16,897 research outputs found

    Examination of a Brief, Self-Paced Online Self-Compassion Intervention Targeting Intuitive Eating and Body Image Outcomes among Men and Women

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    Ideals for appearance and body image are pervasive in Western culture in which men and women are portrayed with unrealistic and often unattainable standards (Ferguson, 2013; Martin, 2010). Exposure and reinforcement have created a culture of social acceptance and internalization of these ideals, contributing to pervasive body image disturbance (i.e., body dissatisfaction; Fallon et al., 2014; Stice, 2001; Thompson & Stice, 2001; Thompson et al., 1999). Research has suggested that body dissatisfaction is expressed differently across sexes (Grossbard et al., 2008), with attention to thin ideals among women and muscular ideals among men. Body dissatisfaction has been linked to numerous poor outcomes, including dieting, unhealthy weight control behaviors, disordered eating, and increased psychopathology. Although dieting is one of the primary mechanisms employed to reduce body dissatisfaction (Thompson & Stice, 2001), research has shown that such efforts are contraindicated as dieting predicts weight gain over time (Pietiläinen et al., 2012) as well as preoccupation with food, disordered eating, eating disorders, emotional distress, and higher body dissatisfaction (Grabe et al., 2007; Johnson & Wardle, 2005; Neumark- Sztianer et al., 2006; Paxton et al., 2006; Tiggemann, 2005). Restrictive dietary behaviors suppress physiological cues to eat (e.g., hunger) that presents a vulnerability to eating in response to alternative cues, both internal (e.g., emotions) and external (e.g., availability of food). Intuitive eating is a non-restrictive approach to eating that encourages adherence to internal physiological cues to indicate when, what, and how much to eat (Tylka, 2006) and has demonstrated an inverse relationship with disordered eating, restrained eating, food preoccupation, dieting, body dissatisfaction, and negative affect (Bruce & Ricciardelli, 2016). Self-compassion, relating to oneself in a caring and supportive manner (Neff, 2003a), has been proposed as a pathway to increase intuitive eating and reduce body dissatisfaction (Neff & Knox, 2017; Schoenefeld & Webb, 2013; Webb & Hardin, 2016). Research has highlighted the efficacy of self-compassion interventions in addressing weight-related concerns (Rahimi-Ardabili et al., 2018) as well as brief experiential exercises for reducing body dissatisfaction (Moffitt et al., 2018). Additionally, there is a growing body of evidence supporting the efficacy of internet-based self-compassion interventions (Mak et al., 2018; Kelman et al., 2018; Nadeau et al., 2020). The purpose of the current study was to examine the effectiveness of a brief, self-paced online self-compassion intervention targeting body image and adaptive eating behaviors and potential mechanisms of change (e.g., self-compassion and psychological flexibility) among undergraduate men and women. This study also examined outcomes among men and women in the area of self-compassion, body dissatisfaction, and intuitive eating as research has highlighted the need to determine who benefits more from self-compassion interventions (Rahimi-Ardabili et al., 2018). The study compared a one-hour, self-guided online self-compassion intervention to an active control condition. The intervention was comprised of psychoeducation, experiential exercises, and mindfulness practice designed to increase self-compassion surrounding body image and eating behaviors. In contrast, the active control condition consisted of self-care recommendations and self-assessments for nutrition, exercise, and sleep. The study was administered over three parts (e.g., baseline, intervention, and follow-up) in which variables of interest were assessed at each time point. Outcome variables included self-compassion, intuitive eating, disordered eating, body appreciation, muscle dysmorphia, internalized weight bias, fear of self-compassion, and psychological inflexibility. Participants were randomized on a 2:1 intervention to control ratio at the second time point in order to make comparisons between groups while simultaneously having sufficient power for examining mediation and moderation within the treatment condition. Overall, 1023 individuals (64% women, Mage = 18.9, 67.4% white) signed informed consent and participated in at least one part of the study whereas 101 participants (71% women, Mage = 19.3, 71% white) completed all three study portions. As predicted, self-compassion was correlated with all variables of interest, and all study variables were correlated with each other (p < .01). In contrast to hypothesized outcomes, the self-compassion condition failed to demonstrate improvements across time or between conditions on all study outcomes. These results persisted when participants were screened for levels of intuitive eating as well. Contrary to prediction, internalized weight bias, muscle dysmorphia, and fear of self-compassion demonstrated increased levels within the intervention condition and decreases in the control condition. There were significant gender differences on multiple outcome variables, with men demonstrating higher levels of self-compassion and body appreciation whereas women endorsed higher levels of disordered eating, internalized weight bias, muscle dysmorphia, and psychological inflexibility. Additionally, there were significant gender interactions for internalized weight bias, body appreciation, and muscle dysmorphia. The interactions existed such that men demonstrated increased internalized weight bias and muscle dysmorphia across time whereas women displayed decreased weight bias and muscle dysmorphia. The opposite pattern was found within body appreciation; women demonstrated increased body appreciation across time while men reported decreased levels of body appreciation. Despite this study’s intent to examine underlying mechanisms of change, the condition in which participants were randomly selected did not have any relationship, positive or negative, with the outcome variables of interest. As such, mediation within the current study was not conducted as it would violate statistical assumptions required to examine this hypothesis. Finally, upon examining the moderating relationship of fear of self-compassion between self-compassion and outcome variables, there were main effects for self-compassion on intuitive eating, emotional eating, internalized weight bias, body appreciation, and psychological inflexibility as well as main effects of fear of self-compassion on psychological inflexibility. There were significant interactions for intuitive eating and emotional eating, such that as fear of self-compassion increased, the effect of self-compassion on intuitive eating decreased, and the effect of self-compassion on reducing emotional eating behaviors decreased. Overall, the brief, self-paced online intervention delivered in the current study did not prove to be an effective means for improving self-compassion, intuitive eating, body appreciation, disordered eating, muscle dysmorphia, and psychological inflexibility. Nevertheless, the relationships between self-compassion and outcome variables of interest throughout the study mirror that of the existing literature. Findings from this study, in general, were also consistent with differences between men and women despite a gap in the research for intervention outcomes. Although fear of self-compassion demonstrated a moderating effect on the relationship between self-compassion and intuitive eating as well as emotional eating, this does not account for the lack of significant findings. The context surrounding this study, such as the COVID-19 pandemic, provided a considerable challenge to examining the efficacy of the current intervention. However, the findings of this study suggest future research will likely need to identify ways to enhance the delivery of experiential exercises that encourage engagement, provide a safe and warm environment for participants, and create flexibility and willingness surrounding painful and difficult experiences in order to undermine internalized and socially accepted beliefs about body image and eating behaviors

