Substructuring approache in state space models for dynamic system parameters identification

In the recent decade, the request for structural health monitoring expertise increased exponentially in the United States. The aging issues that most of the transportation structures are experiencing can put in serious jeopardy the economic system of a region as well as of a country. At the same time, the monitoring of structures is a central topic of discussion in Europe, where the preservation of historical buildings has been addressed over the last four centuries. More recently, various concerns arose about security performance of civil structures after tragic events such the 9/11 or the 2011 Japan earthquake: engineers looks for a design able to resist exceptional loadings due to earthquakes, hurricanes and terrorist attacks. After events of such a kind, the assessment of the remaining life of the structure is at least as important as the initial performance design. Consequently, it appears very clear that the introduction of reliable and accessible damage assessment techniques is crucial for the localization of issues and for a correct and immediate rehabilitation. The System Identification is a branch of the more general Control Theory. In Civil Engineering, this field addresses the techniques needed to find mechanical characteristics as the stiffness or the mass starting from the signals captured by sensors. The objective of the Dynamic Structural Identification (DSI) is to define, starting from experimental measurements, the modal fundamental parameters of a generic structure in order to characterize, via a mathematical model, the dynamic behavior. The knowledge of these parameters is helpful in the Model Updating procedure, that permits to define corrected theoretical models through experimental validation. The main aim of this technique is to minimize the differences between the theoretical model results and in situ measurements of dynamic data. Therefore, the new model becomes a very effective control practice when it comes to rehabilitation of structures or damage assessment. The instrumentation of a whole structure is an unfeasible procedure sometimes because of the high cost involved or, sometimes, because it’s not possible to physically reach each point of the structure. Therefore, numerous scholars have been trying to address this problem. In general two are the main involved methods. Since the limited number of sensors, in a first case, it’s possible to gather time histories only for some locations, then to move the instruments to another location and replay the procedure. Otherwise, if the number of sensors is enough and the structure does not present a complicate geometry, it’s usually sufficient to detect only the principal first modes. This two problems are well presented in the works of Balsamo [1] for the application to a simple system and Jun [2] for the analysis of system with a limited number of sensors. Once the system identification has been carried, it is possible to access the actual system characteristics. A frequent practice is to create an updated FEM model and assess whether the structure fulfills or not the requested functions. Once again the objective of this work is to present a general methodology to analyze big structure using a limited number of instrumentation and at the same time, obtaining the most information about an identified structure without recalling methodologies of difficult interpretation. A general framework of the state space identification procedure via OKID/ERA algorithm is developed and implemented in Matlab. Then, some simple examples are proposed to highlight the principal characteristics and advantage of this methodology. A new algebraic manipulation for a prolific use of substructuring results is developed and implemented.

Species identification of archived fish bones collected from the Mediterranean Sea 100 years ago using molecular techniques

With the discovery that DNA can be successfully recovered from museum collections, a new source of genetic information has been provided to extend our comprehension of the evolutionary history of species. However, historical specimens are often mislabeled or report incorrect information of origin, thus accurate identification of specimens is essential. Due to the highly damaged nature of ancient DNA many pitfalls exist and particular precautions need to be considered in order to perform genetic analysis. In this study we analyze 208 historical remains of pelagic fishes collected in the beginning of the 20th century. Through the adaptation of existing protocols, usually applied to human remains, we manage to successfully retrieve valuable genetic material from almost all of the examined samples using a guanidine and silica column-based approach. The combined use of two mitochondrial markers cytochrome-oxidase-1(mtDNA COI) and Control Region (mtDNA CR), and the nuclear marker first internal transcriber space (ITS1) allowed us to identify the majority of the examined specimens using traditional PCR and Sanger sequencing techniques. The creation of primers capable of amplifying heavily degraded DNA have great potential for future uses, both in ancient and in modern investigation. The methodologies developed in this study can in fact be applied for other ancient fish specimens as well as cooked or canned samples.

Preliminary identification of dehalogenation specificities exhibited by dehalorespiring bacteria from a marine microbial community

The ability of a previously PCB-enriched microbial culture from Venice Lagoon marine sediments to dechlorinate pentachlorophenol (PCP) and 2,3,5-trichlorophenol (2,3,5-TCP) was confirmed under anaerobic conditions in microcosms consisting of site water and sediment. Dechlorination activities against Aroclor 1254 PCB mixture were also confirmed as control. Pentachlorophenol was degraded to 2,4,6-TCP (75.92±0.85 mol%), 3,5-DCP (6.40±0.75 mol%), and phenol (15.40±0.87 mol%). From the distribution of the different dechlorination products accumulated in the PCP-spiked cultures over time, two dechlorination pathways for PCP were proposed: (i) PCP to 2,3,4,6-TeCP, then to 2,4,6-TCP through the removal of both meta double-flanked chlorine substituents (main pathway); (ii) alternately, PCP to 2,3,5,6-TeCP, 2,3,5-TCP, 3,5-DCP, then phenol, through the removal of the para double-flanked chlorine, followed by ortho single-flanked chlorines, and finally meta unflanked chlorines (minor pathway). Removal of meta double-flanked chlorines is thus preferred over all other substituents. 2,3,5-TCP, that completely lacks double-flanked chlorines, was degraded to 3,5-DCP through removal of the ortho single-flanked chlorine, with a 99.6% reduction in initial concentration of 2,3,5-TCP by week 14. 16S rRNA PCR-DGGE using Chloroflexi-specific primers revealed a different role of the two microorganisms VLD-1 and VLD-2, previously identified as dechlorinators in the Aroclor 1254 PCB-enriched community, in the dehalogenation of chlorophenols. VLD-1 was observed both in PCP- and TCP-dechlorinating communities, whereas VLD-2 only in TCP-dechlorinating communities. This indicates that VLD-1 and VLD-2 may both dechlorinate ortho single-flanked chlorines, but only VLD-1 is able to remove double-flanked meta or para chlorines.