the total loss of both organic matters and available mineral constituents, and the mesophilic bacteria activity, were critical factors affecting the C/N ratio during the investigated food waste composting. This first application of GASCA to composting modelling indicated that more direct search algorithms could be coupled with SCA or other multivariate analysis methods to analyze complicated relationships during composting and many other environmental processes. - Research Highlights: {yields} A genetic algorithm aided stepwise cluster analysis method in food waste composting. {yields} Nonlinear relationships between the selected state variables and the C/N ratio. {yields} Introduced proxy tables

Geostatistical Analysis of Mesoscale Spatial Variability and Error in SeaWiFS and MODIS/Aqua Global Ocean Color Data

Science.gov (United States)

Glover, David M.; Doney, Scott C.; Oestreich, William K.; Tullo, Alisdair W.

2018-01-01

Mesoscale (10-300 km, weeks to months) physical variability strongly modulates the structure and dynamics of planktonic marine ecosystems via both turbulent advection and environmental impacts upon biological rates. Using structure function analysis (geostatistics), we quantify the mesoscale biological signals within global 13 year SeaWiFS (1998-2010) and 8 year MODIS/Aqua (2003-2010) chlorophyll a ocean color data (Level-3, 9 km resolution). We present geographical distributions, seasonality, and interannual variability of key geostatistical parameters: unresolved variability or noise, resolved variability, and spatial range. Resolved variability is nearly identical for both instruments, indicating that geostatistical techniques isolate a robust measure of biophysical mesoscale variability largely independent of measurement platform. In contrast, unresolved variability in MODIS/Aqua is substantially lower than in SeaWiFS, especially in oligotrophic waters where previous analysis identified a problem for the SeaWiFS instrument likely due to sensor noise characteristics. Both records exhibit a statistically significant relationship between resolved mesoscale variability and the low-pass filtered chlorophyll field horizontal gradient magnitude, consistent with physical stirring acting on large-scale gradient as an important factor supporting observed mesoscale variability. Comparable horizontal length scales for variability are found from tracer-based scaling arguments and geostatistical decorrelation. Regional variations between these length scales may reflect scale dependence of biological mechanisms that also create variability directly at the mesoscale, for example, enhanced net phytoplankton growth in coastal and frontal upwelling and convective mixing regions. Global estimates of mesoscale biophysical variability provide an improved basis for evaluating higher resolution, coupled ecosystem-ocean general circulation models, and data assimilation.

Individual and Work-Related Factors Influencing Burnout of Mental Health Professionals: A Meta-Analysis

Science.gov (United States)

Lim, Nayoung; Kim, Eun Kyoung; Kim, Hyunjung; Yang, Eunjoo; Lee, Sang Min

2010-01-01

The current study identifies and assesses individual and work-related factors as correlates of burnout among mental health professionals. Results of a meta-analysis indicate that age and work setting variables are the most significant indicators of emotional exhaustion and depersonalization. In terms of level of personal accomplishment, the age…

Variability Bugs in Highly Configurable Systems: A Qualitative Analysis

DEFF Research Database (Denmark)

Abal, Iago; Melo, Jean; Stanciulescu, Stefan

2018-01-01

Variability-sensitive verification pursues effective analysis of the exponentially many variants in number of features of a program family. Several variability-aware techniques have been proposed, but researchers still lack examples of concrete bugs induced by variability, occurring in real large......-scale systems. A collection of real world bugs is needed to evaluate tool implementations of variability-sensitive analyses by testing them on real bugs. We present a qualitative study of 98 diverse variability bugs collected from bug-fixing commits in the Apache, BusyBox, Linux kernel and Marlin repositories....... We analyze each of the bugs, and record the results in a database. For each bug, we create a self-contained simplified C99 version and a simplified patch, in order to help researchers who are not experts on these subject studies to understand them, so that they can use it for evaluation...

Factors contributing to radiocaesium variability in upland sheep flocks in west Cumbria (United Kingdom)

International Nuclear Information System (INIS)

Beresford, N.A.; Barnett, C.L.; Wright, S.M.; Howard, B.J.; Crout, N.M.J.

2007-01-01

Following the Chernobyl accident in 1986, restrictions were placed on the movement and slaughter of sheep within upland areas of the UK because radiocaesium activity concentrations in their meat exceeded 1000 Bq kg -1 fresh weight. Some farms remain under restriction in 2007. From 1991 to 1993 detailed studies were conducted on three sheep farms within the restricted area of west Cumbria to systematically assess the various parameters which may contribute to the observed variability in radiocaesium activity concentrations within sheep flocks. This paper reports the spatial variation in soil and vegetation activity concentrations across the grazed areas at these farms and determines the influence of grazing behaviour on variability in 137 Cs activity concentrations between individual sheep within the flocks. Together with previously reported results, these new data are used to draw conclusions on the factors determining variability within the three flocks. However, the factors are too site specific to be able to generalise the findings to other farms within the restricted areas of the UK

Antiretroviral treatment adherence as a mediating factor between psychosocial variables and HIV viral load.

Science.gov (United States)

Attonito, Jennifer; Dévieux, Jessy G; Lerner, Brenda D G; Hospital, Michelle M; Rosenberg, Rhonda

2014-01-01

Psychosocial factors may directly impact HIV health measures such as viral load (VL) whether or not patients are taking antiretroviral treatment (ART) consistently. Structural equation modeling plus Baron and Kenny's (1986) four-step approach were used to test a mediated model predicting VL among 246 HIV-infected adults who were on ART. Exogenous variables were social support, barriers to adherence, and stress. Moderators were alcohol use, marijuana use, and neurocognitive impairment. A small positive association between marijuana use and ART adherence approached significance. Only barriers to adherence predicted a decrease in adherence rates and an increase in VL. No other factors were significantly associated with either VL or adherence, and no interaction effects between exogenous variables and moderators were identified. The association between barriers to adherence and VL was partially mediated by ART adherence. Findings provide modest support for a direct link between psychosocial variables and a virologic response to ART. Copyright © 2014 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

Factor analysis

CERN Document Server

Gorsuch, Richard L

2013-01-01

Comprehensive and comprehensible, this classic covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and sufficient discussion of applications for effective use. This includes not only theory but also the empirical evaluations of the importance of mathematical distinctions for applied scientific analysis.

Variables As Currency: Linking Meta-Analysis Research and Data Paths in Sciences

Directory of Open Access Journals (Sweden)

Hua Qin

2014-11-01

Full Text Available Meta-analyses are studies that bring together data or results from multiple independent studies to produce new and over-arching findings. Current data curation systems only partially support meta-analytic research. Some important meta-analytic tasks, such as the selection of relevant studies for review and the integration of research datasets or findings, are not well supported in current data curation systems. To design tools and services that more fully support meta-analyses, we need a better understanding of meta-analytic research. This includes an understanding of both the practices of researchers who perform the analyses and the characteristics of the individual studies that are brought together. In this study, we make an initial contribution to filling this gap by developing a conceptual framework linking meta-analyses with data paths represented in published articles selected for the analysis. The framework focuses on key variables that represent primary/secondary datasets or derived socio-ecological data, contexts of use, and the data transformations that are applied. We introduce the notion of using variables and their relevant information (e.g., metadata and variable relationships as a type of currency to facilitate synthesis of findings across individual studies and leverage larger bodies of relevant source data produced in small science research. Handling variables in this manner provides an equalizing factor between data from otherwise disparate data-producing communities. We conclude with implications for exploring data integration and synthesis issues as well as system development.

An Analysis of Social Class Classification Based on Linguistic Variables

Institute of Scientific and Technical Information of China (English)

QU Xia-sha

2016-01-01

Since language is an influential tool in social interaction, the relationship of speech and social factors, such as social class, gender, even age is worth studying. People employ different linguistic variables to imply their social class, status and iden-tity in the social interaction. Thus the linguistic variation involves vocabulary, sounds, grammatical constructions, dialects and so on. As a result, a classification of social class draws people’s attention. Linguistic variable in speech interactions indicate the social relationship between people. This paper attempts to illustrate three main linguistic variables which influence the social class, and further sociolinguistic studies need to be more concerned about.

Temporal and spatial variability of rainfall distribution and ...

African Journals Online (AJOL)

Rainfall and evapotranspiration are the two major climatic factors affecting agricultural production. This study examined the extent and nature of rainfall variability from measured data while estimation of evapotranspiration was made from recorded weather data. Analysis of rainfall variability is made by the rainfall anomaly ...

Multi-scale approach to the environmental factors effects on spatio-temporal variability of Chironomus salinarius (Diptera: Chironomidae) in a French coastal lagoon

Science.gov (United States)

Cartier, V.; Claret, C.; Garnier, R.; Fayolle, S.; Franquet, E.

2010-03-01

The complexity of the relationships between environmental factors and organisms can be revealed by sampling designs which consider the contribution to variability of different temporal and spatial scales, compared to total variability. From a management perspective, a multi-scale approach can lead to time-saving. Identifying environmental patterns that help maintain patchy distribution is fundamental in studying coastal lagoons, transition zones between continental and marine waters characterised by great environmental variability on spatial and temporal scales. They often present organic enrichment inducing decreased species richness and increased densities of opportunist species like C hironomus salinarius, a common species that tends to swarm and thus constitutes a nuisance for human populations. This species is dominant in the Bolmon lagoon, a French Mediterranean coastal lagoon under eutrophication. Our objective was to quantify variability due to both spatial and temporal scales and identify the contribution of different environmental factors to this variability. The population of C. salinarius was sampled from June 2007 to June 2008 every two months at 12 sites located in two areas of the Bolmon lagoon, at two different depths, with three sites per area-depth combination. Environmental factors (temperature, dissolved oxygen both in sediment and under water surface, sediment organic matter content and grain size) and microbial activities (i.e. hydrolase activities) were also considered as explanatory factors of chironomid densities and distribution. ANOVA analysis reveals significant spatial differences regarding the distribution of chironomid larvae for the area and the depth scales and their interaction. The spatial effect is also revealed for dissolved oxygen (water), salinity and fine particles (area scale), and for water column depth. All factors but water column depth show a temporal effect. Spearman's correlations highlight the seasonal effect

Testing of technology readiness index model based on exploratory factor analysis approach

Science.gov (United States)

Ariani, AF; Napitupulu, D.; Jati, RK; Kadar, JA; Syafrullah, M.

2018-04-01

SMEs readiness in using ICT will determine the adoption of ICT in the future. This study aims to evaluate the model of technology readiness in order to apply the technology on SMEs. The model is tested to find if TRI model is relevant to measure ICT adoption, especially for SMEs in Indonesia. The research method used in this paper is survey to a group of SMEs in South Tangerang. The survey measures the readiness to adopt ICT based on four variables which is Optimism, Innovativeness, Discomfort, and Insecurity. Each variable contains several indicators to make sure the variable is measured thoroughly. The data collected through survey is analysed using factor analysis methodwith the help of SPSS software. The result of this study shows that TRI model gives more descendants on some indicators and variables. This result can be caused by SMEs owners’ knowledge is not homogeneous about either the technology that they are used, knowledge or the type of their business.

The effects of exposure to environmental factors on Heart Rate Variability: An ecological perspective

International Nuclear Information System (INIS)

Schnell, Izhak; Potchter, Oded; Epstein, Yoram; Yaakov, Yaron; Hermesh, Hagai; Brenner, Shmuel; Tirosh, Emanuel

2013-01-01

The impact of human exposure to environmental factors on Heart Rate Variability (HRV) was examined in the urban space of Tel-Aviv-Jaffa. Four environmental factors were investigated: thermal and social loads; CO concentrations and noise. Levels of HRV are explained mainly by subjective social stresses, noise and CO. The most interesting result is the fact that while subjective social stress and noise increase HRV, low levels of CO are reducing HRV to some extent moderating the impact of subjective social stress and noise. Beyond the poisoning effect of CO and the fact that extremely low levels of HRV associated with high dozes of CO increase risk for life, low levels of CO may have a narcotic effect, as it is measured by HRV. The effects of thermal loads on HRV are negligible probably due to the use of behavioral means in order to neutralize heat and cold effects. -- Highlights: ► The impact of human exposure to environmental factors on Heart Rate Variability (HRV) was examined. ► Previous studies measured human exposure to pollution by fixed monitoring stations. ► This study measured actual personal exposure by mini sensors. ► High level of subjective social load and noise increase HRV. ► Low levels of CO may have a narcotic effect, as it is measured by HRV. -- The research focuses on the effects of environmental factors; noise, subjective social stress, thermal load and CO on Heart Rate Variability

Exploratory Bi-factor Analysis: The Oblique Case

OpenAIRE

Jennrich, Robert L.; Bentler, Peter M.

2011-01-01

Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford (1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler (2011) introduced an exploratory form of bi-factor analysis that does not require one to provide an explicit bi-factor structure a priori. They use exploratory factor analysis and a bi-factor rotation criterion designed to produce a rotated loading mat...

Determining the spatial variability of wetland soil bulk density, organic matter, and the conversion factor between organic matter and organic carbon across coastal Louisiana, U.S.A.

Science.gov (United States)

Wang, Hongqing; Piazza, Sarai C.; Sharp, Leigh A.; Stagg, Camille L.; Couvillion, Brady R.; Steyer, Gregory D.; McGinnis, Thomas E.

2016-01-01

Soil bulk density (BD), soil organic matter (SOM) content, and a conversion factor between SOM and soil organic carbon (SOC) are often used in estimating SOC sequestration and storage. Spatial variability in BD, SOM, and the SOM–SOC conversion factor affects the ability to accurately estimate SOC sequestration, storage, and the benefits (e.g., land building area and vertical accretion) associated with wetland restoration efforts, such as marsh creation and sediment diversions. There are, however, only a few studies that have examined large-scale spatial variability in BD, SOM, and SOM–SOC conversion factors in coastal wetlands. In this study, soil cores, distributed across the entire coastal Louisiana (approximately 14,667 km2) were used to examine the regional-scale spatial variability in BD, SOM, and the SOM–SOC conversion factor. Soil cores for BD and SOM analyses were collected during 2006–09 from 331 spatially well-distributed sites in the Coastwide Reference Monitoring System network. Soil cores for the SOM–SOC conversion factor analysis were collected from 15 sites across coastal Louisiana during 2006–07. Results of a split-plot analysis of variance with incomplete block design indicated that BD and SOM varied significantly at a landscape level, defined by both hydrologic basins and vegetation types. Vertically, BD and SOM varied significantly among different vegetation types. The SOM–SOC conversion factor also varied significantly at the landscape level. This study provides critical information for the assessment of the role of coastal wetlands in large regional carbon budgets and the estimation of carbon credits from coastal restoration.

Epigenetic variability in conversion to psychosis: novel findings from an innovative longitudinal methylomic analysis.

Science.gov (United States)

Kebir, Oussama; Chaumette, Boris; Krebs, Marie-Odile

2018-04-26

Conversion to psychosis is a longitudinal process during which several epigenetic changes have been described. We tested the hypothesis that epigenetic variability in the methylomes of ultra-high risk (UHR) individuals may contribute to the risk of conversion. We studied a longitudinal cohort of UHR individuals (n = 39) and compared two groups (converters, n = 14 vs. non-converters, n = 25). A longitudinal methylomic study was conducted using Infinium HumanMethylation450 BeadChip covering half a million cytosine-phosphate-guanine (CpG) sites across the human genome from whole-blood samples. We used two statistical methods to investigate the variability of methylation probes. (i) The search for longitudinal variable methylation probes (VMPs) based on median comparisons identified two VMPs in converters only. The first CpG was located in the MACROD2 gene and the second CpG was in an intergenic region at 8q24.21. (ii) The detection of outliers using variance analysis related to private epimutations identified a dozen CpGs in converters only and highlighted two genes (RAC1 and SPHK1) from the sphingolipid signaling pathway. Our study is the first to support increased methylome variability during conversion to psychosis. We speculate that stochastic factors could increase DNA methylation variability and have a role in the complex pathophysiology of conversion to psychosis as well as in other psychiatric diseases.

Minimizing inter-microscope variability in dental microwear texture analysis

Science.gov (United States)

Arman, Samuel D.; Ungar, Peter S.; Brown, Christopher A.; DeSantis, Larisa R. G.; Schmidt, Christopher; Prideaux, Gavin J.

2016-06-01

A common approach to dental microwear texture analysis (DMTA) uses confocal profilometry in concert with scale-sensitive fractal analysis to help understand the diets of extinct mammals. One of the main benefits of DMTA over other methods is the repeatable, objective manner of data collection. This repeatability, however, is threatened by variation in results of DMTA of the same dental surfaces yielded by different microscopes. Here we compare DMTA data of five species of kangaroos measured on seven profilers of varying specifications. Comparison between microscopes confirms that inter-microscope differences are present, but we show that deployment of a number of automated treatments to remove measurement noise can help minimize inter-microscope differences. Applying these same treatments to a published hominin DMTA dataset shows that they alter some significant differences between dietary groups. Minimising microscope variability while maintaining interspecific dietary differences requires then that these factors are balanced in determining appropriate treatments. The process outlined here offers a solution for allowing comparison of data between microscopes, which is essential for ongoing DMTA research. In addition, the process undertaken, including considerations of other elements of DMTA protocols also promises to streamline methodology, remove measurement noise and in doing so, optimize recovery of a reliable dietary signature.

A new detrended semipartial cross-correlation analysis: Assessing the important meteorological factors affecting API

International Nuclear Information System (INIS)

Shen, Chen-Hua

2015-01-01

To analyze the unique contribution of meteorological factors to the air pollution index (API), a new method, the detrended semipartial cross-correlation analysis (DSPCCA), is proposed. Based on both a detrended cross-correlation analysis and a DFA-based multivariate-linear-regression (DMLR), this method is improved by including a semipartial correlation technique, which is used to indicate the unique contribution of an explanatory variable to multiple correlation coefficients. The advantages of this method in handling nonstationary time series are illustrated by numerical tests. To further demonstrate the utility of this method in environmental systems, new evidence of the primary contribution of meteorological factors to API is provided through DMLR. Results show that the most important meteorological factors affecting API are wind speed and diurnal temperature range, and the explanatory ability of meteorological factors to API gradually strengthens with increasing time scales. The results suggest that DSPCCA is a useful method for addressing environmental systems. - Highlights: • A detrended multiple linear regression is shown. • A detrended semipartial cross correlation analysis is proposed. • The important meteorological factors affecting API are assessed. • The explanatory ability of meteorological factors to API gradually strengthens with increasing time scales.

A new detrended semipartial cross-correlation analysis: Assessing the important meteorological factors affecting API

Energy Technology Data Exchange (ETDEWEB)

Shen, Chen-Hua, E-mail: shenandchen01@163.com [College of Geographical Science, Nanjing Normal University, Nanjing 210046 (China); Jiangsu Center for Collaborative Innovation in Geographical Information Resource, Nanjing 210046 (China); Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing 210046 (China)

2015-12-04

To analyze the unique contribution of meteorological factors to the air pollution index (API), a new method, the detrended semipartial cross-correlation analysis (DSPCCA), is proposed. Based on both a detrended cross-correlation analysis and a DFA-based multivariate-linear-regression (DMLR), this method is improved by including a semipartial correlation technique, which is used to indicate the unique contribution of an explanatory variable to multiple correlation coefficients. The advantages of this method in handling nonstationary time series are illustrated by numerical tests. To further demonstrate the utility of this method in environmental systems, new evidence of the primary contribution of meteorological factors to API is provided through DMLR. Results show that the most important meteorological factors affecting API are wind speed and diurnal temperature range, and the explanatory ability of meteorological factors to API gradually strengthens with increasing time scales. The results suggest that DSPCCA is a useful method for addressing environmental systems. - Highlights: • A detrended multiple linear regression is shown. • A detrended semipartial cross correlation analysis is proposed. • The important meteorological factors affecting API are assessed. • The explanatory ability of meteorological factors to API gradually strengthens with increasing time scales.

Worry About Caregiving Performance: A Confirmatory Factor Analysis

Directory of Open Access Journals (Sweden)

Ruijie Li

2018-03-01

Full Text Available Recent studies on the Zarit Burden Interview (ZBI support the existence of a unique factor, worry about caregiving performance (WaP, beyond role and personal strain. Our current study aims to confirm the existence of WaP within the multidimensionality of ZBI and to determine if predictors of WaP differ from the role and personal strain. We performed confirmatory factor analysis (CFA on 466 caregiver-patient dyads to compare between one-factor (total score, two-factor (role/personal strain, three-factor (role/personal strain and WaP, and four-factor models (role strain split into two factors. We conducted linear regression analyses to explore the relationships between different ZBI factors with socio-demographic and disease characteristics, and investigated the stage-dependent differences between WaP with role and personal strain by dyadic relationship. The four-factor structure that incorporated WaP and split role strain into two factors yielded the best fit. Linear regression analyses reveal that different variables significantly predict WaP (adult child caregiver and Neuropsychiatric Inventory Questionnaire (NPI-Q severity from role/personal strain (adult child caregiver, instrumental activities of daily living, and NPI-Q distress. Unlike other factors, WaP was significantly endorsed in early cognitive impairment. Among spouses, WaP remained low across Clinical Dementia Rating (CDR stages until a sharp rise in CDR 3; adult child and sibling caregivers experience a gradual rise throughout the stages. Our results affirm the existence of WaP as a unique factor. Future research should explore the potential of WaP as a possible intervention target to improve self-efficacy in the milder stages of burden.

Initiating an ergonomic analysis. A process for jobs with highly variable tasks.

Science.gov (United States)

Conrad, K M; Lavender, S A; Reichelt, P A; Meyer, F T

2000-09-01

Occupational health nurses play a vital role in addressing ergonomic problems in the workplace. Describing and documenting exposure to ergonomic risk factors is a relatively straightforward process in jobs in which the work is repetitive. In other types of work, the analysis becomes much more challenging because tasks may be repeated infrequently, or at irregular time intervals, or under different environmental and temporal conditions, thereby making it difficult to observe a "representative" sample of the work performed. This article describes a process used to identify highly variable job tasks for ergonomic analyses. The identification of tasks for ergonomic analysis was a two step process involving interviews and a survey of firefighters and paramedics from a consortium of 14 suburban fire departments. The interviews were used to generate a list of frequently performed, physically strenuous job tasks and to capture clear descriptions of those tasks and associated roles. The goals of the survey were to confirm the interview findings across the entire target population and to quantify the frequency and degree of strenuousness of each task. In turn, the quantitative results from the survey were used to prioritize job tasks for simulation. Although this process was used to study firefighters and paramedics, the approach is likely to be suitable for many other types of occupations in which the tasks are highly variable in content and irregular in frequency.

Factors affecting construction performance: exploratory factor analysis

Science.gov (United States)

Soewin, E.; Chinda, T.

2018-04-01

The present work attempts to develop a multidimensional performance evaluation framework for a construction company by considering all relevant measures of performance. Based on the previous studies, this study hypothesizes nine key factors, with a total of 57 associated items. The hypothesized factors, with their associated items, are then used to develop questionnaire survey to gather data. The exploratory factor analysis (EFA) was applied to the collected data which gave rise 10 factors with 57 items affecting construction performance. The findings further reveal that the items constituting ten key performance factors (KPIs) namely; 1) Time, 2) Cost, 3) Quality, 4) Safety & Health, 5) Internal Stakeholder, 6) External Stakeholder, 7) Client Satisfaction, 8) Financial Performance, 9) Environment, and 10) Information, Technology & Innovation. The analysis helps to develop multi-dimensional performance evaluation framework for an effective measurement of the construction performance. The 10 key performance factors can be broadly categorized into economic aspect, social aspect, environmental aspect, and technology aspects. It is important to understand a multi-dimension performance evaluation framework by including all key factors affecting the construction performance of a company, so that the management level can effectively plan to implement an effective performance development plan to match with the mission and vision of the company.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

Science.gov (United States)

Zhang, Zhenzhen; O'Neill, Marie S; Sánchez, Brisa N

2016-04-01

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM 2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

Quantitative effects of composting state variables on C/N ratio through GA-aided multivariate analysis.

Science.gov (United States)

Sun, Wei; Huang, Guo H; Zeng, Guangming; Qin, Xiaosheng; Yu, Hui

2011-03-01

It is widely known that variation of the C/N ratio is dependent on many state variables during composting processes. This study attempted to develop a genetic algorithm aided stepwise cluster analysis (GASCA) method to describe the nonlinear relationships between the selected state variables and the C/N ratio in food waste composting. The experimental data from six bench-scale composting reactors were used to demonstrate the applicability of GASCA. Within the GASCA framework, GA searched optimal sets of both specified state variables and SCA's internal parameters; SCA established statistical nonlinear relationships between state variables and the C/N ratio; to avoid unnecessary and time-consuming calculation, a proxy table was introduced to save around 70% computational efforts. The obtained GASCA cluster trees had smaller sizes and higher prediction accuracy than the conventional SCA trees. Based on the optimal GASCA tree, the effects of the GA-selected state variables on the C/N ratio were ranged in a descending order as: NH₄+-N concentration>Moisture content>Ash Content>Mean Temperature>Mesophilic bacteria biomass. Such a rank implied that the variation of ammonium nitrogen concentration, the associated temperature and the moisture conditions, the total loss of both organic matters and available mineral constituents, and the mesophilic bacteria activity, were critical factors affecting the C/N ratio during the investigated food waste composting. This first application of GASCA to composting modelling indicated that more direct search algorithms could be coupled with SCA or other multivariate analysis methods to analyze complicated relationships during composting and many other environmental processes. Copyright © 2010 Elsevier B.V. All rights reserved.

