An improved method to detect damage using modal strain energy based damage index

This paper presents two novel concepts to enhance the accuracy of damage detection using the Modal Strain Energy based Damage Index (MSEDI) with the presence of noise in the mode shape data. Firstly, the paper presents a sequential curve fitting technique that reduces the effect of noise on the calculation process of the MSEDI, more effectively than the two commonly used curve fitting techniques; namely, polynomial and Fourier’s series. Secondly, a probability based Generalized Damage Localization Index (GDLI) is proposed as a viable improvement to the damage detection process. The study uses a validated ABAQUS finite-element model of a reinforced concrete beam to obtain mode shape data in the undamaged and damaged states. Noise is simulated by adding three levels of random noise (1%, 3%, and 5%) to the mode shape data. Results show that damage detection is enhanced with increased number of modes and samples used with the GDLI.

Improved treatment of nicotine addiction and emerging pulmonary drug delivery

Nicotine addiction remains the leading cause of death and disease in developed and developing nations and a major cause of mortality around the world. Currently, nicotine replacement therapies (NRTs), bupropion, and varenicline are approved by the regulatory agencies as first-line treatments for nicotine addiction. Emerging evidence indicates that varenicline and bupropion have some therapeutic limitations for treating nicotine addiction with oral route of administration. Thus, continued investigation of innovative drug delivery for nicotine addiction remains a critical priority. This review will discuss some novel strategies and future directions for pulmonary drug delivery, an emerging route of administration for smoking cessation. It is anticipated that the advancement of knowledge on pulmonary drug delivery will provide better management for nicotine addiction and other addictive disorders.

Subcarrier pairing and relay assignment with improved user fairness in a multi-user cooperative OFDM system

We consider a joint relay selection and subcarrier allocation problem that minimizes the total system power for a multi-user, multi-relay and single source cooperative OFDM based two hop system. The system is constrained to all users having a specific subcarrier requirement (user fairness). However no specific fairness constraints for relays are considered. To ensure the optimum power allocation, the subcarriers in two hops are paired with each other. We obtain an optimal subcarrier allocation for the single user case using a similar method to what is described in [1] and modify the algorithm for multiuser scenario. Although the optimality is not achieved in multiuser case the probability of all users being served fairly is improved significantly with a relatively low cost trade off.

Improved surgical safety after laparoscopic compared to open surgery for apparent early stage endometrial cancer : results from a randomised controlled trial

AIM: To compare Total Laparoscopic Hysterectomy (TLH) and Total Abdominal Hysterectomy (TAH) with regard to surgical safety. METHODS: Between October 2005 and June 2010, 760 patients with apparent early stage endometrial cancer were enroled in a multicentre, randomised clinical trial (LACE) comparing outcomes following TLH or TAH. The main study end points for this analysis were surgical adverse events (AE), hospital length of stay, conversion from laparoscopy to laparotomy, including 753 patients who completed at least 6 weeks of follow-up. Postoperative AEs were graded according to Common Toxicity Criteria (V3), and those immediately life-threatening, requiring inpatient hospitalisation or prolonged hospitalisation, or resulting in persistent or significant disability/incapacity were regarded as serious AEs. RESULTS: The incidence of intra-operative AEs was comparable in either group. The incidence of post-operative AE CTC grade 3+ (18.6% in TAH, 12.9% in TLH, p 0.03) and serious AE (14.3% in TAH, 8.2% in TLH, p 0.007) was significantly higher in the TAH group compared to the TLH group. Mean operating time was 132 and 107 min, and median length of hospital stay was 2 and 5 days in the TLH and TAH group, respectively (p<0.0001). The decline of haemoglobin from baseline to day 1 postoperatively was 2g/L less in the TLH group (p 0.006). CONCLUSIONS: Compared to TAH, TLH is associated with a significantly decreased risk of major surgical AEs. A laparoscopic surgical approach to early stage endometrial cancer is safe.

Encouraging, assisting and time to EAT : improved nutritional intake for older medical patients receiving Protected Mealtimes and/or additional nursing feeding assistance

Background & Aims: Inadequate feeding assistance and mealtime interruptions during hospitalisation may contribute to malnutrition and poor nutritional intake in older people. This study aimed to implement and compare three interventions designed to specifically address mealtime barriers and improve energy intakes of medical inpatients aged ≥65 years. Methods: Pre-post study compared three mealtime assistance interventions: PM: Protected Mealtimes with multidisciplinary education; AIN: additional assistant-in-nursing (AIN) with dedicated meal role; PM+AIN: combined intervention. Dietary intake of 254 patients (pre: n=115, post: n=141; mean age 80±8) was visually estimated on a single day in the first week of hospitalisation and compared with estimated energy requirements. Assistance activities were observed and recorded. Results: Mealtime assistance levels significantly increased in all interventions (p<0.01). Post-intervention participants were more likely to achieve adequate energy intake (OR=3.4, p=0.01), with no difference noted between interventions (p=0.29). Patients with cognitive impairment or feeding dependency appeared to gain substantial benefit from mealtime assistance interventions. Conclusions: Protected Mealtimes and additional AIN assistance (implemented alone or in combination) may produce modest improvements in nutritional intake. Targeted feeding assistance for certain patient groups holds promise; however, alternative strategies are required to address the complex problem of malnutrition in this population.

