Managing clients’ expectations at the outset of online Cognitive Behavioural Therapy (CBT) for depression

Background Engaging clients in psychotherapy by managing their expectations is important for therapeutic success. Initial moments in first sessions of therapy are thought to afford an opportunity to establish a shared understanding of how therapy will proceed. However there is little evidence from analysis of actual sessions of therapy to support this. Objective This study utilised recordings to examine how therapists manage clients’ expectations during the first two sessions of online Cognitive Behavioural Therapy (CBT). Methods Expectation management was investigated through conversation analysis of sessions from 176 client-therapist dyads involved in online CBT. The primary focus of analysis was expectation management during the initial moments of first sessions, with a secondary focus on expectations at subsequent points. Analysis Clients’ expectations for therapy were most commonly managed during the initial moments of first sessions of therapy. At this point, most therapists either outlined the tasks of the first and subsequent sessions (n=36), or the first session only (n=108). On other occasions (n = 32), no attempt was made to manage clients’ expectations by outlining what would happen in therapy. Observations of the interactional consequences of such an absence suggest clients may struggle to engage with the therapeutic process in the absence of appropriate expectation management by therapists. Conclusion Clients may more readily engage from the outset of therapy when provided with an explanation that manages their expectation of what is involved. Therapists can accomplish this by projecting how therapy will proceed, particularly beyond the initial session.

Swelling induced finite strain flexure in a rectangular block of an isotropic elastic material

The deformation of a rectangular block into an annular wedge is studied with respect to the state of swelling interior to the block. Nonuniform swelling fields are shown to generate these flexure deformations in the absence of resultant forces and bending moments. Analytical expressions for the deformation fields demonstrate these effects for both incompressible and compressible generalizations of conventional hyperelastic materials. Existing results in the absence of a swelling agent are recovered as special cases.

Predicting stock market returns and volatility with investor sentiment : evidence from eight developed countries

We test the predictive ability of investor sentiment on the return and volatility at the aggregate market level in the U.S., four largest European countries and three Asia-Pacific countries. We find that in the U.S., France and Italy periods of high consumer confidence levels are followed by low market returns. In Japan both the level and the change in consumer confidence boost the market return in the next month. Further, shifts in sentiment significantly move conditional volatility in most of the countries, and in Italy such impacts lead to an increase in returns by 4.7% in the next month.

Investor sentiment as conditioning information in asset pricing

This paper assesses whether incorporating investor sentiment as conditioning information in asset-pricing models helps capture the impacts of the size, value, liquidity and momentum effects on risk-adjusted returns of individual stocks. We use survey sentiment measures and a composite index as proxies for investor sentiment. In our conditional framework, the size effect becomes less important in the conditional CAPM and is no longer significant in all the other models examined. Furthermore, the conditional models often capture the value, liquidity and momentum effects.

Asymptotic minimax robust and misspecified Lorden quickest change detection for dependent stochastic processes

The quick detection of an abrupt unknown change in the conditional distribution of a dependent stochastic process has numerous applications. In this paper, we pose a minimax robust quickest change detection problem for cases where there is uncertainty about the post-change conditional distribution. Our minimax robust formulation is based on the popular Lorden criteria of optimal quickest change detection. Under a condition on the set of possible post-change distributions, we show that the widely known cumulative sum (CUSUM) rule is asymptotically minimax robust under our Lorden minimax robust formulation as a false alarm constraint becomes more strict. We also establish general asymptotic bounds on the detection delay of misspecified CUSUM rules (i.e. CUSUM rules that are designed with post- change distributions that differ from those of the observed sequence). We exploit these bounds to compare the delay performance of asymptotically minimax robust, asymptotically optimal, and other misspecified CUSUM rules. In simulation examples, we illustrate that asymptotically minimax robust CUSUM rules can provide better detection delay performance at greatly reduced computation effort compared to competing generalised likelihood ratio procedures.