A factor analysis to detect factors influencing building national brand

Directory of Open Access Journals (Sweden)

Naser Azad

Full Text Available Developing a national brand is one of the most important issues for development of a brand. In this study, we present factor analysis to detect the most important factors in building a national brand. The proposed study uses factor analysis to extract the most influencing factors and the sample size has been chosen from two major auto makers in Iran called Iran Khodro and Saipa. The questionnaire was designed in Likert scale and distributed among 235 experts. Cronbach alpha is calculated as 84%, which is well above the minimum desirable limit of 0.70. The implementation of factor analysis provides six factors including “cultural image of customers”, “exciting characteristics”, “competitive pricing strategies”, “perception image” and “previous perceptions”.

Stress Intensity Factor for Interface Cracks in Bimaterials Using Complex Variable Meshless Manifold Method

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Hongfen Gao

2014-01-01

Full Text Available This paper describes the application of the complex variable meshless manifold method (CVMMM to stress intensity factor analyses of structures containing interface cracks between dissimilar materials. A discontinuous function and the near-tip asymptotic displacement functions are added to the CVMMM approximation using the framework of complex variable moving least-squares (CVMLS approximation. This enables the domain to be modeled by CVMMM without explicitly meshing the crack surfaces. The enriched crack-tip functions are chosen as those that span the asymptotic displacement fields for an interfacial crack. The complex stress intensity factors for bimaterial interfacial cracks were numerically evaluated using the method. Good agreement between the numerical results and the reference solutions for benchmark interfacial crack problems is realized.

Regression analysis of nuclear plant capacity factors

International Nuclear Information System (INIS)

Stocks, K.J.; Faulkner, J.I.

1980-07-01

Operating data on all commercial nuclear power plants of the PWR, HWR, BWR and GCR types in the Western World are analysed statistically to determine whether the explanatory variables size, year of operation, vintage and reactor supplier are significant in accounting for the variation in capacity factor. The results are compared with a number of previous studies which analysed only United States reactors. The possibility of specification errors affecting the results is also examined. Although, in general, the variables considered are statistically significant, they explain only a small portion of the variation in the capacity factor. The equations thus obtained should certainly not be used to predict the lifetime performance of future large reactors

Factors influencing crime rates: an econometric analysis approach

Science.gov (United States)

Bothos, John M. A.; Thomopoulos, Stelios C. A.

2016-05-01

The scope of the present study is to research the dynamics that determine the commission of crimes in the US society. Our study is part of a model we are developing to understand urban crime dynamics and to enhance citizens' "perception of security" in large urban environments. The main targets of our research are to highlight dependence of crime rates on certain social and economic factors and basic elements of state anticrime policies. In conducting our research, we use as guides previous relevant studies on crime dependence, that have been performed with similar quantitative analyses in mind, regarding the dependence of crime on certain social and economic factors using statistics and econometric modelling. Our first approach consists of conceptual state space dynamic cross-sectional econometric models that incorporate a feedback loop that describes crime as a feedback process. In order to define dynamically the model variables, we use statistical analysis on crime records and on records about social and economic conditions and policing characteristics (like police force and policing results - crime arrests), to determine their influence as independent variables on crime, as the dependent variable of our model. The econometric models we apply in this first approach are an exponential log linear model and a logit model. In a second approach, we try to study the evolvement of violent crime through time in the US, independently as an autonomous social phenomenon, using autoregressive and moving average time-series econometric models. Our findings show that there are certain social and economic characteristics that affect the formation of crime rates in the US, either positively or negatively. Furthermore, the results of our time-series econometric modelling show that violent crime, viewed solely and independently as a social phenomenon, correlates with previous years crime rates and depends on the social and economic environment's conditions during previous years.

Factor analysis for the adoption of nuclear technology in diagnosis and treatment of chronic diseases

International Nuclear Information System (INIS)

Sato, Renato Cesar; Zouain, Desiree Moraes

2012-01-01

To identify and evaluate latent variables (variables that are not directly observed) for adopting and using nuclear technologies in diagnosis and treatment of chronic diseases. The measurement and management of these latent factors are important for health care due to complexities of the sector. Methods: An exploratory factor analysis study was conducted among 52 physicians practicing in the areas of Cardiology, Neurology and Oncology in the State of Sao Paulo who agreed to participate in the study between 2009 and 2010. Data were collected using an attitude measurement questionnaire, and analyzed according to the principal component method with Varimax rotation. Results: The component matrix after factor rotation showed three elucidative groups arranged according to demand for nuclear technology: clinical factors, structural factors, and technological factors. Clinical factors included questionnaire answers referring to medical history, previous interventions, complexity and chronicity of the disease. Structural factors included patient age, physician's practice area, and payment ability. Technological factors included prospective growth in the use of nuclear technology and availability of services. Conclusions: The clinical factors group dimension identified in the study included patient history, prior interventions, and complexity and chronicity of the disease. This dimension is the main motivating for adopting nuclear technology in diagnosis and treatment of chronic diseases. (author)

Exploratory Factor Analysis of SCL90-R Symptoms Relevant to Psychosis

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Javad Amini

2011-10-01

Full Text Available "nObjective: Inconsistent results have been reported regarding the symptom dimensions relevant to psychosis in symptoms check list revised (SCL90-R, i.e., "psychoticism" and "paranoid ideation". Therefore, some studies have suggested different factor structures for questions of these two dimensions, and proposed two newly defined dimensions of "schizotypal signs" and "schizophrenia nuclear symptoms". We conducted an exploratory factor analysis on the items of these two dimensions in a general population sample in Iran. "nMethod: A total of 2158 subjects residing in Southern Tehran (capital of Iran were interviewed using the psychoticism and paranoid ideation questions in SCL90-R to assess severity of these symptom dimensions. Factor analysis was done through SAS 9.1.3 PROC FACTOR using Promax rotation (power=3 on the matrix of "polychoric correlations among variables" as the input data. "nResults: Two factors were retained by the proportion criterion. Considering loadings >= 0.5 as minimum criteria for factor loadings, 7 out of 10 questions  from psychoticism ,and 3 out of 6 questions from paranoid ideation were retained, and others were eliminated. The factor labels proposed by the questionnaire suited the extracted factors and were retained. Internal consistency for each of the dimensions was acceptable (Cronbach's alpha 0.7 and 0.74 for paranoid ideation and psychoticism respectively. Composite scores showed a half-normal distribution for both dimensions which is predictable for instruments that detect psychotic symptoms. "nConclusion: Results were in contrast with similar studies, and questioned them by suggesting a different factor structure obtained from a statistically large population. The population in a developing nation (Iran in this study and the socio-cultural differences in developed settings are the potential sources for discrepancies between this analysis and previous reports.

The role of climate and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria

Science.gov (United States)

Abdussalam, Auwal; Thornes, John; Leckebusch, Gregor

2015-04-01

Nigeria has a number of climate-sensitive infectious diseases; one of the most important of these diseases that remains a threat to public health is cholera. This study investigates the influences of both meteorological and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria. A stepwise multiple regression models are used to estimate the influence of the year-to-year variations of cholera cases and deaths for individual states in the country and as well for three groups of states that are classified based on annual rainfall amount. Specifically, seasonal mean maximum and minimum temperatures and annual rainfall totals were analysed with annual aggregate count of cholera cases and deaths, taking into account of the socioeconomic factors that are potentially enhancing vulnerability such as: absolute poverty, adult literacy, access to pipe borne water and population density. Result reveals that the most important explanatory meteorological and socioeconomic variables in explaining the spatiotemporal variability of the disease are rainfall totals, seasonal mean maximum temperature, absolute poverty, and accessibility to pipe borne water. The influences of socioeconomic factors appeared to be more pronounced in the northern part of the country, and vice-versa in the case of meteorological factors. Also, cross validated models output suggests a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.

Hydrological and environmental variables outperform spatial factors in structuring species, trait composition, and beta diversity of pelagic algae.

Science.gov (United States)

Wu, Naicheng; Qu, Yueming; Guse, Björn; Makarevičiūtė, Kristė; To, Szewing; Riis, Tenna; Fohrer, Nicola

2018-03-01

There has been increasing interest in algae-based bioassessment, particularly, trait-based approaches are increasingly suggested. However, the main drivers, especially the contribution of hydrological variables, of species composition, trait composition, and beta diversity of algae communities are less studied. To link species and trait composition to multiple factors (i.e., hydrological variables, local environmental variables, and spatial factors) that potentially control species occurrence/abundance and to determine their relative roles in shaping species composition, trait composition, and beta diversities of pelagic algae communities, samples were collected from a German lowland catchment, where a well-proven ecohydrological modeling enabled to predict long-term discharges at each sampling site. Both trait and species composition showed significant correlations with hydrological, environmental, and spatial variables, and variation partitioning revealed that the hydrological and local environmental variables outperformed spatial variables. A higher variation of trait composition (57.0%) than species composition (37.5%) could be explained by abiotic factors. Mantel tests showed that both species and trait-based beta diversities were mostly related to hydrological and environmental heterogeneity with hydrological contributing more than environmental variables, while purely spatial impact was less important. Our findings revealed the relative importance of hydrological variables in shaping pelagic algae community and their spatial patterns of beta diversities, emphasizing the need to include hydrological variables in long-term biomonitoring campaigns and biodiversity conservation or restoration. A key implication for biodiversity conservation was that maintaining the instream flow regime and keeping various habitats among rivers are of vital importance. However, further investigations at multispatial and temporal scales are greatly needed.

Quantification of variability and uncertainty in lawn and garden equipment NOx and total hydrocarbon emission factors.

Science.gov (United States)

Frey, H Christopher; Bammi, Sachin

2002-04-01

Variability refers to real differences in emissions among multiple emission sources at any given time or over time for any individual emission source. Variability in emissions can be attributed to variation in fuel or feedstock composition, ambient temperature, design, maintenance, or operation. Uncertainty refers to lack of knowledge regarding the true value of emissions. Sources of uncertainty include small sample sizes, bias or imprecision in measurements, nonrepresentativeness, or lack of data. Quantitative methods for characterizing both variability and uncertainty are demonstrated and applied to case studies of emission factors for lawn and garden (L&G) equipment engines. Variability was quantified using empirical and parametric distributions. Bootstrap simulation was used to characterize confidence intervals for the fitted distributions. The 95% confidence intervals for the mean grams per brake horsepower/hour (g/hp-hr) emission factors for two-stroke engine total hydrocarbon (THC) and NOx emissions were from -30 to +41% and from -45 to +75%, respectively. The confidence intervals for four-stroke engines were from -33 to +46% for THCs and from -27 to +35% for NOx. These quantitative measures of uncertainty convey information regarding the quality of the emission factors and serve as a basis for calculation of uncertainty in emission inventories (EIs).

A Variable Stiffness Analysis Model for Large Complex Thin-Walled Guide Rail

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Wang Xiaolong

2016-01-01

Full Text Available Large complex thin-walled guide rail has complicated structure and no uniform low rigidity. The traditional cutting simulations are time consuming due to huge computation especially in large workpiece. To solve these problems, a more efficient variable stiffness analysis model has been propose, which can obtain quantitative stiffness value of the machining surface. Applying simulate cutting force in sampling points using finite element analysis software ABAQUS, the single direction variable stiffness rule can be obtained. The variable stiffness matrix has been propose by analyzing multi-directions coupling variable stiffness rule. Combining with the three direction cutting force value, the reasonability of existing processing parameters can be verified and the optimized cutting parameters can be designed.

Variables relacionadas con los factores protectores en estudiantes de una universidad pública colombiana, 2016

OpenAIRE

Jaramillo, C.P.; Vélez, C.; Hoyos, M.; Escobar, M.P.; Pico, M.E.

2017-01-01

Introducción: Los factores protectores se definen como aquellos que fomentan los comportamientos favorables y positivos y desestimulan e inhiben los comportamientos de riesgo, limitando los efectos de los riesgos a los cuales se exponen los individuos. El fomento de factores protectores facilita el desarrollo de comportamientos y hábitos saludables así como la prevención de factores de riesgo. Objetivo: Analizar las variables relacionadas con los factores protectores en estudiantes de una uni...

Factor analysis of multivariate data

Digital Repository Service at National Institute of Oceanography (India)

Fernandes, A.A.; Mahadevan, R.

A brief introduction to factor analysis is presented. A FORTRAN program, which can perform the Q-mode and R-mode factor analysis and the singular value decomposition of a given data matrix is presented in Appendix B. This computer program, uses...

[Relations between biomedical variables: mathematical analysis or linear algebra?].

Science.gov (United States)

Hucher, M; Berlie, J; Brunet, M

1977-01-01

The authors, after a short reminder of one pattern's structure, stress on the possible double approach of relations uniting the variables of this pattern: use of fonctions, what is within the mathematical analysis sphere, use of linear algebra profiting by matricial calculation's development and automatiosation. They precise the respective interests on these methods, their bounds and the imperatives for utilization, according to the kind of variables, of data, and the objective for work, understanding phenomenons or helping towards decision.

Statistical analysis of corn yields responding to climate variability at various spatio-temporal resolutions

Science.gov (United States)

Jiang, H.; Lin, T.

2017-12-01

Rain-fed corn production systems are subject to sub-seasonal variations of precipitation and temperature during the growing season. As each growth phase has varied inherent physiological process, plants necessitate different optimal environmental conditions during each phase. However, this temporal heterogeneity towards climate variability alongside the lifecycle of crops is often simplified and fixed as constant responses in large scale statistical modeling analysis. To capture the time-variant growing requirements in large scale statistical analysis, we develop and compare statistical models at various spatial and temporal resolutions to quantify the relationship between corn yield and weather factors for 12 corn belt states from 1981 to 2016. The study compares three spatial resolutions (county, agricultural district, and state scale) and three temporal resolutions (crop growth phase, monthly, and growing season) to characterize the effects of spatial and temporal variability. Our results show that the agricultural district model together with growth phase resolution can explain 52% variations of corn yield caused by temperature and precipitation variability. It provides a practical model structure balancing the overfitting problem in county specific model and weak explanation power in state specific model. In US corn belt, precipitation has positive impact on corn yield in growing season except for vegetative stage while extreme heat attains highest sensitivity from silking to dough phase. The results show the northern counties in corn belt area are less interfered by extreme heat but are more vulnerable to water deficiency.

Multiple Linear Regression Analysis of Factors Affecting Real Property Price Index From Case Study Research In Istanbul/Turkey

Science.gov (United States)

Denli, H. H.; Koc, Z.

2015-12-01

Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.

A new approach for modelling variability in residential construction projects

Directory of Open Access Journals (Sweden)

Mehrdad Arashpour

2013-06-01

Full Text Available The construction industry is plagued by long cycle times caused by variability in the supply chain. Variations or undesirable situations are the result of factors such as non-standard practices, work site accidents, inclement weather conditions and faults in design. This paper uses a new approach for modelling variability in construction by linking relative variability indicators to processes. Mass homebuilding sector was chosen as the scope of the analysis because data is readily available. Numerous simulation experiments were designed by varying size of capacity buffers in front of trade contractors, availability of trade contractors, and level of variability in homebuilding processes. The measurements were shown to lead to an accurate determination of relationships between these factors and production parameters. The variability indicator was found to dramatically affect the tangible performance measures such as home completion rates. This study provides for future analysis of the production homebuilding sector, which may lead to improvements in performance and a faster product delivery to homebuyers.

A new approach for modelling variability in residential construction projects

Directory of Open Access Journals (Sweden)

Mehrdad Arashpour

2013-06-01

Full Text Available The construction industry is plagued by long cycle times caused by variability in the supply chain. Variations or undesirable situations are the result of factors such as non-standard practices, work site accidents, inclement weather conditions and faults in design. This paper uses a new approach for modelling variability in construction by linking relative variability indicators to processes. Mass homebuilding sector was chosen as the scope of the analysis because data is readily available. Numerous simulation experiments were designed by varying size of capacity buffers in front of trade contractors, availability of trade contractors, and level of variability in homebuilding processes. The measurements were shown to lead to an accurate determination of relationships between these factors and production parameters. The variability indicator was found to dramatically affect the tangible performance measures such as home completion rates. This study provides for future analysis of the production homebuilding sector, which may lead to improvements in performance and a faster product delivery to homebuyers. 

GIS and correlation analysis of geo-environmental variables ...

African Journals Online (AJOL)

GIS and correlation analysis of geo-environmental variables influencing malaria prevalence in the Saboba district of Northern Ghana. ... The study also applied spline interpolation technique to map malaria prevalence in the district using standardised malaria incidence. The result indicates that distance to marshy areas is ...

Factor analysis and scintigraphy

International Nuclear Information System (INIS)

Di Paola, R.; Penel, C.; Bazin, J.P.; Berche, C.

1976-01-01

The goal of factor analysis is usually to achieve reduction of a large set of data, extracting essential features without previous hypothesis. Due to the development of computerized systems, the use of largest sampling, the possibility of sequential data acquisition and the increase of dynamic studies, the problem of data compression can be encountered now in routine. Thus, results obtained for compression of scintigraphic images were first presented. Then possibilities given by factor analysis for scan processing were discussed. At last, use of this analysis for multidimensional studies and specially dynamic studies were considered for compression and processing [fr

Analysis about factors affecting the degree of damage of buildings in earthquake

International Nuclear Information System (INIS)

Jia, Jing; Yan, Jinghong

2015-01-01

Earthquakes have been affecting human's safety through human's history. Previous studies on earthquake, mostly, focused on the performance of buildings or evaluating damages. This paper, however, compares different factors that have influence on the damage of buildings with a case study in Wenchuan earthquake, using multiple linear regression methodology, so as to identify to what extend this factors influence the buildings’ damages, then give the rank of importance of these factors. In this process, authors take the type of structure as a dummy variable to compare the degree of damages caused by different types of structure, which were barely studied before. Besides, Factor Analysis Methodology(FA) will be adapted to classify factors, the results of which will simplify later study. The outcome of this study would make a big difference in optimizing the seismic design and improving residential seismic quality. (paper)

Analysis of factors important for the occurrence of Campylobacter in Danish broiler flocks

DEFF Research Database (Denmark)

Sommer, Helle Mølgaard; Heuer, Ole Eske; Sørensen, Anna Irene Vedel

2013-01-01

a multivariate analysis including all 43 variables. A multivariate analysis was conducted using a generalized linear model, and the correlations between the houses from the same farms were accounted for by adding a variance structure to the model. The procedures for analyses included backward elimination...... of positive flocks/total number of flocks delivered over the 2-year period).The following factors were found to be significantly associated with the occurrence of Campylobacter in the broiler flocks: old broiler houses, late introduction of whole wheat in the feed, relatively high broiler age at slaughter...

In-depth analysis of the causal factors of incidents reported in the Greek petrochemical industry

Energy Technology Data Exchange (ETDEWEB)

Konstandinidou, Myrto [Institute of Nuclear Technology-Radiation Protection, National Center for Scientific Research ' Demokritos' , Aghia Paraskevi 15310 (Greece); Nivolianitou, Zoe, E-mail: zoe@ipta.demokritos.gr [Institute of Nuclear Technology-Radiation Protection, National Center for Scientific Research ' Demokritos' , Aghia Paraskevi 15310 (Greece); Kefalogianni, Eirini; Caroni, Chrys [School of Applied Mathematical and Physical Sciences, National Technical University of Athens, 9 Iroon Polytexneiou Str., Zografou Campus, 157 80 Athens (Greece)

2011-11-15

This paper presents a statistical analysis of all reported incidents in the Greek petrochemical industry from 1997 to 2003. A comprehensive database has been developed to include industrial accidents (fires, explosions and substance releases), occupational accidents, incidents without significant consequences and near misses. The study concentrates on identifying and analyzing the causal factors related to different consequences of incidents, in particular, injury, absence from work and material damage. Methods of analysis include logistic regression with one of these consequences as dependent variable. The causal factors that are considered cover four major categories related to organizational issues, equipment malfunctions, human errors (of commission or omission) and external causes. Further analyses aim to confirm the value of recording near misses by comparing their causal factors with those of more serious incidents. The statistical analysis highlights the connection between the human factor and the underlying causes of accidents or incidents. - Highlights: > The research work is original, based on field data collected directly from the petrochemical industry. > It deals with the in-depth statistical analysis of accident data on human-organizational causes. > It researches underlying causes of accidents and the parameters affecting them. > The causal factors that are considered cover four big taxonomies. > Near misses are worth recording for comparing their causal factors with more serious incidents.

Insights into gait disorders: walking variability using phase plot analysis, Huntington's disease.

Science.gov (United States)

Collett, Johnny; Esser, Patrick; Khalil, Hanan; Busse, Monica; Quinn, Lori; DeBono, Katy; Rosser, Anne; Nemeth, Andrea H; Dawes, Helen

2014-09-01

Huntington's disease (HD) is a progressive inherited neurodegenerative disorder. Identifying sensitive methodologies to quantitatively measure early motor changes have been difficult to develop. This exploratory observational study investigated gait variability and symmetry in HD using phase plot analysis. We measured the walking of 22 controls and 35 HD gene carriers (7 premanifest (PreHD)), 16 early/mid (HD1) and 12 late stage (HD2) in Oxford and Cardiff, UK. The unified Huntington's disease rating scale-total motor scores (UHDRS-TMS) and disease burden scores (DBS) were used to quantify disease severity. Data was collected during a clinical walk test (8.8 or 10 m) using an inertial measurement unit attached to the trunk. The 6 middle strides were used to calculate gait variability determined by spatiotemporal parameters (co-efficient of variation (CoV)) and phase plot analysis. Phase plots considered the variability in consecutive wave forms from vertical movement and were quantified by SDA (spatiotemporal variability), SDB (temporal variability), ratio ∀ (ratio SDA:SDB) and Δangleβ (symmetry). Step time CoV was greater in manifest HD (p0.05). Phase plot analysis identified differences between manifest HD and controls for SDB, Ratio ∀ and Δangle (all pplot analysis may be a sensitive method of detecting gait changes in HD and can be performed quickly during clinical walking tests. Copyright © 2014 Elsevier B.V. All rights reserved.

PRINCIPAL COMPONENT ANALYSIS OF FACTORS DETERMINING PHOSPHATE ROCK DISSOLUTION ON ACID SOILS

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Yusdar Hilman

2016-10-01

Full Text Available Many of the agricultural soils in Indonesia are acidic and low in both total and available phosphorus which severely limits their potential for crops production. These problems can be corrected by application of chemical fertilizers. However, these fertilizers are expensive, and cheaper alternatives such as phosphate rock (PR have been considered. Several soil factors may influence the dissolution of PR in soils, including both chemical and physical properties. The study aimed to identify PR dissolution factors and evaluate their relative magnitude. The experiment was conducted in Soil Chemical Laboratory, Universiti Putra Malaysia and Indonesian Center for Agricultural Land Resources Research and Development from January to April 2002. The principal component analysis (PCA was used to characterize acid soils in an incubation system into a number of factors that may affect PR dissolution. Three major factors selected were soil texture, soil acidity, and fertilization. Using the scores of individual factors as independent variables, stepwise regression analysis was performed to derive a PR dissolution function. The factors influencing PR dissolution in order of importance were soil texture, soil acidity, then fertilization. Soil texture factors including clay content and organic C, and soil acidity factor such as P retention capacity interacted positively with P dissolution and promoted PR dissolution effectively. Soil texture factors, such as sand and silt content, soil acidity factors such as pH, and exchangeable Ca decreased PR dissolution.

