Damage quantification techniques in acoustic emission monitoring

Acoustic emission (AE) analysis is one of the several diagnostic techniques available nowadays for structural health monitoring (SHM) of engineering structures. Some of its advantages over other techniques include high sensitivity to crack growth and capability of monitoring a structure in real time. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). In AE technique, these stress waves are recorded by means of suitable sensors placed on the surface of a structure. Recorded signals are subsequently analysed to gather information about the nature of the source. By enabling early detection of crack growth, AE technique helps in planning timely retrofitting or other maintenance jobs or even replacement of the structure if required. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. Large amount of data is generated during AE testing, hence effective data analysis is necessary, especially for long term monitoring uses. Appropriate analysis of AE data for quantification of damage level is an area that has received considerable attention. Various approaches available for damage quantification for severity assessment are discussed in this paper, with special focus on civil infrastructure such as bridges. One method called improved b-value analysis is used to analyse data collected from laboratory testing.

The microRNA expression signature on modified titanium implant surfaces influences genetic mechanisms leading to osteogenic differentiation

Topographically and chemically modified titanium implants are recognized to have improved osteogenic properties; however, the molecular regulation of this process remains unknown. This study aimed to determine the microRNA profile and the potential regulation of osteogenic differentiation following early exposure of osteoprogenitor cells to sand-blasted, large-grit acid-etched (SLA) and hydrophilic SLA (modSLA) surfaces. Firstly, the osteogenic characteristics of the primary osteoprogenitor cells were confirmed using ALP activity and Alizarin Red S staining. The effect of smooth (SMO), SLA and modSLA surfaces on the TGF-β/BMP (BMP2, BMP6, ACVR1) and non-canonical WNT/Ca2+ (WNT5A, FZD6) pathways, as well as the integrins ITGB1 and ITGA2, was determined. It was revealed that the modified titanium surfaces could induce the activation of TGF-β/BMP and non-canonical WNT/Ca2+ signaling genes. The expression pattern of microRNAs (miRNAs) related to cell differentiation was evaluated. Statistical analysis of the differentially regulated miRNAs indicated that 35 and 32 miRNAs were down-regulated on the modSLA and SLA surfaces respectively, when compared with the smooth surface (SMO). Thirty-one miRNAs that were down-regulated were common to both modSLA and SLA. There were 10 miRNAs up-regulated on modSLA and nine on SLA surfaces, amongst which eight were the same as observed on modSLA. TargetScan predictions for the down-regulated miRNAs revealed genes of the TGF-β/BMP and non-canonical Ca2+ pathways as targets. This study demonstrated that modified titanium implant surfaces induce differential regulation of miRNAs, which potentially regulate the TGF-β/BMP and WNT/Ca2+ pathways during osteogenic differentiation on modified titanium implant surfaces.

Using fiber Bragg grating in geothermal monitoring

Earthquake precursor monitoring is the foundation of earthquake prediction and geothermal monitoring is one of the basic methods of earthquake precursor monitoring. High temperature well contains more information and therefore its monitoring is more important. However, electric sensors are hard to meet the monitoring requirements of high sensitivity and long lifetime. For a better observation of the earthquake precursor, a high sensitive fiber Bragg grating (FBG) temperature sensor is designed to monitoring a well at 87.5±1◦C. The performance of the FBG sensor demonstrates that it’s quite possible that applying FBG to high-sensitivity temperature-monitoring fields, such as geothermal monitoring. As far as we known, it is the first time that trying a high sensitive FBG temperature sensor in a practical application, let alone in the field of geothermal monitoring.

Extending persistent monitoring by combining ocean models and Markov decision processes

Ocean processes are complex and have high variability in both time and space. Thus, ocean scientists must collect data over long time periods to obtain a synoptic view of ocean processes and resolve their spatiotemporal variability. One way to perform these persistent observations is to utilise an autonomous vehicle that can remain on deployment for long time periods. However, such vehicles are generally underactuated and slow moving. A challenge for persistent monitoring with these vehicles is dealing with currents while executing a prescribed path or mission. Here we present a path planning method for persistent monitoring that exploits ocean currents to increase navigational accuracy and reduce energy consumption.

Genetic diversity in three redclaw crayfish (Cherax quadricarinatus, von Martens) lines developed in culture in China

The redclaw crayfish Cherax quadricarinatus (von Martens) accounts for the entire commercial production of freshwater crayfish in Australia. Two forms have been recognized, an 'Eastern' form in northern Queensland and a 'Western' form in the Northern Territory and far northern Western Australia. To date, only the Eastern form has been exported overseas for culture (including to China). The genetic structure of three Chinese redclaw crayfish culture lines from three different geographical locations in China (Xiamen in Fujian Province, Guangzhou in Guangdong Province and Chongming in Shanghai) were investigated for their levels and patterns of genetic diversity using microsatellite markers. Twenty-eight SSR markers were isolated and used to analyse genetic diversity levels in three redclaw crayfish culture lines in China. This study set out to improve the current understanding of the molecular genetic characteristics of imported strains of redclaw crayfish reared in China. Microsatellite analysis revealed moderate allelic and high gene diversity in all three culture lines. Polymorphism information content estimates for polymorphic loci varied between 0.1168 and 0.8040, while pairwise F ST values among culture lines were moderate (0.0020-0.1244). The highest estimate of divergence was evident between the Xiamen and Guangzhou populations.

