The effectiveness of an internet-based psychological treatment program for female sexual dysfunction

Introduction.  There has been limited evaluation of the effectiveness of psychological interventions for female sexual dysfunction (FSD). Furthermore, none of these studies have evaluated the effectiveness of these programs delivered over the Internet.

Aim.  The current study evaluated an Internet-based psychological treatment program for FSD. Revive consisted of three well-established and validated treatment components: communication skills training, sensate focus exercises, and regular contact with a therapist.

Main Outcome Measures.  Outcome measures included: the Female Sexual Function Index; the Depression, Anxiety, Stress Scale; the Sexual Function Scale; and the Personal Assessment of Intimacy in Relationships.

Methods.  In total, 39 women (17 in treatment group and 22 in control group) completed the program. The mean age for the treatment group was 34.91 (standard deviation [SD] = 10.27) and 33.30 years (SD = 9.34) for the control group.

Results.  The results demonstrated that women who completed treatment reported improved sexual and relationship functioning in comparison with those who received no treatment.

Conclusions.  These findings highlight the suitability of the Internet for providing psychological treatment for FSD and for targeting the individual and relationship factors often associated with these disorders.

Optimizing the quality of bootstrap-based prediction intervals

The bootstrap method is one of the most widely used methods in literature for construction of confidence and prediction intervals. This paper proposes a new method for improving the quality of bootstrap-based prediction intervals. The core of the proposed method is a prediction interval-based cost function, which is used for training neural networks. A simulated annealing method is applied for minimization of the cost function and neural network parameter adjustment. The developed neural networks are then used for estimation of the target variance. Through experiments and simulations it is shown that the proposed method can be used to construct better quality bootstrap-based prediction intervals. The optimized prediction intervals have narrower widths with a greater coverage probability compared to traditional bootstrap-based prediction intervals.

Virtual haptic cell model for operator training

Microrobotic cell injection is an area of growing research interest. Typically, operators rely on visual feedback to perceive the microscale environment and are subject to lengthy training times and low success rates. Haptic interaction offers the ability to utilise the operator’s haptic modality and to enhance operator performance. Our earlier work presented a haptically enabled system for assisting the operator with certain aspects of the cell injection task. The system aimed to enhance the operator’s controllability of the micropipette through a logical mapping between the haptic device and microrobot, as well as introducing virtual fixtures for haptic guidance. The system was also designed in such a way that given the availability of appropriate force sensors, haptic display of the cell penetration force is straightforward. This work presents our progress towards a virtual replication of the system, aimed at facilitating offline operator training. It is suggested that operators can use the virtual system to train offline and later transfer their skills to the physical system. In order to achieve the necessary representation of the cell within the virtual system, methods based on a particle-based cell model are utilised. In addition to providing the necessary visual representation, the cell model provides the ability to estimate cell penetration forces and haptically display them to the operator. Two different approaches to achieving the virtual system are discussed.

A chemotactic-based model for spatial activity recognition

Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by changing motile behaviour in relation to environmental dynamics. Using chemotactic principles, we propose the chemotactic model and evaluate its classification performance in a smart house environment. The model exhibits high classification accuracy (99%) with a diverse 10 class activity dataset and outperforms the discrete hidden Markov model (HMM). High accuracy (>89%) is also maintained across small training sets and through incorporation of varying degrees of artificial noise into testing sequences. Importantly, unlike other bottom–up spatial activity recognition models, we show that the chemotactic model is capable of recognizing simple interwoven activities.

Efficient tensor based face recognition

This paper addresses the limitation of current multilinear PCA based techniques, in terms of pro- hibitive computational cost of testing and poor gen- eralisation in some scenarios, when applied to large training databases. We define person-specific eigen-modes to obtain a set of projection bases, wherein a particular basis captures variation across light- ings and viewpoints for a particular person. A new recognition approach is developed utilizing these bases. The proposed approach performs on a par with the existing multilinear approaches, whilst sig- nificantly reducing the complexity order of the testing algorithm.

High level segmentation of instructional videos based on content density

Automatically partitioning instructional videos into topic sections is a challenging problem in e-learning environments for efficient content management and cataloging. This paper addresses this problem by proposing a novel density function to delineate sections underscored by changes in topics in instructional and training videos. The content density function draws guidance from the observation that topic boundaries coincide with the ebb and flow of the 'density' of content shown in these videos. Based on this function, we propose two methods for high-level segmentation by determining topic boundaries. We study the performance of the two methods on eight training videos, and our experimental results demonstrate the effectiveness and robustness of the two proposed high-level segmentation algorithms for learning media.

Narrative structure analysis with education and training videos for e-learning

This paper deals with the problem ofstructuralizing education and training videos for high-level semantics extraction and nonlinear media presentation in e-learning applications. Drawing guidance from production knowledge in instructional media, we propose six main narrative structures employed in education and training videos for both motivation and demonstration during learning and practical training. We devise a powerful audiovisual feature set, accompanied by a hierarchical decision tree-based classification system to determine and discriminate between these structures. Based on a two-liered hierarchical model, we demonstrate that we can achieve an accuracy of 84.7% on a comprehensive set of education and training video data.