Latent vs. Observed Variables : Analysis of Irrigation Water Efficiency Using SEM and SUR

NARCIS (Netherlands)

Tang, Jianjun; Folmer, Henk

In this paper we compare conceptualising single factor technical and allocative efficiency as indicators of a single latent variable, or as separate observed variables. In the former case, the impacts on both efficiency types are analysed by means of structural equationmodeling (SEM), in the latter

A Systematic Review and Meta-Analysis of the Effects of Flavanol-Containing Tea, Cocoa and Apple Products on Body Composition and Blood Lipids: Exploring the Factors Responsible for Variability in Their Efficacy

Science.gov (United States)

González-Sarrías, Antonio; Pinto, Paula; Dall’Asta, Margherita; Rodríguez-Mateos, Ana; Dumont, Julie; Massaro, Marika; Sánchez-Meca, Julio; Morand, Christine

2017-01-01

Several randomized controlled trials (RCTs) and meta-analyses support the benefits of flavanols on cardiometabolic health, but the factors affecting variability in the responses to these compounds have not been properly assessed. The objectives of this meta-analysis were to systematically collect the RCTs-based-evidence of the effects of flavanol-containing tea, cocoa and apple products on selected biomarkers of cardiometabolic risk and to explore the influence of various factors on the variability in the responses to the consumption of these products. A total of 120 RCTs were selected. Despite a high heterogeneity, the intake of the flavanol-containing products was associated using a random model with changes (reported as standardized difference in means (SDM)) in body mass index (−0.15, p < 0.001), waist circumference (−0.29, p < 0.001), total-cholesterol (−0.21, p < 0.001), LDL-cholesterol (−0.23, p < 0.001), and triacylglycerides (−0.11, p = 0.027), and with an increase of HDL-cholesterol (0.15, p = 0.005). Through subgroup analyses, we showed the influence of baseline-BMI, sex, source/form of administration, medication and country of investigation on some of the outcome measures and suggest that flavanols may be more effective in specific subgroups such as those with a BMI ≥ 25.0 kg/m2, non-medicated individuals or by specifically using tea products. This meta-analysis provides the first robust evidence of the effects induced by the consumption of flavanol-containing tea, cocoa and apple products on weight and lipid biomarkers and shows the influence of various factors that can affect their bioefficacy in humans. Of note, some of these effects are quantitatively comparable to those produced by drugs, life-style changes or other natural products. Further, RCTs in well-characterized populations are required to fully comprehend the factors affecting inter-individual responses to flavanol and thereby improve flavanols efficacy in the prevention of

A Systematic Review and Meta-Analysis of the Effects of Flavanol-Containing Tea, Cocoa and Apple Products on Body Composition and Blood Lipids: Exploring the Factors Responsible for Variability in Their Efficacy

Directory of Open Access Journals (Sweden)

Antonio González-Sarrías

2017-07-01

Full Text Available Several randomized controlled trials (RCTs and meta-analyses support the benefits of flavanols on cardiometabolic health, but the factors affecting variability in the responses to these compounds have not been properly assessed. The objectives of this meta-analysis were to systematically collect the RCTs-based-evidence of the effects of flavanol-containing tea, cocoa and apple products on selected biomarkers of cardiometabolic risk and to explore the influence of various factors on the variability in the responses to the consumption of these products. A total of 120 RCTs were selected. Despite a high heterogeneity, the intake of the flavanol-containing products was associated using a random model with changes (reported as standardized difference in means (SDM in body mass index (−0.15, p < 0.001, waist circumference (−0.29, p < 0.001, total-cholesterol (−0.21, p < 0.001, LDL-cholesterol (−0.23, p < 0.001, and triacylglycerides (−0.11, p = 0.027, and with an increase of HDL-cholesterol (0.15, p = 0.005. Through subgroup analyses, we showed the influence of baseline-BMI, sex, source/form of administration, medication and country of investigation on some of the outcome measures and suggest that flavanols may be more effective in specific subgroups such as those with a BMI ≥ 25.0 kg/m2, non-medicated individuals or by specifically using tea products. This meta-analysis provides the first robust evidence of the effects induced by the consumption of flavanol-containing tea, cocoa and apple products on weight and lipid biomarkers and shows the influence of various factors that can affect their bioefficacy in humans. Of note, some of these effects are quantitatively comparable to those produced by drugs, life-style changes or other natural products. Further, RCTs in well-characterized populations are required to fully comprehend the factors affecting inter-individual responses to flavanol and thereby improve flavanols efficacy in the

Sympathetic Activity, Assessed by Power Spectral Analysis of Heart Rate Variability, in White-Coat, Masked and Sustained Hypertension Versus True Normotension

Czech Academy of Sciences Publication Activity Database

Fagard, R.H.; Stolarz, K.; Kuznetsova, T.; Seidlerová, J.; Tikhonoff, V.; Grodzicki, T.; Nikitin, Y.; Filipovský, J.; Peleška, Jan; Casiglia, E.; Thijs, L.; Staessen, J.A.; Kawecka-Jaszcz, K.

2007-01-01

Roč. 25, č. 11 (2007), s. 2280-2285 ISSN 0263-6352 Institutional research plan: CEZ:AV0Z10300504 Keywords : heart rate variability * masked hypertension * power spectral analysis * sympathetic activity * white-coat hypertension Subject RIV: FA - Cardiovascular Diseases incl. Cardiotharic Surgery Impact factor: 4.364, year: 2007

Unbiased proteomics analysis demonstrates significant variability in mucosal immune factor expression depending on the site and method of collection.

Directory of Open Access Journals (Sweden)

Kenzie M Birse

Full Text Available Female genital tract secretions are commonly sampled by lavage of the ectocervix and vaginal vault or via a sponge inserted into the endocervix for evaluating inflammation status and immune factors critical for HIV microbicide and vaccine studies. This study uses a proteomics approach to comprehensively compare the efficacy of these methods, which sample from different compartments of the female genital tract, for the collection of immune factors. Matching sponge and lavage samples were collected from 10 healthy women and were analyzed by tandem mass spectrometry. Data was analyzed by a combination of differential protein expression analysis, hierarchical clustering and pathway analysis. Of the 385 proteins identified, endocervical sponge samples collected nearly twice as many unique proteins as cervicovaginal lavage (111 vs. 61 with 55% of proteins common to both (213. Each method/site identified 73 unique proteins that have roles in host immunity according to their gene ontology. Sponge samples enriched for specific inflammation pathways including acute phase response proteins (p = 3.37×10(-24 and LXR/RXR immune activation pathways (p = 8.82×10(-22 while the role IL-17A in psoriasis pathway (p = 5.98×10(-4 and the complement system pathway (p = 3.91×10(-3 were enriched in lavage samples. Many host defense factors were differentially enriched (p<0.05 between sites including known/potential antimicrobial factors (n = 21, S100 proteins (n = 9, and immune regulatory factors such as serpins (n = 7. Immunoglobulins (n = 6 were collected at comparable levels in abundance in each site although 25% of those identified were unique to sponge samples. This study demonstrates significant differences in types and quantities of immune factors and inflammation pathways collected by each sampling technique. Therefore, clinical studies that measure mucosal immune activation or factors assessing HIV transmission should utilize

Analysis of the spatial variability of crop yield and soil properties in small agricultural plots

Directory of Open Access Journals (Sweden)

Vieira Sidney Rosa

2003-01-01

Full Text Available The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in São Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol and a Red Yellow Latossol (Ultisol. Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.

PATH ANALYSIS WITH LOGISTIC REGRESSION MODELS : EFFECT ANALYSIS OF FULLY RECURSIVE CAUSAL SYSTEMS OF CATEGORICAL VARIABLES

OpenAIRE

Nobuoki, Eshima; Minoru, Tabata; Geng, Zhi; Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University; Department of Applied Mathematics, Faculty of Engineering, Kobe University; Department of Probability and Statistics, Peking University

2001-01-01

This paper discusses path analysis of categorical variables with logistic regression models. The total, direct and indirect effects in fully recursive causal systems are considered by using model parameters. These effects can be explained in terms of log odds ratios, uncertainty differences, and an inner product of explanatory variables and a response variable. A study on food choice of alligators as a numerical exampleis reanalysed to illustrate the present approach.

Statictical Analysis Of The Conditioning Factors Of Urban Electric Consumption

International Nuclear Information System (INIS)

Segura D'Rouville, Juan Joel; Suárez Carreño, Franyelit María

2017-01-01

This research work presents the analysis of the most important factors that condition the urban residential electricity consumption. This study shows the quantitative parameters conditioning the electricity consumption. This sector of analysis has been chosen because there is disaggregated information of which are the main social and technological factors that determine its behavior, growth, with the objective of elaborating policies in the management of the electric consumption. The electrical demand considered as the sum of the powers of all the equipment that are used in each of the instants of a full day, is related to the electrical consumption, which is not but the value of the power demanded by a determined consumer Multiplied by the time in which said demand is maintained. In this report we propose the design of a probabilistic model of prediction of electricity consumption, taking into account mainly influential social and technological factors. The statistical process of this database is done through the Stat Graphics software version 4.1, for its extensive didactic in the accomplishment of calculations and associated methods. Finally, the correlation of the variables was performed to classify the determinants in a specific way and thus to determine the consumption of the dwellings. (author)

Global sugar market – the analysis of factors influencing supply and demand

Directory of Open Access Journals (Sweden)

Lenka Rumánková

2013-01-01

Full Text Available This article deals with an analysis of the world sugar market, and specifically focuses on the supply and demand of refined sugar and their main determinants. The article first identifies the main determinants of the world supply of and demand for sugar, and further, their effect on such variables is quantified. Further, the component correlations on the selected market are analyzed. This consists of the identification of the factors affecting the production of refined sugar, as one of the main elements of the supply of sugar, as well as an analysis of the world price of sugar, as one of the significant factors affecting the world sugar market. The said correlations are quantified with the utilization of regression analysis on the basis of time series of the individual variables within the years 1980–2010. On the basis of the conducted analysis, the main determinants of the sugar supply on the world market within the analyzed period, for which an effect has been established both from an economic viewpoint, as well as from a statistical viewpoint, can be considered to be sugar reserves, its price and the acreage of sugarcane. The main determinant of the demand for sugar is, according to the conducted analysis, the global GDP on a new value level, as well as converted to one inhabitant. Further, the analysis also established the effect of the price of sugar and its reserves on the world production of refined sugar, and, last but not least, also the long-term tendency in the development of the world price of sugar. The analysis has proven significant influence of refined sugar supply, reserves of refined sugar, its price and area of sugar cane on sugar supply. Then, the analysis detected GDP as the main determinant of the sugar demand and the long memory in sugar prices. Finally, the influence of delayed price, reserves and delayed reserves on production has been proven.

Analysis Of Factors Affecting Demand Red Chili Pepper Capsicum Annum L In Solok And Effort Fulfillment

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Zulfitriyana

2015-08-01

Full Text Available Research on the analysis of the factors that influence the demand for red chilli Capsicum annuum L in Solok and compliance efforts implemented in March s.d April 2016. The purpose of this study consisted of 1 analyze the factors affecting the demand for red chili in Solok 2 analyze the elasticity of demand for red chili in Solok 3 know the effort that can be done to meet the demand of red chilli in Solok. To achieve the objectives of the first and second use secondary data for 15 fifteen years and to achieve the objectives the third used primary data. The method used is descriptive analytical method a method that is used to describe phenomena that exist which takes place in the present or past. The variables were observed in this study is the X1 price of red chilli X2 the price of green chili X3 onion prices X4 population X5 income and Y the number of requests red chili which is then analyzed by multiple linear regression elasticity of demand and SWOT. The results of that research addressing the factors that influence the demand for red chili in Solok is the price of red chilli itself the price of green chili as a substitute goods the number of population and income while onion prices affect the amount of red chili demand in Solok. But simultaneously variable X1 red chili prices X2 the price of green chili X3 onion prices X4 population and X5 income strongly influence demand red chili in Solok where the F test results show that F count F table 212.262 3600 with a significance level 0.000 0.010 and the most influential variable is the variable X4 population with the greatest value of beta Coefficients is 1100. Based on analysis of the elasticity of demand is known that red chili pepper is a normal good is inelastic to price elasticity coefficient value amp603p of -0.120. Green chili is substituting goods and shallots are complements of red chili with cross elasticity coefficient amp603px1 and amp603px2 respectively by 0293 and -0.635. While the

Physical activity as a health factor modifying heart rate variability (HRV

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Nowosielska-Swadzba Danuta

2015-03-01

Full Text Available Purpose: The aim of the research was the evaluation of the selected HRV factors of the training volleyball players in two training periods and non-training people. Materials and methods : The study involved 8 leading volleyball players aged 20-23 and 13 non-training persons aged 19-26. The study of the training players was conducted twice: in the pre-competition and in the competition period. The study for the non-training persons was conducted once. The selected factors of the spectral analysis have been evaluated: TP [ms 2], share of LF and HF power [n.u], LF/HF indicator and time analysis factors: RR [ms], HR [1/min], RMSSD [ms]. Results : Statistically significant differences appeared only in the selected time analysis factors (RR, HR, between the group of the training and non-training persons. Other differences in the evaluated parameters were not statistically significant. Conclusions : Physical activity influences on the HRV growth. HRV measurement may serve for the control of the changes taking place in the AUN under the influence of the physical activity.

Analysis of factors associated with traffic injury severity on rural roads in Iran.

Science.gov (United States)

Kashani, Ali Tavakoli; Shariat-Mohaymany, Afshin; Ranjbari, Andishe

2012-01-01

Iran is a country with one of the highest rates of traffic crash fatality and injury, and seventy percent of these fatalities happen on rural roads. The objective of this study is to identify the significant factors influencing injury severity among drivers involved in crashes on two kinds of major rural roads in Iran: two-lane, two-way roads and freeways. According to the dataset, 213569 drivers were involved in rural road crashes in Iran, over the 3 years from 2006 to 2008. The Classification And Regression Tree method (CART) was applied for 13 independent variables, and one target variable of injury severity with 3 classes of no-injury, injury and fatality. Some of the independent variables were cause of crash, collision type, weather conditions, road surface conditions, driver's age and gender and seat belt usage. The CART model was trained by 70% of these data, and tested with the rest. It was indicated that seat belt use is the most important safety factor for two-lane, two-way rural roads, but on freeways, the importance of this variable is less. Cause of crash, also turned out to be the next most important variable. The results showed that for two-lane, two-way rural roads, "improper overtaking" and "speeding", and for rural freeways, "inattention to traffic ahead", "vehicle defect", and "movement of pedestrians, livestock and unauthorized vehicles on freeways" are the most serious causes of increasing injury severity. The analysis results revealed seat belt use, cause of crash and collision type as the most important variables influencing the injury severity of traffic crashes. To deal with these problems, intensifying police enforcement by means of mobile patrol vehicles, constructing overtaking lanes where necessary, and prohibiting the crossing of pedestrians and livestock and the driving of unauthorized vehicles on freeways are necessary. Moreover, creating a rumble strip on the two edges of roads, and paying attention to the design consistency of

Methods for the Quasi-Periodic Variability Analysis in Blazars Y. Liu ...

Indian Academy of Sciences (India)

the variability analysis in blazars in optical and radio bands, to search for possible quasi-periodic signals. 2. Power spectral density (PSD). In statistical signal processing and physics, the power spectral density (PSD) is a positive real function of a frequency variable associated with a stationary stochas- tic process. Intuitively ...

VALUE OF HEART RATE VARIABILITY ANALYSIS IN DIAGNOSTICS OF THE EMOTIONAL STATE

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І. Chaykovskyi

2012-11-01

Full Text Available The is presented the development of method for evaluation of emotional state of man, what suitable for use at the workplace based on analysis of heart rate (HR variability. 28 healthy volunteers were examined. 3 audiovisual clips were consistently presented on the display of the personal computer for each of them. One clip contained information originating the positive emotions, the second one – negative emotions, the third one – neutral. All possible pairs of the emotional states were analysed with help of one- and multi-dimensional linear discriminant analysis based on HR variability. Showing the emotional video-clips (of both signs causes reliable slowing of HR frequency and also some decreasing of HR variability. In addition, negative emotions cause regularizing and simplification of structural organization of heart rhythm. Accuracy of discrimination for pair “emotional – neutral” video clips was 98 %, for pair “rest – neutral” was 74 %, for pair “positive – negative” was 91 %. Analysis of HR variability enables to determine the emotional state of observed person at the workplace with high reliability.

Variability of in vivo recovery of factor IX after infusion of monoclonal antibody purified factor IX concentrates in patients with hemophilia B. The Mononine Study Group.

Science.gov (United States)

White, G C; Shapiro, A D; Kurczynski, E M; Kim, H C; Bergman, G E

1995-05-01

Monoclonal antibody purified factor IX concentrate, Mononine (Armour Pharmaceutical Company, Kankakee, Illinois, USA), is a recently developed replacement factor concentrate for the treatment of patients with hemophilia B. The pharmacokinetic properties of monoclonal antibody purified factor IX concentrate (MAb Factor IX concentrate) have been evaluated in only small samples of patients, and little is known about those factors that might influenced in vivo recovery of factor IX after infusion is a larger patient population. In vivo recovery of factor IX was therefore evaluated for 80 different indications in 72 patients who received MAb Factor IX concentrate for the management of spontaneous or trauma-induced bleeding, or as prophylaxis with surgery. The average recovery after infusions for presurgical pharmacokinetic analysis (mean +/- standard deviation) was 1.28 +/- 0.56 U/dl rise per U/kg infused (range 0.41-2.80), and the average recovery after all infusions for treatment was 1.23 +/- 0.49 U/dl rise per U/kg infused (range - 0.35-2.92). Recovery values for multiple MAb Factor IX doses in a given patient were also variable; the average recovery was 1.22 +/- 0.53 U/dl rise per U/kg given, and standard deviations ranged from 0.03 to 1.26. Patient age, weight, and MAb Factor IX concentrate dose minimally but significantly influenced factor IX recovery. There was no significant effect of either race, history of previous thrombotic complications during treatment with other replacement factor concentrates, or bleeding state on recovery. All of the patients treated with this preparation experienced excellent hemostasis, and no thrombotic complications were observed.

A Statistical Analysis of Cointegration for I(2) Variables

DEFF Research Database (Denmark)

Johansen, Søren

1995-01-01

be conducted using the ¿ sup2/sup distribution. It is shown to what extent inference on the cointegration ranks can be conducted using the tables already prepared for the analysis of cointegration of I(1) variables. New tables are needed for the test statistics to control the size of the tests. This paper...... contains a multivariate test for the existence of I(2) variables. This test is illustrated using a data set consisting of U.K. and foreign prices and interest rates as well as the exchange rate....

Longitudinal intra- and inter-individual variability in young swimmers' performance and determinant competition factors

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Jorge Estrela Morais

2014-09-01

Full Text Available The main purpose of this study was to follow-up the intra- and inter-individual variability of young swimmers' performance and determinant factors over two competitive seasons. Thirty young swimmers (14 boys: 12.33±0.65 years-old; 16 girls: 11.15±0.55 years-old were followed-up throughout two consecutive seasons (seven evaluation moments. Performance (100m freestyle, anthropometric, kinematic, hydrodynamic and efficiency features were evaluated. A gender and skill-level effect was observed. Boys improved in a higher amount (% comparing to girls. Overall, swimmers in skill-level 2 (both genders presented a higher intra-individual variability. Performance and anthropometrics showed a significant inter-individual variability in most moments, but hydrodynamics, kinematics and efficiency did not. Within each skill-level hydrodynamics, kinematics and efficiency were the variables that showed a high inter-individual variability. As a gender and skill-level effect was noticed in an age-group of young swimmers, coaches and practitioners should put the focus in specific and customized training plans for each skill-level of swimmers.

An analysis of prediction skill of monthly mean climate variability

Energy Technology Data Exchange (ETDEWEB)

Kumar, Arun; Chen, Mingyue; Wang, Wanqiu [Climate Prediction Center, National Centers for Environmental Prediction (CPC/NCEP), Camp Springs, MD (United States)

2011-09-15

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30-40 days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability. (orig.)

Partial differential equations with variable exponents variational methods and qualitative analysis

CERN Document Server

Radulescu, Vicentiu D

2015-01-01

Partial Differential Equations with Variable Exponents: Variational Methods and Qualitative Analysis provides researchers and graduate students with a thorough introduction to the theory of nonlinear partial differential equations (PDEs) with a variable exponent, particularly those of elliptic type. The book presents the most important variational methods for elliptic PDEs described by nonhomogeneous differential operators and containing one or more power-type nonlinearities with a variable exponent. The authors give a systematic treatment of the basic mathematical theory and constructive meth

Analysis of spatiotemporal variability of C-factor derived from remote sensing data

Czech Academy of Sciences Publication Activity Database

Pechanec, V.; Mráz, A.; Benc, A.; Cudlín, Pavel

2018-01-01

Roč. 12, č. 1 (2018), č. článku 016022. ISSN 1931-3195 R&D Projects: GA MŠk(CZ) LO1415 Grant - others:EHP,MF ČR(CZ) EHP-CZ02-OV-1-014-2014 Program:CZ02 Institutional support: RVO:86652079 Keywords : leaf-area index * soil-erosion * water erosion * ecosystem * model * vegetation * europe * fluxes * scale * ndvi * C-factor * soil erosion * erosion models * remote sensing * vegetation Subject RIV: EH - Ecology, Behaviour OBOR OECD: Environmental sciences (social aspects to be 5.7) Impact factor: 1.107, year: 2016

Factors affecting the inter-annual to centennial timescale variability of Indian summer monsoon rainfall

Science.gov (United States)

Malik, Abdul; Brönnimann, Stefan

2018-06-01

The Modes of Ocean Variability (MOV) namely Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and El Niño Southern Oscillation (ENSO) can have significant impacts on Indian Summer Monsoon Rainfall (ISMR) on different timescales. The timescales at which these MOV interacts with ISMR and the factors which may perturb their relationship with ISMR need to be investigated. We employ De-trended Cross-Correlation Analysis (DCCA), and De-trended Partial-Cross-Correlation Analysis (DPCCA) to study the timescales of interaction of ISMR with AMO, PDO, and ENSO using observational dataset (AD 1854-1999), and atmosphere-ocean-chemistry climate model simulations with SOCOL-MPIOM (AD 1600-1999). Further, this study uses De-trended Semi-Partial Cross-Correlation Analysis (DSPCCA) to address the relation between solar variability and the ISMR. We find statistically significant evidence of intrinsic correlations of ISMR with AMO, PDO, and ENSO on different timescales, consistent between model simulations and observations. However, the model fails to capture modulation in intrinsic relationship between ISRM and MOV due to external signals. Our analysis indicates that AMO is a potential source of non-stationary relationship between ISMR and ENSO. Furthermore, the pattern of correlation between ISMR and Total Solar Irradiance (TSI) is inconsistent between observations and model simulations. The observational dataset indicates statistically insignificant negative intrinsic correlation between ISMR and TSI on decadal-to-centennial timescales. This statistically insignificant negative intrinsic correlation is transformed to statistically significant positive extrinsic by AMO on 61-86-year timescale. We propose a new mechanism for Sun-monsoon connection which operates through AMO by changes in summer (June-September; JJAS) meridional gradient of tropospheric temperatures (ΔTTJJAS). There is a negative (positive) intrinsic correlation between ΔTTJJAS (AMO) and

In-depth analysis of the causal factors of incidents reported in the Greek petrochemical industry

International Nuclear Information System (INIS)

Konstandinidou, Myrto; Nivolianitou, Zoe; Kefalogianni, Eirini; Caroni, Chrys

2011-01-01

This paper presents a statistical analysis of all reported incidents in the Greek petrochemical industry from 1997 to 2003. A comprehensive database has been developed to include industrial accidents (fires, explosions and substance releases), occupational accidents, incidents without significant consequences and near misses. The study concentrates on identifying and analyzing the causal factors related to different consequences of incidents, in particular, injury, absence from work and material damage. Methods of analysis include logistic regression with one of these consequences as dependent variable. The causal factors that are considered cover four major categories related to organizational issues, equipment malfunctions, human errors (of commission or omission) and external causes. Further analyses aim to confirm the value of recording near misses by comparing their causal factors with those of more serious incidents. The statistical analysis highlights the connection between the human factor and the underlying causes of accidents or incidents. - Highlights: → The research work is original, based on field data collected directly from the petrochemical industry. → It deals with the in-depth statistical analysis of accident data on human-organizational causes. → It researches underlying causes of accidents and the parameters affecting them. → The causal factors that are considered cover four big taxonomies. → Near misses are worth recording for comparing their causal factors with more serious incidents.

Factors Affecting Turkish Students' Achievement in Mathematics

Science.gov (United States)

Demir, Ibrahim; Kilic, Serpil; Depren, Ozer

2009-01-01

Following past researches, student background, learning strategies, self-related cognitions in mathematics and school climate variables were important for achievement. The purpose of this study was to identify a number of factors that represent the relationship among sets of interrelated variables using principal component factor analysis and…

Exploratory Bi-Factor Analysis: The Oblique Case

Science.gov (United States)

Jennrich, Robert I.; Bentler, Peter M.

2012-01-01

Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…

Climate change - An uncertainty factor in risk analysis of contaminated land

International Nuclear Information System (INIS)

Augustsson, Anna; Filipsson, Monika; Oberg, Tomas; Bergbaeck, Bo

2011-01-01

Metals frequently occur at contaminated sites, where their potential toxicity and persistence require risk assessments that consider possible long-term changes. Changes in climate are likely to affect the speciation, mobility, and risks associated with metals. This paper provides an example of how the climate effect can be inserted in a commonly used exposure model, and how the exposure then changes compared to present conditions. The comparison was made for cadmium (Cd) exposure to 4-year-old children at a highly contaminated iron and steel works site in southeastern Sweden. Both deterministic and probabilistic approaches (through probability bounds analysis, PBA) were used in the exposure assessment. Potential climate-sensitive variables were determined by a literature review. Although only six of the total 39 model variables were assumed to be sensitive to a change in climate (groundwater infiltration, hydraulic conductivity, soil moisture, soil:water distribution, and two bioconcentration factors), the total exposure was clearly affected. For example, by altering the climate-sensitive variables in the order of 15% to 20%, the deterministic estimate of exposure increased by 27%. Similarly, the PBA estimate of the reasonable maximum exposure (RME, defined as the upper bound of the 95th percentile) increased by almost 20%. This means that sites where the exposure in present conditions is determined to be slightly below guideline values may in the future exceed these guidelines, and risk management decisions could thus be affected. The PBA, however, showed that there is also a possibility of lower exposure levels, which means that the changes assumed for the climate-sensitive variables increase the total uncertainty in the probabilistic calculations. This highlights the importance of considering climate as a factor in the characterization of input data to exposure assessments at contaminated sites. The variable with the strongest influence on the result was the

Confirmatory Factor Analysis of the ISB - Burnout Syndrome Inventory

Directory of Open Access Journals (Sweden)

Ana Maria T. Benevides-Pereira

2017-05-01

Full Text Available AimBurnout is a dysfunctional reaction to chronic occupational stress. The present study analysis the psychometric qualities of the Burnout Syndrome Inventory (ISB through Confirmatory Factor Analysis (CFA.MethodEmpirical study in a multi-centre and multi-occupational sample (n = 701 using the ISB. The Part I assesses antecedent factors: Positive Organizational Conditions (PC and Negative Organizational Conditions (NC. The Part II assesses the syndrome: Emotional Exhaustion (EE, Dehumanization (DE, Emotional Distancing (ED and Personal Accomplishment (PA.ResultsThe highest means occurred in the positive scales CP (M = 23.29, SD = 5.89 and PA (M = 14.84, SD = 4.71. Negative conditions showed the greatest variability (SD = 6.03. Reliability indexes were reasonable, with the lowest rate at .77 for DE and the highest rate .91 for PA. The CFA revealed RMSEA = .057 and CFI = .90 with all scales regressions showing significant values (β = .73 until β = .92.ConclusionThe ISB showed a plausible instrument to evaluate burnout. The two sectors maintained the initial model and confirmed the theoretical presupposition. This instrument makes possible a more comprehensive idea of the labour context, and one or another part may be used separately according to the needs and the aims of the assessor.

Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

Science.gov (United States)

Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

2017-12-01

Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three

Analyzing the Impacts of Alternated Number of Iterations in Multiple Imputation Method on Explanatory Factor Analysis

Directory of Open Access Journals (Sweden)

Duygu KOÇAK

2017-11-01

Full Text Available The study aims to identify the effects of iteration numbers used in multiple iteration method, one of the methods used to cope with missing values, on the results of factor analysis. With this aim, artificial datasets of different sample sizes were created. Missing values at random and missing values at complete random were created in various ratios by deleting data. For the data in random missing values, a second variable was iterated at ordinal scale level and datasets with different ratios of missing values were obtained based on the levels of this variable. The data were generated using “psych” program in R software, while “dplyr” program was used to create codes that would delete values according to predetermined conditions of missing value mechanism. Different datasets were generated by applying different iteration numbers. Explanatory factor analysis was conducted on the datasets completed and the factors and total explained variances are presented. These values were first evaluated based on the number of factors and total variance explained of the complete datasets. The results indicate that multiple iteration method yields a better performance in cases of missing values at random compared to datasets with missing values at complete random. Also, it was found that increasing the number of iterations in both missing value datasets decreases the difference in the results obtained from complete datasets.

Expanded flux variability analysis on metabolic network of Escherichia coli

Institute of Scientific and Technical Information of China (English)

CHEN Tong; XIE ZhengWei; OUYANG Qi

2009-01-01

Flux balance analysis,based on the mass conservation law in a cellular organism,has been extensively employed to study the interplay between structures and functions of cellular metabolic networks.Consequently,the phenotypes of the metabolism can be well elucidated.In this paper,we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions,such as flexibility,modularity and essentiality,by exploring the trend of the range,the maximum and the minimum flux of reactions.We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints.The average variability of all reactions decreases dramatically when the growth rate increases.Consider the noise effect on the metabolic system,we thus argue that the microorganism may practically grow under a suboptimal state.Besides,under the EFVA framework,the reactions are easily to be grouped into catabolic and anabolic groups.And the anabolic groups can be further assigned to specific biomass constitute.We also discovered the growth rate dependent essentiality of reactions.

Variable precision rough set for multiple decision attribute analysis

Institute of Scientific and Technical Information of China (English)

Lai; Kin; Keung

2008-01-01

A variable precision rough set (VPRS) model is used to solve the multi-attribute decision analysis (MADA) problem with multiple conflicting decision attributes and multiple condition attributes. By introducing confidence measures and a β-reduct, the VPRS model can rationally solve the conflicting decision analysis problem with multiple decision attributes and multiple condition attributes. For illustration, a medical diagnosis example is utilized to show the feasibility of the VPRS model in solving the MADA...

A preliminary analysis of the variability of ionospheric characteristics from ionosonde data

International Nuclear Information System (INIS)

Mosert, M.; Ezquer, R.; Miro, G.; Corbella, R.; Zerda, L. de la

2003-01-01

We use ionosonde data obtained at El Arenosillo (37.1, 353.2), Tucuman (-26.9, 294.6), San Juan (-31.5, 294.5), Buenos Aires (-34.6, 301.7), Ushuaia (-54.8, 291.7) and Puerto Belgrano (-77.9, 321.4) to study the variability of the critical frequencies foE, foF1, foF2 and the propagation factor M(3000)F2 as a function of local time, season, latitude and solar cycle. Two variability indexes were introduced: Clo= Qlo/ median and Cup= Qup/ median, in order to quantify the variability of the parameters. The results indicate that these parameters are helpful to the development of a quantitative model of the variability of the different ionospheric parameters. (author)

Modeling Psychological Attributes in Psychology – An Epistemological Discussion: Network Analysis vs. Latent Variables

Science.gov (United States)

Guyon, Hervé; Falissard, Bruno; Kop, Jean-Luc

2017-01-01

Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes. PMID:28572780

Short-term variability of Johor River discharge based on wavelet analysis

Science.gov (United States)

Ahmad, N.; Kamaruddin, S. A.; Heryansyah, A.

2015-02-01

River discharge provides a direct measure of water quantity and availability of water for specific uses. It also provides the basis for understanding river basin processes and is essential for interpreting and understanding river flow characteristics. This study investigates the temporal variability of river discharge records of Johor River. Wavelet analysis of discharge records for 30 years was carried out to characterize the river flow variability. Our results indicate that Johor River discharge data shows a significant short-term variability of between 0.6 to 2.5 years.

Evaluation of standardized and applied variables in predicting treatment outcomes of polytrauma patients.

Science.gov (United States)

Aksamija, Goran; Mulabdic, Adi; Rasic, Ismar; Muhovic, Samir; Gavric, Igor

2011-01-01

Polytrauma is defined as an injury where they are affected by at least two different organ systems or body, with at least one life-threatening injuries. Given the multilevel model care of polytrauma patients within KCUS are inevitable weaknesses in the management of this category of patients. To determine the dynamics of existing procedures in treatment of polytrauma patients on admission to KCUS, and based on statistical analysis of variables applied to determine and define the factors that influence the final outcome of treatment, and determine their mutual relationship, which may result in eliminating the flaws in the approach to the problem. The study was based on 263 polytrauma patients. Parametric and non-parametric statistical methods were used. Basic statistics were calculated, based on the calculated parameters for the final achievement of research objectives, multicoleration analysis, image analysis, discriminant analysis and multifactorial analysis were used. From the universe of variables for this study we selected sample of n = 25 variables, of which the first two modular, others belong to the common measurement space (n = 23) and in this paper defined as a system variable methods, procedures and assessments of polytrauma patients. After the multicoleration analysis, since the image analysis gave a reliable measurement results, we started the analysis of eigenvalues, that is defining the factors upon which they obtain information about the system solve the problem of the existing model and its correlation with treatment outcome. The study singled out the essential factors that determine the current organizational model of care, which may affect the treatment and better outcome of polytrauma patients. This analysis has shown the maximum correlative relationships between these practices and contributed to development guidelines that are defined by isolated factors.

On the explaining-away phenomenon in multivariate latent variable models.

Science.gov (United States)

van Rijn, Peter; Rijmen, Frank

2015-02-01

Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.

Cardiovascular variability and introversion/extroversion, neuroticism and psychoticism.

Science.gov (United States)

Burdick, J A; Van Dyck, B; Von Bargen, W J

1982-01-01

Forty-eight subjects were measured during a 10 min rest period for pulse wave velocity (PWV) and heart rate (HR) level and variability, using a Cyborg BL 907 instrument. These subjects were also evaluated by means of the Eysenck Personality Questionnaire for I-E, N, P and L. These data were factor analyzed. Five factors were identified which were accounted for 80.6% of the variance. These factors were: 'cardiovascular lability', 'heart rate time trends', 'cardiovascular balance', 'sex effects' and 'self reports'. The EPQ measurements separated from the physiological measurements in the factor analysis and none were found to be significantly loaded on any physiological variables. On the other hand, significant physiological correlations were found with N. This study adds a possible blood pressure and heart rate descripter to N.

PISA 2012 Analysis of School Variables Affecting Problem-Solving Competency: Turkey-Serbia Comparison

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Emine YAVUZ

2017-12-01

Full Text Available According to the OECD's PISA 2012 Turkey problem-solving report, Turkey and Serbia are at the same mathematical literacy level. However, Serbia's average of problem-solving competency is said to be higher than Turkey's. In this study, school variables that affect problem-solving competency of the two countries were examined and compared. The method of the study was causal comparison method, and HLM analysis was performed on data of 4494 students from 147 schools in Turkey sample and 4059 students from 132 schools in Serbia sample separately. As a result of HLM analysis, "obstacle and family donation" variable for Serbia and "abandon, teacher morale and mathematics competition" variable for Turkey were statistically significant. Although it was found that for each countries different variables influence the problem-solving competency, it was quite remarkable that these variables are in common in that they are components of the school climate concept.

Self-Compassion Scale: IRT Psychometric Analysis, Validation, and Factor Structure – Slovak Translation

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Júlia Halamová

2018-01-01

Full Text Available The present study verifies the psychometric properties of the Slovak version of the Self-Compassion Scale through item response theory, factor-analysis, validity analyses and norm development. The surveyed sample consisted of 1,181 participants (34% men and 66% women with a mean age of 30.30 years (SD = 12.40. Two general factors (Self-compassionate responding and Self-uncompassionate responding were identified, whereas there was no support for a single general factor of the scale and six subscales. The results of the factor analysis were supported by an independent sample of 676 participants. Therefore, the use of total score for the whole scale would be inappropriate. In Slovak language the Self-Compassion Scale should be used in the form of two general subscales (Self-compassionate responding and Self-uncompassionate responding. In line with our theoretical assumptions, we obtained relatively high Spearman’s correlation coefficients between the Self-Compassion Scale and related external variables, demonstrating construct validity for the scale. To sum up, the Slovak translation of The Self-Compassion Scale is a reliable and valid instrument that measures Self-compassionate responding and Self-uncompassionate responding.

Interannual and spatial variability of maple syrup yield as related to climatic factors

Science.gov (United States)

Houle, Daniel

2014-01-01

Sugar maple syrup production is an important economic activity for eastern Canada and the northeastern United States. Since annual variations in syrup yield have been related to climate, there are concerns about the impacts of climatic change on the industry in the upcoming decades. Although the temporal variability of syrup yield has been studied for specific sites on different time scales or for large regions, a model capable of accounting for both temporal and regional differences in yield is still lacking. In the present study, we studied the factors responsible for interregional and interannual variability in maple syrup yield over the 2001–2012 period, by combining the data from 8 Quebec regions (Canada) and 10 U.S. states. The resulting model explained 44.5% of the variability in yield. It includes the effect of climatic conditions that precede the sapflow season (variables from the previous growing season and winter), the effect of climatic conditions during the current sapflow season, and terms accounting for intercountry and temporal variability. Optimal conditions for maple syrup production appear to be spatially restricted by less favourable climate conditions occurring during the growing season in the north, and in the south, by the warmer winter and earlier spring conditions. This suggests that climate change may favor maple syrup production northwards, while southern regions are more likely to be negatively affected by adverse spring conditions. PMID:24949244

Summer U.S. Surface Air Temperature Variability: Controlling Factors and AMIP Simulation Biases

Science.gov (United States)

Merrifield, A.; Xie, S. P.

2016-02-01

This study documents and investigates biases in simulating summer surface air temperature (SAT) variability over the continental U.S. in the Coupled Model Intercomparison Project (CMIP5) Atmospheric Model Intercomparison Project (AMIP). Empirical orthogonal function (EOF) and multivariate regression analyses are used to assess the relative importance of circulation and the land surface feedback at setting summer SAT over a 30-year period (1979-2008). In observations, regions of high SAT variability are closely associated with midtropospheric highs and subsidence, consistent with adiabatic theory (Meehl and Tebaldi 2004, Lau and Nath 2012). Preliminary analysis shows the majority of the AMIP models feature high SAT variability over the central U.S., displaced south and/or west of observed centers of action (COAs). SAT COAs in models tend to be concomitant with regions of high sensible heat flux variability, suggesting an excessive land surface feedback in these models modulate U.S. summer SAT. Additionally, tropical sea surface temperatures (SSTs) play a role in forcing the leading EOF mode for summer SAT, in concert with internal atmospheric variability. There is evidence that models respond to different SST patterns than observed. Addressing issues with the bulk land surface feedback and the SST-forced component of atmospheric variability may be key to improving model skill in simulating summer SAT variability over the U.S.

Nonlinear canonical correlation analysis with k sets of variables

NARCIS (Netherlands)

van der Burg, Eeke; de Leeuw, Jan

1987-01-01

The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correlation analysis (CCA). First, two sets CCA is introduced. Two sets CCA is a technique that computes linear combinations of sets of variables that correlate in an optimal way. Two sets CCA is then

Analysis, Test and Verification in The Presence of Variability (Dagstuhl Seminar 13091)

DEFF Research Database (Denmark)

2014-01-01

-aware tool chains. We brought together 46 key researchers from three continents, working on quality assurance challenges that arise from introducing variability, and some who do not work with variability, but that are experts in their respective areas in the broader domain of software analysis or testing...

Sensitivity analysis and power for instrumental variable studies.

Science.gov (United States)

Wang, Xuran; Jiang, Yang; Zhang, Nancy R; Small, Dylan S

2018-03-31

In observational studies to estimate treatment effects, unmeasured confounding is often a concern. The instrumental variable (IV) method can control for unmeasured confounding when there is a valid IV. To be a valid IV, a variable needs to be independent of unmeasured confounders and only affect the outcome through affecting the treatment. When applying the IV method, there is often concern that a putative IV is invalid to some degree. We present an approach to sensitivity analysis for the IV method which examines the sensitivity of inferences to violations of IV validity. Specifically, we consider sensitivity when the magnitude of association between the putative IV and the unmeasured confounders and the direct effect of the IV on the outcome are limited in magnitude by a sensitivity parameter. Our approach is based on extending the Anderson-Rubin test and is valid regardless of the strength of the instrument. A power formula for this sensitivity analysis is presented. We illustrate its usage via examples about Mendelian randomization studies and its implications via a comparison of using rare versus common genetic variants as instruments. © 2018, The International Biometric Society.

Analysis of Performance Factors for Accounting and Finance Related Business Courses in A Distance Education Environment

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Serdar BENLIGIRAY

2017-07-01

Full Text Available The objective of this study is to explore business courses performance factors with a focus on accounting and finance. Course score interrelations are assumed to represent interpretable constructs of these factors. Factor analysis is proposed to identify the constructs that explain the correlations. Factor analysis results identify three sub-groups of business core courses. The first group is labeled as management-oriented courses. Accounting, finance and economics courses are separated in two groups: the prior courses group and the subsequent courses group. The clustering order of these three groups was attributed to underlying performance factor similarities. Then, the groups are compared by the pre-assessed ratings of course specific skills and knowledge. The comparison suggests that course requirements for skills and knowledge were the latent variables for the factor analysis. Moreover, multivariate regression analyses are employed to reveal the required level of verbal and quantitative skills for the groups. Management-oriented courses are differentiated from others with requiring verbal skills, managerial skills and knowledge more. Introductory courses require quantitative and analytical reasoning skills more than the subsequent courses in accounting, finance and economics. Mathematics course score fails to be a suitable proxy of numerical processing skills as an accounting course performance factor.

A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

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Zizhou Lao

2018-05-01

Full Text Available For model-based state of charge (SOC estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF strategy is introduced to improve forgetting factor recursive least squares (FFRLS to variable forgetting factor recursive least squares (VFF-RLS. A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

A comprehensive analysis of factors influencing the injury severity of large-truck crashes.

Science.gov (United States)

Zhu, Xiaoyu; Srinivasan, Sivaramakrishnan

2011-01-01

Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data - these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined. Copyright © 2010 Elsevier Ltd. All rights reserved.

KAP Surveys and Dengue Control in Colombia: Disentangling the Effect of Sociodemographic Factors Using Multiple Correspondence Analysis.

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Diana Rocío Higuera-Mendieta

2016-09-01

Full Text Available During the last few decades, several studies have analyzed and described knowledge, attitudes, and practices (KAP of populations regarding dengue. However, few studies have applied geometric data analytic techniques to generate indices from KAP domains. Results of such analyses have not been used to determine the potential effects of sociodemographic variables on the levels of KAP. The objective was to determine the sociodemographic factors related to different levels of KAP regarding dengue in two hyper-endemic cities of Colombia, using a multiple correspondence analysis (MCA technique. In the context of a cluster randomized trial, 3,998 households were surveyed in Arauca and Armenia between 2012 and 2013. To generate KAP indexes, we performed a MCA followed by a hierarchical cluster analysis to classify each score in different groups. A quantile regression for each of the score groups was conducted. KAP indexes explained 56.1%, 79.7%, and 83.2% of the variance, with means of 4.2, 1.4, and 3.2 and values that ranged from 1 to 7, 7 and 11, respectively. The highest values of the index denoted higher levels of knowledge and practices. The attitudes index did not show the same relationship and was excluded from the analysis. In the quantile regression, age (0.06; IC: 0.03, 0.09, years of education (0.14; IC: 0.06, 0.22, and history of dengue in the family (0.21; IC: 0.12, 0.31 were positively related to lower levels of knowledge regarding dengue. The effect of such factors gradually decreased or disappeared when knowledge was higher. The practices indexes did not evidence a correlation with sociodemographic variables. These results suggest that the transformation of categorical variables into a single index by the use of MCA is possible when analyzing knowledge and practices regarding dengue from KAP questionnaires. Additionally, the magnitude of the effect of socioeconomic variables on the knowledge scores varies according to the levels of

Using principal component analysis to understand the variability of PDS 456

Science.gov (United States)

Parker, M. L.; Reeves, J. N.; Matzeu, G. A.; Buisson, D. J. K.; Fabian, A. C.

2018-02-01

We present a spectral-variability analysis of the low-redshift quasar PDS 456 using principal component analysis. In the XMM-Newton data, we find a strong peak in the first principal component at the energy of the Fe absorption line from the highly blueshifted outflow. This indicates that the absorption feature is more variable than the continuum, and that it is responding to the continuum. We find qualitatively different behaviour in the Suzaku data, which is dominated by changes in the column density of neutral absorption. In this case, we find no evidence of the absorption produced by the highly ionized gas being correlated with this variability. Additionally, we perform simulations of the source variability, and demonstrate that PCA can trivially distinguish between outflow variability correlated, anticorrelated and un-correlated with the continuum flux. Here, the observed anticorrelation between the absorption line equivalent width and the continuum flux may be due to the ionization of the wind responding to the continuum. Finally, we compare our results with those found in the narrow-line Seyfert 1 IRAS 13224-3809. We find that the Fe K UFO feature is sharper and more prominent in PDS 456, but that it lacks the lower energy features from lighter elements found in IRAS 13224-3809, presumably due to differences in ionization.

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments.

Science.gov (United States)

Shao, Kan; Allen, Bruce C; Wheeler, Matthew W

2017-10-01

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations. © 2016 Society for Risk Analysis.

Analysis of factors associated with traffic injury severity on rural roads in Iran

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Andishe Ranjbari

2012-01-01

Full Text Available BACKGROUND: Iran is a country with one of the highest rates of traffic crash fatality and injury, and seventy percent of these fatalities happen on rural roads. The objective of this study is to identify the significant factors influencing injury severity among drivers involved in crashes on two kinds of major rural roads in Iran: two-lane, two-way roads and freeways. METHODS: According to the dataset, 213569 drivers were involved in rural road crashes in Iran, over the 3 years from 2006 to 2008. The Classification And Regression Tree method (CART was applied for 13 independent variables, and one target variable of injury severity with 3 classes of no-injury, injury and fatality. Some of the independent variables were cause of crash, collision type, weather conditions, road surface conditions, driver's age and gender and seat belt usage. The CART model was trained by 70% of these data, and tested with the rest. RESULTS: It was indicated that seat belt use is the most important safety factor for two-lane, two-way rural roads, but on freeways, the importance of this variable is less. Cause of crash, also turned out to be the next most important variable. The results showed that for two-lane, two-way rural roads, "improper overtaking" and "speeding", and for rural freeways, "inattention to traffic ahead", "vehicle defect", and "movement of pedestrians, livestock and unauthorized vehicles on freeways" are the most serious causes of increasing injury severity. CONCLUSIONS: The analysis results revealed seat belt use, cause of crash and collision type as the most important variables influencing the injury severity of traffic crashes. To deal with these problems, intensifying police enforcement by means of mobile patrol vehicles, constructing overtaking lanes where necessary, and prohibiting the crossing of pedestrians and livestock and the driving of unauthorized vehicles on freeways are necessary. Moreover, creating a rumble strip on the two edges of

Analysis of Lung Tumor Motion in a Large Sample: Patterns and Factors Influencing Precise Delineation of Internal Target Volume

International Nuclear Information System (INIS)

Knybel, Lukas; Cvek, Jakub; Molenda, Lukas; Stieberova, Natalie; Feltl, David

2016-01-01

Purpose/Objective: To evaluate lung tumor motion during respiration and to describe factors affecting the range and variability of motion in patients treated with stereotactic ablative radiation therapy. Methods and Materials: Log file analysis from online respiratory tumor tracking was performed in 145 patients. Geometric tumor location in the lungs, tumor volume and origin (primary or metastatic), sex, and tumor motion amplitudes in the superior-inferior (SI), latero-lateral (LL), and anterior-posterior (AP) directions were recorded. Tumor motion variability during treatment was described using intrafraction/interfraction amplitude variability and tumor motion baseline changes. Tumor movement dependent on the tumor volume, position and origin, and sex were evaluated using statistical regression and correlation analysis. Results: After analysis of >500 hours of data, the highest rates of motion amplitudes, intrafraction/interfraction variation, and tumor baseline changes were in the SI direction (6.0 ± 2.2 mm, 2.2 ± 1.8 mm, 1.1 ± 0.9 mm, and −0.1 ± 2.6 mm). The mean motion amplitudes in the lower/upper geometric halves of the lungs were significantly different (P 15 mm were observed only in the lower geometric quarter of the lungs. Higher tumor motion amplitudes generated higher intrafraction variations (R=.86, P 3 mm indicated tumors contacting mediastinal structures or parietal pleura. On univariate analysis, neither sex nor tumor origin (primary vs metastatic) was an independent predictive factor of different movement patterns. Metastatic lesions in women, but not men, showed significantly higher mean amplitudes (P=.03) and variability (primary, 2.7 mm; metastatic, 4.9 mm; P=.002) than primary tumors. Conclusion: Online tracking showed significant irregularities in lung tumor movement during respiration. Motion amplitude was significantly lower in upper lobe tumors; higher interfraction amplitude variability indicated tumors in contact

Variable forgetting factor mechanisms for diffusion recursive least squares algorithm in sensor networks

Science.gov (United States)

Zhang, Ling; Cai, Yunlong; Li, Chunguang; de Lamare, Rodrigo C.

2017-12-01

In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithms and derive mathematical expressions for the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed low-complexity VFF-DRLS algorithms achieve superior performance to the existing DRLS algorithm with fixed forgetting factor when applied to scenarios of distributed parameter and spectrum estimation. Besides, the simulation results also demonstrate a good match for our proposed analytical expressions.

Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten

Science.gov (United States)

Lü, Chengxu; Jiang, Xunpeng; Zhou, Xingfan; Zhang, Yinqiao; Zhang, Naiqian; Wei, Chongfeng; Mao, Wenhua

2017-10-01

Wet gluten is a useful quality indicator for wheat, and short wave near infrared spectroscopy (NIRS) is a high performance technique with the advantage of economic rapid and nondestructive test. To study the feasibility of short wave NIRS analyzing wet gluten directly from wheat seed, 54 representative wheat seed samples were collected and scanned by spectrometer. 8 spectral pretreatment method and genetic algorithm (GA) variable selection method were used to optimize analysis. Both quantitative and qualitative model of wet gluten were built by partial least squares regression and discriminate analysis. For quantitative analysis, normalization is the optimized pretreatment method, 17 wet gluten sensitive variables are selected by GA, and GA model performs a better result than that of all variable model, with R2V=0.88, and RMSEV=1.47. For qualitative analysis, automatic weighted least squares baseline is the optimized pretreatment method, all variable models perform better results than those of GA models. The correct classification rates of 3 class of 30% wet gluten content are 95.45, 84.52, and 90.00%, respectively. The short wave NIRS technique shows potential for both quantitative and qualitative analysis of wet gluten for wheat seed.

Stochastic Analysis of the Efficiency of a Wireless Power Transfer System Subject to Antenna Variability and Position Uncertainties.

Science.gov (United States)

Rossi, Marco; Stockman, Gert-Jan; Rogier, Hendrik; Vande Ginste, Dries

2016-07-19

The efficiency of a wireless power transfer (WPT) system in the radiative near-field is inevitably affected by the variability in the design parameters of the deployed antennas and by uncertainties in their mutual position. Therefore, we propose a stochastic analysis that combines the generalized polynomial chaos (gPC) theory with an efficient model for the interaction between devices in the radiative near-field. This framework enables us to investigate the impact of random effects on the power transfer efficiency (PTE) of a WPT system. More specifically, the WPT system under study consists of a transmitting horn antenna and a receiving textile antenna operating in the Industrial, Scientific and Medical (ISM) band at 2.45 GHz. First, we model the impact of the textile antenna's variability on the WPT system. Next, we include the position uncertainties of the antennas in the analysis in order to quantify the overall variations in the PTE. The analysis is carried out by means of polynomial-chaos-based macromodels, whereas a Monte Carlo simulation validates the complete technique. It is shown that the proposed approach is very accurate, more flexible and more efficient than a straightforward Monte Carlo analysis, with demonstrated speedup factors up to 2500.

