29 resultados para Cherokee County Circuit, Probate and Family Court Systems
Resumo:
Objective Behavioural inhibition (BI) in early childhood is associated with increased risk for anxiety. The present research examines BI alongside family environment factors, specifically maternal negativity and overinvolvement, maternal anxiety and mother-child attachment, with a view to providing a broader understanding of the development of child anxiety. Method Participants were 202 children classified at age 4 as either behaviourally inhibited (N=102) or uninhibited (N=100). Family environment, BI and child anxiety were assessed at baseline and child anxiety and BI were assessed again two-years later when participants were aged 6 years. Results After controlling for baseline anxiety, inhibited participants were significantly more likely to meet criteria for a diagnosis of social phobia and generalized anxiety disorder at follow-up. Path analysis suggested that maternal anxiety significantly affected child anxiety over time, even after controlling for the effects of BI and baseline anxiety. No significant paths from parenting or attachment to child anxiety were found. Maternal overinvolvement was significantly associated with BI at follow-up. Conclusions At age 4, BI, maternal anxiety and child anxiety represent risk factors for anxiety at age 6. Furthermore, overinvolved parenting increases risk for BI at age 6, which may then lead to the development of anxiety in later childhood.
Resumo:
Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.
Resumo:
The objective of this article is to review the scientific literature on airflow distribution systems and ventilation effectiveness to identify and assess the most suitable room air distribution methods for various spaces. In this study, different ventilation systems are classified according to specific requirements and assessment procedures. This study shows that eight ventilation methods have been employed in the built environment for different purposes and tasks. The investigation shows that numerous studies have been carried out on ventilation effectiveness but few studies have been done regarding other aspects of air distribution. Amongst existing types of ventilation systems, the performance of each ventilation methods varies from one case to another due to different usages of the ventilation system in a room and the different assessment indices used. This review shows that the assessment of ventilation effectiveness or efficiency should be determined according to each task of the ventilation system, such as removal of heat, removal of pollutant, supply fresh air to the breathing zone or protecting the occupant from cross infection. The analysis results form a basic framework regarding the application of airflow distribution for the benefit of designers, architects, engineers, installers and building owners.
Resumo:
The field of systems thinking is both broad and diverse. This paper tries to provide assistance to outsiders wishing to find out what systems thinking is and also to insiders interested in exploring areas of the systems movement other than their own. A selection of books, papers and articles is given. Each has a full reference and a brief annotation, this being an account of, and a critical comment on, its content. The selection does not aim to be definitive or authoritative and obviously displays the predilections of the authors. However, the hope is that it will convey a sense of the intellectual and practical endeavours that, to the authors, constitute systems thinking and that it may aid the exploration of the range of holistic ideas that people have found useful in thinking about and acting in the world.
Resumo:
During the past decade, brain–computer interfaces (BCIs) have rapidly developed, both in technological and application domains. However, most of these interfaces rely on the visual modality. Only some research groups have been studying non-visual BCIs, primarily based on auditory and, sometimes, on somatosensory signals. These non-visual BCI approaches are especially useful for severely disabled patients with poor vision. From a broader perspective, multisensory BCIs may offer more versatile and user-friendly paradigms for control and feedback. This chapter describes current systems that are used within auditory and somatosensory BCI research. Four categories of noninvasive BCI paradigms are employed: (1) P300 evoked potentials, (2) steady-state evoked potentials, (3) slow cortical potentials, and (4) mental tasks. Comparing visual and non-visual BCIs, we propose and discuss different possible multisensory combinations, as well as their pros and cons. We conclude by discussing potential future research directions of multisensory BCIs and related research questions
Resumo:
This study investigated the effects of increased genetic diversity in winter wheat (Triticum aestivum L.), either from hybridization across genotypes or from physical mixing of lines, on grain yield, grain quality, and yield stability in different cropping environments. Sets of pure lines (no diversity), chosen for high yielding ability or high quality, were compared with line mixtures (intermediate level of diversity), and lines crossed with each other in composite cross populations (CCPn, high diversity). Additional populations containing male sterility genes (CCPms) to increase outcrossing rates were also tested. Grain yield, grain protein content, and protein yield were measured at four sites (two organically-managed and two conventionally-managed) over three years, using seed harvested locally in each preceding year. CCPn and mixtures out-yielded the mean of the parents by 2.4% and 3.6%, respectively. These yield differences were consistent across genetic backgrounds but partly inconsistent across cropping environments and years. Yield stability measured by environmental variance was higher in CCPn and CCPms than the mean of the parents. An index of yield reliability tended to be higher in CCPn, CCPms and mixtures than the mean of the parents. Lin and Binns’ superiority values of yield and protein yield were consistently and significantly lower (i.e. better) in the CCPs than in the mean of the parents, but not different between CCPs and mixtures. However, CCPs showed greater early ground cover and plant height than mixtures. When compared with the (locally non-predictable) best-yielding pure line, CCPs and mixtures exhibited lower mean yield and somewhat lower yield reliability but comparable superiority values. Thus, establishing CCPs from smaller sets of high-performing parent lines might optimize their yielding ability. On the whole, the results demonstrate that using increased within-crop genetic diversity can produce wheat crops with improved yield stability and good yield reliability across variable and unpredictable cropping environments.
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This study examines when “incremental” change is likely to trigger “discontinuous” change, using the lens of complex adaptive systems theory. Going beyond the simulations and case studies through which complex adaptive systems have been approached so far, we study the relationship between incremental organizational reconfigurations and discontinuous organizational restructurings using a large-scale database of U.S. Fortune 50 industrial corporations. We develop two types of escalation process in organizations: accumulation and perturbation. Under ordinary conditions, it is perturbation rather than the accumulation that is more likely to trigger subsequent discontinuous change. Consistent with complex adaptive systems theory, organizations are more sensitive to both accumulation and perturbation in conditions of heightened disequilibrium. Contrary to expectations, highly interconnected organizations are not more liable to discontinuous change. We conclude with implications for further research, especially the need to attend to the potential role of managerial design and coping when transferring complex adaptive systems theory from natural systems to organizational systems.
Resumo:
Objective. To compare mental health, coping and family-functioning in parents of young people with obsessive-compulsive disorder (OCD), anxiety disorders, and no known mental health problems. Method. Parents of young people with OCD (N=28), other anxiety disorders (N=28), and no known mental health problems (N=62) completed the Brief Symptom Inventory (Derogatis, 1993), the Coping Responses Inventory (Moos, 1990), and the McMaster family assessment device (Epstein, Baldwin, & Bishop, 1983). Results. Parents of children with OCD and anxiety disorders had poorer mental health and used more avoidant coping than parents of non-clinical children. There were no group differences in family-functioning. Conclusion. The similarities across the parents of clinically referred children suggest that there is a case for encouraging active parental involvement in the treatment of OCD in young people.
Resumo:
This study has explored the prediction errors of tropical cyclones (TCs) in the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) for the Northern Hemisphere summer period for five recent years. Results for the EPS are contrasted with those for the higher-resolution deterministic forecasts. Various metrics of location and intensity errors are considered and contrasted for verification based on IBTrACS and the numerical weather prediction (NWP) analysis (NWPa). Motivated by the aim of exploring extended TC life cycles, location and intensity measures are introduced based on lower-tropospheric vorticity, which is contrasted with traditional verification metrics. Results show that location errors are almost identical when verified against IBTrACS or the NWPa. However, intensity in the form of the mean sea level pressure (MSLP) minima and 10-m wind speed maxima is significantly underpredicted relative to IBTrACS. Using the NWPa for verification results in much better consistency between the different intensity error metrics and indicates that the lower-tropospheric vorticity provides a good indication of vortex strength, with error results showing similar relationships to those based on MSLP and 10-m wind speeds for the different forecast types. The interannual variation in forecast errors are discussed in relation to changes in the forecast and NWPa system and variations in forecast errors between different ocean basins are discussed in terms of the propagation characteristics of the TCs.
Resumo:
This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.