Search

This paper explores the influence of load models on decisions of undervoltage load shedding in power systems. A controlled load rejection can be used as an emergency countermeasure to avoid widespread blackout when system voltages are unstable. In this paper, dynamic simulations of a small power system using both static and dynamic load models are presented. When using a static load model, the system includes an explicit model of a transformer with load tap changer. The aim is to demonstrate how different load models influence the analysis and calculation of the amount of load needed to be shed to stabilise the system voltage. Keywords: Load modelling, Load shedding, Long-term dynamics.

Abstract Background Relationships between cognitive deficits and brain morphological changes observed in schizophrenia are alternately explained by less gray matter in the brain cerebral cortex, by alterations in neural circuitry involving the basal ganglia, and by alteration in cerebellar structures and related neural circuitry. This work explored a model encompassing all of these possibilities to identify the strongest morphological relationships to cognitive skill in schizophrenia. Methods Seventy-one patients with schizophrenia and sixty-five healthy control subjects were characterized by neuropsychological tests covering six functional domains. Measures of sixteen brain morphological structures were taken using semi-automatic and fully manual tracing of MRI images, with the full set of measures completed on thirty of the patients and twenty controls. Group differences were calculated. A Bayesian decision-theoretic method identified those morphological features, which best explained neuropsychological test scores in the context of a multivariate response linear model with interactions. Results Patients performed significantly worse on all neuropsychological tests except some regarding executive function. The most prominent morphological observations were enlarged ventricles, reduced posterior superior vermis gray matter volumes, and increased putamen gray matter volumes in the patients. The Bayesian method associated putamen volumes with verbal learning, vigilance, and (to a lesser extent) executive function, while caudate volumes were associated with working memory. Vermis regions were associated with vigilance, executive function, and, less strongly, visuo-motor speed. Ventricular volume was strongly associated with visuo-motor speed, vocabulary, and executive function. Those neuropsychological tests, which were strongly associated to ventricular volume, showed only weak association to diagnosis, possibly because ventricular volume was regarded a proxy for diagnosis. Diagnosis was strongly associated with the other neuropsychological tests, implying that the morphological associations for these tasks reflected morphological effects and not merely group volumetric differences. Interaction effects were rarely associated, indicating that volumetric relationships to neuropsychological performance were similar for both patients and controls. Conclusion The association of subcortical and cerebellar structures to verbal learning, vigilance, and working memory supports the importance of neural connectivity to these functions. The finding that a morphological indicator of diagnosis (ventricular volume) provided more explanatory power than diagnosis itself for visuo-motor speed, vocabulary, and executive function suggests that volumetric abnormalities in the disease are more important for cognition than non-morphological features.

This paper presents a method to quantify and rank transmission system components by their importance for system reliability under different load scenarios. Each component is ranked by three separate importance indexes based on its expected outage rate and impact on: 1) system security margin; 2) load supply; and 3) generation units. By studying these three interests individually, a more complete view of the risks to system reliability can be assessed. The method is demonstrated on a detailed power system model (7000 components) of a significant part of the Great Britain transmission system at 400 and 275 kV. The results show how sensitive the component indexes are to the load scenario. The method provides an input for decision-making when planning maintenance and new investment and can be used as a complement to deterministic criteria. [ABSTRACT FROM PUBLISHER]

A number of different gene polymorphisms have been found to dispose for the development of schizophrenia. However, no single gene polymorphism is sufficient for the precipitation of schizophrenia. Swedish psychosis patients (n=103) and control subjects (n=89) were analyzed for 36 single nucleotide polymorphisms in 30 candidate genes for schizophrenia. Evidence of association was analyzed with Bayesian statistical methods. Variants in the genes coding for dopamine-D2 receptor, brain-derived neurotrophic factor (BDNF), neuropeptide Y (NPY), neuregulin 1, reelin and synapsin 3 showed association with schizophrenia, although few subjects were found in the minority allele for the two latter variants. The six gene variants, all with suspected connection to schizophrenia, were found to be risk factors when considered in combination, but not separately. The results indicate that the Bayesian statistical method gives additional possibilities in the search for risk factors for schizophrenia or other complex disorders. [ABSTRACT FROM AUTHOR]

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations. [ABSTRACT FROM AUTHOR]