Stochastic Analysis of the Efficiency of a Wireless Power Transfer System Subject to Antenna Variability and Position Uncertainties

Directory of Open Access Journals (Sweden)

Marco Rossi

2016-07-01

Full Text Available The efficiency of a wireless power transfer (WPT system in the radiative near-field is inevitably affected by the variability in the design parameters of the deployed antennas and by uncertainties in their mutual position. Therefore, we propose a stochastic analysis that combines the generalized polynomial chaos (gPC theory with an efficient model for the interaction between devices in the radiative near-field. This framework enables us to investigate the impact of random effects on the power transfer efficiency (PTE of a WPT system. More specifically, the WPT system under study consists of a transmitting horn antenna and a receiving textile antenna operating in the Industrial, Scientific and Medical (ISM band at 2.45 GHz. First, we model the impact of the textile antenna’s variability on the WPT system. Next, we include the position uncertainties of the antennas in the analysis in order to quantify the overall variations in the PTE. The analysis is carried out by means of polynomial-chaos-based macromodels, whereas a Monte Carlo simulation validates the complete technique. It is shown that the proposed approach is very accurate, more flexible and more efficient than a straightforward Monte Carlo analysis, with demonstrated speedup factors up to 2500.

Stochastic Analysis of the Efficiency of a Wireless Power Transfer System Subject to Antenna Variability and Position Uncertainties

Science.gov (United States)

Rossi, Marco; Stockman, Gert-Jan; Rogier, Hendrik; Vande Ginste, Dries

2016-01-01

The efficiency of a wireless power transfer (WPT) system in the radiative near-field is inevitably affected by the variability in the design parameters of the deployed antennas and by uncertainties in their mutual position. Therefore, we propose a stochastic analysis that combines the generalized polynomial chaos (gPC) theory with an efficient model for the interaction between devices in the radiative near-field. This framework enables us to investigate the impact of random effects on the power transfer efficiency (PTE) of a WPT system. More specifically, the WPT system under study consists of a transmitting horn antenna and a receiving textile antenna operating in the Industrial, Scientific and Medical (ISM) band at 2.45 GHz. First, we model the impact of the textile antenna’s variability on the WPT system. Next, we include the position uncertainties of the antennas in the analysis in order to quantify the overall variations in the PTE. The analysis is carried out by means of polynomial-chaos-based macromodels, whereas a Monte Carlo simulation validates the complete technique. It is shown that the proposed approach is very accurate, more flexible and more efficient than a straightforward Monte Carlo analysis, with demonstrated speedup factors up to 2500. PMID:27447632

Factors influencing bone scan quality

International Nuclear Information System (INIS)

Adams, F.G.; Shirley, A.W.

1983-01-01

A reliable subjective method of assessing bone scan quality is described. A large number of variables which theoretically could influence scan quality were submitted to regression and factor analysis. Obesity, age, sex and abnormality of scan were found to be significant but weak variables. (orig.)

Variability, correlation and path coefficient analysis of seedling traits ...

African Journals Online (AJOL)

Indirect selection is a useful means for improving yield in cotton crop. The objective of the present study was to determine the genetic variability, broad sense heritability, genetic advance and correlation among the six seedling traits and their direct and indirect effects on cotton yield by using path coefficient analysis.

Load flow analysis for variable speed offshore wind farms

DEFF Research Database (Denmark)

Chen, Zhe; Zhao, Menghua; Blaabjerg, Frede

2009-01-01

factors such as the different wind farm configurations, the control of wind turbines and the power losses of pulse width modulation converters are considered. The DC/DC converter model is proposed and integrated into load flow algorithm by modifying the Jacobian matrix. Two iterative methods are proposed...... and integrated into the load flow algorithm: one takes into account the control strategy of converters and the other considers the power losses of converters. In addition, different types of variable speed wind turbine systems with different control methods are investigated. Finally, the method is demonstrated......A serial AC-DC integrated load flow algorithm for variable speed offshore wind farms is proposed. It divides the electrical system of a wind farm into several local networks, and different load flow methods are used for these local networks sequentially. This method is fast, more accurate, and many...

Phytoscreening with SPME: Variability Analysis.

Science.gov (United States)

Limmer, Matt A; Burken, Joel G

2015-01-01

Phytoscreening has been demonstrated at a variety of sites over the past 15 years as a low-impact, sustainable tool in delineation of shallow groundwater contaminated with chlorinated solvents. Collection of tree cores is rapid and straightforward, but low concentrations in tree tissues requires sensitive analytics. Solid-phase microextraction (SPME) is amenable to the complex matrix while allowing for solvent-less extraction. Accurate quantification requires the absence of competitive sorption, examined here both in laboratory experiments and through comprehensive examination of field data. Analysis of approximately 2,000 trees at numerous field sites also allowed testing of the tree genus and diameter effects on measured tree contaminant concentrations. Collectively, while these variables were found to significantly affect site-adjusted perchloroethylene (PCE) concentrations, the explanatory power of these effects was small (adjusted R(2) = 0.031). 90th quantile chemical concentrations in trees were significantly reduced by increasing Henry's constant and increasing hydrophobicity. Analysis of replicate tree core data showed no correlation between replicate relative standard deviation (RSD) and wood type or tree diameter, with an overall median RSD of 30%. Collectively, these findings indicate SPME is an appropriate technique for sampling and analyzing chlorinated solvents in wood and that phytoscreening is robust against changes in tree type and diameter.

A Geographic Information-Assisted Temporal Mixture Analysis for Addressing the Issue of Endmember Class and Endmember Spectra Variability

Directory of Open Access Journals (Sweden)

Wenliang Li

2017-03-01

Full Text Available Spectral mixture analysis (SMA is a common approach for parameterizing biophysical fractions of urban environment and widely applied in many fields. For successful SMA, the selection of endmember class and corresponding spectra has been assumed as the most important step. Thanks to the spatial heterogeneity of natural and urban landscapes, the variability of endmember class and corresponding spectra has been widely considered as the profound error source in SMA. To address the challenging problems, we proposed a geographic information-assisted temporal mixture analysis (GATMA. Specifically, a logistic regression analysis was applied to analyze the relationship between land use/land covers and surrounding socio-economic factors, and a classification tree method was used to identify the present status of endmember classes throughout the whole study area. Furthermore, an ordinary kriging analysis was employed to generate a spatially varying endmember spectra at all pixels in the remote sensing image. As a consequence, a fully constrained temporal mixture analysis was conducted for examining the fractional land use land covers. Results show that the proposed GATMA achieved a promising accuracy with an RMSE of 6.81%, SE of 1.29% and MAE of 2.6%. In addition, comparative analysis result illustrates that a significant accuracy improvement has been found in the whole study area and both developed and less developed areas, and this demonstrates that the variability of endmember class and endmember spectra is essential for unmixing analysis.

Variability in connectivity patterns of fish with ontogenetic migrations: Modelling effects of abiotic and biotic factors

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Susanne Eva Tanner

2015-10-01

Full Text Available Connectivity is a critical property of marine fish populations as it drives population replenishment, determines colonization patterns and the resilience of populations to harvest. Understanding connectivity patterns is particularly important in species that present ontogenetic migrations and segregated habitat use during their life history, such as marine species with estuarine nursery areas. Albeit challenging, fish movement can be estimated and quantified using different methodologies depending on the life history stages of interest (e.g. biophysical modelling, otolith chemistry, genetic markers. Relative contributions from estuarine nursery areas to the adult coastal populations were determined using otolith elemental composition and maximum likelihood estimation for four commercially important species (Dicentrarchus labrax, Plathichtys flesus, Solea senegalensis and Solea solea and showed high interannual variability. Here, the effects of abiotic and biotic factors on the observed variability in connectivity rates and extent between estuarine juvenile and coastal adult subpopulations are investigated using generalized linear models (GLM and generalized mixed models (GMM. Abiotic factors impacting both larval and juvenile life history stages are included in the models (e.g. wind force and direction, NAO, water temperature while biotic factors relative to the estuarine residency of juvenile fish are evaluated (e.g. juvenile density, food availability. Factors contributing most to the observed variability in connectivity rates are singled out and compared among species. General trends are identified and results area discussed in the general context of identifying potential management frameworks applicable to different life stages and which may prove useful for ontogenetically migrating species.

Joint variable frame rate and length analysis for speech recognition under adverse conditions

DEFF Research Database (Denmark)

Tan, Zheng-Hua; Kraljevski, Ivan

2014-01-01

This paper presents a method that combines variable frame length and rate analysis for speech recognition in noisy environments, together with an investigation of the effect of different frame lengths on speech recognition performance. The method adopts frame selection using an a posteriori signal......-to-noise (SNR) ratio weighted energy distance and increases the length of the selected frames, according to the number of non-selected preceding frames. It assigns a higher frame rate and a normal frame length to a rapidly changing and high SNR region of a speech signal, and a lower frame rate and an increased...... frame length to a steady or low SNR region. The speech recognition results show that the proposed variable frame rate and length method outperforms fixed frame rate and length analysis, as well as standalone variable frame rate analysis in terms of noise-robustness....

Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis.

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Adeola, Abiodun M; Botai, Joel O; Rautenbach, Hannes; Adisa, Omolola M; Ncongwane, Katlego P; Botai, Christina M; Adebayo-Ojo, Temitope C

2017-11-08

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease's transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998-2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables' and malaria cases' time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature ( R ² = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in

Causality and cointegration analysis between macroeconomic variables and the Bovespa.

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da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

2014-01-01

The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.

Lake variability: Key factors controlling mercury concentrations in New York State fish

International Nuclear Information System (INIS)

Simonin, Howard A.; Loukmas, Jefferey J.; Skinner, Lawrence C.; Roy, Karen M.

2008-01-01

A 4 year study surveyed 131 lakes across New York State beginning in 2003 to improve our understanding of mercury and gather information from previously untested waters. Our study focused on largemouth and smallmouth bass, walleye and yellow perch, common piscivorous fish shown to accumulate high mercury concentrations and species important to local fisheries. Fish from Adirondack and Catskill Forest Preserve lakes generally had higher mercury concentrations than those from lakes in other areas of the state. Variability between nearby individual lakes was observed, and could be due to differences in water chemistry, lake productivity or the abundance of wetlands in the watershed. We found the following factors impact mercury bioaccumulation: fish length, lake pH, specific conductivity, chlorophyll a, mercury concentration in the water, presence of an outlet dam and amount of contiguous wetlands. - Lake water chemistry variables, dams, and wetlands play major roles in determining fish mercury concentrations

CADDIS Volume 4. Data Analysis: Advanced Analyses - Controlling for Natural Variability

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Methods for controlling natural variability, predicting environmental conditions from biological observations method, biological trait data, species sensitivity distributions, propensity scores, Advanced Analyses of Data Analysis references.

A review of factors explaining variability in fentanyl pharmacokinetics; focus on implications for cancer patients

NARCIS (Netherlands)

Kuip, E.J.M.; Zandvliet, M.L.; Koolen, S.L.; Mathijssen, R.H.; Rijt, C.C. van der

2017-01-01

Fentanyl is a strong opioid that is available for various administration routes, and which is widely used to treat cancer-related pain. Many factors influence the fentanyl pharmacokinetics leading to a wide inter- and intrapatient variability. This systematic review summarizes multiple studied

Omitted Variable Sensitivity Analysis with the Annotated Love Plot

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Hansen, Ben B.; Fredrickson, Mark M.

2014-01-01

The goal of this research is to make sensitivity analysis accessible not only to empirical researchers but also to the various stakeholders for whom educational evaluations are conducted. To do this it derives anchors for the omitted variable (OV)-program participation association intrinsically, using the Love plot to present a wide range of…

Early variability in the conceptualisation of "sustainable development and human factors".

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Thatcher, Andrew

2012-01-01

The sub-discipline of "sustainable development and human factors" is relatively new, first being used in 2006 with a Technical Committee of the IEA being established only in 2009 and a similar special interest group on "green ergonomics" at the Institute of Ergonomics and Human Factors being established in 2010. In general though, the definitions and practice of "sustainable development" is highly contentious and ambiguous across a range of disciplines. This paper examines the diversity of definitions and approaches to sustainable development and human factors in the early papers in this sub-discipline. An examination of 45 chapters and papers (from 2008 to 2011) reveals a surprising consistency in the definitions used for sustainable development but also a large proportion of the papers where no definitions are given at all. The majority of papers were, however, biased towards an economic capital and social capital emphasis, which is to be expected of work traditionally in the ergonomics paradigm. Further, most papers were theoretical in nature demonstrating a great opportunity for empirical work. The variability in definitions is discussed in relation to the future challenges facing the growth of this emergent sub-discipline and opportunities for further theoretical and empirical work.

Research on Open-Closed-Loop Iterative Learning Control with Variable Forgetting Factor of Mobile Robots

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Hongbin Wang

2016-01-01

Full Text Available We propose an iterative learning control algorithm (ILC that is developed using a variable forgetting factor to control a mobile robot. The proposed algorithm can be categorized as an open-closed-loop iterative learning control, which produces control instructions by using both previous and current data. However, introducing a variable forgetting factor can weaken the former control output and its variance in the control law while strengthening the robustness of the iterative learning control. If it is applied to the mobile robot, this will reduce position errors in robot trajectory tracking control effectively. In this work, we show that the proposed algorithm guarantees tracking error bound convergence to a small neighborhood of the origin under the condition of state disturbances, output measurement noises, and fluctuation of system dynamics. By using simulation, we demonstrate that the controller is effective in realizing the prefect tracking.

Analysis and Prediction of Micromilling Stability with Variable Tool Geometry

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Ziyang Cao

2014-11-01

Full Text Available Micromilling can fabricate miniaturized components using micro-end mill at high rotational speeds. The analysis of machining stability in micromilling plays an important role in characterizing the cutting process, estimating the tool life, and optimizing the process. A numerical analysis and experimental method are presented to investigate the chatter stability in micro-end milling process with variable milling tool geometry. The schematic model of micromilling process is constructed and the calculation formula to predict cutting force and displacements is derived. This is followed by a detailed numerical analysis on micromilling forces between helical ball and square end mills through time domain and frequency domain method and the results are compared. Furthermore, a detailed time domain simulation for micro end milling with straight teeth and helical teeth end mill is conducted based on the machine-tool system frequency response function obtained through modal experiment. The forces and displacements are predicted and the simulation result between variable cutter geometry is deeply compared. The simulation results have important significance for the actual milling process.

Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations.

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Deng, Chenhui; Plan, Elodie L; Karlsson, Mats O

2016-06-01

Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.

Incorporation of gene-specific variability improves expression analysis using high-density DNA microarrays

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Spitznagel Edward

2003-11-01

Full Text Available Abstract Background The assessment of data reproducibility is essential for application of microarray technology to exploration of biological pathways and disease states. Technical variability in data analysis largely depends on signal intensity. Within that context, the reproducibility of individual probe sets has not been hitherto addressed. Results We used an extraordinarily large replicate data set derived from human placental trophoblast to analyze probe-specific contribution to variability of gene expression. We found that signal variability, in addition to being signal-intensity dependant, is probe set-specific. Importantly, we developed a novel method to quantify the contribution of this probe set-specific variability. Furthermore, we devised a formula that incorporates a priori-computed, replicate-based information on probe set- and intensity-specific variability in determination of expression changes even without technical replicates. Conclusion The strategy of incorporating probe set-specific variability is superior to analysis based on arbitrary fold-change thresholds. We recommend its incorporation to any computation of gene expression changes using high-density DNA microarrays. A Java application implementing our T-score is available at http://www.sadovsky.wustl.edu/tscore.html.

gHRV: Heart rate variability analysis made easy.

Science.gov (United States)

Rodríguez-Liñares, L; Lado, M J; Vila, X A; Méndez, A J; Cuesta, P

2014-08-01

In this paper, the gHRV software tool is presented. It is a simple, free and portable tool developed in python for analysing heart rate variability. It includes a graphical user interface and it can import files in multiple formats, analyse time intervals in the signal, test statistical significance and export the results. This paper also contains, as an example of use, a clinical analysis performed with the gHRV tool, namely to determine whether the heart rate variability indexes change across different stages of sleep. Results from tests completed by researchers who have tried gHRV are also explained: in general the application was positively valued and results reflect a high level of satisfaction. gHRV is in continuous development and new versions will include suggestions made by testers. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Variability analysis of AGN: a review of results using new statistical criteria

Science.gov (United States)

Zibecchi, L.; Andruchow, I.; Cellone, S. A.; Romero, G. E.; Combi, J. A.

We present here a re-analysis of the variability results of a sample of active galactic nuclei (AGN), which have been observed on several sessions with the 2.15 m "Jorge Sahade" telescope (CASLEO), San Juan, Argentina, and whose results are published (Romero et al. 1999, 2000, 2002; Cellone et al. 2000). The motivation for this new analysis is the implementation, dur- ing the last years, of improvements in the statistical criteria applied, taking quantitatively into account the incidence of the photometric errors (Cellone et al. 2007). This work is framed as a first step in an integral study on the statistical estimators of AGN variability. This study is motivated by the great diversity of statistical tests that have been proposed to analyze the variability of these objects. Since we note that, in some cases, the results of the object variability depend on the test used, we attempt to make a com- parative study of the various tests and analyze, under the given conditions, which of them is the most efficient and reliable.

The Infinitesimal Jackknife with Exploratory Factor Analysis

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Zhang, Guangjian; Preacher, Kristopher J.; Jennrich, Robert I.

2012-01-01

The infinitesimal jackknife, a nonparametric method for estimating standard errors, has been used to obtain standard error estimates in covariance structure analysis. In this article, we adapt it for obtaining standard errors for rotated factor loadings and factor correlations in exploratory factor analysis with sample correlation matrices. Both…

Analysis Of Factors Causing Delays On Harun Nafsi - Hm Rifadin Street In Samarinda East Kalimantan Maintenance Project

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Fadli

2017-12-01

Full Text Available This study aims to identify analyze and describe the factors that affect the project maintenance delay on Harun Nafsi - HM. Rifadin Street in Samarinda East Kalimantan. This research uses qualitative research method by utilizing questionnaires. The 30 participating respondents consist of 14 project implementers and 16 field implementers. The data are analyzed by descriptive statistical technique factor analysis and linear regression analysis. The results show that the factors influencing the delay of maintenance project of Harun Nafis - HM Rifadin Street include 1 time factor and workmanship factor 2 human resources and natural factors 3 geographical conditions late approval plans change and labor strikes and 4 non-optimal working levels and changes in the scope of the project during the work are still ongoing. Based on multiple linear regression analysis coefficient of determination value of 0.824 is obtained. It means that the four factors studied affect 82.4 of project delays and the rest of 27.6 is influenced by other variables out of this study. The results of this study also indicate that the dominant factor for road maintenance project delays is the fourth factor of the factors mentioned. The effort that the contractor needs to undertake is not to expand the employment contract if the project is underway or the contractor does not have the capability to complete another project.

Left ventricular wall motion abnormalities evaluated by factor analysis as compared with Fourier analysis

International Nuclear Information System (INIS)

Hirota, Kazuyoshi; Ikuno, Yoshiyasu; Nishikimi, Toshio

1986-01-01

Factor analysis was applied to multigated cardiac pool scintigraphy to evaluate its ability to detect left ventricular wall motion abnormalities in 35 patients with old myocardial infarction (MI), and in 12 control cases with normal left ventriculography. All cases were also evaluated by conventional Fourier analysis. In most cases with normal left ventriculography, the ventricular and atrial factors were extracted by factor analysis. In cases with MI, the third factor was obtained in the left ventricle corresponding to wall motion abnormality. Each case was scored according to the coincidence of findings of ventriculography and those of factor analysis or Fourier analysis. Scores were recorded for three items; the existence, location, and degree of asynergy. In cases of MI, the detection rate of asynergy was 94 % by factor analysis, 83 % by Fourier analysis, and the agreement in respect to location was 71 % and 66 %, respectively. Factor analysis had higher scores than Fourier analysis, but this was not significant. The interobserver error of factor analysis was less than that of Fourier analysis. Factor analysis can display locations and dynamic motion curves of asynergy, and it is regarded as a useful method for detecting and evaluating left ventricular wall motion abnormalities. (author)

Multivariate Analysis of Factors Influencing Length of Hospital Stay after Coronary Artery Bypass Surgery in Tehran, Iran

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Amin Torabipour

2016-03-01

Full Text Available Length of hospital stay (LOS is a key indicator for hospital management. Reducing hospital stay is a priority for all health systems. We aimed to determine the length of hospital stay following Coronary Artery Bypass Surgery (CABG based on its clinical and non-clinical factors. A cross-sectional study of 649 consecutive patients who underwent coronary artery bypass graft surgery was conducted in Imam Khomeini and Shariati university hospitals, Tehran, Iran. Data was analyzed by using non-parametric univariate tests and multiple linier regression models. Thirty seven independent variables including pre-operative, intra-operative and post-operative variables were analyzed. Finally, an appropriate model was constructed based on the associated factors. The results showed that 70.3% of the patients were male, and the mean age of the patients was 59.3 ± 10.4 years. The Mean (±SD and median of the LOS were 11.7 ± 7.1 and 9 days, respectively. Of 37 investigated variables, 24 qualitative and quantitative variables were significantly associated with length of stay (p<0.05. Multiple linear regression analysis showed that independent variables including age, medical insurance type, body mass index, and prior myocardial infarction; admission day, admission season, Cross-clamp time, pump usage, admission type, the number of laboratory tests and the number of specialty consultation had more effect on the hospital stay. We concluded that some significant factors influencing hospital stay after CABG were predictable and modifiable by hospital managers and decision makers to manage hospital beds.

Exploratory factor analysis in Rehabilitation Psychology: a content analysis.

Science.gov (United States)

Roberson, Richard B; Elliott, Timothy R; Chang, Jessica E; Hill, Jessica N

2014-11-01

Our objective was to examine the use and quality of exploratory factor analysis (EFA) in articles published in Rehabilitation Psychology. Trained raters examined 66 separate exploratory factor analyses in 47 articles published between 1999 and April 2014. The raters recorded the aim of the EFAs, the distributional statistics, sample size, factor retention method(s), extraction and rotation method(s), and whether the pattern coefficients, structure coefficients, and the matrix of association were reported. The primary use of the EFAs was scale development, but the most widely used extraction and rotation method was principle component analysis, with varimax rotation. When determining how many factors to retain, multiple methods (e.g., scree plot, parallel analysis) were used most often. Many articles did not report enough information to allow for the duplication of their results. EFA relies on authors' choices (e.g., factor retention rules extraction, rotation methods), and few articles adhered to all of the best practices. The current findings are compared to other empirical investigations into the use of EFA in published research. Recommendations for improving EFA reporting practices in rehabilitation psychology research are provided.

Real analysis foundations and functions of one variable

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Laczkovich, Miklós

2015-01-01

Based on courses given at Eötvös Loránd University (Hungary) over the past 30 years, this introductory textbook develops the central concepts of the analysis of functions of one variable - systematically, with many examples and illustrations, and in a manner that builds upon, and sharpens, the students' mathematical intuition. The modular organization of the book makes it adaptable for either semester or year-long introductory courses, while the wealth of material allows for it to be used at various levels of student sophistication in all programs where analysis is a part of the curriculum, including teachers' education. In the spirit of learning-by-doing, Real Analysis includes more than 500 engaging exercises for the student keen on mastering the basics of analysis. There are frequent hints and occasional complete solutions provided for the more challenging exercises making it an ideal choice for independent study. The book includes a solid grounding in the basics of logic and proofs, sets, and real numb...

Theory of sampling: four critical success factors before analysis.

Science.gov (United States)

Wagner, Claas; Esbensen, Kim H

2015-01-01

Food and feed materials characterization, risk assessment, and safety evaluations can only be ensured if QC measures are based on valid analytical data, stemming from representative samples. The Theory of Sampling (TOS) is the only comprehensive theoretical framework that fully defines all requirements to ensure sampling correctness and representativity, and to provide the guiding principles for sampling in practice. TOS also defines the concept of material heterogeneity and its impact on the sampling process, including the effects from all potential sampling errors. TOS's primary task is to eliminate bias-generating errors and to minimize sampling variability. Quantitative measures are provided to characterize material heterogeneity, on which an optimal sampling strategy should be based. Four critical success factors preceding analysis to ensure a representative sampling process are presented here.

Systematic Risk Factors for Australian Stock Market Returns: a Cointegration Analysis

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Mazharul H. Kazi

2008-12-01

Full Text Available This paper identifies the systematic risk factors for the Australian stock market by applyingthe cointegration technique of Johansen. In conformity with the finance literature andinvestors’ common intuition, relevant a priori variables are chosen to proxy for Australiansystematic risk factors. The results show that only a few systematic risk factors are dominantfor Australian stock market price movements in the long-run while short-run dynamics are inplace. It is observed that the linear combination of all a priori variables is cointegratedalthough not all variables are significantly influential. The findings show that bank interestrate, corporate profitability, dividend yield, industrial production and, to a lesser extent, globalmarket movements are significantly influencing the Australian stock market returns in thelong-run; while in the short-run it is being adjusted each quarter by its own performance,interest rate and global stock market movements of previous quarter.

