Analysis of spherical indentation of superelastic shape memory alloys

Dimensional analysis and the finite element method are applied in this paper to study spherical indentation of superelastic shape memory alloys. The scaling relationships derived from dimensional analysis bridge the indentation response and the mechanical properties of a superelastic shape memory alloy. Several key variables of a superelastic indentation curve are revealed and examined. We prove that the bifurcation force in a superelastic indentation curve only relies on the forward transformation stress and the elastic properties of the initial austenite; and the return force in a superelastic indentation curve only relies on the reverse transformation stress and the elastic properties of the initial austenite. Furthermore, the dimensionless functions to determine the bifurcation force and the return force are proved to be identical. These results not only enhance our understanding of spherical indentation of superelastic shape memory alloys, but also provide the theoretical basis for developing a practicable method to calibrate the mechanical properties of a superelastic material from the spherical indentation test.

Scaling up community-based obesity prevention in Australia : background and evaluation design of the health promoting communities : being active eating well initiative

Background : There is only limited evidence available on how best to prevent childhood obesity and community-based interventions hold promise, as several successful interventions have now been published. The Victorian Government has recently funded six disadvantaged communities across Victoria, Australia for three years to promote healthy eating and physical activity for children, families, and adults in a community-based participatory manner. Five of these intervention communities are situated in Primary Care Partnerships and are the subject of this paper. The interventions will comprise a mixture of capacity-building, environmental, and whole-of-community approaches with targeted and population-level interventions. The specific intervention activities will be determined locally within each community through stakeholder and community consultation. Implementation of the interventions will occur through funded positions in primary care and local government. This paper describes the design of the evaluation of the five primary care partnership-based initiatives in the 'Go for your life' Health Promoting Communities: Being Active Eating Well (HPC:BAEW) initiative.

Methods/Design : A mixed method and multi-level evaluation of the HPC:BAEW initiative will capture process, impact and outcome data and involve both local and state-wide evaluators. There will be a combined analysis across the five community intervention projects with outcomes compared to a comparison group using a cross-sectional, quasi-experimental design. The evaluation will capture process, weight status, socio-demographic, obesity-related behavioral and environmental data in intervention and comparison areas. This will be achieved using document analysis, paper-based questionnaires, interviews and direct measures of weight, height and waist circumference from participants (children, adolescents and adults).

Discussion : This study will add significant evidence on how to prevent obesity at a population level in disadvantaged and ethnically diverse communities. The outcomes will have direct influence on policy and practice and guide the development and implementation of future obesity prevention efforts in Australia and internationally.

Phylogeny affects estimation of metabolic scaling in mammals

The relationship between body size and metabolic rate is a crucial issue in organismal biology and evolution. There has been considerable debate over whether the scaling exponent of the relationship is 0.75 (Kleiber’s Law) or 0.67. Here we show that determination of this exponent for mammals depends on both the evolutionary tree and the regression model used in the comparative analysis. For example, more recent molecular-based phylogenies tend to support a 0.67 exponent, whereas older phylogenies, mostly based on morphological data, suggest a 0.75 exponent. However, molecular phylogenies yield more variable results than morphological phylogenies and thus are not currently helping to resolve the issue.

Stereo vision and wavelets/multiwavelets analysis : 3d reconstruction using wavelets/multiwavelets theory and stereo vision

During the image formation process of the camera, explicit 3D information about the scene or objects in the scene are lost. Therefore, 3D structure or depth information has to be inferred implicitly from the 2D intensity images. This problem is com- monly referred to as 3D reconstruction. In this work a complete 3D reconstruction algorithm is presented, capable of reconstructing dimensionally accurate 3D models of the objects, based on stereo vision and multi-resolution analysis. The developed system uses a reference depth model of the objects under observation to improve the disparity maps, estimated. Only a few features are extracted from that reference model, which are the relative location of the discontinuities and the z-dimensional extremes of objects depth. The maximum error deviation of the estimated depth along the surfaces is less than 0.5mm and along the discontinuities is less than 1.5mm. The developed system is invariant to illuminative variations, and orientation, location and scaling of the objects under consideration, which makes the developed system highly robust.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

Scaling-up from an implementation trial to state-wide coverage : results from the preliminary Melbourne Diabetes Prevention Study