Altered copy

In 2008, Jenny Roche commissioned Jodi Melnick to make the solo Business of the Bloom on her. Four years later, Roche re-enters this piece and draws on their many years of working together to inhabits its ideas and impulses from a new perspective. In this new 'altered copy', Roche refers to this previous work; embellishing, redirecting and abstracting moments. By reinterpreting both her earlier self and footage of Melnick dancing a reworked version of the solo in performance, Roche connects with the polyvalent nature of both interpretation and memory. This reworked version outlines the relationship that dancers have to movement traces that circulate after a dancing event. Suzanne Ravn (2009) found that dancers experienced the reapperance of movement traces from previous works when moving. Martin Nachbar (2012) writing from his first-person perspective as a dancer, describes the dancer who discerns between the past of movement that is remembered and the current moment of its performance. He outlines how past and present movement experiences cohabitate in the embodied present. Timmy de Laet (2012) identifies the emerging trait of re-enactment in contemporary dance, in which contemporary artists such as Nachbar dialogue with past choreographies to comment on the various mnemonic dimensions of dance. De Laet (2012) explains that the function of these works are not to preserve the past choreographies as might be achieved through reconstruction but to contemplate creatively issues of ephemerality and preservation of dance. Altered copy addresses this dialogue between the past and present body.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

A biomechanical analysis of growing rods used in the management of early onset scoliosis

INTRODUCTION Managing spinal deformities in young children is challenging, particularly early-onset scoliosis (EOS). Any progressive spinal deformity particularly in early life presents significant health risks for the child and a challenge for the treating surgeon. Surgical intervention is often required if EOS has been unresponsive to conservative treatment particularly with rapidly progressive curves. An emerging treatment option particularly for EOS is fusionless scoliosis surgery. Similar to bracing this surgical option potentially harnesses growth, motion and function of the spine along with correcting spinal deformity. Dual growing rods is one such fusionless treatment, which aims to modulate growth of the vertebrae. The aim of this study was to ascertain the extent to which semi-constrained growing rods (Medtronic, Memphis, TN) with a telescopic sleeve component, reduce rotational constraint on the spine compared with standard rigid rods and hence potentially provide a more physiological mechanical environment for the growing spine. METHODS Six 40-60kg English Large White porcine spines served as a model for the paediatric human spine. Each spine was dissected into 7 level thoracolumbar multi-segment unit (MSU) spines, removing all non-ligamentous soft tissues. Appropriately sized semi-constrained growing rods and rigid rods were secured by multi-axial screws (Medtronic) prior to testing in alternating sequences for each spine. Pure nondestructive moments of +/4Nm at a constant rotation rate of 8deg/s was applied to the mounted MSU spines. Displacement of each level was captured using an Optotrak (Northern Digital Inc, Waterloo, ON). The range of motion (ROM), neutral zone (NZ) size and stiffness (Nm/deg) were calculated from the Instron load-displacement data and intervertebral ROM was calculated through a MATLAB algorithm from Optotrak data. RESULTS Irrespective of sequence order rigid rods significantly reduced the total ROM (deg) than compared to semi-constrained rods (p<0.05) and resulted in a significantly stiffer (Nm/deg) spine for both left and right axial rotation testing (p<0.05). Analysing the intervertebral motion within the instrumented levels, rigid rods showed reduced ROM (Deg) than compared to semi-constrained growing rods and the un-instrumented (UN-IN) test sequences. CONCLUSION The semi-constrained growing rods maintained rotation similar to UN-IN spines while the rigid rods showed significantly reduced axial rotation across all instrumented levels. Clinically the effect of semi-constrained growing rods evaluated in this study is that they will allow growth via the telescopic rod components while maintaining the axial rotation ability of the spine, which may also reduce the occurrence of the crankshaft phenomenon.