Risk factors for Type 2 Diabetes Mellitus in college students: association with sociodemographic variables

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Adman Câmara Soares Lima

2014-06-01

Full Text Available OBJECTIVE: identify the modifiable risk factors for type 2 diabetes mellitus in college students and associate these factors with their sociodemographic variables.METHOD: cross-sectional study, involving 702 college students from Fortaleza-CE, Brazil. Sociodemographic, anthropometric, physical exercise data and blood pressure and fasting plasma glucose levels were collected.RESULTS: the most prevalent risk factor was sedentariness, followed by overweight, central obesity, high fasting plasma glucose and arterial hypertension. A statistically significant association was found between overweight and sex (p=0.000, age (p=0.004 and marital status (p=0.012, as well as between central obesity and age (p=0.018 and marital status (p=0.007 and between high fasting plasma glucose and sex (p=0.033.CONCLUSION: distinct risk factors were present in the study population, particularly sedentariness and overweight.

Lithuanian Population Aging Factors Analysis

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Agnė Garlauskaitė

2015-05-01

Full Text Available The aim of this article is to identify the factors that determine aging of Lithuania’s population and to assess the influence of these factors. The article shows Lithuanian population aging factors analysis, which consists of two main parts: the first describes the aging of the population and its characteristics in theoretical terms. Second part is dedicated to the assessment of trends that influence the aging population and demographic factors and also to analyse the determinants of the aging of the population of Lithuania. After analysis it is concluded in the article that the decline in the birth rate and increase in the number of emigrants compared to immigrants have the greatest impact on aging of the population, so in order to show the aging of the population, a lot of attention should be paid to management of these demographic processes.

Confirming theoretical pay constructs of a variable pay scheme

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Sibangilizwe Ncube

2013-05-01

Full Text Available Orientation: Return on the investment in variable pay programmes remains controversial because their cost versus contribution cannot be empirically justified. Research purpose: This study validates the findings of the model developed by De Swardt on the factors related to successful variable pay programmes. Motivation for the study: Many organisations blindly implement variable pay programmes without any means to assess the impact these programmes have on the company’s performance. This study was necessary to validate the findings of an existing instrument that validates the contribution of variable pay schemes. Research design, approach and method: The study was conducted using quantitative research. A total of 300 completed questionnaires from a non-purposive sample of 3000 participants in schemes across all South African industries were returned and analysed. Main findings: Using exploratory and confirmatory factor analysis, it was found that the validation instrument developed by De Swardt is still largely valid in evaluating variable pay schemes. The differences between the study and the model were reported. Practical/managerial implications: The study confirmed the robustness of an existing model that enables practitioners to empirically validate the use of variable pay plans. This model assists in the design and implementation of variable pay programmes that meet critical success factors. Contribution/value-add: The study contributed to the development of a measurement instrument that will assess whether a variable pay plan contributes to an organisation’s success.

Foot-and-Mouth Disease Virus Serotype O Phylodynamics: Genetic Variability Associated with Epidemiological Factors in Pakistan

DEFF Research Database (Denmark)

Brito, B. P.; Perez, A. M.; Jamal, S. M.

2013-01-01

One of the most challenging aspects of foot-and-mouth disease (FMD) control is the high genetic variability of the FMD virus (FMDV). In endemic settings such as the Indian subcontinent, this variability has resulted in the emergence of pandemic strains that have spread widely and caused devastating...... outbreaks in disease-free areas. In countries trying to control and eradicate FMD using vaccination strategies, the constantly evolving and wide diversity of field FMDV strains is an obstacle for identifying vaccine strains that are successful in conferring protection against infection with field viruses....... Consequently, quantitative knowledge on the factors that are associated with variability of the FMDV is prerequisite for preventing and controlling FMD in the Indian subcontinent. A hierarchical linear model was used to assess the association between time, space, host species and the genetic variability...

Causality and cointegration analysis between macroeconomic variables and the Bovespa.

Directory of Open Access Journals (Sweden)

Fabiano Mello da Silva

Full Text Available The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI, industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa. The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988 causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988 long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.

Analysis of risk factors and morphological ultrasound image for gallbladder polyp in adults living in Busan and Gyeongnam provinces

Energy Technology Data Exchange (ETDEWEB)

An, Hyeon [Dept. of Radiology, Inje University Busan Paik Hospital, Busan (Korea, Republic of); Hwang, Chul Hwan [Dept. of Radiation Oncology, Pusan National University Hospital, Busan (Korea, Republic of); Kim, Chang Soo; Ko, Sung Jin [Dept. of Radiological Science, College of Health Sciences, Catholic University of Pusan, Busan (Korea, Republic of)

2016-09-15

This study were to evaluate risk factors of GB polpy in Busan and Gyeongnam area. This study was performed with patients by abdominal ultrasonography among the patients who came to the P hospital from January to May 2016. Among them, risk factors were analyzed on 399 people at the same time when abdominal ultrasonography and hematological test. The statistical analysis of risk factors related to the GB ployp was performed by independent t-test and chi-square test. In consider of difference verification result for calculations odds ratio about independent variables, multiple logistic regression analysis to conduct verify adequacy by calculating forecasting model from variable. As a result, GB polyp risk factors have relevance to male, HBsAg positive, triglyceride. GB polyp risk factors confirmed to male, HBsAg positive, triglyceride were calculated forecasting model and forecasting probability value. Forecasting probability sensitivity 61.0%, specificity 76.8%, ROC area under curve 0.735 showed, it confirmed validity of forecasting model. When analyzing the GB polyps morphologically, among the GB polyp types observed from abdominal ultrasonography, the hyperechoic and homogeneous pattern with neck was the largest as shown from 27.5% and two GB polyps were shown most from 38%, sizes were shown most by maximum diameter, 5 to 10mm from 53%. As a disease accompany with GB polyp showed mild fatty liver(23%), diffuse hepatopathy(21%)

Radiograph and passive data analysis using mixed variable optimization

Science.gov (United States)

Temple, Brian A.; Armstrong, Jerawan C.; Buescher, Kevin L.; Favorite, Jeffrey A.

2015-06-02

Disclosed herein are representative embodiments of methods, apparatus, and systems for performing radiography analysis. For example, certain embodiments perform radiographic analysis using mixed variable computation techniques. One exemplary system comprises a radiation source, a two-dimensional detector for detecting radiation transmitted through a object between the radiation source and detector, and a computer. In this embodiment, the computer is configured to input the radiographic image data from the two-dimensional detector and to determine one or more materials that form the object by using an iterative analysis technique that selects the one or more materials from hierarchically arranged solution spaces of discrete material possibilities and selects the layer interfaces from the optimization of the continuous interface data.

Problems with the factor analysis of items: Solutions based on item response theory and item parcelling

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Gideon P. De Bruin

2004-10-01

Full Text Available The factor analysis of items often produces spurious results in the sense that unidimensional scales appear multidimensional. This may be ascribed to failure in meeting the assumptions of linearity and normality on which factor analysis is based. Item response theory is explicitly designed for the modelling of the non-linear relations between ordinal variables and provides a strong alternative to the factor analysis of items. Items may also be combined in parcels that are more likely to satisfy the assumptions of factor analysis than do the items. The use of the Rasch rating scale model and the factor analysis of parcels is illustrated with data obtained with the Locus of Control Inventory. The results of these analyses are compared with the results obtained through the factor analysis of items. It is shown that the Rasch rating scale model and the factoring of parcels produce superior results to the factor analysis of items. Recommendations for the analysis of scales are made. Opsomming Die faktorontleding van items lewer dikwels misleidende resultate op, veral in die opsig dat eendimensionele skale as meerdimensioneel voorkom. Hierdie resultate kan dikwels daaraan toegeskryf word dat daar nie aan die aannames van lineariteit en normaliteit waarop faktorontleding berus, voldoen word nie. Itemresponsteorie, wat eksplisiet vir die modellering van die nie-liniêre verbande tussen ordinale items ontwerp is, bied ’n aantreklike alternatief vir die faktorontleding van items. Items kan ook in pakkies gegroepeer word wat meer waarskynlik aan die aannames van faktorontleding voldoen as individuele items. Die gebruik van die Rasch beoordelingskaalmodel en die faktorontleding van pakkies word aan die hand van data wat met die Lokus van Beheervraelys verkry is, gedemonstreer. Die resultate van hierdie ontledings word vergelyk met die resultate wat deur ‘n faktorontleding van die individuele items verkry is. Die resultate dui daarop dat die Rasch

Confirmatory Factor Analysis untuk Mengetahui Pemanfaatan Multimedia Learning pada Perguruan Tinggi Swasta di Kota Semarang

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Achmad Solechan

2014-07-01

Full Text Available Dominant indicator of the use of multimedia in learning needs to be studied using the Confirmatory Factor Analysis. This study aims to determine the most dominant factor affecting the use of multimedia in learning, this study uses the Technology Acceptance Modelling theory. This study uses the technique of Judgment Sampling Area or a sampling technique that is based on the determination of the research area. Determination of the study area are four private universities in Semarang, namely USM, Udinus, STMIK Provisi and Unisbank, so the overall number of respondents as many as 200 students. This study shows that: 1-the greatest contribution value of the Perceived Usefulness latent variable that is multimedia learning increase the effectiveness of learning in the classroom, 2-the greatest contribution value of the Confirmation latent variable, that is Lecturer Services using multimedia learning is preferred, 3-the greatest contribution value of the Perceived Ease of Use latent variable that is teaching materials using multimedia learning is something that is easy for students to understand. This study also shows that : 4-the greatest contribution value of the Satisfaction latent variable that is multimedia learning used by Lecturer in teaching and learning in the classroom is able to provide information in accordance with the information that students need, and 5-the greatest contribution value of the Continued IT Usage Intention latent variable that is students interested in understanding the material, which is taught by Lecturer, if Lecturer teaching using multimedia based than conventional models

Dynamic analysis of hybrid energy systems under flexible operation and variable renewable generation – Part II: Dynamic cost analysis

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Garcia, Humberto E.; Mohanty, Amit; Lin, Wen-Chiao; Cherry, Robert S.

2013-01-01

Dynamic analysis of HES (hybrid energy systems) under flexible operation and variable renewable generation is considered in this two-part communication to better understand various challenges and opportunities associated with the high system variability arising from the integration of renewable energy into the power grid. Advanced HES solutions are investigated in which multiple forms of energy commodities, such as electricity and chemical products, may be exchanged. In particular, a comparative dynamic cost analysis is conducted in this part two of the communication to determine best HES options. The cost function includes a set of metrics for computing fixed costs, such as fixed operations and maintenance and overnight capital costs, and also variable operational costs, such as cost of operational variability, variable operations and maintenance cost, and cost of environmental impact, together with revenues. Assuming natural gas, coal, and nuclear as primary heat sources, preliminary results identify the level of renewable penetration at which a given advanced HES option (e.g., a nuclear hybrid) becomes increasingly more economical than a traditional electricity-only generation solution. Conditions are also revealed under which carbon resources may be better utilized as carbon sources for chemical production rather than as combustion material for electricity generation. - Highlights: ► Dynamic analysis of HES to investigate challenges related to renewable penetration. ► Evaluation of dynamic synergies among HES constituents on system performance. ► Comparison of traditional versus advanced HES candidates. ► Dynamic cost analysis of HES candidates to investigate their economic viability. ► Identification of conditions under which an energy commodity may be best utilized

Bivariate analysis of the genetic variability among some accessions of African Yam Bean (Sphenostylis stenocarpa (Hochst ex A. RichHarms

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Solomon Tayo AKINYOSOYE

2017-12-01

Full Text Available Variability is an important factor to consider in crop improvement programmes. This study was conducted in two years to assess genetic variability and determine relationship between seed yield, its components and tuber production characters among twelve accessions of African yam bean. Data collected were subjected to combined analysis of variance (ANOVA, Principal Component Analysis (PCA, hierarchical and K-means clustering analyses. Results obtained revealed that genotype by year (G × Y interaction had significant effects on some of variables measured (days to first flowering, days to 50 % flowering, number of pod per plant, pod length, seed yield and tuber yield per plant in this study.The first five principal components (PC with Eigen values greater than 1.0 accounted for about 66.70 % of the total variation, where PC1 and PC 2 accounted for 39.48 % of variation and were associated with seed and tuber yield variables. Three heterotic groups were clearly delineated among genotypes with accessions AY03 and AY10 identified for high seed yield and tuber yield respectively. Non-significant relationship that existed between tuber and seed yield per plant of these accessions was recommended for further test in various agro-ecologies for their suitability, adaptability and possible exploitation of heterosis to further improve the accessions.

Hand Fatigue Analysis Using Quantitative Evaluation of Variability in Drawing Patterns

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mohamadali Sanjari

2015-02-01

Full Text Available Background & aim: Muscle fatigue is defined as the reduced power generation capacity of a muscle or muscle group after activity which can lead to a variety of lesions. The purpose of the present study was to define the fatigue analysis by quantitative analysis using drawing patterns. Methods: the present cross-sectional study was conducted on 37 healthy volunteers (6 men and 31 women aged 18-30 years. Before & immediately after a fatigue protocol, quantitative assessment of hand drawing skills was performed by drawing repeated, overlapping, and concentric circles. The test was conducted in three sessions with an interval of 48-72 hours. Drawing was recorded by a digital tablet. Data were statistically analyzed using paired t-test and repeated measure ANOVA. Result: In drawing time series data analysis, at fatigue level of 100%, the variables standard deviation along x axis (SDx, standard deviation of velocity on both x and y axis (SDVx and SDVy and resultant vector velocity standard deviation (SDVR, showed significant differences after fatigue (P<0.05. In comparison of variables after the three fatigue levels, SDx showed significant difference (P<0.05. Conclusions: structurally full fatigue showed significant differences with other levels of fatigue, so it contributed to significant variability in drawing parameters. The method used in the present study recognized the fatigue in high frequency motion as well.

Bias and Bias Correction in Multi-Site Instrumental Variables Analysis of Heterogeneous Mediator Effects

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Reardon, Sean F.; Unlu, Faith; Zhu, Pei; Bloom, Howard

2013-01-01

We explore the use of instrumental variables (IV) analysis with a multi-site randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, as assumption known in the instrumental variables literature as the…

ANÁLISIS DE LAS VARIABLES ANTROPOMÉTRICAS Y FÍSICO TÉCNICAS EN VOLEIBOL FEMENINO [Analysis of anthropometric and physical techniques in women´s volleyball

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Natalia Valladares Iglesias

2016-12-01

Volleyball has been considered a highly complex sport because of their technical, tactical, physical, psychological and anthropometric factors requirements. Over the years there has been an increase in the homogeneity of the characteristics of volleyball players in high performance due to talent acquisition with similar skills. The main interest of this study was to determine the performance of a sample of 53 volleyball players participating in different official women's volleyball competition. The variables studied are: category, anthropometric variables (body mass, height, endomorphy, mesomorphy, ectomorphy, upper body strength (throwing ball, measure the speed hitting without jump and with jump spike and lower body strength (CMJ, ABK, DJ and jump spike. Statistical analysis consisted of a Shapiro-Wilks test, to determine the normality of the sample. The comparative analysis between categories was performed using an analysis of variance (ANOVA a factor. The results showed a significant positive increase in physical, technical and anthropometric characteristics of the players with the increased level of competition, and a better use of technical gestures, reflecting an increase in performance thereof.

Variability of the western Galician upwelling system (NW Spain) during an intensively sampled annual cycle. An EOF analysis approach

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Herrera, J. L.; Rosón, G.; Varela, R. A.; Piedracoba, S.

2008-07-01

The key features of the western Galician shelf hydrography and dynamics are analyzed on a solid statistical and experimental basis. The results allowed us to gather together information dispersed in previous oceanographic works of the region. Empirical orthogonal functions analysis and a canonical correlation analysis were applied to a high-resolution dataset collected from 47 surveys done on a weekly frequency from May 2001 to May 2002. The main results of these analyses are summarized bellow. Salinity, temperature and the meridional component of the residual current are correlated with the relevant local forcings (the meridional coastal wind component and the continental run-off) and with a remote forcing (the meridional temperature gradient at latitude 37°N). About 80% of the salinity and temperature total variability over the shelf, and 37% of the residual meridional current total variability are explained by two EOFs for each variable. Up to 22% of the temperature total variability and 14% of the residual meridional current total variability is devoted to the set up of cross-shore gradients of the thermohaline properties caused by the wind-induced Ekman transport. Up to 11% and 10%, respectively, is related to the variability of the meridional temperature gradient at the Western Iberian Winter Front. About 30% of the temperature total variability can be explained by the development and erosion of the seasonal thermocline and by the seasonal variability of the thermohaline properties of the central waters. This thermocline presented unexpected low salinity values due to the trapping during spring and summer of the high continental inputs from the River Miño recorded in 2001. The low salinity plumes can be traced on the Galician shelf during almost all the annual cycle; they tend to be extended throughout the entire water column under downwelling conditions and concentrate in the surface layer when upwelling favourable winds blow. Our evidences point to the

Identifying factors affecting optimal management of agricultural water

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Masoud Samian

2015-01-01

In addition to quantitative methodology such as descriptive statistics and factor analysis a qualitative methodology was employed for dynamic simulation among variables through Vensim software. In this study, the factor analysis technique was used through the Kaiser-Meyer-Olkin (KMO and Bartlett tests. From the results, four key elements were identified as factors affecting the optimal management of agricultural water in Hamedan area. These factors were institutional and legal factors, technical and knowledge factors, economic factors and social factors.

[Effects of land use and environmental factors on the variability of soil quality indicators in hilly Loess Plateau region of China].

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Xu, Ming-Xiang; Liu, Guo-Bin; Zhao, Yun-Ge

2011-02-01

Classical statistics methods were adopted to analyze the soil quality variability, its affecting factors, and affecting degree at a regional scale (700 km2) in the central part of hilly Loess Plateau region of China. There existed great differences in the variability of test soil quality indicators. Soil pH, structural coefficient, silt content, specific gravity, bulk density, total porosity, capillary porosity, and catalase activity were the indicators with weak variability; soil nutrients (N, P, and K) contents, CaCO3 content, cation exchange capacity (CEC), clay content, micro-aggregate mean mass diameter, aggregate mean mass diameter, water-stable aggregates, respiration rate, microbial quotient, invertase and phosphatase activities, respiratory quotient, and microbial carbon and nitrogen showed medium variation; while soil labile organic carbon and phosphorus contents, erosion-resistance, permeability coefficient, and urease activity were the indicators with strong variability. The variability of soil CaCO3, total P and K, CEC, texture, and specific gravity, etc. was correlated with topography and other environmental factors, while the variability of dynamic soil quality indicators, including soil organic matter content, nitrogen content, water-stable aggregates, permeability, microbial biomass carbon and nitrogen, enzyme activities, and respiration rate, was mainly correlated with land use type. Overall, land use pattern explained 97% of the variability of soil quality indicators in the region. It was suggested that in the evaluation of soil quality in hilly Loess Plateau region, land use type and environmental factors should be fully considered.

Universal analytical scattering form factor for shell-, core-shell, or homogeneous particles with continuously variable density profile shape.

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Foster, Tobias

2011-09-01

A novel analytical and continuous density distribution function with a widely variable shape is reported and used to derive an analytical scattering form factor that allows us to universally describe the scattering from particles with the radial density profile of homogeneous spheres, shells, or core-shell particles. Composed by the sum of two Fermi-Dirac distribution functions, the shape of the density profile can be altered continuously from step-like via Gaussian-like or parabolic to asymptotically hyperbolic by varying a single "shape parameter", d. Using this density profile, the scattering form factor can be calculated numerically. An analytical form factor can be derived using an approximate expression for the original Fermi-Dirac distribution function. This approximation is accurate for sufficiently small rescaled shape parameters, d/R (R being the particle radius), up to values of d/R ≈ 0.1, and thus captures step-like, Gaussian-like, and parabolic as well as asymptotically hyperbolic profile shapes. It is expected that this form factor is particularly useful in a model-dependent analysis of small-angle scattering data since the applied continuous and analytical function for the particle density profile can be compared directly with the density profile extracted from the data by model-free approaches like the generalized inverse Fourier transform method. © 2011 American Chemical Society

MULTIVARIATE ANALYSIS OF RISK FACTORS FOR PREMATURITY IN SOUTHERN BRAZIL

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Willian Augusto de Melo

2014-05-01

Full Text Available This study assessed the risk factors associated with preterm birth through a cross-sectional study in 4,440 newborns. Examined the factors associated between maternal sociodemographic variables (age, marital status, education and occupation, obstetric (pregnancy and delivery type and number of prenatal visits and neonatal (sex, race/color, birth weight and Apgar. Data were analyzed by multivariate logistic regression technique. Among the 480 (10.8% preterm risk factors were prevalent type of pregnancy (OR=6.48, number of prenatal visits (OR=2.09, Apgar score at first (OR=2.00 and fifth minute (OR=2.14 and birth weight (OR=31.8 indicating that these variables are directly associated with the occurrence of prematurity. The identification of risk factors should be the object of attention of health professionals and services to support effective measures to promote health to the general population; especially for women in fertile included some criteria of gestational risk.

Analysis of Lung Tumor Motion in a Large Sample: Patterns and Factors Influencing Precise Delineation of Internal Target Volume

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Knybel, Lukas [Department of Oncology, University Hospital Ostrava, Ostrava (Czech Republic); VŠB-Technical University of Ostrava, Ostrava (Czech Republic); Cvek, Jakub, E-mail: Jakub.cvek@fno.cz [Department of Oncology, University Hospital Ostrava, Ostrava (Czech Republic); Molenda, Lukas; Stieberova, Natalie; Feltl, David [Department of Oncology, University Hospital Ostrava, Ostrava (Czech Republic)

2016-11-15

Purpose/Objective: To evaluate lung tumor motion during respiration and to describe factors affecting the range and variability of motion in patients treated with stereotactic ablative radiation therapy. Methods and Materials: Log file analysis from online respiratory tumor tracking was performed in 145 patients. Geometric tumor location in the lungs, tumor volume and origin (primary or metastatic), sex, and tumor motion amplitudes in the superior-inferior (SI), latero-lateral (LL), and anterior-posterior (AP) directions were recorded. Tumor motion variability during treatment was described using intrafraction/interfraction amplitude variability and tumor motion baseline changes. Tumor movement dependent on the tumor volume, position and origin, and sex were evaluated using statistical regression and correlation analysis. Results: After analysis of >500 hours of data, the highest rates of motion amplitudes, intrafraction/interfraction variation, and tumor baseline changes were in the SI direction (6.0 ± 2.2 mm, 2.2 ± 1.8 mm, 1.1 ± 0.9 mm, and −0.1 ± 2.6 mm). The mean motion amplitudes in the lower/upper geometric halves of the lungs were significantly different (P<.001). Motion amplitudes >15 mm were observed only in the lower geometric quarter of the lungs. Higher tumor motion amplitudes generated higher intrafraction variations (R=.86, P<.001). Interfraction variations and baseline changes >3 mm indicated tumors contacting mediastinal structures or parietal pleura. On univariate analysis, neither sex nor tumor origin (primary vs metastatic) was an independent predictive factor of different movement patterns. Metastatic lesions in women, but not men, showed significantly higher mean amplitudes (P=.03) and variability (primary, 2.7 mm; metastatic, 4.9 mm; P=.002) than primary tumors. Conclusion: Online tracking showed significant irregularities in lung tumor movement during respiration. Motion amplitude was significantly lower in upper lobe

Analysis of factors for early hypothyroidism after 131I treatment of hyperthyroidism

International Nuclear Information System (INIS)

Zhou Youjun; Quan Xinsheng; Zhang Ling; He Meiqiong

2008-01-01

To explore the factors for the early hypothyroidism following 131 I treatment of hyperthyroidism, clinic data of 120 hyperthyroidism patients including 8 independent and 1 dependent variables after one year 131 I treatment were analyzed by logistic regression analysis method. The results showed that the average 131 I dosage given to one gram thyroid tissue was correlated positively with early hypothyroidism occurrence, and the weight of thyroid was negatively correlated to early hypothyroidism occurrence. The positive and negative prediction accuracy of the early hypothyroidism were 53.3% and 96.1% respectively, and the total prediction accuracy was 46.7%. The results suggest that the 131 I dosage and the weight of thyroid are key factors for early hypothyroidism; the appropriate adjustment of 131 I dosage based on the thyroid mass could prevent the early hypothyroidism occurrence in certain degree. (authors)

First course in factor analysis

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Comrey, Andrew L

2013-01-01

The goal of this book is to foster a basic understanding of factor analytic techniques so that readers can use them in their own research and critically evaluate their use by other researchers. Both the underlying theory and correct application are emphasized. The theory is presented through the mathematical basis of the most common factor analytic models and several methods used in factor analysis. On the application side, considerable attention is given to the extraction problem, the rotation problem, and the interpretation of factor analytic results. Hence, readers are given a background of

Sparse modeling of spatial environmental variables associated with asthma.

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Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Multivariate analysis between air pollutants and meteorological variables in Seoul

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Kim, J.; Lim, J.

2005-01-01

Multivariate analysis was conducted to analyze the relationship between air pollutants and meteorological variables measured in Seoul from January 1 to December 31, 1999. The first principal component showed the contrast effect between O 3 and the other pollutants. The second principal component showed the contrast effect between CO, SO 2 , NO 2 , and O 3 , PM 10 , TSP. Based on the cluster analysis, three clusters represented different air pollution levels, seasonal characteristics of air pollutants, and meteorological conditions. Discriminant analysis with air environment index (AEI) was carried out to develop an air pollution index function. (orig.)

