Relationship between expectation management and client retention in online Cognitive Behavioural Therapy

Background Engaging clients from the outset of psychotherapy is important for therapeutic success. However, there is little research evaluating therapists’ initial attempts to engage clients. This article reports retrospective analysis of data from a trial of online Cognitive Behavioural Therapy (CBT) for depression. Qualitative and quantitative methods were used to evaluate how therapists manage clients’ expectations at the outset of therapy and its relationship with client retention in the therapeutic intervention. Aims To develop a system to codify expectation management in initial sessions of online CBT and evaluate its relationship with retention. Method Initial qualitative research using conversation analysis identified three different communication practices used by therapists at the start of first sessions: no expectation management, some expectation management, and comprehensive expectation management. These findings were developed into a coding scheme that enabled substantial inter-rater agreement (weighted Kappa = 0.78; 95% CI: 0.52 to 0.94) and was applied to all trial data. Results Adjusting for a range of client variables, primary analysis of data from 147 clients found comprehensive expectation management was associated with clients remaining in therapy for 1.4 sessions longer than those who received no expectation management (95% CI: -0.2 to 3.0). This finding was supported by a sensitivity analysis including an additional 21 clients (1.6 sessions, 95% CI: 0.2 to 3.1). Conclusions Using a combination of qualitative and quantitative methods, this study suggests a relationship between expectation management and client retention in online CBT for depression, which has implications for professional practice. A larger prospective study would enable a more precise estimate of retention.

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

A coupled SPH-DEM model for micro-scale structural deformations of plant cells during drying

A single plant cell was modeled with smoothed particle hydrodynamics (SPH) and a discrete element method (DEM) to study the basic micromechanics that govern the cellular structural deformations during drying. This two-dimensional particle-based model consists of two components: a cell fluid model and a cell wall model. The cell fluid was approximated to a highly viscous Newtonian fluid and modeled with SPH. The cell wall was treated as a stiff semi-permeable solid membrane with visco-elastic properties and modeled as a neo-Hookean solid material using a DEM. Compared to existing meshfree particle-based plant cell models, we have specifically introduced cell wall–fluid attraction forces and cell wall bending stiffness effects to address the critical shrinkage characteristics of the plant cells during drying. Also, a moisture domain-based novel approach was used to simulate drying mechanisms within the particle scheme. The model performance was found to be mainly influenced by the particle resolution, initial gap between the outermost fluid particles and wall particles and number of particles in the SPH influence domain. A higher order smoothing kernel was used with adaptive smoothing length to improve the stability and accuracy of the model. Cell deformations at different states of cell dryness were qualitatively and quantitatively compared with microscopic experimental findings on apple cells and a fairly good agreement was observed with some exceptions. The wall–fluid attraction forces and cell wall bending stiffness were found to be significantly improving the model predictions. A detailed sensitivity analysis was also done to further investigate the influence of wall–fluid attraction forces, cell wall bending stiffness, cell wall stiffness and the particle resolution. This novel meshfree based modeling approach is highly applicable for cellular level deformation studies of plant food materials during drying, which characterize large deformations.

Technology, Natural Resources and Economic Growth: Improving the Environment for a Greener Future

Description Through a combination of global data analysis and focused country level analysis, this timely book provides answers to the most pertinent country and industry specific questions defining the current relationship between technology, natural resources and economic growth. Contents Contents: Preface Part I: Global Analysis 1. Economic Growth and the Environment 2. Energy Substitution and Carbon Dioxide Emissions 3. Pollution, Natural Resources, and Economic Growth 4. Trade Openness and Environmental Quality 5. Environmental Productivity 6. Energy Price-induced Technological Change 7. Trade-induced Technological Change 8. Regional Economic Integration Part II: Country-Level Analysis 9. Emissions Trading in the United States 10. Increasing Returns to Pollution Abatement in the United States 11. Policy-induced Competitiveness in the United States 12. Trade Liberalization, Technology, and the Environment 13. Policy Implementation and its Effectiveness in China 14. Clean Technological Inventions in Japan 15. Intervention of Economic Policy and its Nonlinear Effects in Japan 16. The Next Emerging Giants: India and Africa 17. Conclusion Index Further information Through a combination of global data analysis and focused country level analysis, this timely book provides answers to the most pertinent country and industry specific questions defining the current relationship between technology, natural resources and economic growth. Shunsuke Managi takes a distinctive approach by focusing on the design and implementation of environmental regulations that encourage technological progress and, in doing so, looks at ways to ensure productivity improvements in the face of increasingly stringent environmental regulations and natural resource depletion. The findings in this important book demonstrate how successful environmental policies can contribute to efficiency by encouraging, rather than inhibiting, technological innovation. Technology, Natural Resources and Economic Growth will provide a valuable resource for a wide readership including postgraduate students, researchers, academics and policy makers working in the fields of environmental and ecological economics.

