Microbial risk assessment is emerging as a new discipline in risk assessment. A systematic approach to microbial risk assessment is presented that employs data analysis for developing parsimonious models and accounts formally for the variability and uncertainty of model inputs using analysis of variance and Monte Carlo simulation. The purpose of the paper is to raise and examine issues in conducting microbial risk assessments. The enteric pathogen Escherichia coli O157:H7 was selected as an example for this study due to its significance to public health. The framework for our work is consistent with the risk assessment components described by the National Research Council in 1983 (hazard identification; exposure assessment; dose-response assessment; and risk characterization). Exposure assessment focuses on hamburgers, cooked a range of temperatures from rare to well done, the latter typical for fast food restaurants. Features of the model include predictive microbiology components that account for random stochastic growth and death of organisms in hamburger. For dose-response modeling, Shigella data from human feeding studies were used as a surrogate for E. coli O157:H7. Risks were calculated using a threshold model and an alternative nonthreshold model. The 95% probability intervals for risk of illness for product cooked to a given internal temperature spanned five orders of magnitude for these models. The existence of even a small threshold has a dramatic impact on the estimated risk.
This study presents an H∞ controller design for time-delay T-S fuzzy systems based on the fuzzy Lyapunov method, which is defined in terms of fuzzy blending quadratic Lyapunov functions. The delay-dependent robust stability criterion is derived in terms of the fuzzy Lyapunov method to guarantee the stability of time-delay T-S fuzzy systems subjected to disturbances. Based on the delay-dependent condition and parallel distributed compensation (PDC) scheme, the controller design problem is transformed into solving linear matrix inequalities (LMI).
Early detection and intervention strategies for schizophrenia are receiving increasingly more attention. Dermatoglyphic patterns, such as the degree of asymmetry of the fingerprints, have been hypothesized to be indirect measures for early abnormal developmental processes that can lead to later psychiatric disorders such as schizophrenia. However, previous results have been inconsistent in trying to establish the association between dermatoglyphics and schizophrenia. The goal of this work is to try to resolve this problem by borrowing well-developed techniques from the field of fingerprint matching. Two dermatoglyphic asymmetry measures are proposed that draw on the orientation field of homologous fingers. To test the capability of these measures, fingerprint images were acquired digitally from 40 schizophrenic patients and 51 normal individuals. Based on these images, no statistically significant association between conventional dermatoglyphic asymmetry measures and schizophrenia was found. In contrast, the sample means of the proposed measures consistently identified the patient group as having a higher degree of asymmetry than the control group. These results suggest that the proposed measures are promising for detecting the dermatoglyphic patterns that can differentiate the patient and control groups.
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