    The applied psychology of addictive orientations : studies in a 12-step treatment context.

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    The clinical data for the studies was collected at The PROMIS Recovery Centre, a Minnesota Model treatmentc entre for addictions,w hich encouragesth e membership and use of the 12 step Anonymous Fellowships, and is abstinence based. The area of addiction is contextualised in a review chapter which focuses on research relating to the phenomenon of cross addiction. A study examining the concept of "addictive orientations" in male and female addicts is described, which develops a study conductedb y StephensonM, aggi, Lefever, & Morojele (1995). This presents study found a four factor solution which appeared to be subdivisions of the previously found Hedonism and Nurturance factors. Self orientated nurturance (both food dimensions, shopping and caffeine), Other orientated nurturance (both compulsive helping dimensions and work), Sensation seeking hedonism (Drugs, prescription drugs, nicotine and marginally alcohol), and Power related hedonism (Both relationship dimensions, sex and gambling. This concept of "addictive orientations" is further explored in a non-clinical population, where again a four factor solution was found, very similar to that in the clinical population. This was thought to indicate that in terms of addictive orientation a pattern already exists in this non-clinical population and that consideration should be given to why this is the case. These orientations are examined in terms of gender differences. It is suggested that the differences between genders reflect power-related role relationships between the sexes. In order to further elaborate the significance and meaning behind these orientations, the next two chapters look at the contribution of personality variables and how addictive orientations relate to psychiatric symptomatology. Personality variables were differentially, and to a considerable extent predictably involved with the four factors for both males and females.Conscientiousness as positively associated with "Other orientated Nurturance" and negatively associated with "Sensation seeking hedonism" (particularly for men). Neuroticism had a particularly strong association with the "Self orientated Nurturance" factor in the female population. More than twice the symptomatology variance was explained by the factor scores for females than it was for males. The most important factorial predictors for psychiatric symptomatology were the "Power related hedonism" factor for males, and "Self oriented nurturance" for females. The results are discussed from theoretical and treatment perspectives

    The labour supply and retirement of older workers: an empirical analysis

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    This thesis examines the labour supply of older workers, their movement into retirement, and any movement out of retirement and back into work. In particular the labour force participation, labour supply and wage elasticity and other income elasticity of work hours are estimated for older workers and compared to younger workers. The thesis goes on to look at the movement into retirement for older workers as a whole by examining cohorts by gender, wave and age. The thesis also presents a descriptive and quantitative • examination of the changes in income and happiness that occur as an individual retires. Finally the thesis examines the reasons why an individual may return to work from v . retirement. The results of the findings suggest: that younger workers are significantly more responsive to wage and household income changes than older worker

    Ergodic properties and response theory for a stochastic two-layer model of geophysical fluid dynamics.