The geographical co-distribution and socio-ecological drivers of childhood pneumonia and diarrhoea in Queensland, Australia

This study aimed to explore the spatiotemporal patterns, geographic co-distribution, and socio-ecological drivers of childhood pneumonia and diarrhea in Queensland. A Bayesian conditional autoregressive model was used to quantify the impacts of socio-ecological factors on both childhood pneumonia and diarrhea at a postal area level. A distinct seasonality of childhood pneumonia and diarrhea was found. Childhood pneumonia and diarrhea mainly distributed in northwest of Queensland. Mount Isa was the high-risk cluster where childhood pneumonia and diarrhea co-distributed. Emergency department visits (EDVs) for pneumonia increased by 3% per 10-mm increase in monthly average rainfall, in wet seasons. In comparison, a 10-mm increase in monthly average rainfall may increase 4% of EDVs for diarrhea. Monthly average temperature was negatively associated with EDVs for childhood diarrhea, in wet seasons. Low socioeconomic index for areas (SEIFA) was associated with high EDVs for childhood pneumonia. Future pneumonia and diarrhea prevention and control measures in Queensland should focus more on Mount Isa.

Building macroscale models from microscale probabilistic models : a general probabilistic approach for nonlinear diffusion and multispecies phenomena

A discrete agent-based model on a periodic lattice of arbitrary dimension is considered. Agents move to nearest-neighbor sites by a motility mechanism accounting for general interactions, which may include volume exclusion. The partial differential equation describing the average occupancy of the agent population is derived systematically. A diffusion equation arises for all types of interactions and is nonlinear except for the simplest interactions. In addition, multiple species of interacting subpopulations give rise to an advection-diffusion equation for each subpopulation. This work extends and generalizes previous specific results, providing a construction method for determining the transport coefficients in terms of a single conditional transition probability, which depends on the occupancy of sites in an influence region. These coefficients characterize the diffusion of agents in a crowded environment in biological and physical processes.

Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold

This paper employs a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011. Understanding the price behavior of commodity prices and the volatility transmission mechanism between these markets and the stock exchanges are crucial for each participant, including governments, traders, portfolio managers, consumers, and producers. For return and volatility spillover, the results show significant transmission among the S&P 500 and commodity markets. The past shocks and volatility of the S&P 500 strongly influenced the oil and gold markets. This study finds that the highest conditional correlations are between the S&P 500 and gold index and the S&P 500 and WTI index. We also analyze the optimal weights and hedge ratios for commodities/S&P 500 portfolio holdings using the estimates for each index. Overall, our findings illustrate several important implications for portfolio hedgers for making optimal portfolio allocations, engaging in risk management and forecasting future volatility in equity and commodity markets. © 2013 Elsevier B.V.

Do socially responsible investment indexes outperform conventional indexes?

The question of whether more Socially Responsible (SR) firms outperform or underperform other conventional firms has been debated in the economic literature. In this study, using the Socially Responsible Investment (SRI) indexes and conventional stock indexes in the US, the UK and Japan, first and second moments of firm performance distributions are estimated based on the Markov Switching (MS) model. We find two distinct regimes (bear and bull) in the SRI markets as well as the stock markets for all the three countries. These regimes occur with the same timing in both types of market. No statistical difference in means and volatilities generated from the SRI indexes and conventional indexes in either region was found. Furthermore, we find strong comovements between the two indexes in both the regimes.

Bayesian models for spatio-temporal assessment of disease

This thesis has contributed to the advancement of knowledge in disease modelling by addressing interesting and crucial issues relevant to modelling health data over space and time. The research has led to the increased understanding of spatial scales, temporal scales, and spatial smoothing for modelling diseases, in terms of their methodology and applications. This research is of particular significance to researchers seeking to employ statistical modelling techniques over space and time in various disciplines. A broad class of statistical models are employed to assess what impact of spatial and temporal scales have on simulated and real data.

Regulatory reforms and productivity : an empirical analysis of the Japanese electricity industry

The Japanese electricity industry has experienced regulatory reforms since the mid-1990s. This article measures productivity in Japan's steam power-generation sector and examines the effect of reforms on the productivity of this industry over the period 1978-2003. We estimate the Luenberger productivity indicator, which is a generalization of the commonly used Malmquist productivity index, using a data envelopment analysis approach. Factors associated with productivity change are investigated through dynamic generalized method of moments (GMM) estimation of panel data. Our empirical analysis shows that the regulatory reforms have contributed to productivity growth in the steam power-generation sector in Japan.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.