Processing of Stimulus Repetition and Change in the Somatosensory System: Recordings of Electrical and Magnetic Brain Responses

In the present work, effects of stimulus repetition and change in a continuous stimulus stream on the processing of somatosensory information in the human brain were studied. Human scalp-recorded somatosensory event-related potentials (ERPs) and magnetoencephalographic (MEG) responses rapidly diminished with stimulus repetition when mechanical or electric stimuli were applied to fingers. On the contrary, when the ERPs and multi-unit a ctivity (MUA) were directly recorded from the primary (SI) and secondary (SII) somatosensory cortices in a monkey, there was no marked decrement in the somatosensory responses as a function of stimulus repetition. These results suggest that this rate effect is not due to the response diminution in the SI and SII cortices. Obviously the responses to the first stimulus after a long "silent" period are nhanced due to unspecific initial orientation, originating in more broadly distributed and/or deeper neural structures, perhaps in the prefrontal cortices. With fast repetition rates not only the late unspecific but also some early specific somatosensory ERPs were diminished in amplitude. The fast decrease of the ERPs as a function of stimulus repetition is mainly due to the disappearance of the orientation effect and with faster repetition rates additively due to stimulus specific refractoriness. A sudden infrequent change in the continuous stimulus stream also enhanced somatosensory MEG responses to electric stimuli applied to different fingers. These responses were quite similar to those elicited by the deviant stimuli alone when the frequent standard stimuli were omitted. This enhancement was obviously due to the release from refractoriness because the neural structures generating the responses to the infrequent deviants had more time to recover from the refractoriness than the respective structures for the standards. Infrequent deviant mechanical stimuli among frequent standard stimuli also enhanced somatosensory ERPs and, in addition, they elicited a new negative wave which did not occur in the deviants-alone condition. This extra negativity could be recorded to deviations in the stimulation site and in the frequency of the vibratory stimuli. This response is probably a somatosensory analogue of the auditory mismatch negativity (MMN) which has been suggested to reflect a neural mismatch process between the sensory input and the sensory memory trace.

Quality of ALICE SSD modules revealed by electrical tests

ALICE (A Large Ion Collider Experiment) is an experiment at CERN (European Organization for Nuclear Research), where a heavy-ion detector is dedicated to exploit the unique physics potential of nucleus-nucleus interactions at LHC (Large Hadron Collider) energies. In a part of that project, 716 so-called type V4 modules were assembles in Detector Laboratory of Helsinki Institute of Physics during the years 2004 - 2006. Altogether over a million detector strips has made this project the most massive particle detector project in the science history of Finland. One ALICE SSD module consists of a double-sided silicon sensor, two hybrids containing 12 HAL25 front end readout chips and some passive components, such has resistors and capacitors. The components are connected together by TAB (Tape Automated Bonding) microcables. The components of the modules were tested in every assembly phase with comparable electrical tests to ensure the reliable functioning of the detectors and to plot the possible problems. The components were accepted or rejected by the limits confirmed by ALICE collaboration. This study is concentrating on the test results of framed chips, hybrids and modules. The total yield of the framed chips is 90.8%, hybrids 96.1% and modules 86.2%. The individual test results have been investigated in the light of the known error sources that appeared during the project. After solving the problems appearing during the learning-curve of the project, the material problems, such as defected chip cables and sensors, seemed to induce the most of the assembly rejections. The problems were typically seen in tests as too many individual channel failures. Instead, the bonding failures rarely caused the rejections of any component. One sensor type among three different sensor manufacturers has proven to have lower quality than the others. The sensors of this manufacturer are very noisy and their depletion voltage are usually outside of the specification given to the manufacturers. Reaching 95% assembling yield during the module production demonstrates that the assembly process has been highly successful.

Methods and applications for improving parameter prediction models for stand structures in Finland

This thesis report attempts to improve the models for predicting forest stand structure for practical use, e.g. forest management planning (FMP) purposes in Finland. Comparisons were made between Weibull and Johnson s SB distribution and alternative regression estimation methods. Data used for preliminary studies was local but the final models were based on representative data. Models were validated mainly in terms of bias and RMSE in the main stand characteristics (e.g. volume) using independent data. The bivariate SBB distribution model was used to mimic realistic variations in tree dimensions by including within-diameter-class height variation. Using the traditional method, diameter distribution with the expected height resulted in reduced height variation, whereas the alternative bivariate method utilized the error-term of the height model. The lack of models for FMP was covered to some extent by the models for peatland and juvenile stands. The validation of these models showed that the more sophisticated regression estimation methods provided slightly improved accuracy. A flexible prediction and application for stand structure consisted of seemingly unrelated regression models for eight stand characteristics, the parameters of three optional distributions and Näslund s height curve. The cross-model covariance structure was used for linear prediction application, in which the expected values of the models were calibrated with the known stand characteristics. This provided a framework to validate the optional distributions and the optional set of stand characteristics. Height distribution is recommended for the earliest state of stands because of its continuous feature. From the mean height of about 4 m, Weibull dbh-frequency distribution is recommended in young stands if the input variables consist of arithmetic stand characteristics. In advanced stands, basal area-dbh distribution models are recommended. Näslund s height curve proved useful. Some efficient transformations of stand characteristics are introduced, e.g. the shape index, which combined the basal area, the stem number and the median diameter. Shape index enabled SB model for peatland stands to detect large variation in stand densities. This model also demonstrated reasonable behaviour for stands in mineral soils.