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    In this work, ergodic properties of a stochastic medium complexity model for atmosphere and ocean dynamics are analysed. Specifically, we study a two– layer quasi–geostrophic (2LQG) model with the upper layer perturbed by additive noise for geophysical flows. This model is popular in the geosciences, for instance to study the effects of a random wind forcing on the ocean. Yet it is less studied in mathematics, especially if the stochastic perturbation is acting only on one of the layers. In this case the noise is effectively spatially degenerate, posing a significant challenge to the analysis. After showing the model well–posedness, we focus on its long time average behaviour and ergodic properties: existence and uniqueness of an invariant measure (ergodicity), exponential convergence of solutions laws to the invariant measure (exponential stability or spectral gap), differentiability or only Hölder continuity of the invariant measure with respect to system parameters (linear or fractional response). Existence of an invariant measure is shown with classic techniques. Its uniqueness is established using a recent technique from stochastic analysis called asymptotic coupling, to account for the noise spatial degeneracy. This is proved provided a certain passivity condition on the second layer holds. Under the same condition, exponential stability is shown by blending different recent approaches like the asymptotic coupling. An important application of spectral gaps is response theory. The only result on linear response applicable to a large class of SPDEs is the work by Hairer and Madja (2010). We modify their approach treating a class of less regular observables. In particular we give a toolkit for linear and fractional response for SPDEs with moderately degenerate noise using the strength of a deterministic forcing as parameter. We apply such a framework to the 2D stochastic Navier-Stokes equation as test model, and finally to the stochastic 2LQG model

    State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning

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    Anxiety influences how the brain estimates and responds to uncertainty. The consequences of these processes on behaviour have been described in theoretical and empirical studies, yet the associated neural correlates remain unclear. Rhythm-based accounts of Bayesian predictive coding propose that predictions in generative models of perception are represented in alpha (8–12 Hz) and beta oscillations (13–30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance), and are encoded in gamma oscillations (>30 Hz) and associated with suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and precision-weighted prediction errors (pwPE) during learning. Healthy human participants performed a probabilistic reward-based learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the tendency of the reward contingency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-based learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer learning

    Nanoscale dynamics of ice growth on surfaces

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    The freezing of liquid water to form ice is the most common phase transition on our planet, and the ubiquity of ice growth on surfaces offers many engineering and scientific challenges. Ice growth can prevent the operation of — and cause damage to — a broad spectrum of man-made structures and devices, such as aircraft, ships, wind turbines, photovoltaic devices, heat exchangers, and telecommunications equipment. Consequently, ice growth on surfaces is an important topic of study, and undesirable ice growth can impact both life and property. While there is a vast experimental and numerical literature on how modifying surface characteristics affects ice growth, little is understood about the underlying nanoscale mechanisms. This is because icing is challenging to study experimentally, as the nucleation of ice crystals within super-cooled liquid occur on the order of nanometres. Numerical investigations into icing rely on the use of molecular dynamics (MD) simulations, as MD can accurately resolve the nanoscale molecular interactions relevant to ice nucleation and growth. However, there are two issues with the use of MD: a) these simulations are computationally expensive; and b) as nucleation is a rare event when compared to MD timescales, the simulations need to be accelerated to force ice formation to occur, which affects the accuracy of the results obtained. An alternative seeded MD simulation approach is presented in the present work, which reduces the computational cost while still ensuring accurate simulations of ice growth on surfaces. In addition, this approach enables the study of ice growth on vastly more complex surfaces than have been considered thus far. In this thesis, this approach is used to investigate fundamental questions of surface icing, such as: a) the effect of surface wettability and structure on ice growth; and b) the role of the nanometre-thick interfacial region adjacent to the surface on ice growth, which has been shown to be important in previous experiments. The findings presented here should provide an improved understanding on the role of the surface properties on the structure and dynamics of ice growth, and provide a useful framework for future slab-seeded ice growth simulations

    Facial expression recognition and intensity estimation.

