Variable neural networks for adaptive control of nonlinear systems

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example

Relationships between stress management in family living and characteristics of Tennessee homemakers

The purpose of this study was to gain knowledge concerning the relationships of personal and family characteristics to Tennessee Homemakers\u27 stress management. Five areas of stress management were identified for study. These were selected sources of stress, worry, guilt, satisfaction and dissatisfaction. Personal and family charac-teristics studied included extension audience, family structure, educational level, age and employment. Number and types of extension contacts and their relationships to stress management were also studied. Data were obtained from 2789 Tennessee homemakers randomly selected and interviewed by County Extension Home Economists. The one-way analysis of variance F-test and Chi Square were the statistical tests used to determine strength of relationships between variables. A probability level of .05 was considered as being statistically significant. Major findings included: 1. The largest percentages of survey participants were Home Demonstration Club Members, were married with children at home, had attended high school, were age 50-over, were married and only the husband was employed. 2. Meetings were the most frequent type of contact homemakers had with extension, this was followed by newsletters and telephone calls; while the least frequent type of extension contact was visits to the office. 3. A larger percentage of homemakers reported receiving infor-mation from extension agents on handling stress and tension than any other selected topic. 4. Findings indicated that the homemakers\u27 most frequent source of stress was the need for more time to relax; the most frequent source of worry was rearing children; the most frequent source of guilt was not developing own talents; the most frequent source of satisfaction was the need to improve spiritual life; and the most frequent source of dissatisfaction was extent spouse helped with household chores. 5. A higher proportion of homemakers who were married with children, who had attended college, were 30-39 years old and employed (both married and single) indicated they needed more time to relax. 6. A higher proportion of homemakers who were 30-39 years old indicated they needed more time to relax, worried about rearing children, felt guilty about not spending enough time with spouse and felt they needed to improve family relationships than homemakers in any other age group. 7. Findings regarding extension audiences indicated that homemakers who were young non-club members had more stress and worry; EFNEP homemakers had more guilt feelings and a higher level of dissatisfaction. 8. Findings regarding family structures showed that homemakers who were married with no children had more stress; homemakers who were single with children had more worry and a low level of satisfaction; homemakers who were single, no children at home had more guilt. 9. Homemakers with lower levels of education tended to have more guilt, lower levels of satisfaction and higher levels of dissatis-faction than homemakers completing more grades of school. 10. Homemakers who were 30-39 years old indicated more stress, worry, guilt and the lowest level of satisfaction while homemakers age 50-over indicated the least amount of stress, worry, and guilt than any other age group studied. 11. Single, employed homemakers indicated more stress and worry; single, unemployed homemakers had more guilt and a lower level of satisfaction. 12. Homemakers having higher numbers of extension contacts tended to have lower scores on stress, worry, guilt, a higher level of satisfaction and a lower level of dissatisfaction. Implications were given and recommendations for extension programs and further studies were included

Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

Experiences of frontline healthcare workers and their views about support during COVID-19 and previous pandemics: a systematic review and qualitative meta-synthesis

BackgroundHealthcare workers across the world have risen to the demands of treating COVID-19 patients, potentially at significant cost to their own health and wellbeing. There has been increasing recognition of the potential mental health impact of COVID-19 on frontline workers and calls to provide psychosocial support for them. However, little attention has so far been paid to understanding the impact of working on a pandemic from healthcare workers' own perspectives or what their views are about support.MethodsWe searched key healthcare databases (Medline, PsychINFO and PubMed) from inception to September 28, 2020. We also reviewed relevant grey literature, screened pre-print servers and hand searched reference lists of key texts for all published accounts of healthcare workers' experiences of working on the frontline and views about support during COVID-19 and previous pandemics/epidemics. We conducted a meta-synthesis of all qualitative results to synthesise findings and develop an overarching set of themes and sub-themes which captured the experiences and views of frontline healthcare workers across the studies.ResultsThis review identified 46 qualitative studies which explored healthcare workers' experiences and views from pandemics or epidemics including and prior to COVID-19. Meta-synthesis derived eight key themes which largely transcended temporal and geographical boundaries. Participants across all the studies were deeply concerned about their own and/or others' physical safety. This was greatest in the early phases of pandemics and exacerbated by inadequate Personal Protective Equipment (PPE), insufficient resources, and inconsistent information. Workers struggled with high workloads and long shifts and desired adequate rest and recovery. Many experienced stigma. Healthcare workers' relationships with families, colleagues, organisations, media and the wider public were complicated and could be experienced concomitantly as sources of support but also sources of stress.ConclusionsThe experiences of healthcare workers during the COVID-19 pandemic are not unprecedented; the themes that arose from previous pandemics and epidemics were remarkably resonant with what we are hearing about the impact of COVID-19 globally today. We have an opportunity to learn from the lessons of previous crises, mitigate the negative mental health impact of COVID-19 and support the longer-term wellbeing of the healthcare workforce worldwide

