Denial predicts outcome in anxiety following group cognitive behavioral therapy

This study aimed to explore whether participants' pretherapy coping strategies predicted the outcome of group cognitive behavioral therapy (CBT) for anxiety and depression. It was hypothesized that adaptive coping strategies such as the use of active planning and acceptance would be associated with higher reductions, whereas maladaptive coping strategies such as denial and disengagement would be associated with lower reductions in anxious and depressed symptoms following psychotherapy. There were 144 participants who completed group CBT for anxiety and depression. Measures of coping strategies were administered prior to therapy, whereas measures of depression and anxiety were completed both prior to and following therapy. The results showed that higher levels of denial were associated with a poorer outcome, in terms of change in anxiety but not depression, following therapy. These findings suggest the usefulness of using the Denial subscale from the revised Coping Orientation to Problems Experienced (COPE) as a predictor of outcome in group CBT for anxiety.

Metacognition in substance misuse

Metacognitive theory provides a novel conceptual framework to understand the development and maintenance of psychopathology. It emphasizes the importance of stored knowledge guiding the individual’s plan for coping with heightened cognitive-affective arousal. According to the metacognitive model individuals experience strong affective responses and engage in a process of metacognitive appraisal and initiation of coping responses in the pursuit of cognitive-affective self-regulation. This chapter outlines the details of this theoretical approach as applied to substance misuse and the metacognitive treatment components aimed at interrupting the selection of maladaptive coping responses.

Serum HE4 as a prognostic marker in endometrial cancer : a population based study

Objective HE4 has emerged as a promising biomarker in gynaecological oncology. The purpose of this study was to evaluate serum HE4 as a biomarker for high-risk phenotypes in a population-based endometrial cancer cohort. Methods Peri-operative serum HE4 and CA125 were measured in 373 patients identified from the prospective Australian National Endometrial Cancer Study (ANECS). HE4 and CA125 were quantified on the ARCHITECT instrument in a clinically accredited laboratory. Receiver operator curves (ROC), Spearman rank correlation coefficient, and chi-squared and Mann–Whitney tests were used for statistical analysis. Survival analysis was performed using Kaplan–Meier and Cox multivariate regression analyses. Results Median CA125 and HE4 levels were higher in stage III and IV tumours (p < 0.001) and in tumours with outer-half myometrial invasion (p < 0.001). ROC analysis demonstrated that HE4 (area under the curve (AUC) = 0.76) was a better predictor of outer-half myometrial invasion than CA125 (AUC = 0.65), particularly in patients with low-grade endometrioid tumours (AUC 0.77 vs 0.64 for CA125). Cox multivariate analysis demonstrated that elevated HE4 was an independent predictor of recurrence-free survival (HR = 2.40, 95% CI 1.19–4.83, p = 0.014) after adjusting for stage and grade of disease, particularly in the endometrioid subtype (HR = 2.86, 95% CI 1.25–6.51, p = 0.012). Conclusion These findings demonstrate the utility of serum HE4 as a prognostic biomarker in endometrial cancer in a large, population-based study. In particular they highlight the utility of HE4 for pre-operative risk stratification to identify high-risk patients within low-grade endometrioid endometrial cancer patients who might benefit from lymphadenectomy.

Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

Book review: 'The Cornea' edited by H.E. Kaufman, B.A. Baron, M.B. McDonald and S.R. Waltman.

Parsons' Diseases of the Eye, first published in 1907, is one of the foundation texts of modern ophthalmology. It has seen a new edition at approximately 5-year intervals throughout the century. This latest edition incorporates developments that have taken place within the specialty since the 1984 impression, but remains in a virtually unchanged format...

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Affective events theory as a framework for understanding third-party consumer complaints

Poor complaint management may result in organizations losing customers and revenue. Consumers exhibit negative emotional responses when dissatisfied and this may lead to a complaint to a third-party organization. Since little information is available on the role of emotion in the consumer complaint process or how to manage complaints effectively, we offer an emotions perspective by applying Affective Events Theory (AET) to complaint behavior. This study presents the first application of AET in a consumption context and advances a theoretical framework supported by qualitative research for emotional responses to complaints. In contrast to commonly held views on gender and emotion, men as well as women use emotion-focused coping to complain.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Women's health in the global village