Background
The successful Greater Green Triangle Diabetes Prevention Program (GGT DPP), a small implementation trial, has been scaled-up to the Victorian state-wide ‘Life!’ programme with over 10,000 individuals enrolled. The Melbourne Diabetes Prevention Study (MDPS) is an evaluation of the translation from the GGT DPP to the Life! programme. We report results from the preliminary phase (pMDPS) of this evaluation.
Methods
The pMDPS is a randomised controlled trial with 92 individuals aged 50 to 75 at high risk of developing type 2 diabetes randomised to Life! or usual care. Intervention consisted of six structured 90-minute group sessions: five fortnightly sessions and the final session at 8 months. Participants underwent anthropometric and laboratory tests at baseline and 12 months, and provided self-reported psychosocial, dietary, and physical activity measures. Intervention group participants additionally underwent these tests at 3 months. Paired t tests were used to analyse within-group changes over time. Chi-square tests were used to analyse differences between groups in goals met at 12 months. Differences between groups for changes over time were tested with generalised estimating equations and analysis of covariance.
Results
Intervention participants significantly improved at 12 months in mean body mass index (−0.98 kg/m2, standard error (SE) = 0.26), weight (−2.65 kg, SE = 0.72), waist circumference (−7.45 cm, SE = 1.15), and systolic blood pressure (−3.18 mmHg, SE = 1.26), increased high-density lipoprotein-cholesterol (0.07 mmol/l, SE = 0.03), reduced energy from total (−2.00%, SE = 0.78) and saturated fat (−1.54%, SE = 0.41), and increased fibre intake (1.98 g/1,000 kcal energy, SE = 0.47). In controls, oral glucose at 2 hours deteriorated (0.59 mmol/l, SE = 0.27). Only waist circumference reduced significantly (−4.02 cm, SE = 0.95).

Intervention participants significantly outperformed controls over 12 months for body mass index and fibre intake. After baseline adjustment, they also showed greater weight loss and reduced saturated fat versus total energy intake.

At least 5% weight loss was achieved by 32% of intervention participants versus 0% controls.
Conclusions
pMDPS results indicate that scaling-up from implementation trial to state-wide programme is possible. The system design for Life! was fit for purpose of scaling-up from efficacy to effectiveness.

Scaling up the production rate of nanofibers by needleless electrospinning from multiple ring

Mass production of nanofibers is crucial in both laboratory research and industry application of nanofibers. In this study, multiple ring spinnerets have been used to generate needleless electrospinning. Multiple polymer jets were produced from the top of each ring in the spinning process, resulting in thin and uniform nanofibers. Production rate of nanofibers increased gradually with the increase of the number of rings in the spinneret. Spinning performance of multiple ring electrospinning, namely the quality and production rate of the as-spun nanofibers, was dependent on experimental parameters like applied voltage and polymer concentration. Electric field analysis of multiple ring showed that high concentrated electric field was formed on the surface of each ring. Fiber diameter together with production rate of needleless electrospinning was dependent on the strength and distribution of the electric field of the spinneret. Needleless electrospinning from multiple ring can be further applied in both laboratory research and industry where large amount of nanofibers must be employed simultaneously. © 2014 The Korean Fiber Society and Springer Science+Business Media Dordrecht.

Asymptotic throughput capacity analysis of VANETs exploiting mobility diversity

Vehicular ad hoc networks (VANETs) rely on intervehicle relay to extend the communication range of individual vehicles for message transmissions to roadside units (RSUs). With the presence of a large number of quickly moving vehicles in the network, the end-to-end transmission performance from individual vehicles to RSUs through intervehicle relaying is, however, highly unreliable due to the violative intervehicle connectivity. As an effort toward this issue, this paper develops an efficient message routing scheme that can maximize the message delivery throughput from vehicles to RSUs. Specifically, we first develop a mathematical framework to analyze the asymptotic throughput scaling of VANETs. We demonstrate that in an urban-like layout, the achievable uplink throughput per vehicle from vehicle to RSUs scales as Θ(1/ log n) when the number of RSUs scales as Θ(n/log n) with n denoting vehicle population. By noting that the network throughput is bottlenecked by the unbalanced data traffic generated by hotspots of realistic urban areas, which may overload the RSUs nearby, a novel packet-forwarding scheme is proposed to approach the optimal network throughput by exploiting the mobility diversity of vehicles to balance the data traffic across the network. Using extensive simulations based on realistic traffic traces, we demonstrate that the proposed scheme can improve the network throughput approaching the asymptotic throughput capacity.

Experience report: anomaly detection of cloud application operations using log and cloud metric correlation analysis

Failure of application operations is one of the maincauses of system-wide outages in cloud environments. Thisparticularly applies to DevOps operations, such as backup,redeployment, upgrade, customized scaling, and migration that areexposed to frequent interference from other concurrent operations,configuration changes, and resources failure. However, currentpractices fail to provide a reliable assurance of correct execution ofthese kinds of operations. In this paper, we present an approach toaddress this problem that adopts a regression-based analysistechnique to find the correlation between an operation’s activity logsand the operation activity’s effect on cloud resources. Thecorrelation model is then used to derive assertion specifications,which can be used for runtime verification of running operations andtheir impact on resources. We evaluated our proposed approach onAmazon EC2 with 22 rounds of rolling upgrade operations whileother types of operations were running and random faults wereinjected. Our experiment shows that our approach successfullymanaged to raise alarms for 115 random injected faults, with aprecision of 92.3%.