Multi-rotor with suspended load: System dynamics and control toolbox

There is an increasing demand for Unmanned Aerial Systems (UAS) to carry suspended loads as this can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. The constant variation in operating point induced by the slung load also causes conventional controllers to demand increased control effort. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present a novel controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions. The paper describes a System Dynamics and Control Simulation Toolbox for use with MATLAB/SIMULINK which includes a detailed simulation of the multi-rotor and slung load as well as a predictive controller to manage the nonlinear dynamics whilst accounting for system constraints. It is demonstrated that the controller simultaneously tracks specified waypoints and actively damps large slung load oscillations. A linear-quadratic regulator (LQR) is derived and control performance is compared. Results show the improved performance of the predictive controller for a larger flight envelope, including aggressive manoeuvres and large slung load displacements. The computational cost remains relatively small, amenable to practical implementations.

Randomized clinical trial of the timing it right stroke family support program: Research protocol

Background Family caregivers provide invaluable support to stroke survivors during their recovery, rehabilitation, and community re-integration. Unfortunately, it is not standard clinical practice to prepare and support caregivers in this role and, as a result, many experience stress and poor health that can compromise stroke survivor recovery and threaten the sustainability of keeping the stroke survivor at home. We developed the Timing it Right Stroke Family Support Program (TIRSFSP) to guide the timing of delivering specific types of education and support to meet caregivers' evolving needs. The objective of this multi-site randomized controlled trial is to determine if delivering the TIRSFSP across the stroke care continuum improves caregivers' sense of being supported and emotional well-being. Methods/design Our multi-site single-blinded randomized controlled trial will recruit 300 family caregivers of stroke survivors from urban and rural acute care hospitals. After completing a baseline assessment, participants will be randomly allocated to one of three groups: 1) TIRSFSP guided by a stroke support person (health care professional with stroke care experience), delivered in-person during acute care and by telephone for approximately the first six to 12 months post-stroke; 2) caregiver self-directed TIRSFSP with an initial introduction to the program by a stroke support person, or; 3) standard care receiving the educational resource "Let's Talk about Stroke" prepared by the Heart and Stroke Foundation. Participants will complete three follow-up quantitative assessments 3, 6, and 12-months post-stroke. These include assessments of depression, social support, psychological well-being, stroke knowledge, mastery (sense of control over life), caregiving assistance provided, caregiving impact on everyday life, and indicators of stroke severity and disability. Qualitative methods will also be used to obtain information about caregivers' experiences with the education and support received and the impact on caregivers' perception of being supported and emotional well-being. Discussion This research will determine if the TIRSFSP benefits family caregivers by improving their perception of being supported and emotional well-being. If proven effective, it could be recommended as a model of stroke family education and support that meets the Canadian Stroke Best Practice Guideline recommendation for providing timely education and support to families through transitions.

Report: The Canadian Best Practices Educational Toolkits: Translating evidence-based stroke recommendations into practical implementation resources

The Canadian Best Practice Recommendations for Stroke Care are intended to reduce variations in stroke care and facilitate closure of the gap between evidence and practice (Lindsay et al., 2010). The publication of best practice recommendations is only the beginning of this process. The guidelines themselves are not sufficient to change practice and increase consistency in care. Therefore, a key objective of the Canadian Stroke Network (CSN) Best Practices Working Group (BPWG) is to encourage and facilitate ongoing professional development and training for health care professionals providing stroke care. This is addressed through a multi-factorial approach to the creation and dissemination of inter-professional implementation tools and resources. The resources developed by CSN span pre-professional education, ongoing professional development, patient education and may be used to inform systems change. With a focus on knowledge translation, several inter-professional point-of-care tools have been developed by the CSN in collaboration with numerous professional organizations and expert volunteers. These resources are used to facilitate awareness, understanding and applications of evidence-based care across stroke care settings. Similar resources are also developed specifically for stroke patients, their families and informal caregivers, and the general public. With each update of the Canadian Best Practice Recommendations for Stroke Care, the BPWG and topic-specific writing groups propose priority areas for ongoing resource development. In 2010, two of these major educational initiatives were undertaken and recently completed—one to support continuing education for health care professionals regarding secondary stroke prevention and the other to educate families, informal caregivers and the public about pediatric stroke. This paper presents an overview of these two resources, and we encourage health care professionals to integrate these into their personal learning plans and tool kits for patients.

