Some Recent Developments in SHM Based on Nonstationary Time Series Analysis

Many of the algorithms used for structural health monitoring (SHM) are based on, or motivated by, time series analysis. Quite often, detection methods are variants of approaches developed within the statistical process control (SPC) community. Many of the algorithms used represent mature theory and have a rigorous probabilistic or mathematical basis. However, one of the main issues facing SHM practitioners is that the structures of interest rarely respect the assumptions inherent in deriving algorithms. In the case of time series data, SPC-based approaches usually require the data to be stationary and, unfortunately, SHM data are often nonstationary because of benign variations in the environment of the structure of interest, or because of deliberate operational changes in the use of the structure. This nonstationarity can manifest itself as slowly varying trends on the data or in abrupt switches between regimes. Recent work in nonstationary time series methods for SHM has made considerable progress in accommodating nonstationarity and some of that work is discussed within this paper: in terms of understanding slowly varying trends, the cointegration algorithm from econometrics is presented; for understanding abrupt switches, Bayesian mixtures of experts are presented. Another issue in time series analysis is indirectly related to the assumption of linear behavior of structures and the impact of this assumption is briefly considered in terms of its effects on detection thresholds in SPC-like methods; again, progress has been made recently. Some issues still remain, and these are discussed also

Long-term monitoring and data analysis of the Tamar Bridge

Author's manuscript version. the version of record is available from the publisher via: doi:10.1016/j.ymssp.2012.08.026. Copyright © 2012 Elsevier Ltd. All rights reserved.A sound understanding of a structure’s normal condition, including its response to normal environmental and operational variations is desirable for structural health monitoring and necessary for performance monitoring of civil structures. The current paper outlines the extensive monitoring campaign of the Tamar Suspension Bridge as well as analysis carried out in an attempt to understand the bridge’s normal condition. Specifically the effects of temperature, traffic loading and wind speed on the structure’s dynamic response are investigated. Finally, initial steps towards the development of a structural health monitoring system for the Tamar Bridge are addressed. © 2012 Elsevier Ltd. All rights reserved

A brief introduction to recent developments in population-based structural health monitoring

This is the final version. Available from the publisher via the DOI in this record.One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring health inferences and information between structures in an identified population—Population-based SHM (PBSHM). In the case of homogenous populations (sets of nominally-identical structures, like in a wind farm), the idea of the form has been proposed which encodes information about the ideal or typical structure together with information about variations across the population. In the case of sets of disparate structures—heterogeneous populations—transfer learning appears to be a powerful tool for sharing inferences, and is also applicable in the homogenous case. In order to assess the likelihood of transference being meaningful, it has proved useful to develop an abstract representation framework for spaces of structures, so that similarities between structures can formally be assessed; this framework exploits tools from graph theory. The current paper discusses all of these very recent developments and provides illustrative examplesEngineering and Physical Sciences Research Council (EPSRC

Prediction of landing gear loads using machine learning techniques

This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction

Conspecific and Heterospecific Information Use in Bumblebees

Heterospecific social learning has been understudied in comparison to interactions between members of the same species. However, the learning mechanisms behind such information use can allow animals to be flexible in the cues that are used. This raises the question of whether conspecific cues are inherently more influential than cues provided by heterospecifics, or whether animals can simply use any cue that predicts fitness enhancing conditions, including those provided by heterospecifics. To determine how freely social information travels across species boundaries, we trained bumblebees (Bombus terrestris) to learn to use cues provided by conspecifics and heterospecific honey bees (Apis mellifera) to locate valuable floral resources. We found that heterospecific demonstrators did not differ from conspecifics in the extent to which they guided observers' choices, whereas various types of inorganic visual cues were consistently less effective than conspecifics. This was also true in a transfer test where bees were confronted with a novel flower type. However, in the transfer test, conspecifics were slightly more effective than heterospecific demonstrators. We then repeated the experiment with entirely naïve bees that had never foraged alongside conspecifics before. In this case, heterospecific demonstrators were equally efficient as conspecifics both in the initial learning task and the transfer test. Our findings demonstrate that social learning is not a unique process limited to conspecifics and that through associative learning, interspecifically sourced information can be just as valuable as that provided by conspecific individuals. Furthermore the results of this study highlight potential implications for understanding competition within natural pollinator communities

Thiolutin is a zinc chelator that inhibits the Rpn11 and other JAMM metalloproteases