Use of hierarchical cluster analysis to assess the representativeness of a baseline groundwater quality monitoring network: comparison of New Zealand’s national and regional groundwater monitoring programs

Baseline monitoring of groundwater quality aims to characterize the ambient condition of the resource and identify spatial or temporal trends. Sites comprising any baseline monitoring network must be selected to provide a representative perspective of groundwater quality across the aquifer(s) of interest. Hierarchical cluster analysis (HCA) has been used as a means of assessing the representativeness of a groundwater quality monitoring network, using example datasets from New Zealand. HCA allows New Zealand's national and regional monitoring networks to be compared in terms of the number of water-quality categories identified in each network, the hydrochemistry at the centroids of these water-quality categories, the proportions of monitoring sites assigned to each water-quality category, and the range of concentrations for each analyte within each water-quality category. Through the HCA approach, the National Groundwater Monitoring Programme (117 sites) is shown to provide a highly representative perspective of groundwater quality across New Zealand, relative to the amalgamated regional monitoring networks operated by 15 different regional authorities (680 sites have sufficient data for inclusion in HCA). This methodology can be applied to evaluate the representativeness of any subset of monitoring sites taken from a larger network.

Clustering composite SaaS components in cloud computing using a grouping genetic algorithm

Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.

Identity and genetic origins : an ethical exploration of the late discovery of adoptive and donor-insemination offspring status

This thesis is an ethical and empirical exploration of the late discovery of genetic origins in two contexts, adoption and sperm donor-assisted conception. This exploration has two interlinked strands of concern. The first is the identification of ‘late discovery’ as a significant issue of concern, deserving of recognition and acknowledgment. The second concerns the ethical implications of late discovery experiences for the welfare of the child. The apparently simple act of recognition of a phenomenon is a precondition to any analysis and critique of it. This is especially important when the phenomenon arises out of social practices that arouse significant debate in ethical and legal contexts. As the new reproductive technologies and some adoption practices remain highly contested, an ethical exploration of this long neglected experience has the potential to offer new insights and perspectives in a range of contexts. It provides an opportunity to revisit developmental debate on the relative merit or otherwise of biological versus social influences, from the perspective of those who have lived this dichotomy in practise. Their experiences are the human face of the effects arising from decisions taken by others to intentionally separate their biological and social worlds, an action which has then been compounded by family and institutional secrecy from birth. This has been accompanied by a failure to ensure that normative standards and values are upheld for them. Following discovery, these factors can be exacerbated by a lack of recognition and acknowledgement of their concerns by family, friends, community and institutions. Late discovery experiences offer valuable insights to inform discussions on the ethical meanings of child welfare, best interests, parental responsibility, duty of care and child identity rights in this and other contexts. They can strengthen understandings of what factors are necessary for a child to be able to live a reasonably happy or worthwhile life.

A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing

A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.

Towards the development of a gas sensor system for monitoring pollutant gases in the low troposphere using small unmanned aerial vehicles

The world is facing problems due to the effects of increased atmospheric pollution, climate change and global warming. Innovative technologies to identify, quantify and assess fluxes exchange of the pollutant gases between the Earth’s surface and atmosphere are required. This paper proposes the development of a gas sensor system for a small UAV to monitor pollutant gases, collect data and geo-locate where the sample was taken. The prototype has two principal systems: a light portable gas sensor and an optional electric–solar powered UAV. The prototype will be suitable to: operate in the lower troposphere (100-500m); collect samples; stamp time and geo-locate each sample. One of the limitations of a small UAV is the limited power available therefore a small and low power consumption payload is designed and built for this research. The specific gases targeted in this research are NO2, mostly produce by traffic, and NH3 from farming, with concentrations above 0.05 ppm and 35 ppm respectively which are harmful to human health. The developed prototype will be a useful tool for scientists to analyse the behaviour and tendencies of pollutant gases producing more realistic models of them.

