A study on the use of acoustic emission technique as a structural health monitoring tool

As civil infrastructures such as bridges age, there is a concern for safety and a need for cost-effective and reliable monitoring tool. Different diagnostic techniques are available nowadays for structural health monitoring (SHM) of bridges. Acoustic emission is one such technique with potential of predicting failure. 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). AEtechnique involves recording the stress waves bymeans of sensors and subsequent analysis of the recorded signals,which then convey information about the nature of the source. AE can be used as a local SHM technique to monitor specific regions with visible presence of cracks or crack prone areas such as welded regions and joints with bolted connection or as a global technique to monitor the whole structure. Strength of AE technique lies in its ability to detect active crack activity, thus helping in prioritising maintenance work by helping focus on active cracks rather than dormant cracks. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. One is the generation of large amount of data during the testing; hence an effective data analysis and management is necessary, especially for long term monitoring uses. Complications also arise as a number of spurious sources can giveAEsignals, therefore, different source discrimination strategies are necessary to identify genuine signals from spurious ones. Another major challenge is the quantification of damage level by appropriate analysis of data. Intensity analysis using severity and historic indices as well as b-value analysis are some important methods and will be discussed and applied for analysis of laboratory experimental data in this paper.

Genetic evidence for the vital function of osterix in cementogenesis

To date, attempts to regenerate a complete tooth, including the critical periodontal tissues associated with the tooth root, have not been successful. Controversy still exists regarding the origin of the cell source for cellular cementum (epithelial or mesenchymal). This disagreement may be partially due to a lack of understanding of the events leading to the initiation and development of the tooth roots and supportive tissues, such as the cementum. Osterix (OSX) is a transcriptional factor essential for osteogenesis, but its role in cementogenesis has not been addressed. In the present study, we first documented a close relationship between the temporal- and spatial-expression pattern of OSX and the formation of cellular cementum. We then generated 3.6 Col 1-OSX transgenic mice, which displayed accelerated cementum formation vs. WT controls. Importantly, the conditional deletion of OSX in the mesenchymal cells with two different Cre systems (the 2.3 kb Col 1 and an inducible CAG-CreER) led to a sharp reduction in cellular cementum formation (including the cementum mass and mineral deposition rate) and gene expression of dentin matrix protein 1 (DMP1) by cementocytes. However, the deletion of the OSX gene after cellular cementum formed did not alter the properties of the mature cementum as evaluated by backscattered SEM and resin-cast SEM. Transient transfection of Osx in the cementoblasts in vitro significantly inhibited cell proliferation and increased cell differentiation and mineralization. Taken together, these data support 1) the mesenchymal origin of cellular cementum (from PDL progenitor cells); 2) the vital role of OSX in controlling the formation of cellular cementum; and 3) the limited remodeling of cellular cementum in adult mice.

Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Functional and advanced functional and causal relationship diagrams for remote condition monitoring

Analysing the condition of an asset is a big challenge as there can be many aspects which can contribute to the overall functional reliability of the asset that have to be considered. In this paper we propose a two-step functional and causal relationship diagram (FCRD) to address this problem. In the first step, the FCRD is designed to facilitate the analysis of the condition of an asset by evaluating the interdependence (functional and causal) relationships between different components of the asset with the help of a relationship diagram. This is followed by the advanced FCRD (AFCRD) which refines the information from the FCRD into a comprehensive and manageable format. This new two-step methodology for asset condition monitoring is tested and validated for the case of a water treatment plant. © IMechE 2012.

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.