Effect of second-phase particles on shape memory in Fe-Mn-Si-based alloys

The shape memory behaviour of two Fe–Mn–Si-based alloys has been investigated. One alloy was a reference alloy, and the other alloy was
similar in composition except that it contained 0.55 wt% Ti. Following solution treatment and quenching, strip samples were subjected to three types
of treatments; isothermal holding, cold rolling followed by isothermal holding, and hot rolling followed by isothermal holding. These treatments
resulted in the formation of intermetallic precipitates in the Ti-containing alloy, while the reference alloy remained precipitate-free. In comparing
the shape memory of the reference and the particle-containing alloy after identical heat treatments, it was found that the formation of precipitates
had a beneficial effect on the shape memory in all cases. In general, the larger precipitates caused a larger increase in the shape memory. The effect
of particle size on shape memory has been analysed using the current data and published results for a range of precipitate types.

Martensite/particle interactions and the shape memory effect in an Fe–Mn–Si-based alloy

The effect of carbide precipitates with a size range of 30–300 nm on the austenite to martensite transformation has been studied. Such particles are known to enhance shape memory, and it was the aim of this work to clarify how the particles exert a favourable effect on shape memory. Differential scanning calorimetry revealed that the presence of particles increases the amount of thermally induced martensite. X-ray diffraction showed that the presence of particles increases the amount of stress-induced martensite also. Surface-relief produced on a pre-polished surface by bending deformation showed that the particle-containing samples exhibited a more complex and highly tilted surface-relief indicative of the formation of a larger volume fraction of martensite. The reversion characteristics of the particle-containing and solution-treated samples were similar: both showed co-reversion of multiple variants of martensite within the same volume of microstructure. However, a higher volume fraction of martensite reverted for the particle-containing sample on recovery annealing. The increased density of nucleation sites for martensite formation and a higher volume fraction of stress-induced martensite for a given strain are therefore considered to be the main contributions of relatively coarse carbide particles to the improvement of shape memory performance.

Austenite stability in Fe-Mn-Si-based shape memory alloys

The stability of austenite in a number of Fe–Mn–Si-based shape memory alloys has been investigated. It was found that a grain boundary precipitate of BCC structure is formed over a wide range of alloy compositions and heat treatment temperatures. This grain boundary phase has been identified as the chi (χ) phase. Although up to 3 vol.% of the grain boundary precipitate was generated by isothermal aging in the range 500–800 °C, it was found not to markedly affect the mechanical properties or the shape memory effect. Nano-indentation indicated that the hardness and strength of the parent and precipitate phase are very similar, as are their compositions.

Familiarity-based word-frequency effects in recognition memory and implicit priming

Provides evidence that familiarity as well as recollection contributes to the recognition advantage for low-frequency words. The importance for explanations of the low-frequency word advantage to account for the influence of both explicit and implicit memory processes is discussed with emphasis given to developing the dual-process model.

Improving prediction interval quality : a genetic algorithm-based method applied to neural networks

The delta technique has been proposed in literature for constructing
prediction intervals for targets estimated by neural networks. Quality of constructed prediction intervals using this technique highly depends on neural network characteristics. Unfortunately, literature is void of information about how these dependences can be managed in order to optimize prediction intervals. This study attempts to optimize length and coverage probability of prediction intervals through modifying structure and parameters of the underlying neural networks. In an evolutionary optimization, genetic algorithm is applied for finding the optimal values of network size and training hyper-parameters. The applicability and efficiency of the proposed optimization technique is examined and demonstrated using a real case study. It is shown that application of the proposed optimization technique significantly improves quality of constructed prediction intervals in term of length and coverage probability.

Intelligent based mobility models for data management in wireless sensor networks

Wireless sensor networks (WSNs) are proposed as powerful means for fine grained monitoring in different classes of applications at very low cost and for extended periods of time. Among various solutions, supporting WSNs with intelligent mobile platforms for handling the data management, proved its benefits towards extending the network lifetime and enhancing its performance. The mobility model applied highly affects the data latency in the network as well as the sensors’ energy consumption levels. Intelligent-based models taking into consideration the network runtime conditions are adopted to overcome such problems. In this chapter, existing proposals that use intelligent mobility for managing the data in WSNs are surveyed. Different classifications are presented through the chapter to give a complete view on the solutions lying in this domain. Furthermore, these models are compared considering various metrics and design goals.

Design of a new biocompatible ti-based shape memory alloy and its superelastic deformation behaviour

Titanium-nickel (Ti-Ni) shape memory alloys have been widely used for biomedical applications in recent years. However, it is reported that Ni is allergic and possibly carcinogenic for the human body. Therefore, it is desirable to develop new Ni-free Ti-based shape memory alloys for biomedical applications. In the present study, a new Ti-18Nb-5Mo-5Sn (wt.%) alloy, containing only biocompatible alloying elements, was designed with the aid of molecular orbital method and produced by vacuum arc melting. Both β and α″ martensitic phases were found to coexist in the alloy after ice-water quenching, indicating the martensitic transformation. The phase transformation temperatures of the Ti-18Nb-5Mo-5Sn alloy were M_s = 7.3 °C, M_f = −31.0 °C, A_s = 9.9 °C, and A_f = 54.8 °C. Superelasticity was observed in the alloy at a temperature higher than the A_f temperature. A totally recovered strain of 3.5 % was achieved for the newly designed Ti-based shape memory alloy with a pre-strain of 4 %.

