General threat and health-related attention biases in illness anxiety disorder. A brief research report.

Illness anxiety disorder, formerly known as hypochondria, has been conceptualised in the psychological literature as an anxiety disorder, and its dimensional correlate is usually referred to as health anxiety. Similarly to other anxiety disorders, health anxiety has also been investigated in the context of attention biases as maintaining factors. However, so far, there is little consensus in the literature concerning the types of biases most relevant to health anxiety (i.e. facilitation, difficulty in disengaging, or avoidance), and whether biases occur towards generally threatening or specific stimuli. The current study aimed to investigate the presence of all three types of biases in relation to both general-threat and health-related threat pictures in clinical participants with illness anxiety disorder as compared to participants with low levels of health anxiety. The results showed a larger difficulty in disengagement bias for health-related threatening stimuli than for general-threatening stimuli in all participants regardless of group factor. No other significant effects were identified. Thus, attention biases follow a simiar pattern in illness anxiety and low-anxiety participants.

Comparing the ISO-recommended and the cumulative data-reduction algorithms in S-on-1 laser damage test by a reverse approach method.

We compare the ISO-recommended (the standard) data-reduction algorithm used to determine the surface laser-induced damage threshold of optical materials by the S-on-1 test with two newly suggested algorithms, both named "cumulative" algorithms/methods, a regular one and a limit-case one, intended to perform in some respects better than the standard one. To avoid additional errors due to real experiments, a simulated test is performed, named the reverse approach. This approach simulates the real damage experiments, by generating artificial test-data of damaged and non-damaged sites, based on an assumed, known damage threshold fluence of the target and on a given probability distribution function to induce the damage. In this work, a database of 12 sets of test-data containing both damaged and non-damaged sites was generated by using four different reverse techniques and by assuming three specific damage probability distribution functions. The same value for the threshold fluence was assumed, and a Gaussian fluence distribution on each irradiated site was considered, as usual for the S-on-1 test. Each of the test-data was independently processed by the standard and by the two cumulative data-reduction algorithms, the resulting fitted probability distributions were compared with the initially assumed probability distribution functions, and the quantities used to compare these algorithms were determined. These quantities characterize the accuracy and the precision in determining the damage threshold and the goodness of fit of the damage probability curves. The results indicate that the accuracy in determining the absolute damage threshold is best for the ISO-recommended method, the precision is best for the limit-case of the cumulative method, and the goodness of fit estimator (adjusted R-squared) is almost the same for all three algorithms.