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Researching quality and equity in assessment for improved learner outcomes

Quality and equity issues as they relate to assessment practices and policy are becoming increasingly important as Australia introduces a national curriculum and achievement standards. In a context of high-stakes accountability, concerns regarding equity and quality have important implications for teachers‘ practice for the improvement of learner outcomes. This article is based on three research projects that were conducted over the past four years and were funded by the Australian Federal Government. The research focus, emergent questions, the educational contexts, and the rationale for the studies are discussed prior to the presentation of the analysis of the research findings and the implications for teachers‘ practice and policy reform.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution

Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).

Prediction of age at menopause from assessment of ovarian reserve may be improved by using body mass index and smoking status

Objective: Menopause is the consequence of exhaustion of the ovarian follicular pool. AMH, an indirect hormonal marker of ovarian reserve, has been recently proposed as a predictor for age at menopause. Since BMI and smoking status are relevant independent factors associated with age at menopause we evaluated whether a model including all three of these variables could improve AMH-based prediction of age at menopause. Methods: In the present cohort study, participants were 375 eumenorrheic women aged 19–44 years and a sample of 2,635 Italian menopausal women. AMH values were obtained from the eumenorrheic women. Results: Regression analysis of the AMH data showed that a quadratic function of age provided a good description of these data plotted on a logarithmic scale, with a distribution of residual deviates that was not normal but showed significant leftskewness. Under the hypothesis that menopause can be predicted by AMH dropping below a critical threshold, a model predicting menopausal age was constructed from the AMH regression model and applied to the data on menopause. With the AMH threshold dependent on the covariates BMI and smoking status, the effects of these covariates were shown to be highly significant. Conclusions: In the present study we confirmed the good level of conformity between the distributions of observed and AMH-predicted ages at menopause, and showed that using BMI and smoking status as additional variables improves AMH-based prediction of age at menopause.

Improved estimates of percolation and anisotropic permeability from 3-D X-ray microtomography using stochastic analyses and visualization

X-ray microtomography (micro-CT) with micron resolution enables new ways of characterizing microstructures and opens pathways for forward calculations of multiscale rock properties. A quantitative characterization of the microstructure is the first step in this challenge. We developed a new approach to extract scale-dependent characteristics of porosity, percolation, and anisotropic permeability from 3-D microstructural models of rocks. The Hoshen-Kopelman algorithm of percolation theory is employed for a standard percolation analysis. The anisotropy of permeability is calculated by means of the star volume distribution approach. The local porosity distribution and local percolation probability are obtained by using the local porosity theory. Additionally, the local anisotropy distribution is defined and analyzed through two empirical probability density functions, the isotropy index and the elongation index. For such a high-resolution data set, the typical data sizes of the CT images are on the order of gigabytes to tens of gigabytes; thus an extremely large number of calculations are required. To resolve this large memory problem parallelization in OpenMP was used to optimally harness the shared memory infrastructure on cache coherent Non-Uniform Memory Access architecture machines such as the iVEC SGI Altix 3700Bx2 Supercomputer. We see adequate visualization of the results as an important element in this first pioneering study.

‘Working with the team’ : an exploratory study of improved type 2 diabetes management in a new model of integrated primary/secondary care

This study aimed to explore how a new model of integrated primary/secondary care for type 2 diabetes management, the Brisbane South Complex Diabetes Service (BSCDS), related to improved diabetes management in a selected group of patients. We used a qualitative research design to obtain detailed accounts from the BSCDS via semi-structured interviews with 10 patients. The interviews were fully transcribed and systematically coded using a form of thematic analysis. Participants’ responses were grouped in relation to: (1) Patient-centred care; (2) Effective multiprofessional teamwork; and (3) Empowering patients. The key features of this integrated primary/secondary care model were accessibility and its delivery within a positive health care environment, clear and supportive interpersonal communication between patients and health care providers, and patients seeing themselves as being part of the team-based care. The BSCDS delivered patient-centred care and achieved patient engagement in ways that may have contributed to improved type 2 diabetes management in these participants.

Development of improved fire design rules for cold-formed steel wall systems

Traditionally the fire resistance rating of LSF wall systems is based on approximate prescriptive methods developed using limited fire tests. Therefore a detailed research study into the performance of load bearing LSF wall systems under standard fire conditions was undertaken to develop improved fire design rules. It used the extensive fire performance results of eight different LSF wall systems from a series of full scale fire tests and numerical studies for this purpose. The use of previous fire design rules developed for LSF walls subjected to non-uniform elevated temperature distributions based on AISI design manual and Eurocode3 Parts 1.2 and 1.3 was investigated first. New simplified fire design rules based on AS/NZS 4600, North American Specification and Eurocode 3 Part 1.3 were then proposed in this study with suitable allowances for the interaction effects of compression and bending actions. The importance of considering thermal bowing, magnified thermal bowing and neutral axis shift in the fire design was also investigated. A spread sheet based design tool was developed based on the new design rules to predict the failure load ratio versus time and temperature curves for varying LSF wall configurations. The accuracy of the proposed design rules was verified using the test and FEA results for different wall configurations, steel grades, thicknesses and load ratios. This paper presents the details and results of this study including the improved fire design rules for predicting the load capacity of LSF wall studs and the failure times of LSF walls under standard fire conditions.