Osteo-cise : Strong bones for life': protocol for a community-based randomised controlled trial of a multi-modal exercise and osteoporosis education program for older adults at risk of falls and fractures

Background : Osteoporosis affects over 220 million people worldwide, and currently there is no 'cure' for the disease. Thus, there is a need to develop evidence-based, safe and acceptable prevention strategies at the population level that target multiple risk factors for fragility fractures to reduce the health and economic burden of the condition.

Methods : The 'Osteo-cise: Strong Bones for Life' study will investigate the effectiveness and feasibility of a multi-component targeted exercise, osteoporosis education/awareness and behavioural change program for improving bone health and muscle function, and reducing falls risk in community-dwelling older adults at an increased risk of fracture. Men and women aged 60 years or above will participate in an 18-month randomised controlled trial comprising a 12-month structured and supervised community-based program and a 6-month 'research to practise' translational phase. Participants will be randomly assigned to either the 'Osteo-cise' intervention or a self-management control group. The intervention will comprise a multi-modal exercise program incorporating high velocity progressive resistance training, moderate impact weight-bearing exercise and high challenging balance exercises performed three times weekly at local community-based fitness centres. A behavioural change program will be used to enhance exercise adoption and adherence to the program. Community-based osteoporosis education seminars will be conducted to improve participant knowledge and understanding of the risk factors and preventative measures for osteoporosis, falls and fractures. The primary outcomes measures, to be collected at baseline, 6, 12, and 18 months, will include DXA-derived hip and spine bone mineral density measurements and functional muscle power (timed stair-climb test). Secondary outcomes measures include: MRI-assessed distal femur and proximal tibia trabecular bone micro-architecture, lower limb and back maximal muscle strength, balance and function (four square step test, functional reach test, timed up-and-go test and 30-second sit-to-stand), falls incidence and health-related quality of life. Cost-effectiveness will also be assessed.

Discussion : The findings from the Osteo-cise: Strong Bones for Life study will provide new information on the efficacy of a targeted multi-modal community-based exercise program incorporating high velocity resistance training, together with an osteoporosis education and behavioural change program for improving multiple risk factors for falls and fracture in older adults at risk of fragility fracture. Trial Registration: Australian New Zealand Clinical Trials Registry reference ACTRN12609000100291

Training improves interobserver reliability for the diagnosis of scaphoid fracture displacement

Background The diagnosis of displacement in scaphoid fractures is notorious for poor interobserver reliability.

Questions/purposes We tested whether training can improve interobserver reliability and sensitivity, specificity, and accuracy for the diagnosis of scaphoid fracture displacement on radiographs and CT scans.

Methods Sixty-four orthopaedic surgeons rated a set of radiographs and CT scans of 10 displaced and 10 nondisplaced scaphoid fractures for the presence of displacement, using a web-based rating application. Before rating, observers were randomized to a training group (34 observers) and a nontraining group (30 observers). The training group received an online training module before the rating session, and the nontraining group did not. Interobserver reliability for training and nontraining was assessed by Siegel’s multirater kappa and the Z-test was used to test for significance.

Results There was a small, but significant difference in the interobserver reliability for displacement ratings in favor of the training group compared with the nontraining group. Ratings of radiographs and CT scans combined resulted in moderate agreement for both groups. The average sensitivity, specificity, and accuracy of diagnosing displacement of scaphoid fractures were, respectively, 83%, 85%, and 84% for the nontraining group and 87%, 86%, and 87% for the training group. Assuming a 5% prevalence of fracture displacement, the positive predictive value was 0.23 in the nontraining group and 0.25 in the training group. The negative predictive value was 0.99 in both groups.

Conclusions Our results suggest training can improve interobserver reliability and sensitivity, specificity and accuracy for the diagnosis of scaphoid fracture displacement, but the improvements are slight. These findings are encouraging for future research regarding interobserver variation and how to reduce it further.

A multi-objective sensitivity approach to training providers' evaluation and quota allocation planning

The practice of solely relying on the human resources department in the selection process of external training providers has cast doubts and mistrust across other departments as to how trainers are sourced. There are no measurable criteria used by human resource personnel, since most decisions are based on intuitive experience and subjective market knowledge. The present problem focuses on outsourcing of private training programs that are partly government funded, which has been facing accountability challenges. Due to the unavailability of a scientific decision-making approach in this context, a 12-step algorithm is proposed and tested in a Japanese multinational company. The model allows the decision makers to revise their criteria expectations, in turn witnessing the change of the training providers' quota distribution. Finally, this multi-objective sensitivity analysis provides a forward-looking approach to training needs planning and aids decision makers in their sourcing strategy.

Integration of supervised ART-based neural networks with a hybrid genetic algorithm

In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.