A case study of the Iranian national railway and its absolute capacity expansion using analytical models

Identifying railway capacity is an important task that can identify "in principal" whether the network can handle an intended traffic flow, and whether there is any free capacity left for additional train services. Capacity determination techniques can also be used to identify how best to improve an existing network, and at least cost. In this article an optimization approach has been applied to a case study of the Iran national railway, in order to identify its current capacity and to optimally expand it given a variety of technical conditions. This railway is very important in Iran and will be upgraded extensively in the coming years. Hence the conclusions in this article may help in that endeavor. A sensitivity analysis is recommended to evaluate a wider range of possible scenarios. Hence more useful lower and upper bounds can be provided for the performance of the system

Performance profiling for predictive train schedules

In this article an alternate sensitivity analysis is proposed for train schedules. It characterises the schedules robustness or lack thereof and provides unique profiles of performance for different sources of delay and for different values of delay. An approach like this is necessary because train schedules are only a prediction of what will actually happen. They can perform poorly with respect to a variety of performance metrics, when deviations and other delays occur, if for instance they can even be implemented, and as originally intended. The information provided by this analytical approach is beneficial because it can be used as part of a proactive scheduling approach to alter a schedule in advance or to identify suitable courses of action for specific “bad behaviour”. Furthermore this information may be used to quantify the cost of delay. The effect of sectional running time (SRT) deviations and additional dwell time in particular were quantified for three railway schedule performance measures. The key features of this approach were demonstrated in a case study.

Numerical investigation of upstream fuel injection through porous media for scramjet engines via surrogate-assisted evolutionary algorithms

A multi-objective design optimization study has been conducted for upstream fuel injection through porous media applied to the first ramp of a two-dimensional scramjet intake. The optimization has been performed by coupling evolutionary algorithms assisted by surrogate modeling and computational fluid dynamics with respect to three design criteria, that is, the maximization of the absolute mixing quantity, total pressure saving, and fuel penetration. A distinct Pareto optimal front has been obtained, highlighting the counteracting behavior of the total pressure against the mixing efficiency and fuel penetration. The injector location and size have been identified as the key design parameters as a result of a sensitivity analysis, with negligible influence of the porous properties in the configurations and conditions considered in the present study. Flowfield visualization has revealed the underlying physics associated with the effects of these dominant parameters on the shock structure and intensity.

Natural convection due to differential heating of inclined walls and heat source placed on bottom wall of an attic shaped space

Numerical investigation of free convection heat transfer in an attic shaped enclosure with differentially heated two inclined walls and filled with air is performed in this study. The left inclined surface is uniformly heated whereas the right inclined surface is uniformly cooled. There is a heat source placed on the right side of the bottom surface. Rest of the bottom surface is kept as adiabatic. Finite volume based commercial software ANSYS 15 (Fluent) is used to solve the governing equations. Dependency of various flow parameters of fluid flow and heat transfer is analyzed including Rayleigh number, Ra ranging from 103 to 106, heater size from 0.2 to 0.6, heater position from 0.3 to 0.7 and aspect ratio from 0.2 to 1.0 with a fixed Prandtl number of 0.72. Outcomes have been reported in terms of temperature and stream function contours and local Nusselt number for various Ra, heater size, heater position, and aspect ratio. Grid sensitivity analysis is performed and numerically obtained results have been compared with those results available in the literature and found good agreement.

Multi-objective models and techniques for analysing the absolute capacity of railway networks

Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.

Psychological health and the risk of diabetes mellitus in Australian women: A 21-year prospective study

Background Symptoms of depression can be recurrent or limited to one episode. This study discusses the prospective association between psychological health, measured as change in depression symptoms, and the risk of diabetes mellitus in Australian women. Methods Data obtained from the Mater-University of Queensland Study of Pregnancy. Depression was measured using the Delusions-Symptoms: States Inventory. To examine possible transitions over time, depression was grouped into four categories and assessed at different phases over the 21-year period. Multiple logistic regression models and sensitivity analysis to assess the robustness of our analytical strategy were performed. Results Three hundred and one women reported diabetes 21 years after the index pregnancy. Almost one-third of the women who reported depression symptoms continued to report these at a subsequent follow-up (FU) phase. About 1 in 20 women who had not reported depression symptoms at the 5-year FU did so at the subsequent 14-year FU. In prospective analyses, we did not find a significant association between diabetes and negative change (not depressed to depressed, at subsequent phase); however, for women with positive history of symptoms of depression and women with persistent symptoms, there was a 1.97-fold (95% confidence interval [CI]: 1.14–3.40) to 2.23-fold (95% CI: 1.09–4.57) greater risk of diabetes. Conclusions Our study suggests that an increased risk of diabetes is significantly associated with persistent depression symptoms. It highlights the importance of recognizing depression symptoms in terms of women's psychological wellbeing and thus provides a basis for targeting those most at risk.