Association between prostinogen (KLK15) genetic variants and prostate cancer risk and aggressiveness in Australia and a meta-analysis of GWAS data

Background: Kallikrein 15 (KLK15)/Prostinogen is a plausible candidate for prostate cancer susceptibility. Elevated KLK15 expression has been reported in prostate cancer and it has been described as an unfavorable prognostic marker for the disease. Objectives: We performed a comprehensive analysis of association of variants in the KLK15 gene with prostate cancer risk and aggressiveness by genotyping tagSNPs, as well as putative functional SNPs identified by extensive bioinformatics analysis. Methods and Data Sources: Twelve out of 22 SNPs, selected on the basis of linkage disequilibrium pattern, were analyzed in an Australian sample of 1,011 histologically verified prostate cancer cases and 1,405 ethnically matched controls. Replication was sought from two existing genome wide association studies (GWAS): the Cancer Genetic Markers of Susceptibility (CGEMS) project and a UK GWAS study. Results: Two KLK15 SNPs, rs2659053 and rs3745522, showed evidence of association (p, 0.05) but were not present on the GWAS platforms. KLK15 SNP rs2659056 was found to be associated with prostate cancer aggressiveness and showed evidence of association in a replication cohort of 5,051 patients from the UK, Australia, and the CGEMS dataset of US samples. A highly significant association with Gleason score was observed when the data was combined from these three studies with an Odds Ratio (OR) of 0.85 (95% CI = 0.77-0.93; p = 2.7610 24). The rs2659056 SNP is predicted to alter binding of the RORalpha transcription factor, which has a role in the control of cell growth and differentiation and has been suggested to control the metastatic behavior of prostate cancer cells. Conclusions: Our findings suggest a role for KLK15 genetic variation in the etiology of prostate cancer among men of European ancestry, although further studies in very large sample sets are necessary to confirm effect sizes.

Identification of a novel prostate cancer susceptibility variant in the KLK3 gene transcript

Abstract Genome-wide association studies (GWAS) have identified more than 30 prostate cancer (PrCa) susceptibility loci. One of these (rs2735839) is located close to a plausible candidate susceptibility gene, KLK3, which encodes prostate-specific antigen (PSA). PSA is widely used as a biomarker for PrCa detection and disease monitoring. To refine the association between PrCa and variants in this region, we used genotyping data from a two-stage GWAS using samples from the UK and Australia, and the Cancer Genetic Markers of Susceptibility (CGEMS) study. Genotypes were imputed for 197 and 312 single nucleotide polymorphisms (SNPs) from HapMap2 and the 1000 Genome Project, respectively. The most significant association with PrCa was with a previously unidentified SNP, rs17632542 (combined P = 3.9 × 10−22). This association was confirmed by direct genotyping in three stages of the UK/Australian GWAS, involving 10,405 cases and 10,681 controls (combined P = 1.9 × 10−34). rs17632542 is also shown to be associated with PSA levels and it is a non-synonymous coding SNP (Ile179Thr) in KLK3. Using molecular dynamic simulation, we showed evidence that this variant has the potential to introduce alterations in the protein or affect RNA splicing. We propose that rs17632542 may directly influence PrCa risk.

Novel molecular markers of Chlamydia pecorum genetic diversity in the koala (Phascolarctos cinereus)

Background Chlamydia pecorum is an obligate intracellular bacterium and the causative agent of reproductive and ocular disease in several animal hosts including koalas, sheep, cattle and goats. C. pecorum strains detected in koalas are genetically diverse, raising interesting questions about the origin and transmission of this species within koala hosts. While the ompA gene remains the most widely-used target in C. pecorum typing studies, it is generally recognised that surface protein encoding genes are not suited for phylogenetic analysis and it is becoming increasingly apparent that the ompA gene locus is not congruent with the phylogeny of the C. pecorum genome. Using the recently sequenced C. pecorum genome sequence (E58), we analysed 10 genes, including ompA, to evaluate the use of ompA as a molecular marker in the study of koala C. pecorum genetic diversity. Results Three genes (incA, ORF663, tarP) were found to contain sufficient nucleotide diversity and discriminatory power for detailed analysis and were used, with ompA, to genotype 24 C. pecorum PCR-positive koala samples from four populations. The most robust representation of the phylogeny of these samples was achieved through concatenation of all four gene sequences, enabling the recreation of a "true" phylogenetic signal. OmpA and incA were of limited value as fine-detailed genetic markers as they were unable to confer accurate phylogenetic distinctions between samples. On the other hand, the tarP and ORF663 genes were identified as useful "neutral" and "contingency" markers respectively, to represent the broad evolutionary history and intra-species genetic diversity of koala C. pecorum. Furthermore, the concatenation of ompA, incA and ORF663 sequences highlighted the monophyletic nature of koala C. pecorum infections by demonstrating a single evolutionary trajectory for koala hosts that is distinct from that seen in non-koala hosts. Conclusions While the continued use of ompA as a fine-detailed molecular marker for epidemiological analysis appears justified, the tarP and ORF663 genes also appear to be valuable markers of phylogenetic or biogeographic divisions at the C. pecorum intra-species level. This research has significant implications for future typing studies to understand the phylogeny, genetic diversity, and epidemiology of C. pecorum infections in the koala and other animal species.