Preliminary studies of soil patterns observed in radar images, Bishop area, California

Soil patterns observed by aerial radar photograph

Indicator organisms to determine the use of chilling as a critical point in beef slaughter HACCP

End of project reportDuring chilling, temperatures of carcass surfaces at different sites change over time as do other parameters such as water activity (aw), the structure of the muscle and other tissues, as the carcass enters rigor mortis. Many of these factors are known to have a major effect on cell survival and growth and must be considered in determining the influence of chilling on bacterial survival on carcass surfaces. This study aimed to determine if chilling could be used as a critical control point (CCP) in beef slaughter in relation to pathogens such as E. coli O157:H7 and L. monocytogenes, using E. coli and Listeria innocua as pathogen indicators. The present study was designed to determine the influence of (a) chilling at 10oC for 72 h on the survival of E. coli and (b) chilling at 4oC for 72 h on the survival of L. innocua inoculated at different sites on beef carcasses. Three sites (neck, outside round and brisket) were inoculated (1) immediately after dressing while hot (E. coli and L. innocua) and (2) when cold after chilling (L. innocua). The influence of changes in surface aw was also considered and their relationship to the survival of E. coli and L. innocua over time was assessed. The data are discussed in relation to the use of chilling as a CCP in beef hazard analysis (HACCP) and the monitoring of neck temperature as the most suitable CCP.National Development Pla

A model for dynamic allocation of human attention among multiple tasks

The problem of multi-task attention allocation with special reference to aircraft piloting is discussed with the experimental paradigm used to characterize this situation and the experimental results obtained in the first phase of the research. A qualitative description of an approach to mathematical modeling, and some results obtained with it are also presented to indicate what aspects of the model are most promising. Two appendices are given which (1) discuss the model in relation to graph theory and optimization and (2) specify the optimization algorithm of the model

Modeling human decision making behavior in supervisory control

An optimal decision control model was developed, which is based primarily on a dynamic programming algorithm which looks at all the available task possibilities, charts an optimal trajectory, and commits itself to do the first step (i.e., follow the optimal trajectory during the next time period), and then iterates the calculation. A Bayesian estimator was included which estimates the tasks which might occur in the immediate future and provides this information to the dynamic programming routine. Preliminary trials comparing the human subject's performance to that of the optimal model show a great similarity, but indicate that the human skips certain movements which require quick change in strategy