Analyzing the impact of learning inputs on near-to-far terrain traversability estimation

With the increasing need to adapt to new environments, data-driven approaches have been developed to estimate terrain traversability by learning the rover’s response on the terrain based on experience. Multiple learning inputs are often used to adequately describe the various aspects of terrain traversability. In a complex learning framework, it can be difficult to identify the relevance of each learning input to the resulting estimate. This paper addresses the suitability of each learning input by systematically analyzing the impact of each input on the estimate. Sensitivity Analysis (SA) methods provide a means to measure the contribution of each learning input to the estimate variability. Using a variance-based SA method, we characterize how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We propose an approach built on Analysis of Variance (ANOVA) decomposition to examine the prediction made in a near-to-far learning framework based on multi-task GP regression. We demonstrate the approach by analyzing the impact of driving speed and terrain geometry on the prediction of the rover’s attitude and chassis configuration in a Marsanalogue terrain using our prototype rover Mawson.

Capacity determination and expansion models for rail networks

This thesis focused upon the development of improved capacity analysis and capacity planning techniques for railways. A number of innovations were made and were tested on a case study of a real national railway. These techniques can reduce the time required to perform decision making activities that planners and managers need to perform. As all railways need to be expanded to meet increasing demands, the presumption that analytical capacity models can be used to identify how best to improve an existing network at least cost, was fully investigated. Track duplication was the mechanism used to expanding a network's capacity, and two variant capacity expansion models were formulated. Another outcome of this thesis is the development and validation of bi objective models for capacity analysis. These models regulate the competition for track access and perform a trade-off analysis. An opportunity to develop more general mulch-objective approaches was identified.

Effect of constitutive material models on seismic response of two-story reinforced concrete frame

This paper focuses on the finite element (FE) response sensitivity and reliability analyses considering smooth constitutive material models. A reinforced concrete frame is modeled for FE sensitivity analysis followed by direct differentiation method under both static and dynamic load cases. Later, the reliability analysis is performed to predict the seismic behavior of the frame. Displacement sensitivity discontinuities are observed along the pseudo-time axis using non-smooth concrete and reinforcing steel model under quasi-static loading. However, the smooth materials show continuity in response sensitivity at elastic to plastic transition points. The normalized sensitivity results are also used to measure the relative importance of the material parameters on the structural responses. In FE reliability analysis, the influence of smoothness behavior of reinforcing steel is carefully noticed. More efficient and reasonable reliability estimation can be achieved by using smooth material model compare with bilinear material constitutive model.

Instant coffee with high chlorogenic acid levels protects humans against oxidative damage of macromolecules

Scope: Coffee is among the most frequently consumed beverages. Its consumption is inversely associated to the incidence of diseases related to reactive oxygen species; the phenomenon may be due to its antioxidant properties. Our primary objective was to investigate the impact of consumption of a coffee containing high levels of chlorogenic acids on the oxidation of proteins, DNA and membrane lipids; additionally, other redox biomarkers were monitored in an intervention trial. Methods and results: The treatment group (n=36) consumed instant coffee co-extracted from green and roasted beans, whereas the control consumed water (800 mL/P/day, 5 days). A global statistical analysis of four main biomarkers selected as primary outcomes showed that the overall changes are significant. 8-Isoprostaglandin F2α in urine declined by 15.3%, 3-nitrotyrosine was decreased by 16.1%, DNA migration due to oxidized purines and pyrimidines was (not significantly) reduced in lymphocytes by 12.5 and 14.1%. Other markers such as the total antioxidant capacity were moderately increased; e.g. LDL and malondialdehyde were shifted towards a non-significant reduction. Conclusion: The oxidation of DNA, lipids and proteins associated with the incidence of various diseases and the protection against their oxidative damage may be indicative for beneficial health effects of coffee.

Colonoscopy requirements of population screening for colorectal cancer in New Zealand

Aim: To estimate the colonoscopy burden of introducing population screening for colorectal cancer in New Zealand. Methods: Screening for colorectal cancer using biennial immunochemical faecal occult blood tests offered to people aged 50-74 years of age was modelled using population estimates from Statistics New Zealand for 2011-2031. Modelling to determine colonoscopy requirements was based on participation and test positivity rates from published results of screening programmes. Estimates of the number of procedures required for ongoing adenoma surveillance were calculated using screening literature results of adenoma yield, and New Zealand Guidelines for Adenoma Surveillance. Sensitivity analysis was undertaken on key parameters. Results: For a test positivity of 6.4%, biennial screening using immunochemical faecal occult blood testing with a 60% participation rate, would require 18,000 colonoscopies nationally, increasing to 28,000 by 2031. The majority of procedures are direct referrals from a positive FOBT, with surveillance colonoscopy numbers building over time. Conclusion: Colonoscopy requirements for immunochemical faecal occult blood based population screening for colorectal cancer are high. Significant expansion of services is required and careful management of surveillance procedures to ensure timely delivery of initial colonoscopies whilst maintaining symptomatic services. A model re-run informed by data from the screening pilot will allow improved estimates for the New Zealand setting.