Traditional and well-established applications of airborne remote sensing to archaeology involve standard aerial photographic recording, either oblique or vertical, of archaeological phenomena visible as shadow, soil or cropmarks, all of which require particular conditions before they become visible. Cropmarks have made the most important contribution to archaeological site discovery and are the most complex of the phenomena, whose occurrence relies upon the differential availability of moisture to a crop growing over buried archaeological remains, reflecting different soil depths and precipitation, to create differential growth patterns in times of moisture stress. Consistently successful aerial photographic reconnaissance for cropmarks relies on dry weather and well-drained soils in arable agriculture. There is, thus, in Scotland a bias in the discovery of archaeological sites in favour of drier eastern districts, supporting arable agriculture, compared to the west with its wetter climate focussed on pastureland. Because cropmarks are linked to moisture stress in growing plants they are potentially detectable in bands outside the visible part of the electro-magnetic spectrum. Although historically cropmark detection has used film, whose sensitivity closely approximates the human eye, hyperspectral scanning allows consideration of a wider range of different wavelengths, beyond the visible spectrum, many of which are more sensitive to changes in vegetation status. The main objectives of this research are to test to what extent hyperspectral and multispectral imagery can reveal otherwise invisible archaeological sites surviving as cropmarks; to assess the relative usefulness of the different sensors employed; and to investigate the potential of hyperspectral and multispectral imagery to augment cropmark detection rates in areas less conducive to their production. It uses a range of imagery (CASI 2, ATM and digital vertical photographic data) acquired by NERC ARSF from two case study sites in Lowland Scotland, one in the east and one in the west, selected to facilitate comparison between areas of good and poor cropmark production. Following processing, comparison and detailed analysis of this data, this thesis has demonstrated the high efficacy of this imagery in the identification of archaeological cropmarks; has established the most appropriate range of bandwidths and processing methods applicable to that imagery; has demonstrated its potential for the discovery of previously unrecognised archaeological sites in areas of lowland pasture; and has reinforced the value of systematic block coverage as compared to traditional ‘observer-directed’ archaeological aerial reconnaissance.

The aim is to study a continuous chemical process, and then analyze the hold process of the reactor and build the models which could be trained to realize the fault diagnosis and localization in the process. An experimental system has been built to be the research base. That includes experiment part and record system. To the diagnosis and localization methods, the work presented the methods with the data-based approach, mainly the Bayesian network and RBF network based on GAAPA (Genetic Algorithm with Auto-adapted of Partial Adjustment). The data collected from the experimental system are used to train and test the models.

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Preharvest sprouting (PHS), the germination of seeds on the maternal plant before harvest, is a big challenge for barley producers worldwide. It is attributed mainly to low seed dormancy. The balance between two classical plant hormones, gibberellin (GA) and abscisic acid (ABA) regulates seed dormancy and germination, and the endogenous level of these two hormones in plants is determined by their biosynthesis and catabolism. This thesis characterized the expression patterns of the major GA and ABA metabolism genes in barley cv. Betzes during seed development, and germination in both dormant and non-dormant seeds. The results indicate that specific gene family members of the two hormones play distinct temporal roles in regulating seed development, dormancy onset and release, and germination. Since only two genes encoding the GA deactivating GA 2-oxidase enzyme have been known so far in barley, this study also identified two new GA2ox genes designated as HvGA2ox1 and HvGA2ox3.