Quantitative analysis of spatial variability of geotechnical parameters

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Fang, Xing

2018-04-01

Geotechnical parameters are the basic parameters of geotechnical engineering design, while the geotechnical parameters have strong regional characteristics. At the same time, the spatial variability of geotechnical parameters has been recognized. It is gradually introduced into the reliability analysis of geotechnical engineering. Based on the statistical theory of geostatistical spatial information, the spatial variability of geotechnical parameters is quantitatively analyzed. At the same time, the evaluation of geotechnical parameters and the correlation coefficient between geotechnical parameters are calculated. A residential district of Tianjin Survey Institute was selected as the research object. There are 68 boreholes in this area and 9 layers of mechanical stratification. The parameters are water content, natural gravity, void ratio, liquid limit, plasticity index, liquidity index, compressibility coefficient, compressive modulus, internal friction angle, cohesion and SP index. According to the principle of statistical correlation, the correlation coefficient of geotechnical parameters is calculated. According to the correlation coefficient, the law of geotechnical parameters is obtained.

Identification of Variables and Factors Impacting Consumer Behavior in On-line Shopping in India: An Empirical Study

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Chhikara, Sudesh

On-line shopping is a recent phenomenon in the field of E-Business and is definitely going to be the future of shopping in the world. Most of the companies are running their on-line portals to sell their products/services. Though online shopping is very common outside India, its growth in Indian Market, which is a large and strategic consumer market, is still not in line with the global market. The potential growth of on-line shopping has triggered the idea of conducting a study on on-line shopping in India. The present research paper has used exploratory study to depict and highlight the various categories of factors and variables impacting the behavior of consumers towards on-line shopping in India. The data was collected through in-depth interviews on a sample of 41 respondents from Delhi, Mumbai, Chennai and Bangalore. The results of the study show that on-line shopping in India is basically impacted by five categories of factors like demographics factor, Psychographics factor, Online shopping feature and policies, Technological factor, Security factor. The results of the study are used to present a comprehensive model of on-line shopping which could be further used by the researchers and practitioners for conducting future studies in the similar area. A brief operational definition of all the factors and variables impacting on-line shopping in India is also described. And finally practical implications of the study are also elucidated.

Stability analysis for cellular neural networks with variable delays

International Nuclear Information System (INIS)

Zhang Qiang; Wei Xiaopeng; Xu Jin

2006-01-01

Some sufficient conditions for the global exponential stability of cellular neural networks with variable delay are obtained by means of a method based on delay differential inequality. The method, which does not make use of Lyapunov functionals, is simple and effective for the stability analysis of neural networks with delay. Some previously established results in the literature are shown to be special cases of the presented result

The interface between research on individual difference variables and teaching practice: The case of cognitive factors and personality

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Adriana Biedroń

2016-09-01

Full Text Available While a substantial body of empirical evidence has been accrued about the role of individual differences in second language acquisition, relatively little is still known about how factors of this kind can mediate the effects of instructional practices as well as how empirically-derived insights can inform foreign language pedagogy, both with respect to shaping certain variables and adjusting instruction to individual learner profiles. The present paper is an attempt to shed light on the interface between research on individual difference factors and teaching practice, focusing upon variables which do not easily lend themselves to external manipulation, namely intelligence, foreign language aptitude, working memory and personality, with the role of the last of these in language learning being admittedly the least obvious. In each case, the main research findings will briefly be outlined, their potential for informing instruction will be considered, and, in the final part, the caveats concerning practical applications of research on the variables in question will be spelled out.

Brand Loyalty Factors Affecting the Hotel Elections of Tourists investigation with Respect to the Demographic Variables

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Ahmet Tayfun

2015-12-01

Full Text Available The purpose of this research is to investigate the factors affecting hotel guests’s brand loyalty and these factors is to determine whether there is a difference or not according to the demographic variables. Within this purpose, a survey research was conducted on the guests staying in the five star hotels of Ankara, Turkey. The research was carried out in two stages, namely the pilot study and the main study. As a result of study, while the factors affecting brand loyalty differs based on the gender and the age of the participants (p0,05

Lungworm Infections in German dairy cattle herds--seroprevalence and GIS-supported risk factor analysis.

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Anne-Marie Schunn

Full Text Available In November 2008, a total of 19,910 bulk tank milk (BTM samples were obtained from dairy farms from all over Germany, corresponding to about 20% of all German dairy herds, and analysed for antibodies against the bovine lungworm Dictyocaulus viviparus by use of the recombinant MSP-ELISA. A total number of 3,397 (17.1%; n = 19,910 BTM samples tested seropositive. The prevalences in individual German federal states varied between 0.0% and 31.2% positive herds. A geospatial map was drawn to show the distribution of seropositive and seronegative herds per postal code area. ELISA results were further analysed for associations with land-use and climate data. Bivariate statistical analysis was used to identify potential spatial risk factors for dictyocaulosis. Statistically significant positive associations were found between lungworm seropositive herds and the proportion of water bodies and grassed area per postal code area. Variables that showed a statistically significant association with a positive BTM test were included in a logistic regression model, which was further refined by controlled stepwise selection of variables. The low Pseudo R(2 values (0.08 for the full model and 0.06 for the final model and further evaluation of the model by ROC analysis indicate that additional, unrecorded factors (e.g. management factors or random effects may substantially contribute to lungworm infections in dairy cows. Veterinarians should include lungworms in the differential diagnosis of respiratory disease in dairy cattle, particularly those at pasture. Monitoring of herds through BTM screening for antibodies can help farmers and veterinarians plan and implement appropriate control measures.

Factor analysis of sources of information on organ donation and transplantation in journalism students.

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Martínez-Alarcón, L; Ríos, A; Ramis, G; López-Navas, A; Febrero, B; Ramírez, P; Parrilla, P

2013-01-01

Journalists and the information they disseminate are essential to promote health and organ donation and transplantation (ODT). The attitude of journalism students toward ODT could influence public opinion and help promote this treatment option. The aim of this study was to determine the media through which journalism students receive information on ODT and to analyze the association between the sources of information and psychosocial variables. We surveyed journalism students (n = 129) recruited in compulsory classes. A validated psychosocial questionnaire (self-administered, anonymous) about ODT was used. Student t test and χ(2) test were applied. Questionnaire completion rate was 98% (n = 126). The medium with the greatest incidence on students was television (TV), followed by press and magazines/books. In the factor analysis to determine the impact of the information by its source, the first factor was talks with friends and family; the second was shared by hoardings/publicity posters, health professionals, and college/school; and the third was TV and radio. In the factor analysis between information sources and psychosocial variables, the associations were between information about organ donation transmitted by friends and family and having spoken about ODT with them; by TV, radio, and hoardings and not having spoken in the family; and by TV/radio and the father's and mother's opinion about ODT. The medium with the greatest incidence on students is TV, and the medium with the greatest impact on broadcasting information was conversations with friends, family, and health professionals. This could be useful for society, because they should be provided with clear and concise information. Copyright © 2013 Elsevier Inc. All rights reserved.

State-variable analysis of inelastic deformation of thin-walled tubes. II. Data analysis and simulations

International Nuclear Information System (INIS)

Wire, G.L.; Duncan, D.R.; Cannon, N.S.; Johnson, G.D.; Alexopoulos, P.S.; Li, C.Y.

Inelastic analysis is performed to calculate the deformation of thin-walled, internally pressurized, tube under a variety of loading modes. A state-variable approach was used to describe the material properties. The material parameters of the constitutive equations used were determined based on uniaxial, load relaxation, tensile tests, and internally pressurized tubes under creep and constant-displacement-rate modes of loading. The simulated results were compared with the experimental data. The significance of the comparison is discussed in terms of the validity of a state-variable approach used to describe the deformation properties in mechanical testing

Identification of dietary patterns using factor analysis in an epidemiological study in São Paulo

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Dirce Maria Lobo Marchioni

Full Text Available CONTEXT AND OBJECTIVE: Diet and nutrition are environmental factors in health/disease relationships. From the epidemiological viewpoint, diet represents a complex set of highly correlated exposures. Our objective was to identify patterns of food intake in a group of individuals living in São Paulo, and to develop objective dietary measurements for epidemiological purposes. DESIGN AND LOCAL: Exploratory factor analysis of data in a case-control study in seven teaching hospitals in São Paulo. METHODS: The participants were 517 patients (260 oral cancer cases and 257 controls admitted to the study hospitals between November 1998 and March 2001. The weekly intake frequencies for dairy products, cereals, meat, processed meat, vegetables, pulses, fruits and sweets were assessed by means of a semi-quantitative food frequency questionnaire. Dietary patterns were identified by factor analysis, based on the intake of the eight food groups, using principal component analysis as an extraction method followed by varimax rotation. RESULTS: Factor analysis identified three patterns that accounted for 55% of the total variability within the sample. The first pattern ("prudent" was characterized by vegetable, fruit and meat intake; the second ("traditional" by cereals (mainly rice and pulses (mainly beans; and the third ("snacks" by dairy products and processed meat. CONCLUSION: This study identified food intake patterns through an a posteriori approach. Such analysis may be useful for nutritional intervention programs and, after computing scores for each individual according to the patterns identified, for establishing a relationship between diet and other epidemiological measurements of interest.

Variability search in M 31 using principal component analysis and the Hubble Source Catalogue

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Moretti, M. I.; Hatzidimitriou, D.; Karampelas, A.; Sokolovsky, K. V.; Bonanos, A. Z.; Gavras, P.; Yang, M.

2018-06-01

Principal component analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18 152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long-period variables (LPVs) and non-variables. This projection recovered more than 90 per cent of the known variables and revealed 38 previously unknown variable stars (about 30 per cent more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.

Pedestrian-Vehicle Accidents Reconstruction with PC-Crash®: Sensibility Analysis of Factors Variation

Energy Technology Data Exchange (ETDEWEB)

Martinez Gala, F.

2016-07-01

This paper describes the main findings of a study performed by INSIA-UPM about the improvement of the reconstruction process of real world vehicle-pedestrian accidents using PC-Crash® software, aimed to develop a software tool for the estimation of the variability of the collision speed due to the lack of real values of some parameters required during the reconstruction task. The methodology has been based on a sensibility analysis of the factors variation. A total of 9 factors have been analyzed with the objective of identifying which ones were significant. Four of them (pedestrian height, collision angle, hood height and pedestrian-road friction coefficient) were significant and were included in a full factorial experiment with the collision speed as an additional factor in order to obtain a regression model with up to third level interactions. Two different factorial experiments with the same structure have been performed because of pedestrian gender differences. The tool has been created as a collision speed predictor based on the regression models obtained, using the 4 significant factors and the projection distance measured or estimated in the accident site. The tool has been used on the analysis of real-world reconstructed accidents occurred in the city of Madrid (Spain). The results have been adequate in most cases with less than 10% of deviation between the predicted speed and the one estimated in the reconstructions. (Author)

The impact of organizational factors on-business adoption: An empirical analysis

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Marta García-Moreno

2018-06-01

Full Text Available Purpose: Provide empirical validation of the model developed by García Moreno et al. (2016 on the factors influencing the adoption of e-business in firms. Design/methodology/approach: Consideration is given to the method for measuring each one of the variables included in the model. Use has been made of the e-Business Watch database, which contains measures for the theoretical model’s three categories: firm, technology, and environment. Multinomial logistic regression models have been provided. Findings: The variables included have revealed significant statistical relationships for the model in question, although the intensity of the relationships differs. the variables related to the environment also reveal statistically significant relationships, whereby the attitude of trading partners appears to have a relevant and growing impact on e-business adoption. Research limitations/implications: Data come from just one database: the e-Business Watch database/enriched data from alternative databases could be included. Practical implications: The infrastructure of information and communications technologies (ICTs is confirmed to be a determining factor in e-business development. Nevertheless, the effect of competitor rivalry has a more erratic influence that is encapsulated in a significant relationship in intermediate models, with a sharper increase in the likelihood of being in the category of customer-focused firms, and less internally focused. Social implications: The human capital linked to ICTs is a driving force behind the adoption of these practices. Albeit with a more moderate effect, note should also be taken of the capacity for entering into relationships with third parties within the scope of ICTs, with significant effects that become more robust as they are tested in models that seek to explain the probability of recording higher levels of e-business adoption. Originality/value: The article presents a first empirical analysis to

Periodontitis-associated risk factors in pregnant women

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Maria Dilma Bezerra de Vasconcellos Piscoya

2012-01-01

Full Text Available OBJECTIVE: The main objective of this study was to investigate the risk factors associated with periodontitis in pregnant women. METHODS: This study was conducted in two stages. In Stage 1, a cross-sectional study was conducted to determine the prevalence of periodontitis among 810 women treated at the maternity ward of a university hospital. In Stage 2, the factors associated with periodontitis were investigated in two groups of pregnant women: 90 with periodontitis and 720 without. A hierarchized approach to the evaluation of the risk factors was used in the analysis, and the independent variables related to periodontitis were grouped into two levels: 1 socio-demographic variables; 2a variables related to nutritional status, smoking, and number of pregnancies; and 2b variables related to oral hygiene. Periodontitis was defined as a probing depth > 4 mm and an attachment loss > 3 mm at the same site in four or more teeth. A logistic regression analysis was also performed. RESULTS: The prevalence of periodontitis in this sample was 11%. The variables that remained in the final multivariate model with the hierarchized approach were schooling, family income, smoking, body mass index, and bacterial plaque. CONCLUSION: The factors identified underscore the social nature of the disease, as periodontitis was associated with socioeconomic, demographic status, and poor oral hygiene.

Homogeneity analysis with k sets of variables: An alternating least squares method with optimal scaling features

NARCIS (Netherlands)

van der Burg, Eeke; de Leeuw, Jan; Verdegaal, Renée

1988-01-01

Homogeneity analysis, or multiple correspondence analysis, is usually applied tok separate variables. In this paper we apply it to sets of variables by using sums within sets. The resulting technique is called OVERALS. It uses the notion of optimal scaling, with transformations that can be multiple

Decoupling Solar Variability and Instrument Trends Using the Multiple Same-Irradiance-Level (MuSIL) Analysis Technique

Science.gov (United States)

Woods, Thomas N.; Eparvier, Francis G.; Harder, Jerald; Snow, Martin

2018-05-01

The solar spectral irradiance (SSI) dataset is a key record for studying and understanding the energetics and radiation balance in Earth's environment. Understanding the long-term variations of the SSI over timescales of the 11-year solar activity cycle and longer is critical for many Sun-Earth research topics. Satellite measurements of the SSI have been made since the 1970s, most of them in the ultraviolet, but recently also in the visible and near-infrared. A limiting factor for the accuracy of previous solar variability results is the uncertainties for the instrument degradation corrections, which need fairly large corrections relative to the amount of solar cycle variability at some wavelengths. The primary objective of this investigation has been to separate out solar cycle variability and any residual uncorrected instrumental trends in the SSI measurements from the Solar Radiation and Climate Experiment (SORCE) mission and the Thermosphere, Mesosphere, Ionosphere, Energetic, and Dynamics (TIMED) mission. A new technique called the Multiple Same-Irradiance-Level (MuSIL) analysis has been developed, which examines an SSI time series at different levels of solar activity to provide long-term trends in an SSI record, and the most common result is a downward trend that most likely stems from uncorrected instrument degradation. This technique has been applied to each wavelength in the SSI records from SORCE (2003 - present) and TIMED (2002 - present) to provide new solar cycle variability results between 27 nm and 1600 nm with a resolution of about 1 nm at most wavelengths. This technique, which was validated with the highly accurate total solar irradiance (TSI) record, has an estimated relative uncertainty of about 5% of the measured solar cycle variability. The MuSIL results are further validated with the comparison of the new solar cycle variability results from different solar cycles.

Analysis of risk factors for cluster behavior of dental implant failures.

Science.gov (United States)

Chrcanovic, Bruno Ramos; Kisch, Jenö; Albrektsson, Tomas; Wennerberg, Ann

2017-08-01

Some studies indicated that implant failures are commonly concentrated in few patients. To identify and analyze cluster behavior of dental implant failures among subjects of a retrospective study. This retrospective study included patients receiving at least three implants only. Patients presenting at least three implant failures were classified as presenting a cluster behavior. Univariate and multivariate logistic regression models and generalized estimating equations analysis evaluated the effect of explanatory variables on the cluster behavior. There were 1406 patients with three or more implants (8337 implants, 592 failures). Sixty-seven (4.77%) patients presented cluster behavior, with 56.8% of all implant failures. The intake of antidepressants and bruxism were identified as potential negative factors exerting a statistically significant influence on a cluster behavior at the patient-level. The negative factors at the implant-level were turned implants, short implants, poor bone quality, age of the patient, the intake of medicaments to reduce the acid gastric production, smoking, and bruxism. A cluster pattern among patients with implant failure is highly probable. Factors of interest as predictors for implant failures could be a number of systemic and local factors, although a direct causal relationship cannot be ascertained. © 2017 Wiley Periodicals, Inc.

Predictive factors in patients with hepatocellular carcinoma receiving sorafenib therapy using time-dependent receiver operating characteristic analysis.

Science.gov (United States)

Nishikawa, Hiroki; Nishijima, Norihiro; Enomoto, Hirayuki; Sakamoto, Azusa; Nasu, Akihiro; Komekado, Hideyuki; Nishimura, Takashi; Kita, Ryuichi; Kimura, Toru; Iijima, Hiroko; Nishiguchi, Shuhei; Osaki, Yukio

2017-01-01

To investigate variables before sorafenib therapy on the clinical outcomes in hepatocellular carcinoma (HCC) patients receiving sorafenib and to further assess and compare the predictive performance of continuous parameters using time-dependent receiver operating characteristics (ROC) analysis. A total of 225 HCC patients were analyzed. We retrospectively examined factors related to overall survival (OS) and progression free survival (PFS) using univariate and multivariate analyses. Subsequently, we performed time-dependent ROC analysis of continuous parameters which were significant in the multivariate analysis in terms of OS and PFS. Total sum of area under the ROC in all time points (defined as TAAT score) in each case was calculated. Our cohort included 175 male and 50 female patients (median age, 72 years) and included 158 Child-Pugh A and 67 Child-Pugh B patients. The median OS time was 0.68 years, while the median PFS time was 0.24 years. On multivariate analysis, gender, body mass index (BMI), Child-Pugh classification, extrahepatic metastases, tumor burden, aspartate aminotransferase (AST) and alpha-fetoprotein (AFP) were identified as significant predictors of OS and ECOG-performance status, Child-Pugh classification and extrahepatic metastases were identified as significant predictors of PFS. Among three continuous variables (i.e., BMI, AST and AFP), AFP had the highest TAAT score for the entire cohort. In subgroup analyses, AFP had the highest TAAT score except for Child-Pugh B and female among three continuous variables. In continuous variables, AFP could have higher predictive accuracy for survival in HCC patients undergoing sorafenib therapy.

Fixed capacity and variable member grouping assignment of orthogonal variable spreading factor code tree for code division multiple access networks

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Vipin Balyan

2014-08-01

Full Text Available Orthogonal variable spreading factor codes are used in the downlink to maintain the orthogonality between different channels and are used to handle new calls arriving in the system. A period of operation leads to fragmentation of vacant codes. This leads to code blocking problem. The assignment scheme proposed in this paper is not affected by fragmentation, as the fragmentation is generated by the scheme itself. In this scheme, the code tree is divided into groups whose capacity is fixed and numbers of members (codes are variable. A group with maximum number of busy members is used for assignment, this leads to fragmentation of busy groups around code tree and compactness within group. The proposed scheme is well evaluated and compared with other schemes using parameters like code blocking probability and call establishment delay. Through simulations it has been demonstrated that the proposed scheme not only adequately reduces code blocking probability, but also requires significantly less time before assignment to locate a vacant code for assignment, which makes it suitable for the real-time calls.

Annual and short-term variability in primary productivity by phytoplankton and correlated abiotic factors in the Jurumirim Reservoir (São Paulo, Brazil

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R. Henry

Full Text Available The annual variability of the photosynthetic production (PP by phytoplankton in the lacustrine zone of the Jurumirim Reservoir (São Paulo, Brazil was evaluated in a three-year study to identify recurrent patterns and their causes. Variability in PP was measured daily during two periods of the year (the dry and rainy seasons. An analysis of the PP data failed to identify a recurrent pattern, since the PP values showed no correlation with hydrological factors (rainfall, water level and discharge, and washout nor, apparently, with the water’s nutritional conditions. A principal component analysis revealed that the PP and assimilation ratio were higher when the PO4(3- and N-NH4+ contents were low and the Z EU/Z MIX ratios were at their highest. Areal primary productivity can be predicted based on the ratio between the maximum volumetric productivity and the coefficient of vertical extinction of light. However, the biomass integrated for Z EU was a poor predictor of areal primary productivity. No correlation was found between water temperature and areal and maximum volumetric productivity. Thus, the three-year PP study indicated that the variability pattern is typically chaotic. As for the short-term measurements, the PP was found to be higher in the dry season than in the rainy, although both seasons showed an areal PP variability of 35 to 40%. This pattern was attributed to the daily variation in the nutritional conditions and the magnitude of light penetrating through the water, combined with the mixing of phytoplanktonic cells. A comment about the relationship between primary production by phytoplankton and fish yield is also briefly discussed here.

Influence diagram of physiological and environmental factors affecting heart rate variability: an extended literature overview

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Julien Fatisson

2016-09-01

Full Text Available Heart rate variability (HRV corresponds to the adaptation of the heart to any stimulus. In fact, among the pathologies affecting HRV the most, there are the cardiovascular diseases and depressive disorders, which are associated with high medical cost in Western societies. Consequently, HRV is now widely used as an index of health.In order to better understand how this adaptation takes place, it is necessary to examine which factors directly influence HRV, whether they have a physiological or environmental origin. The primary objective of this research is therefore to conduct a literature review in order to get a comprehensive overview of the subject.The system of these factors affecting HRV can be divided into the following five categories: physiological and pathological factors, environmental factors, lifestyle factors, non-modifiable factors and effects. The direct interrelationships between these factors and HRV can be regrouped into an influence diagram. This diagram can therefore serve as a basis to improve daily clinical practice as well as help design even more precise research protocols.

An integrated multi-study analysis of intra-subject variability in cerebrospinal fluid amyloid-β concentrations collected by lumbar puncture and indwelling lumbar catheter

DEFF Research Database (Denmark)

Lucey, Brendan P; Gonzales, Celedon; Das, Ujjwas

2015-01-01

unknown what effect differences in CSF collection methodology have on Aβ variability. In this study, we sought to determine the effect of different collection methodologies on the stability of CSF Aβ concentrations over time. METHODS: Grouped analysis of CSF Aβ levels from multiple industry and academic...... by enzyme linked immunosorbent assay. Data from all sponsors was converted to percent of the mean for Aβ40 and Aβ42 for comparison. Repeated measures analysis of variance was performed to assess for factors affecting the linear rise of Aβ concentrations over time. RESULTS: Analysis of studies collecting CSF...

Metabolic syndrome in the rural population of Wardha, Central India: An exploratory factor analysis

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Pradeep R Deshmukh

2013-01-01

Full Text Available Background and Objectives: Metabolic syndrome - a plausible precondition for type II diabetes and cardiovascular diseases is also on rise. To understand the mechanistic complexity of metabolic syndrome it is imperative to study the specific contribution of the determinants of metabolic syndrome. Such study can help to identify the most significant factor which may be of use in early detection as well as prevention efforts. Such information is scarcely available from India and especially from rural India. Hence, the present study was undertaken to explore for such factor which might be considered crucial for development of such pathogenesis particularly in rural population of Wardha. Methods: A cross-sectional study comprising of 300 subjects was carried out in rural area of Primary Health Center, attached to medical college with approximate 31,000 populations. The anthropometric parameters such as height, weight, waist circumference were measured. Overnight fasting samples were collected for lipid profile (total cholesterol, triglyceride, high density lipoproteins, low density lipoproteins, very low density lipoproteins and fasting blood glucose levels. The National Cholesterol Education Programme Adult Treatment Panel, ATP-III guidelines were used to categorize the study subjects. As many of the variables are highly intercorrelated, exploratory factor analysis was carried out to reduce the data to a smaller number of independent factors that accounts for the most of the variances in the data. Principal component analysis was used as a method of extraction. Results: For both sexes, three factors were extracted accounting for about 71% variance in the measured variables. An adiposity factor which accounted for highest explained variance (28%, was the initial factor extracted. It was loaded positively by waist circumference, triglyceride, and very low density lipoprotein and negatively loaded by high density lipoprotein. Second factor extracted

Predictive factors in patients eligible for pegfilgrastim prophylaxis focusing on RDI using ordered logistic regression analysis.

Science.gov (United States)

Kanbayashi, Yuko; Ishikawa, Takeshi; Kanazawa, Motohiro; Nakajima, Yuki; Kawano, Rumi; Tabuchi, Yusuke; Yoshioka, Tomoko; Ihara, Norihiko; Hosokawa, Toyoshi; Takayama, Koichi; Shikata, Keisuke; Taguchi, Tetsuya

2018-03-16

Although pegfilgrastim prophylaxis is expected to maintain the relative dose intensity (RDI) of chemotherapy and improve safety, information is limited. However, the optimal selection of patients eligible for pegfilgrastim prophylaxis is an important issue from a medical economics viewpoint. Therefore, this retrospective study identified factors that could predict these eligible patients to maintain the RDI. The participants included 166 cancer patients undergoing pegfilgrastim prophylaxis combined with chemotherapy in our outpatient chemotherapy center between March 2015 and April 2017. Variables were extracted from clinical records for regression analysis of factors related to maintenance of the RDI. RDI was classified into four categories: 100% = 0, 85% or predictive factors in patients eligible for pegfilgrastim prophylaxis to maintain the RDI. Threshold measures were examined using a receiver operating characteristic (ROC) analysis curve. Age [odds ratio (OR) 1.07, 95% confidence interval (CI) 1.04-1.11; P maintenance. ROC curve analysis of the group that failed to maintain the RDI indicated that the threshold for age was 70 years and above, with a sensitivity of 60.0% and specificity of 80.2% (area under the curve: 0.74). In conclusion, younger age, anemia (less), and administration of pegfilgrastim 24-72 h after chemotherapy were significant factors for RDI maintenance.