As we stand at the beginning of the 21st century and behold the world before us, it seems that we are living in a time of profound change. Everywhere we look change seems afoot, demolishing our traditional securities and hastily building new ones in their place. Modern medical science has been an integral part of this change. It is not possible to ignore the advances of modern medicine nor the realities of scientific uncertainties for they are part of the shared context of our lives today. I In the past 50 years we have witnessed the discovery of DNA and more recently the mapping of the human genome, the birth of the world's first in-vitro fertilisation baby, followed by thousands worldwide in the period since, the discovery of human stem cells and the birth of Dolly the cloned sheep in Scotland. Furthermore, the processes of globalisation have ensured that an event that occurs on one side of the globe becomes an item on the evening news on the other side, creating the impression that all change takes place on our doorstep. Some of these events have provoked deep angst in the community, sparking public debate over the ethics of science and the boundaries to be imposed by law. All of these developments have changed the realm of the possible. While these advances in medical science spark debate in the developed countries, in less developed countries high rates of infectious diseases and infant and maternal mortality and the challenges of access to adequate food and clean water are priorities, highlighting international differences in health care. This article explores these differences through an analysis of globalisation and reproduction. It seeks to analyse both the meaning of globalisation and the impact of globalising trends on health laws and policies as regulators of women's health within the global village.

Practical Advice for Entrepreneurs

I am interested in the psychology of entrepreneurship—how entrepreneurs think, decide to act, and feel. I recently realized that while my publications in academic journals have implications for entrepreneurs, those implications have remained relatively hidden in the text of the articles and hidden in articles published in journals largely inaccessible to those involved in the entrepreneurial process. This book is designed to bring the practical implications of my research to the forefront. I decided to take a different approach with this book and not write it for a publisher. I did this because I wanted the ideas to be freely available: (1) I wanted those interested in practical advice for entrepreneurs to be able to freely download, distribute, and use this information (I only ask that the content be properly cited), (2) I wanted to release the chapters independently and make chapters available as they are finished, and; (3) I wanted this work to be a dialogue rather than a one-way conversation—I hope readers email me feedback (positive and negative) so that I can use this information to revise the book. In producing the journal articles underpinning this book, I have had the pleasure of working with many talented and wonderful colleagues—they are cited at the end of each chapter. I hope you find some of the advice in this book useful.

Negative affectivity and responses to work stressors : an experimental study

An experiment tested the hypothesis that individuals high in negative affectivity (NA) show increased stress reactivity to stressors. There were three predictor variables: NA (measured 1 week prior to experimental participation), and two manipulated variables—demand (high/low) and behavioral control (high/low). First-year psychology students (n=256) were randomly allocated to one of the four experimental conditions. Measures obtained were initial and post-task negative mood, coping strategies, task satisfaction, and performance (subjective and objective). Participants with high levels of NA reported more post-task negative mood in response to high demand conditions, compared to participants with low NA. A similar pattern of results emerged for task satisfaction, particularly in response to high demand-low behavioral control situations. Mediation analyses suggested this was because participants with high NA used more emotion-focused coping strategies. The study provides support for the stress reactivity role of NA in the stressor-strain process.

Self-determination, control, and reactions to changes in workload : a work simulation

The objective of this experimental study is to capture the dynamic temporal processes that occur in changing work settings and to test how work control and individuals' motivational predispositions interact to predict reactions to these changes. To this aim, we examine the moderating effects of global self-determined and non-self-determined motivation, at different levels of work control, on participants' adaptation and stress reactivity to changes in workload during four trials of an inbox activity. Workload was increased or decreased at Trial 3, and adaptation to this change was examined via fluctuations in anxiety, coping, motivation, and performance. In support of the hypotheses, results revealed that, for non-self-determined individuals, low work control was stress-buffering and high work control was stress-exacerbating when predicting anxiety and intrinsic motivation. In contrast, for self-determined individuals, high work control facilitated the adaptive use of planning coping in response to a change in workload. Overall, this pattern of results demonstrates that, while high work control was anxiety-provoking and demotivating for non-self-determined individuals, self-determined individuals used high work control to implement an adaptive antecedent-focused emotion regulation strategy (i.e., planning coping) to meet situational demands. Other interactive effects of global motivation emerged on anxiety, active coping, and task performance. These results and their practical implications are discussed.

General self-efficacy influences affective task reactions during a work simulation : the temporal effects of changes in workload at different levels of control

This study investigated the effects of workload, control, and general self-efficacy on affective task reactions (i.e., demands-ability fit, active coping, and anxiety) during a work simulation. The main goals were: (1) to determine the extent general self-efficacy moderates the effects of demand and control on affective task reactions, and; (2) to determine if this varies as a function of changes in workload. Participants (N=141) completed an inbox activity under conditions of low or high control and within low and high workload conditions. The order of trials varied so that workload increased or decreased. Results revealed individuals with high general self-efficacy reported better demands-abilities fit and active coping as well as less anxiety. Three interactive effects were found. First, it was found that high control increased demands-abilities fit from trial 1 to trial 2, but only when workload decreased. Second, it was found that low efficacious individuals active coping increased in trial 2, but only under high control. Third, it was found that high control helped high efficacious individuals manage anxiety when workload decreased. However, for individuals with low general self-efficacy, neither high nor low control alleviated anxiety (i.e., whether workload increased or decreased over time).