Proceedings of the 2009 R-research seminar

The Deakin Motion.Lab was excited to have Scott deLahunta and Wayne McGregor from Wayne McGregorIRandom Dance visit in late July 2009.

Scott deLahunta is the director of R-Research, the research arm of Wayne McGregor|Random Dance based in London. McGregor is a multi-award winning, world renowned contemporary dance choreographer, and along with directing his own company, is also resident choreographer at The Royal Ballet in London.

Together with Deakin Motion.Lab's Kim Vincs, deLahunta and McGregor gave a public presentation of their research, their philosophies, and the future of dance and technology, focussing specifically on the use of motion capture.

Scott deLahunta's visit was supported by the British Council, the Australian Research Council Discovery Program (DP0987101). Thanks also to Random Dance, to R-Research Seminar co-presenter Dancehouse and to the Australian Ballet who supported Wayne McGregor's visit to Australia.

Construction of neural network-based prediction intervals using particle swarm optimization

Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with point forecasts and predictions. This paper adopts and develops the lower upper bound estimation (LUBE) method for construction of PIs using neural network (NN) models. This method is fast and simple and does not require calculation of heavy matrices, as required by traditional methods. Besides, it makes no assumption about the data distribution. A new width-based index is proposed to quantitatively check how much PIs are informative. Using this measure and the coverage probability of PIs, a multi-objective optimization problem is formulated to train NN models in the LUBE method. The optimization problem is then transformed into a training problem through definition of a PI-based cost function. Particle swarm optimization (PSO) with the mutation operator is used to minimize the cost function. Experiments with synthetic and real-world case studies indicate that the proposed PSO-based LUBE method can construct higher quality PIs in a simpler and faster manner.

Managing patient deterioration : a protocol for enhancing undergraduate nursing students’ competence through web-based simulation and feedback techniques

Aims: To describe a funded proposal for the development of an on-line evidence based educational program for the management of deteriorating patients.
Background: There are international concerns regarding the management of deteriorating patients with issues around the ‘failure to rescue’. The primary response to these issues has been the development of medical emergency teams with little focus on the education of primary first responders.
Design/Methods: A mixed methods triangulated convergent design. In this four phase proposal we plan to 1. examine nursing student team ability to manage deteriorating patients and based upon these findings 2. develop web based educational material, including interactive scenarios. This educational material will be tested and refined in the third Phase 3, prior to evaluation and dissemination in the final phase.
Conclusion: This project aims to enhance knowledge development for the management of deteriorating patients through rigorous assessment of team performance and to produce a contemporary evidence-based online training program.

CADET - Centre for Advanced Design in Engineering Training

BACKGROUND : The Centre for Advanced Design in Engineering Training (CADET) is a partnership of Deakin University and the Gordon Institute of TAFE that will improve access and pathways into careers to address Australia’s critical engineering skills shortage (Walton, C). Local high schools, Belmont High and Matthew Flinders Girls Secondary College are included as strategic partners. CADET is proposed to be a teaching and learning facility providing a project focused modern engineering approach to students at regional schools and TAFE as well as Deakin’s degree programs. CADET will emphasize engineering design and development through virtual and physical modelling, simulation and prototyping – skills at the heart of the 21st century engineering challenges, and will serve as an attractor to engineering and related professions.

PURPOSE : The purpose of this paper is to present an argument toward the development of a Centre for advanced design in engineering training. CADET is proposed to increase the awareness and attractiveness of engineering as an education and career option, particularly for women, in regional schools, provide under one roof state-of-the-art engineering design, modelling and prototyping facilities, facilitate access and articulation pathways between school, VET and Higher Education, increase the physical capacity to serve student demand in western Victoria, and reinvigorate engineering as an essential component of a skilled regional economy.

DESIGN/METHOD : The evidenced based argument towards the proposed centre for advanced design in engineering training is based on a detailed literature review as well as a research study with industry representatives in engineering design. The learning principles of the model are also investigated and aligned to the proposed centre.

RESULTS : CADET is a change to the way engineering has traditionally been taught. The outcomes of CADET will be to provide a broad range of contemporary/relevant teaching programs, improve the social benefits gained from teaching programs, improve retention rates, advance partnerships that link with rural and regional victoria, and collaborate with local communities to encourage governments to support regional capacity building. Through focus group interviews and open discussions with industry and academia over the past 12 months on the integration of design skills in engineering education, results indicate that the following key skills are essential elements required for a successful project oriented design based learning curriculum are creative & innovative skills, successful industry engagement, and awareness of design skills in early years. Feedback also showed that 80% of the industry representatives are looking to recruit graduates who acquired design-equipped skill and 60% indicated that they want graduates who acquired knowledge through projects.

CONCLUSIONS : CADET projected benefits are significant at the strategic and operational levels. They include access for more women in engineering, facilitates articulation pathways between VET and HE, targeted recognised critical current engineering skills shortage in Australia, improvement of regional access, attractiveness and participation in tertiary education, achievement of a significant improvement in the teaching-research nexus.