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Determining lubricant film thickness between contacting bodies under elastohydrodynamic (EHD) conditions is often simulated by using a ball/cylinder and transparent disc apparatus together with an interferometry technique. The simulated contact will have a point or elliptic shape and the light used can be white or monochromatic. The interference pattern is normally photographed with a regular camera or a video camera and the pictures are then evaluated by the naked eye of the observer. In most cases, only central or minimum thicknesses are evaluated. In this paper an image processing method for the analysis of film thickness is presented. This method makes it possible to extract considerably more information about film thickness fluctuations than is achievable by the naked eye. The method primarily matches hue (but also saturation and intensity values) from digitized colour interferometric images of the unknown film shapes with calibration values obtained with known geometric shapes. The method is shown to work well in the range from 95 up to 700 nm with white light and makes the results unbiased by the observer. Furthermore, absolute film thickness can be evaluated without prior knowledge about the fringe order in the interferogram.

Autism is a developmental disorder with possibly multiple pathophysiologies. It has been theorized that cortical feature maps in individuals with autism are inadequate for forming abstract codes and representations. Cortical feature maps make it possible to classify stimuli, such as phonemes of speech, disregarding incidental detail. Hierarchies of such maps are instrumental in creating abstract codes and representations of objects and events. Self-Organizing Maps (SOMs) are artificial neural networks that offer insights into the development of cortical feature maps.Attentional impairment is prevalent in autism, but whether it is caused by attention-shift impairment or strong familiarity preference or negative response to novelty is a matter of debate. We model attention shift during self-organization by presenting a SOM with stimuli from two sources in four different modes, namely, novelty seeking (regarded as normal learning), attention-shift impairment (shifts are made with a low probability), familiarity preference (shifts made with a lower probability to the source that is the less familiar to the SOM of the two sources), and familiarity preference in conjunction with attention-shift impairment.The resulting feature maps from learning with novelty seeking and with attention-shift impairment are much the same except that learning with attention-shift impairment often yields maps with a somewhat better discrimination capacity than learning with novelty seeking. In contrast, the resulting maps from learning with strong familiarity preference are adapted to one of the sources at the expense of the other, and if one of the sources has a set of stimuli with smaller variability, the resulting maps are adapted to stimuli from that source. When familiarity preference is less pronounced, the resulting maps may become normal or fully restricted to one of the sources, and in that case, always the source with smaller variability if such a source is present. Such learning, in a system with many different maps, will result in very uneven capacities.Learning with familiarity preference in conjunction with attention-shift impairment surprisingly has higher probability for the development of normal maps than learning with familiarity preference alone.

Narrow neural columns have been suggested to be a neuroanatomical abnormality in autism. A previous hypothetical explanation, an unbalance between excitatory and inhibitory lateral feedback in the neocortex, has been found to be difficult to reconcile with the relatively high comorbidity of autism with epilepsy. Two alternative explanations are discussed, an early low capacity for producing serotonin, documented in autism, and insufficient production of nitric oxide. An early low level of serotonin has in animal experiments caused narrow neural columns. Insufficient nitric oxide is known from neural network theory to cause narrow neural columns.

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