Comparative analysis of NOAA REFM and SNB 3 GEO tools for the forecast of the fluxes of high-energy electrons at GEO

Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field Bz observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast

“Fix the system … the people who are in it are not the ones that are broken” A qualitative study exploring UK academic researchers’ views on support at work

Recent evidence suggests that it remains difficult for academic researchers to preserve global well-being when working in the UK higher education sector. Our study aimed to explore academic researchers' perspectives on how they feel their mental health and well-being could be better supported within the UK higher education system. Using a combination of semi-structured and narrative interviewing techniques, we gathered the perspectives of 26 researchers. Narrative and reflexive thematic analysis were then used on the data collected. Our findings highlight the need to tackle systemic issues such job insecurity and unrealistically high workloads, given the risk they can pose to researchers' mental health and well-being. Our findings also highlight the key influence of managers and supervisors in creating a supportive environment, and the importance of going beyond what support is offered. That is, it is vital to effectively promote any existing or emerging support systems, and to be proactive in offering this support. Given the diversity identified in researchers’ individual situations, it is important that support is flexible and takes into consideration individual requirements and preferences. Higher education authorities and institutions need to determine how they can foster a healthy, caring environment for researchers working in this sector going forwards

Proof of concept evaluation of a project using ‘conversations inviting change’ methodology to support the development of in-place systems leadership in local care hubs

This evaluation took place between March and August 2018 and was commissioned by the NHS Leadership Academy on behalf of Kent and Medway Sustainability and Transformation Plan (STP) and delivered by East Kent Community Education Provider Network (EK CEPN). The programme consisted of three development sessions carried out over two sites, or multi-professional teams known as hubs, clusters or primary care networks by an experienced facilitator using the model ‘conversations inviting change’. This model embodies a narrative approach that recognises the domains identified by the National Leadership Academy of individual effectiveness, relationships and connectivity, innovation and improvement, learning and capacity building (NHS Leadership Academy, 2017)

Experiences of mental health professionals supporting front-line health and social care workers during COVID-19: qualitative study

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is having a well-documented impact on the mental health of front-line health and social care workers (HSCWs). However, little attention has been paid to the experiences of, and impact on, the mental health professionals who were rapidly tasked with supporting them. AIMS: We set out to redress this gap by qualitatively exploring UK mental health professionals' experiences, views and needs while working to support the well-being of front-line HSCWs during the COVID-19 pandemic. METHOD: Mental health professionals working in roles supporting front-line HSCWs were recruited purposively and interviewed remotely. Transcripts of the interviews were analysed by the research team following the principles of reflexive thematic analysis. RESULTS: We completed interviews with 28 mental health professionals from varied professional backgrounds, career stages and settings across the UK. Mental health professionals were motivated and driven to develop new clinical pathways to support HSCWs they perceived as colleagues and many experienced professional growth. However, this also came at some costs, as they took on additional responsibilities and increased workloads, were anxious and uncertain about how best to support this workforce and tended to neglect their own health and well-being. Many were professionally isolated and were affected vicariously by the traumas and moral injuries that healthcare workers talked about in sessions. CONCLUSIONS: This research highlights the urgent need to consider the mental well-being, training and support of mental health professionals who are supporting front-line workers

Neural Network Based Variable Structure Control for Nonlinear Discrete Systems

Neural network based variable structure control is proposed for the design of nonlinear discrete systems. Sliding mode control is used to provide good stability and robustness performance for nonlinear systems. An affine nonlinear neural predictor is introduced to predict the outputs of the nonlinear process and to make the variable structure control algorithm simple and easy to implement. When the predictor model is inaccurate, variable structure control with sliding modes is used to improve the stability of the system. A recursive weight learning algorithm for the neural networks based affine nonlinear predictor is also developed and the convergence of both the weights and the estimation error is analysed