Assessment of atrial septal defect morphology by transthoracic three dimensional echocardiography using standard grey scale and Doppler myocardial imaging techniques: comparison with magnetic resonance imaging and intraoperative findings

The properties of chemically cured and light-cured composite resins were recorded at baseline and at intervals over seven years, while the materials were exposed to controlled storage conditions as well as to various conditions typical of clinical situations. For chemically cured resins in clinical conditions, mechanical properties decreased, and working and setting times increased over four years; if refrigerated (controlled), properties remained constant past seven years. For light-cured resins, test results were constant over the entire seven-year test period regardless of storage conditions. An accelerated aging protocol was developed to allow for the evaluation of the relative storage stability of new and similar materials.     

Effects of piracetam on the performance of rats in a delayed match-to-position task

The growing use of antibody-based separation methods has paralleled the expansion of immunochemical detection methods in moving beyond the clinical diagnostic field to applications in environmental monitoring. In recent years high-performance immunoaffinity chromatography, which began as a separation technique in biochemical and clinical research, has been adapted for separating and quantifying environmental pollutants. Bioaffinity offers a selective biological basis for separation that can be incorporated into a modular analytical process for more efficient environmental analysis. The use of immunoaffinity chromatography for separation complements the use of immunoassay for detection. A widely used immunochemical detection method for environmental analyses is enzyme immunoassay. The objective of this paper is to review the status of bioaffinity-based analytical procedures for environmental applications and human exposure assessment studies. Environmental methods based on bioaffinity range from mature immunoassays to emerging techniques such as immunosensors and immunoaffinity chromatography procedures for small molecules.     

Direct and two-step methods for closed-loop identification: a comparison of asymptotic and finite data set performance

The asymptotic and finite data behavior of some closed-loop identification methods are investigated. It is shown that, when the output power is limited, closed-loop identification can generally identify models with smaller variance than open-loop identification. Several variations on some two-step identification methods are compared with the direct identification method. High order FIR models are used as process models to avoid bias issues arising from inadequate model structures for the processes. Comparisons are, therefore, made based on the variance of the identified process models both for asymptotic situations and for finite data sets. Process model bias resulting from improper selection of the noise and sensitivity function models is also investigated. In this context, the results support the use of direct identification methods on closed-loop data.     

Multi-output process identification

In model based control of multivariate processes, it has been common practice to identify a multi-input single-output (MISO) model for each output separately and then combine the individual models into a final MIMO model. If models for all outputs are independently parameterized then this approach is optimal. However, if there are common or correlated parameters among models for different output variables and/or correlated noise, then performing identification on all outputs simultaneously can lead to better and more robust models. In this paper, theoretical justifications for using multi-output identification for a multivariate process are presented and the potential benefits from using them are investigated via simulations on two process examples: a quality control example and an extractive distillation column. The identification of both the parsimonious transfer function models using multivariate prediction error methods, and of non-parsimonious finite impulse response (FIR) models using multivariate statistical regression methods such as partial least squares (PLS2), canonical correlation regression (CCR) and reduced rank regression (RRR) are considered. The multi-output identification results are compared to traditional single-output identification from several points of view: best predictions, closeness of the model to the true process, the precision of the identified models in frequency domain, stability robustness of the resulting model based control system, and multivariate control performance. The multi-output identification methods are shown to be superior to the single-output methods on the basis of almost all the criteria. Improvements in the prediction of individual outputs and in the closeness of the model to the true process are only marginal. The major benefits are in the stability and performance robustness of controllers based on the identified models. In this sense the multi-output identification methods are more ‘control relevant’.     

Middle and long latency auditory evoked potentials in cat. II. Component distributions and dependence on stimulus factors

The middle (10-50 ms) and long (50-600 ms) latency periods of the auditory evoked potential (AEP) were investigated in muscle-paralyzed, artificially respired cats with respect to two issues: (1) the distribution of components across the skull, and (2) the effects of changing stimulus intensity on component latencies and amplitudes. The distributional data were gathered during a behavioral study in which four behavioral tasks related to classical pupillary conditioning were used to vary attentional and arousal processes. The distributions across the skull surface (averaged across tasks) of 12 peaks and troughs (P10, N13, P17, N22, P31, N41, P55, N70, N100, N140, P260 and N520) and seven principal components derived from the set of waveforms collected during this experiment are reported. Both peak amplitudes and principal component scores were distributed differentially across the skull surface. In the second experiment, acoustic stimulus intensity was varied, and AEPs collected from a vertex and temporal electrode site. In general, increasing stimulus intensity had a stronger influence on the earlier portions of the AEP, where increased amplitude and decreased latency was the rule, than on later ones. The relationships between cat and human AEP components were discussed based on both the data presented in this paper and in previous papers.     

Approximate analysis of M/G/c/c state-dependent queueing networks

Congestion is ever present in most practical situations. We describe a methodology for approximate analysis of open state-dependent M/G/c/c queueing networks in which the service rate is subject to congestion, that is, it is a function of the number of customers in the system. Important performance measurements are easily computed with high accuracy, such as the blocking probability, throughput, expected number of customers in the system (known also as work-in-process), and expected waiting time. The methodology forms a basic building block useful in many practical applications and contexts. Computational results demonstrate that the methodology provides accurate results in many topological configurations as well as in the analysis of network evacuation problems in high-rise buildings.     

PRACTICAL INCONSISTENCY MANAGEMENT FOR CRITICAL-TASKS DECISION-SUPPORT SYSTEMS

Abstract

In this paper, the problem of logical consistency in critical-tasks decision systems is addressed. It is shown how local search techniques can be used to check the (in)consistency of very large propositional knowledge bases (KBs). Using new heuristic findings. we can now implement efficient forms of diagnostic reasoning in the context of very large ICBs modeling deep models of complex devices and including patterns of default reasoning.