Cultural heritage constitutive materials can provide excellent substrates for microbial colonization, highly influenced by thermo-hygrometric parameters. In cultural heritage-related environments, a detrimental microbial load may be present both on manufacts surface and in the aerosol. In this study, bacterial and fungal colonisation has been investigated in three Sicilian confined environments (archive, cave and hypogea), each with peculiar structures and different thermo-hygrometric parameters. Particular attention has been paid to microorganisms able to induce artifacts biodeterioration and to release biological particles in the aerosol (spores, cellular debrides, toxins and allergens) potentially dangerous for the human health (visitors/users). Results provided information on the composition of the biological consortia, highlighting also the symbiotic relationships between micro (cyanobacteria, bacteria and fungi) and macro-organisms (plants, bryophyte and insects). The results of this integrated approach, including molecular biology techniques, are essential for a complete understanding of both microbial colonization of the cultural objects and the potential relationship with illness to human.

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The paper describes the activities carried out for developing and testing Back Propagation Neural Networks (BPNN) for the gas turbine engine diagnostics. One of the aims of this study was to analyze the problems encountered during training using large number of patterns. Each pattern contains information about the engine thermodynamic behaviour when there is a fault in progress. Moreover the research studied different architectures of BPNN for testing their capability to recognize patterns even when information is noised. The results showed that it is possible to set-up and optimize suitable and robust Neural Networks useful for gas turbine diagnostics. The methods of Gas Path Analysis furnish the necessary data and information about engine behaviour. The best architecture, among the ones studied, is formed by 13, 26 and 47 neurons in the input, hidden and output layer respectively. The investigated Nets have shown that the best encoding of faults is the one using a unitary diagonal matrix. Moreover the calculation have identified suitable laws of learning rate factor (LRF) for improving the learning rate. Finally the authors used two different computers. The first one has a classical architecture (sequential, vectorial and parallel). The second one is the Neural Computer, SYNAPSE-1, developed by Siemens.

Eight cultivars of Citrus aurantium L., showing peculiar morphological traits of the fruits and leaves, were analyzed genetically. Inter simple sequence repeat (ISSR) was chosen as molecular markers because they represent a highly efficient system for investigating variability at intraspecific level. The particular morphological traits were discussed, the genetic identity and distance matrix based on Nei index was calculated, and the unweighted pair group method with arithmetic mean (UPGMA) dendrogram was generated. A total of 53 clearly distinguished DNA bands were considered for the ISSR analysis of which 24 were polymorphic. A basic C. aurantium fingerprinting pattern was obtained. The ISSR profiles allowed to discriminate only two cultivars, while the other six ones did not show polymorphisms, although their phenotypes were notably different, indicating that mutations must have occurred without significantly modifying the DNA length between the simple sequence repeats. The results showed a very low level of genetic variability among the cultivars; ‘‘Canaliculata’’ formed a separated cluster with C. sinensis Osbeck, suggesting a probable hybrid origin derived from crossing between sour and sweet orange. This study confirmed the reliability and reproducibly of ISSR markers for Citrus fingerprinting purposes, although some morphological differences could not be detected; therefore, both morphological and genetic characters must be considered for cultivar or variety identification