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    Doctoral Degree. University of KwaZulu-Natal, Durban.Facial Expression is one of the profound non-verbal channels through which human emotion state is inferred from the deformation or movement of face components when facial muscles are activated. Facial Expression Recognition (FER) is one of the relevant research fields in Computer Vision (CV) and Human-Computer Interraction (HCI). Its application is not limited to: robotics, game, medical, education, security and marketing. FER consists of a wealth of information. Categorising the information into primary emotion states only limit its performance. This thesis considers investigating an approach that simultaneously predicts the emotional state of facial expression images and the corresponding degree of intensity. The task also extends to resolving FER ambiguous nature and annotation inconsistencies with a label distribution learning method that considers correlation among data. We first proposed a multi-label approach for FER and its intensity estimation using advanced machine learning techniques. According to our findings, this approach has not been considered for emotion and intensity estimation in the field before. The approach used problem transformation to present FER as a multilabel task, such that every facial expression image has unique emotion information alongside the corresponding degree of intensity at which the emotion is displayed. A Convolutional Neural Network (CNN) with a sigmoid function at the final layer is the classifier for the model. The model termed ML-CNN (Multilabel Convolutional Neural Network) successfully achieve concurrent prediction of emotion and intensity estimation. ML-CNN prediction is challenged with overfitting and intraclass and interclass variations. We employ Visual Geometric Graphics-16 (VGG-16) pretrained network to resolve the overfitting challenge and the aggregation of island loss and binary cross-entropy loss to minimise the effect of intraclass and interclass variations. The enhanced ML-CNN model shows promising results and outstanding performance than other standard multilabel algorithms. Finally, we approach data annotation inconsistency and ambiguity in FER data using isomap manifold learning with Graph Convolutional Networks (GCN). The GCN uses the distance along the isomap manifold as the edge weight, which appropriately models the similarity between adjacent nodes for emotion predictions. The proposed method produces a promising result in comparison with the state-of-the-art methods.Author's List of Publication is on page xi of this thesis

    Understanding and Engineering of Sub-gap States in Photodetection

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    Emerging applications for light sensing, including wearable electronics, internet of things and autonomous driving, are pushing conventional semiconductors technologies to their limits when it comes to ease of fabrication, power consumption and device design. Organic semiconductors are considered next-generation absorber materials for photodetection in the visible and near infrared part of the electromagnetic spectrum, which hold some promise of addressing the aforementioned problems of conventional materials. So far, only a handful of companies are putting organic semiconductors to the test for commercial photodetectors, however, research on organic photodetectors is thriving – in particular on photodetectors with a diode architecture called photodiodes. The goal is to make flexible, light-weight devices with improved performance metrics and high stability to realize viable alternatives to conventional photodiodes. The performance limits of organic photodiodes are often associated with the presence of electronic states with energies below the bandgap edge – the so-called sub-gap states. A powerful tool to study the properties of sub-gap states is to measure the external quantum efficiency (EQE), however, the subsequent analysis is complicated by the presence of static disorder and optical interference. In the first part of this work, it is shown how the true absorption coefficient can be extracted from a series of interference affected sub-gap EQE spectra of organic photodiodes with different thicknesses. In consequence, the effect of chemical structure modification on the absorption coefficient in the spectral range of charge transfer absorption is demonstrated. By adjusting the molecular energy levels through target chemical substitutions, a redshift and an increase of the oscillator strength are achieved. The increased spectral coverage in the near infrared is then exploited in micro-cavity photodiodes. The second part of this work deals with the sub-gap absorption coefficient of donor and acceptor materials and how it is affected by the molecular energy level offset. For materials with low energetic offset, it is shown that the sub-gap absorption coefficient follows the Urbach rule in the spectral range of excitonic absorption, dictating the broadening of the sub-gap absorption coefficient at energies right below the bandgap. Lastly, the origin of the high dark current in organic photodiodes is identified as non-radiative recombination via mid-gap trap states. An upper limit to the specific detectivity is calculated that is expected viable in organic photodiodes. The findings of this thesis contribute to the understanding of the sub-gap states by studying their absorption features and distinguishing them from the ubiquitous optical interference effects. The spectroscopic observation of mid-gap trap states is linked to the dark current generation dictating the upper performance limits of organic photodiodes
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