A hybrid of modified PSO and local search on a multi-robot search system

Particle swarm optimization (PSO), a new population based algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area.Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms,a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.

Quantitative genetic modeling of lateral ventricular shape and volume using multi-atlas fluid image alignment in twins

Despite substantial progress in measuring the 3D profile of anatomical variations in the human brain, their genetic and environmental causes remain enigmatic. We developed an automated system to identify and map genetic and environmental effects on brain structure in large brain MRI databases . We applied our multi-template segmentation approach ("Multi-Atlas Fluid Image Alignment") to fluidly propagate hand-labeled parameterized surface meshes into 116 scans of twins (60 identical, 56 fraternal), labeling the lateral ventricles. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps revealed 3D heritability patterns, and their significance, with and without adjustments for global brain scale. These maps visualized detailed profiles of environmental versus genetic influences on the brain, extending genetic models to spatially detailed, automatically computed, 3D maps.

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response. © 2007 Elsevier Inc. All rights reserved.

Combining meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group

Meta-analyses estimate a statistical effect size for a test or an analysis by combining results from multiple studies without necessarily having access to each individual study's raw data. Multi-site meta-analysis is crucial for imaging genetics, as single sites rarely have a sample size large enough to pick up effects of single genetic variants associated with brain measures. However, if raw data can be shared, combining data in a "mega-analysis" is thought to improve power and precision in estimating global effects. As part of an ENIGMA-DTI investigation, we use fractional anisotropy (FA) maps from 5 studies (total N=2, 203 subjects, aged 9-85) to estimate heritability. We combine the studies through meta-and mega-analyses as well as a mixture of the two - combining some cohorts with mega-analysis and meta-analyzing the results with those of the remaining sites. A combination of mega-and meta-approaches may boost power compared to meta-analysis alone.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

Probabilistic multi-tensor estimation using the tensor distribution function

Diffusion weighted magnetic resonance (MR) imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of 6 directions, second-order tensors can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve crossing fiber tracts. Recently, a number of high-angular resolution schemes with greater than 6 gradient directions have been employed to address this issue. In this paper, we introduce the Tensor Distribution Function (TDF), a probability function defined on the space of symmetric positive definite matrices. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the diffusion orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function.

Analyzing multi-fiber reconstruction in high angular resolution diffusion imaging using the tensor distribution function

High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the tradeoff between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusionsensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.

Translating training in the NYU Caregiver Intervention in Australia: Maintaining fidelity and meeting graduate standards in an online continuing professional education setting

The aim of this study was to develop an Internet-based self-directed training program for Australian healthcare workers to facilitate learning and competence in delivery of a proven intervention for caregivers of people with dementia: The New York University Caregiver Intervention (NYUCI). The NYUCI is a nonpharmacological, multicomponent intervention for spousal caregivers. It is aimed at maintaining well-being by increasing social support and decreasing family discord, thereby delaying or avoiding nursing home placement of the person with dementia. Training in the NYUCI in the United States has, until now, been conducted in person to trainee practitioners. The Internet-based intervention was developed simultaneously for trainees in the U.S. and Australia. In Australia, due to population geography, community healthcare workers, who provide support to older adult caregivers of people with dementia, live and work in many regional and rural areas. Therefore, it was especially important to have online training available to make it possible to realize the health and economic benefits of using an existing evidence-based intervention. This study aimed to transfer knowledge of training in, and delivery of, the NYUCI for an Australian context and consumers. This article details the considerations given to contextual differences and to learners’ skillset differences in translating the NYUCI for Australia.

Facilitating sustainable professional part-time work: A question of design?

Despite significant socio-demographic and economic shifts in the contours of work over the past 40 years, there has been surprisingly little change in the way work is designed. Current understandings of the content and structure of jobs are predominantly underpinned by early 20th century theories derived from the manufacturing industry where employees worked independently of each other in stand-alone organisations. It is only in the last 10 years that elaborations and extensions to job/work design theory have been posed, which accommodate some of the fundamental shifts in contemporary work settings, yet these extended frameworks have received little empirical attention. Utilising contemporary features of work design and a sample of professional service workers, the purpose of this study is to examine to what extent and how part-time roles are designed relative to equivalent full-time roles. The findings contribute to efforts to design effective part-time roles that balance organisational and individual objectives.