Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

1600 RN

Due to a changing employment arena, healthcare organizations are hiring more new graduate RNs into acute care units. MMC’s usual process is to put new hires into night shift. Historically, night shifts have less resource availability. These combined factors left staff feeling unsupported; patient care could be compromised when less support is available to those in the beginning of their careers

Fault Tolerance and Scaling in e-Science Cloud Applications: Observations from the Continuing Development of MODISAzure

It can be natural to believe that many of the traditional issues of scale have been eliminated or at least greatly reduced via cloud computing. That is, if one can create a seemingly wellfunctioning cloud application that operates correctly on small or moderate-sized problems, then the very nature of cloud programming abstractions means that the same application will run as well on potentially significantly larger problems. In this paper, we present our experiences taking MODISAzure, our satellite data processing system built on the Windows Azure cloud computing platform, from the proof-of-concept stage to a point of being able to run on significantly larger problem sizes (e.g., from national-scale data sizes to global-scale data sizes). To our knowledge, this is the longest-running eScience application on the nascent Windows Azure platform. We found that while many infrastructure-level issues were thankfully masked from us by the cloud infrastructure, it was valuable to design additional redundancy and fault-tolerance capabilities such as transparent idempotent task retry and logging to support debugging of user code encountering unanticipated data issues. Further, we found that using a commercial cloud means anticipating inconsistent performance and black-box behavior of virtualized compute instances, as well as leveraging changing platform capabilities over time. We believe that the experiences presented in this paper can help future eScience cloud application developers on Windows Azure and other commercial cloud providers

The prevalence and incidence of mental ill-health in adults with autism and intellectual disabilities

The prevalence, and incidence, of mental ill-health in adults with intellectual disabilities and autism were compared with the whole population with intellectual disabilities, and with controls, matched individually for age, gender, ability-level, and Down syndrome. Although the adults with autism had a higher point prevalence of problem behaviours compared with the whole adult population with intellectual disabilities, compared with individually matched controls there was no difference in prevalence, or incidence of either problem behaviours or other mental ill-health. Adults with autism who had problem behaviours were less likely to recover over a two-year period than were their matched controls. Apparent differences in rates of mental ill-health are accounted for by factors other than autism, including Down syndrome and ability level

Skunk River Review Autumn 1994, vol 6

https://openspace.dmacc.edu/skunkriver/1009/thumbnail.jp

The effectiveness, acceptability and cost-effectiveness of psychosocial interventions for maltreated children and adolescents: an evidence synthesis.

BACKGROUND: Child maltreatment is a substantial social problem that affects large numbers of children and young people in the UK, resulting in a range of significant short- and long-term psychosocial problems. OBJECTIVES: To synthesise evidence of the effectiveness, cost-effectiveness and acceptability of interventions addressing the adverse consequences of child maltreatment. STUDY DESIGN: For effectiveness, we included any controlled study. Other study designs were considered for economic decision modelling. For acceptability, we included any study that asked participants for their views. PARTICIPANTS: Children and young people up to 24 years 11 months, who had experienced maltreatment before the age of 17 years 11 months. INTERVENTIONS: Any psychosocial intervention provided in any setting aiming to address the consequences of maltreatment. MAIN OUTCOME MEASURES: Psychological distress [particularly post-traumatic stress disorder (PTSD), depression and anxiety, and self-harm], behaviour, social functioning, quality of life and acceptability. METHODS: Young Persons and Professional Advisory Groups guided the project, which was conducted in accordance with Cochrane Collaboration and NHS Centre for Reviews and Dissemination guidance. Departures from the published protocol were recorded and explained. Meta-analyses and cost-effectiveness analyses of available data were undertaken where possible. RESULTS: We identified 198 effectiveness studies (including 62 randomised trials); six economic evaluations (five using trial data and one decision-analytic model); and 73 studies investigating treatment acceptability. Pooled data on cognitive-behavioural therapy (CBT) for sexual abuse suggested post-treatment reductions in PTSD [standardised mean difference (SMD) -0.44 (95% CI -4.43 to -1.53)], depression [mean difference -2.83 (95% CI -4.53 to -1.13)] and anxiety [SMD -0.23 (95% CI -0.03 to -0.42)]. No differences were observed for post-treatment sexualised behaviour, externalising behaviour, behaviour management skills of parents, or parental support to the child. Findings from attachment-focused interventions suggested improvements in secure attachment [odds ratio 0.14 (95% CI 0.03 to 0.70)] and reductions in disorganised behaviour [SMD 0.23 (95% CI 0.13 to 0.42)], but no differences in avoidant attachment or externalising behaviour. Few studies addressed the role of caregivers, or the impact of the therapist-child relationship. Economic evaluations suffered methodological limitations and provided conflicting results. As a result, decision-analytic modelling was not possible, but cost-effectiveness analysis using effectiveness data from meta-analyses was undertaken for the most promising intervention: CBT for sexual abuse. Analyses of the cost-effectiveness of CBT were limited by the lack of cost data beyond the cost of CBT itself. CONCLUSIONS: It is not possible to draw firm conclusions about which interventions are effective for children with different maltreatment profiles, which are of no benefit or are harmful, and which factors encourage people to seek therapy, accept the offer of therapy and actively engage with therapy. Little is known about the cost-effectiveness of alternative interventions. LIMITATIONS: Studies were largely conducted outside the UK. The heterogeneity of outcomes and measures seriously impacted on the ability to conduct meta-analyses. FUTURE WORK: Studies are needed that assess the effectiveness of interventions within a UK context, which address the wider effects of maltreatment, as well as specific clinical outcomes. STUDY REGISTRATION: This study is registered as PROSPERO CRD42013003889. FUNDING: The National Institute for Health Research Health Technology Assessment programme

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

The landscape model: a model for exploring trade-offs between agricultural production and the environment

We describe a model framework that simulates spatial and temporal interactions in agricultural landscapes and that can be used to explore trade-offs between production and environment so helping to determine solutions to the problems of sustainable food production. Here we focus on models of agricultural production, water movement and nutrient flow in a landscape. We validate these models against data from two long-term experiments, (the first a continuous wheat experiment and the other a permanent grass-land experiment) and an experiment where water and nutrient flow are measured from isolated catchments. The model simulated wheat yield (RMSE 20.3–28.6%), grain N (RMSE 21.3–42.5%) and P (RMSE 20.2–29% excluding the nil N plots), and total soil organic carbon particularly well (RMSE 3.1 − 13.8 %), the simulations of water flow were also reasonable (RMSE 180.36 and 226.02%). We illustrate the use of our model framework to explore trade-offs between production and nutrient losses