RELIGIOSITY AS AN INTERVENING VARIABLE IN THE CONSUMPTION PATTERN OF MOSLEM COMMUNITY

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Habibi A.

2018-04-01

Full Text Available Consumption is one of the basic indicators of human’s life. The level of human satisfaction is always influenced by the level of economic and social change in the culture of a region. According to Kotler, religion is part of a culture that can shape people's behavior. The purpose of this study is to analyze the influence of contextual factors and religiosity on food consumption patterns in Bandar Lampung, as well as to investigate the religiosity variables as an intervening variable on the contextual factors on food consumption patterns in Bandar Lampung. The results of the analysis showed that the relative and contextual factors cannot influence the consumers' buying behavior directly but influence the religiosity (as an intervening variable and the pattern of consumption indirectly.

Bias and Bias Correction in Multisite Instrumental Variables Analysis of Heterogeneous Mediator Effects

Science.gov (United States)

Reardon, Sean F.; Unlu, Fatih; Zhu, Pei; Bloom, Howard S.

2014-01-01

We explore the use of instrumental variables (IV) analysis with a multisite randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, an assumption known in the IV literature as the exclusion restriction.…

Wavelet and receiver operating characteristic analysis of heart rate variability

Science.gov (United States)

McCaffery, G.; Griffith, T. M.; Naka, K.; Frennaux, M. P.; Matthai, C. C.

2002-02-01

Multiresolution wavelet analysis has been used to study the heart rate variability in two classes of patients with different pathological conditions. The scale dependent measure of Thurner et al. was found to be statistically significant in discriminating patients suffering from hypercardiomyopathy from a control set of normal subjects. We have performed Receiver Operating Characteristc (ROC) analysis and found the ROC area to be a useful measure by which to label the significance of the discrimination, as well as to describe the severity of heart dysfunction.

Analysis of mineral phases in coal utilizing factor analysis

International Nuclear Information System (INIS)

Roscoe, B.A.; Hopke, P.K.

1982-01-01

The mineral phase inclusions of coal are discussed. The contribution of these to a coal sample are determined utilizing several techniques. Neutron activation analysis in conjunction with coal washability studies have produced some information on the general trends of elemental variation in the mineral phases. These results have been enhanced by the use of various statistical techniques. The target transformation factor analysis is specifically discussed and shown to be able to produce elemental profiles of the mineral phases in coal. A data set consisting of physically fractionated coal samples was generated. These samples were analyzed by neutron activation analysis and then their elemental concentrations examined using TTFA. Information concerning the mineral phases in coal can thus be acquired from factor analysis even with limited data. Additional data may permit the resolution of additional mineral phases as well as refinement of theose already identified

Relationship between climatic variables and the variation in bulk tank milk composition using canonical correlation analysis.

Science.gov (United States)

Stürmer, Morgana; Busanello, Marcos; Velho, João Pedro; Heck, Vanessa Isabel; Haygert-Velho, Ione Maria Pereira

2018-06-04

A number of studies have addressed the relations between climatic variables and milk composition, but these works used univariate statistical approaches. In our study, we used a multivariate approach (canonical correlation) to study the impact of climatic variables on milk composition, price, and monthly milk production at a dairy farm using bulk tank milk data. Data on milk composition, price, and monthly milk production were obtained from a dairy company that purchased the milk from the farm, while climatic variable data were obtained from the National Institute of Meteorology (INMET). The data are from January 2014 to December 2016. Univariate correlation analysis and canonical correlation analysis were performed. Few correlations between the climatic variables and milk composition were found using a univariate approach. However, using canonical correlation analysis, we found a strong and significant correlation (r c  = 0.95, p value = 0.0029). Lactose, ambient temperature measures (mean, minimum, and maximum), and temperature-humidity index (THI) were found to be the most important variables for the canonical correlation. Our study indicated that 10.2% of the variation in milk composition, pricing, and monthly milk production can be explained by climatic variables. Ambient temperature variables, together with THI, seem to have the most influence on variation in milk composition.

Variability of indoor and outdoor VOC measurements: An analysis using variance components

International Nuclear Information System (INIS)

Jia, Chunrong; Batterman, Stuart A.; Relyea, George E.

2012-01-01

This study examines concentrations of volatile organic compounds (VOCs) measured inside and outside of 162 residences in southeast Michigan, U.S.A. Nested analyses apportioned four sources of variation: city, residence, season, and measurement uncertainty. Indoor measurements were dominated by seasonal and residence effects, accounting for 50 and 31%, respectively, of the total variance. Contributions from measurement uncertainty (<20%) and city effects (<10%) were small. For outdoor measurements, season, city and measurement variation accounted for 43, 29 and 27% of variance, respectively, while residence location had negligible impact (<2%). These results show that, to obtain representative estimates of indoor concentrations, measurements in multiple seasons are required. In contrast, outdoor VOC concentrations can use multi-seasonal measurements at centralized locations. Error models showed that uncertainties at low concentrations might obscure effects of other factors. Variance component analyses can be used to interpret existing measurements, design effective exposure studies, and determine whether the instrumentation and protocols are satisfactory. - Highlights: ► The variability of VOC measurements was partitioned using nested analysis. ► Indoor VOCs were primarily controlled by seasonal and residence effects. ► Outdoor VOC levels were homogeneous within neighborhoods. ► Measurement uncertainty was high for many outdoor VOCs. ► Variance component analysis is useful for designing effective sampling programs. - Indoor VOC concentrations were primarily controlled by seasonal and residence effects; and outdoor concentrations were homogeneous within neighborhoods. Variance component analysis is a useful tool for designing effective sampling programs.

Data base for the analysis of compositional characteristics of coal seams and macerals. Final report - Part 10. Variability in the inorganic content of United States' coals: a multivariate statistical study

Energy Technology Data Exchange (ETDEWEB)

Glick, D.C.; Davis, A.

1984-07-01

The multivariate statistical techniques of correlation coefficients, factor analysis, and cluster analysis, implemented by computer programs, can be used to process a large data set and produce a summary of relationships between variables and between samples. These techniques were used to find relationships for data on the inorganic constituents of US coals. Three hundred thirty-five whole-seam channel samples from six US coal provinces were analyzed for inorganic variables. After consideration of the attributes of data expressed on ash basis and whole-coal basis, it was decided to perform complete statistical analyses on both data sets. Thirty variables expressed on whole-coal basis and twenty-six variables expressed on ash basis were used. For each inorganic variable, a frequency distribution histogram and a set of summary statistics was produced. These were subdivided to reveal the manner in which concentrations of inorganic constituents vary between coal provinces and between coal regions. Data collected on 124 samples from three stratigraphic groups (Pottsville, Monongahela, Allegheny) in the Appalachian region were studied using analysis of variance to determine degree of variability between stratigraphic levels. Most variables showed differences in mean values between the three groups. 193 references, 71 figures, 54 tables.

Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery.

Science.gov (United States)

Yun, Yong-Huan; Deng, Bai-Chuan; Cao, Dong-Sheng; Wang, Wei-Ting; Liang, Yi-Zeng

2016-03-10

Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable. Copyright © 2016 Elsevier B.V. All rights reserved.

Postpartum Depression in Women: A Risk Factor Analysis.

Science.gov (United States)

Zaidi, Farheen; Nigam, Aruna; Anjum, Ruby; Agarwalla, Rashmi

2017-08-01

Postpartum Depression (PPD) is a known entity affecting not only the women but the whole family. It affects women more harshly and chronically due to their increased stress sensitivity, maladaptive coping strategies and multiple social roles in the community. To estimate the commonly associated risk factors of PPD among the women coming to a tertiary hospital in New Delhi, India. It was a longitudinal study conducted at the antenatal clinic for a period of one year. Total 260 women were screened at > 36 weeks of gestation, of which 149 postnatal women completed the questionnaire for PPD at six weeks of their delivery. The inform consent, demographical data and obstetrical details from each participant was taken before commencing the screening. Various risk factors and their association were determined by odds-ratio and significant association was accepted at order to identify the most important confounding variables, logistic regression analysis was used. PPD is a common mental health problem seen among the postnatal women as it was found in 12.75% (19 out of 149) of subjects at six weeks of their delivery. Moreover, it has significant association with the young maternal age (p-value=0.040), birth of the female child (p-value=0.015), previous stressful life events (p-value= 0.003), low self-esteem and feeling of loneliness (p-value=0.007). This study provides important information regarding the risk factors associated with development of PPD in this region of India. Female sex of the new born and the younger age play an important role in the development of PPD.

Variables predicting elevated portal pressure in alcoholic liver disease. Results of a multivariate analysis

DEFF Research Database (Denmark)

Krogsgaard, K; Christensen, E; Gluud, C

1987-01-01

In 46 alcoholic patients the association of wedged-to-free hepatic-vein pressure with other variables (clinical, histologic, hemodynamic, and liver function data) was studied by means of multiple regression analysis, taking the wedged-to-free hepatic-vein pressure as the dependent variable. Four ...

Analysis of the Variables that Affect Bookstore Customer Satisfaction

Directory of Open Access Journals (Sweden)

Elis Ratna Wulan

2016-02-01

Full Text Available Competition, which becomes more widespread, complex, and intense, drives companies to must be able to see the various opportunities that exist and to identify strategies for creating customer satisfaction. As an example of a company must be able to produce products with good quality, reasonable price, facilities, and companies are able to create a positive image in the eyes of consumers. This strategy is quite important in facing the competitive level of competition with rival firms. This research is aimed to analysis simultaneously or partially positive effect of the facilities, prices and corporate image on customer satisfaction, as well as analyzing the most dominant variable in effecting bookstore customer satisfaction. Data used in this research are primary data from Tmbookstore customer in Cianjur city, West Java Indonesia, which were collected from respondents using valid and reliable questionnaire. A total of 100 respondents were selected from Tmbookstore visitors by accidental sampling. Data were analyzed using multiple regression analysis. Results of the research indicate that product, price, location, and simultaneously affect to bookstore consumer satisfaction. Partially, only two of the three variables that affect bookstore consumer satisfaction namely price and company image. Image of the company is the most a dominant impact on bookstore customer satisfaction.

Analysis of the Variables that Affect Bookstore Customer Satisfaction

Directory of Open Access Journals (Sweden)

Elis Ratna Wulan

2015-06-01

Full Text Available Competition, which becomes more widespread, complex, and intense, drives companies to must be able to see the various opportunities that exist and to identify strategies for creating customer satisfaction. As an example of a company must be able to produce products with good quality, reasonable price, facilities, and companies are able to create a positive image in the eyes of consumers. This strategy is quite important in facing the competitive level of competition with rival firms. This research is aimed to analysis simultaneously or partially positive effect of the facilities, prices and corporate image on customer satisfaction, as well as analyzing the most dominant variable in effecting bookstore customer satisfaction.  Data used in this research are primary data from Tmbookstore customer in Cianjur city, West Java Indonesia, which were collected from respondents using valid and reliable questionnaire. A total of 100 respondents were selected from Tmbookstore visitors by accidental sampling. Data were analyzed using multiple regression analysis. Results of the research indicate that product, price, location, and simultaneously affect to bookstore consumer satisfaction. Partially, only two of the three variables that affect bookstore consumer satisfaction namely price and company image. Image of the company is the most a dominant impact on bookstore customer satisfaction.

Analysis of Global Sensitivity of Landing Variables on Landing Loads and Extreme Values of the Loads in Carrier-Based Aircrafts

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Jin Zhou

2018-01-01

Full Text Available When a carrier-based aircraft is in arrested landing on deck, the impact loads on landing gears and airframe are closely related to landing states. The distribution and extreme values of the landing loads obtained during life-cycle analysis provide an important basis for buffering parameter design and fatigue design. In this paper, the effect of the multivariate distribution was studied based on military standards and guides. By establishment of a virtual prototype, the extended Fourier amplitude sensitivity test (EFAST method is applied on sensitivity analysis of landing variables. The results show that sinking speed and rolling angle are the main influencing factors on the landing gear’s course load and vertical load; sinking speed, rolling angle, and yawing angle are the main influencing factors on the landing gear’s lateral load; and sinking speed is the main influencing factor on the barycenter overload. The extreme values of loads show that the typical condition design in the structural strength analysis is safe. The maximum difference value of the vertical load of the main landing gear is 12.0%. This research may provide some reference for structure design of landing gears and compilation of load spectrum for carrier-based aircrafts.

Meta-analysis of variables affecting mouse protection efficacy of whole organism Brucella vaccines and vaccine candidates

Science.gov (United States)

2013-01-01

Background Vaccine protection investigation includes three processes: vaccination, pathogen challenge, and vaccine protection efficacy assessment. Many variables can affect the results of vaccine protection. Brucella, a genus of facultative intracellular bacteria, is the etiologic agent of brucellosis in humans and multiple animal species. Extensive research has been conducted in developing effective live attenuated Brucella vaccines. We hypothesized that some variables play a more important role than others in determining vaccine protective efficacy. Using Brucella vaccines and vaccine candidates as study models, this hypothesis was tested by meta-analysis of Brucella vaccine studies reported in the literature. Results Nineteen variables related to vaccine-induced protection of mice against infection with virulent brucellae were selected based on modeling investigation of the vaccine protection processes. The variable "vaccine protection efficacy" was set as a dependent variable while the other eighteen were set as independent variables. Discrete or continuous values were collected from papers for each variable of each data set. In total, 401 experimental groups were manually annotated from 74 peer-reviewed publications containing mouse protection data for live attenuated Brucella vaccines or vaccine candidates. Our ANOVA analysis indicated that nine variables contributed significantly (P-value Brucella vaccine protection efficacy: vaccine strain, vaccination host (mouse) strain, vaccination dose, vaccination route, challenge pathogen strain, challenge route, challenge-killing interval, colony forming units (CFUs) in mouse spleen, and CFU reduction compared to control group. The other 10 variables (e.g., mouse age, vaccination-challenge interval, and challenge dose) were not found to be statistically significant (P-value > 0.05). The protection level of RB51 was sacrificed when the values of several variables (e.g., vaccination route, vaccine viability, and

Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables

Science.gov (United States)

Adams, Matthew D.; Arain, Altaf; Papatheodorou, Stefania; Koutrakis, Petros; Mahmoud, Moataz

2018-01-01

Background. Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors. Objectives. We systematically reviewed the evidence on the association between air pollution and cardiorespiratory diseases (hospital admissions and mortality), including variability by energy, transportation, socioeconomic status, and air quality. Search Methods. We conducted a literature search (PubMed and Web of Science) for studies published between 2006 and May 11, 2016. Selection Criteria. We included studies if they met all of the following criteria: (1) considered at least 1 of these air pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, or particulate matter (PM2.5 or PM10); (2) reported risk for hospital admissions, mortality, or both; (3) presented individual results for respiratory diseases, cardiovascular diseases, or both; (4) considered the age groups younger than 5 years, older than 65 years, or all ages; and (5) did not segregate the analysis by gender. Data Collection and Analysis. We extracted data from each study, including location, health outcome, and risk estimates. We performed a meta-analysis to estimate the overall effect and to account for both within- and between-study heterogeneity. Then, we applied a model selection (least absolute shrinkage and selection operator) to assess the modifier variables, and, lastly, we performed meta-regression analyses to evaluate the modifier variables contributing to heterogeneity among studies. Main Results. We assessed 2183 studies, of which we selected 529 for in-depth review, and 70 articles fulfilled our study inclusion criteria. The 70 studies selected for meta-analysis encompass more than 30 million events across 28 countries. We found positive associations between cardiorespiratory diseases and different air pollutants. For example, when we considered only the association between PM2.5 and

Short timescale variability in the faint sky variability survey

NARCIS (Netherlands)

Morales-Rueda, L.; Groot, P.J.; Augusteijn, T.; Nelemans, G.A.; Vreeswijk, P.M.; Besselaar, E.J.M. van den

2006-01-01

We present the V-band variability analysis of the Faint Sky Variability Survey (FSVS). The FSVS combines colour and time variability information, from timescales of 24 minutes to tens of days, down to V = 24. We find that �1% of all point sources are variable along the main sequence reaching �3.5%

Conversion from laparoscopic to open cholecystectomy: Multivariate analysis of preoperative risk factors

Directory of Open Access Journals (Sweden)

Khan M

2005-01-01

Full Text Available BACKGROUND: Laparoscopic cholecystectomy has become the gold standard in the treatment of symptomatic cholelithiasis. Some patients require conversion to open surgery and several preoperative variables have been identified as risk factors that are helpful in predicting the probability of conversion. However, there is a need to devise a risk-scoring system based on the identified risk factors to (a predict the risk of conversion preoperatively for selected patients, (b prepare the patient psychologically, (c arrange operating schedules accordingly, and (d minimize the procedure-related cost and help overcome financial constraints, which is a significant problem in developing countries. AIM: This study was aimed to evaluate preoperative risk factors for conversion from laparoscopic to open cholecystectomy in our setting. SETTINGS AND DESIGNS: A case control study of patients who underwent laparoscopic surgery from January 1997 to December 2001 was conducted at the Aga Khan University Hospital, Karachi, Pakistan. MATERIALS AND METHODS: All those patients who were converted to open surgery (n = 73 were enrolled as cases. Two controls who had successful laparoscopic surgery (n = 146 were matched with each case for operating surgeon and closest date of surgery. STATISTICAL ANALYSIS USED: Descriptive statistics were computed and, univariate and multivariate analysis was done through multiple logistic regression. RESULTS: The final multivariate model identified two risk factors for conversion: ultrasonographic signs of inflammation (adjusted odds ratio [aOR] = 8.5; 95% confidence interval [CI]: 3.3, 21.9 and age > 60 years (aOR = 8.1; 95% CI: 2.9, 22.2 after adjusting for physical signs, alkaline phosphatase and BMI levels. CONCLUSION: Preoperative risk factors evaluated by the present study confirm the likelihood of conversion. Recognition of these factors is important for understanding the characteristics of patients at a higher risk of conversion.

Variability of consumer impacts from energy efficiency standards

Energy Technology Data Exchange (ETDEWEB)

McMahon, James E.; Liu, Xiaomin

2000-06-15

A typical prospective analysis of the expected impact of energy efficiency standards on consumers is based on average economic conditions (e.g., energy price) and operating characteristics. In fact, different consumers face different economic conditions and exhibit different behaviors when using an appliance. A method has been developed to characterize the variability among individual households and to calculate the life-cycle cost of appliances taking into account those differences. Using survey data, this method is applied to a distribution of consumers representing the U.S. Examples of clothes washer standards are shown for which 70-90% of the population benefit, compared to 10-30% who are expected to bear increased costs due to new standards. In some cases, sufficient data exist to distinguish among demographic subgroups (for example, low income or elderly households) who are impacted differently from the general population. Rank order correlations between the sampled input distributions and the sampled output distributions are calculated to determine which variability inputs are main factors. This ''importance analysis'' identifies the key drivers contributing to the range of results. Conversely, the importance analysis identifies variables that, while uncertain, make so little difference as to be irrelevant in deciding a particular policy. Examples will be given from analysis of water heaters to illustrate the dominance of the policy implications by a few key variables.

Integrating factors and conservation theorems for Hamilton's canonical equations of motion of variable mass nonholonomic nonconservative dynamical systems

Institute of Scientific and Technical Information of China (English)

李仁杰; 乔永芬; 刘洋

2002-01-01

We present a general approach to the construction of conservation laws for variable mass nonholonomic noncon-servative systems. First, we give the definition of integrating factors, and we study in detail the necessary conditionsfor the existence of the conserved quantities. Then, we establish the conservation theorem and its inverse theorem forHamilton's canonical equations of motion of variable mass nonholonomic nonconservative dynamical systems. Finally,we give an example to illustrate the application of the results.

Experimental Analysis and Discussion on the Damage Variable of Frozen Loess

Directory of Open Access Journals (Sweden)

Cong Cai

2017-01-01

Full Text Available The damage variable is very important to study damage evolution of material. Taking frozen loess as an example, a series of triaxial compression and triaxial loading-unloading tests are performed under five strain rates of 5.0 × 10−6–1.3 × 10−2/s at a temperature of −6°C. A damage criterion of frozen loess is defined and a damage factor Dc is introduced to satisfy the requirements of the engineering application. The damage variable of frozen loess is investigated using the following four methods: the stiffness degradation method, the deformation increase method, the dissipated energy increase method, and the constitutive model deducing method during deformation process. In addition, the advantages and disadvantages of the four methods are discussed when they are used for frozen loess material. According to the discussion, the plastic strain may be the most appropriate variable to characterize the damage evolution of frozen loess during the deformation process based on the material properties and the nature of the material service.

Scalable conditional induction variables (CIV) analysis

KAUST Repository

Oancea, Cosmin E.; Rauchwerger, Lawrence

2015-01-01

challenges to automatic parallelization. Because the complexity of such induction variables is often due to their conditional evaluation across the iteration space of loops we name them Conditional Induction Variables (CIV). This paper presents a flow

A database strategy for new variables

Science.gov (United States)

B. Tyler Wilson; Ali Conner; Glenn Christensen; John Shaw; Jason Meade; Larry Royer

2012-01-01

The introduction of new variables into the annual inventory system of the U.S. Forest Service’s Forest Inventory and Analysis (FIA) program can create issues with population estimates since evaluations (or expansion factors) based on a full cycle’s worth of data should not be used with new data that have not been collected for a full cycle. This manuscript provides...

Data analysis as a source of variability of the HLA-peptide multimer assay

DEFF Research Database (Denmark)

Gouttefangeas, Cécile; Chan, Cliburn; Attig, Sebastian

2015-01-01

by laboratories performing ex vivo T cell immune monitoring. In particular, analysis currently relies on a manual, step-by-step strategy employing serial gating decisions based on visual inspection of one- or two-dimensional plots. It is therefore operator dependent and subjective. In the context of continuing......Multiparameter flow cytometry is an indispensable method for assessing antigen-specific T cells in basic research and cancer immunotherapy. Proficiency panels have shown that cell sample processing, test protocols and data analysis may all contribute to the variability of the results obtained...... efforts to support inter-laboratory T cell assay harmonization, the CIMT Immunoguiding Program organized its third proficiency panel dedicated to the detection of antigen-specific CD8(+) T cells by HLA-peptide multimer staining. We first assessed the contribution of manual data analysis to the variability...

Importance analysis for models with correlated variables and its sparse grid solution

International Nuclear Information System (INIS)

Li, Luyi; Lu, Zhenzhou

2013-01-01

For structural models involving correlated input variables, a novel interpretation for variance-based importance measures is proposed based on the contribution of the correlated input variables to the variance of the model output. After the novel interpretation of the variance-based importance measures is compared with the existing ones, two solutions of the variance-based importance measures of the correlated input variables are built on the sparse grid numerical integration (SGI): double-loop nested sparse grid integration (DSGI) method and single loop sparse grid integration (SSGI) method. The DSGI method solves the importance measure by decreasing the dimensionality of the input variables procedurally, while SSGI method performs importance analysis through extending the dimensionality of the inputs. Both of them can make full use of the advantages of the SGI, and are well tailored for different situations. By analyzing the results of several numerical and engineering examples, it is found that the novel proposed interpretation about the importance measures of the correlated input variables is reasonable, and the proposed methods for solving importance measures are efficient and accurate. -- Highlights: •The contribution of correlated variables to the variance of the output is analyzed. •A novel interpretation for variance-based indices of correlated variables is proposed. •Two solutions for variance-based importance measures of correlated variables are built

Analysis of genetic variability in the Czech Dachshund population using microsatellite markers

Czech Academy of Sciences Publication Activity Database

Přibáňová, M.; Horák, Pavel; Schröffelová, D.; Urban, T.; Bechyňová, Renata; Musilová, L.

2009-01-01

Roč. 126, - (2009), s. 311-318 ISSN 0931-2668 R&D Projects: GA AV ČR 1QS500450578; GA ČR GD523/03/H076 Institutional research plan: CEZ:AV0Z50450515 Keywords : dachshund * dog * genetic variability * microsatellite Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 1.706, year: 2009

Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters

Science.gov (United States)

Hoshino, Takahiro; Shigemasu, Kazuo

2008-01-01

The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…

The Effects of Variability and Risk in Selection Utility Analysis: An Empirical Comparison.

Science.gov (United States)

Rich, Joseph R.; Boudreau, John W.

1987-01-01

Investigated utility estimate variability for the selection utility of using the Programmer Aptitude Test to select computer programmers. Comparison of Monte Carlo results to other risk assessment approaches (sensitivity analysis, break-even analysis, algebraic derivation of the distribtion) suggests that distribution information provided by Monte…

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

Science.gov (United States)

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

Do climate variables and human density affect Achatina fulica (Bowditch (Gastropoda: Pulmonata shell length, total weight and condition factor?

Directory of Open Access Journals (Sweden)

FS. Albuquerque

Full Text Available The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm. The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

EXOTIC PLANTS IN THE CIBODAS BOTANIC GARDENS REMNANT FOREST: INVENTORY AND CLUSTER ANALYSIS OF SEVERAL ENVIRONMENTAL FACTORS