Thiolutin is a disulfide-containing antibiotic and anti-angiogenic compound produced by Streptomyces. Its biological targets are not known. We show that reduced thiolutin is a zinc chelator that inhibits the JAB1/MPN/Mov34 (JAMM) domain–containing metalloprotease Rpn11, a deubiquitinating enzyme of the 19S proteasome. Thiolutin also inhibits the JAMM metalloproteases Csn5, the deneddylase of the COP9 signalosome; AMSH, which regulates ubiquitin-dependent sorting of cell-surface receptors; and BRCC36, a K63-specific deubiquitinase of the BRCC36-containing isopeptidase complex and the BRCA1–BRCA2-containing complex. We provide evidence that other dithiolopyrrolones also function as inhibitors of JAMM metalloproteases

Discovery of Nuclear-Encoded Genes for the Neurotoxin Saxitoxin in Dinoflagellates

Saxitoxin is a potent neurotoxin that occurs in aquatic environments worldwide. Ingestion of vector species can lead to paralytic shellfish poisoning, a severe human illness that may lead to paralysis and death. In freshwaters, the toxin is produced by prokaryotic cyanobacteria; in marine waters, it is associated with eukaryotic dinoflagellates. However, several studies suggest that saxitoxin is not produced by dinoflagellates themselves, but by co-cultured bacteria. Here, we show that genes required for saxitoxin synthesis are encoded in the nuclear genomes of dinoflagellates. We sequenced >1.2×106 mRNA transcripts from the two saxitoxin-producing dinoflagellate strains Alexandrium fundyense CCMP1719 and A. minutum CCMP113 using high-throughput sequencing technology. In addition, we used in silico transcriptome analyses, RACE, qPCR and conventional PCR coupled with Sanger sequencing. These approaches successfully identified genes required for saxitoxin-synthesis in the two transcriptomes. We focused on sxtA, the unique starting gene of saxitoxin synthesis, and show that the dinoflagellate transcripts of sxtA have the same domain structure as the cyanobacterial sxtA genes. But, in contrast to the bacterial homologs, the dinoflagellate transcripts are monocistronic, have a higher GC content, occur in multiple copies, contain typical dinoflagellate spliced-leader sequences and eukaryotic polyA-tails. Further, we investigated 28 saxitoxin-producing and non-producing dinoflagellate strains from six different genera for the presence of genomic sxtA homologs. Our results show very good agreement between the presence of sxtA and saxitoxin-synthesis, except in three strains of A. tamarense, for which we amplified sxtA, but did not detect the toxin. Our work opens for possibilities to develop molecular tools to detect saxitoxin-producing dinoflagellates in the environment

Life-Cycle and Genome of OtV5, a Large DNA Virus of the Pelagic Marine Unicellular Green Alga Ostreococcus tauri

Large DNA viruses are ubiquitous, infecting diverse organisms ranging from algae to man, and have probably evolved from an ancient common ancestor. In aquatic environments, such algal viruses control blooms and shape the evolution of biodiversity in phytoplankton, but little is known about their biological functions. We show that Ostreococcus tauri, the smallest known marine photosynthetic eukaryote, whose genome is completely characterized, is a host for large DNA viruses, and present an analysis of the life-cycle and 186,234 bp long linear genome of OtV5. OtV5 is a lytic phycodnavirus which unexpectedly does not degrade its host chromosomes before the host cell bursts. Analysis of its complete genome sequence confirmed that it lacks expected site-specific endonucleases, and revealed the presence of 16 genes whose predicted functions are novel to this group of viruses. OtV5 carries at least one predicted gene whose protein closely resembles its host counterpart and several other host-like sequences, suggesting that horizontal gene transfers between host and viral genomes may occur frequently on an evolutionary scale. Fifty seven percent of the 268 predicted proteins present no similarities with any known protein in Genbank, underlining the wealth of undiscovered biological diversity present in oceanic viruses, which are estimated to harbour 200Mt of carbon

Phylogenomic analysis of the Chlamydomonas genome unmasks proteins potentially involved in photosynthetic function and regulation

Chlamydomonas reinhardtii, a unicellular green alga, has been exploited as a reference organism for identifying proteins and activities associated with the photosynthetic apparatus and the functioning of chloroplasts. Recently, the full genome sequence of Chlamydomonas was generated and a set of gene models, representing all genes on the genome, was developed. Using these gene models, and gene models developed for the genomes of other organisms, a phylogenomic, comparative analysis was performed to identify proteins encoded on the Chlamydomonas genome which were likely involved in chloroplast functions (or specifically associated with the green algal lineage); this set of proteins has been designated the GreenCut. Further analyses of those GreenCut proteins with uncharacterized functions and the generation of mutant strains aberrant for these proteins are beginning to unmask new layers of functionality/regulation that are integrated into the workings of the photosynthetic apparatus