Titanium-based shape memory alloys and scaffolds for biomedical applications

Porous Ti-50.5Ni shape memory alloys with different porosities were produced using a space-holder sintering method. A new Ni-free Ti-based shape memory alloy, Ti-18Nb-5Mo-5Sn, was developed for potential biomedical applications, and a novel one-step hydrothermal process was applied to produce hydroxyapatite coatings on the surface of Ti alloy.

Simulation-based learning for control of complex conveyor networks

This research investigated the problem of path planning in complex conveyor networks. A reinforcement learning approach was applied to derive a control strategy for routing traffic. The derived strategy was verified in real world systems and was found to improve network performance by prioritising traffic flows and balancing network load.

Load forecasting and neural networks : a prediction interval-based perspective

Successfully determining competitive optimal schedules for electricity generation intimately hinges on the forecasts of loads. The nonstationarity and high volatility of loads make their accurate prediction somewhat problematic. Presence of uncertainty in data also significantly degrades accuracy of point predictions produced by deterministic load forecasting models. Therefore, operation planning utilizing these predictions will be unreliable. This paper aims at developing prediction intervals rather than producing exact point prediction. Prediction intervals are theatrically more reliable and practical than predicted values. The delta and Bayesian techniques for constructing prediction intervals for forecasted loads are implemented here. To objectively and comprehensively assess quality of constructed prediction intervals, a new index based on length and coverage probability of prediction intervals is developed. In experiments with real data, and through calculation of global statistics, it is shown that neural network point prediction performance is unreliable. In contrast, prediction intervals developed using the delta and Bayesian techniques are satisfactorily narrow, with a high coverage probability.

Can improving working memory prevent academic difficulties? a school based randomised controlled trial

Background: Low academic achievement is common and is associated with adverse outcomes such as grade repetition, behavioural disorders and unemployment. The ability to accurately identify these children and intervene before they experience academic failure would be a major advance over the current ‘wait to fail’ model. Recent research suggests that a possible modifiable factor for low academic achievement is working memory, the ability to temporarily store and manipulate information in a ‘mental workspace’. Children with working memory difficulties are at high risk of academic failure. It has recently been demonstrated that working memory can be improved with adaptive training tasks that encourage improvements in working memory capacity. Our trial will determine whether the intervention is efficacious as a selective prevention strategy for young children at risk of academic difficulties and is cost-effective.

Methods/Design: This randomised controlled trial aims to recruit 440 children with low working memory after a school-based screening of 2880 children in Grade one. We will approach caregivers of all children from 48 participating primary schools in metropolitan Melbourne for consent. Children with low working memory will be randomised to usual care or the intervention. The intervention will consist of 25 computerised working memory training sessions, which take approximately 35 minutes each to complete. Follow-up of children will be conducted at 6, 12 and 24 months post-randomisation through child face-to-face assessment, parent and teacher surveys and data from government authorities. The primary outcome is academic achievement at 12 and 24 months, and other outcomes include child behaviour, attention, health-related quality of life, working memory, and health and educational service
utilisation.

Discussion: A successful start to formal learning in school sets the stage for future academic, psychological and economic well-being. If this preventive intervention can be shown to be efficacious, then we will have the potential to prevent academic underachievement in large numbers of at-risk children, to offer a ready-to-use intervention to the Australian school system and to build international research partnerships along the health education interface, in order to carry our further studies of effectiveness and generalisability.

Adaptive data based neural network leader-follower control of multi-agent networks

In this paper, we propose a data based neural network leader-follower control for multi-agent networks where each agent is described by a class of high-order uncertain nonlinear systems with input perturbation. The control laws are developed using multiple-surface sliding control technique. In particular, novel set of sliding variables are proposed to guarantee leader-follower consensus on the sliding surfaces. Novel switching is proposed to overcome the unavailability of instantaneous control output from the neighbor. By utilizing RBF neural network and Fourier series to approximate the unknown functions, leader-follower consensus can be reached, under the condition that the dynamic equations of all agents are unknown. An O(n) data based algorithm is developed, using only the network’s measurable input/output data to generate the distributed virtual control laws. Simulation results demonstrate the effectiveness of the approach.

A cache-based mobility-aware scheme for real-time continuous media delivery in wireless networks

Providing real-time or continuous media (CM) application services in wireless networks poses a significant challenge, as it requires timely delivery of data in a best-effort network. In this paper, we propose a cache-based scheme for mobility-aware, CM applications. The proposed scheme exploits a previously proposed caching strategy to complement Mobile-IP by placing services closer to migrated mobile nodes. The central idea of this work is based on the migration of sessions in order to facilitate uninterrupted delivery of CM in mobile environments. The performance of the proposed scheme is investigated by simulation studies. In particular, the effect of the proposed scheme on several QoS parameters under varying conditions of mobility and CM data is measured.

A policy-based web service redundancy detection in wireless sensor networks

Wireless sensor networks (WSNs) are attractive for monitoring and gathering physical information (e.g. temperature) via lots of deployed sensors. For the applications in WSNs, Web service is one of the recommended frameworks to publish, invoke, and manage services. However, the standard Web service description language (WSDL), defines only the service input and output while ignoring the corresponding input-to-output mapping relationships. This presents a serious challenge in distinguishing services with similar input and output interface. In this paper, we address this challenge by embedding the service policy into the traditional WSDL2.0 schema to describe the input-to-output mapping relationships. The service policy is then transformed into a policy binary tree so that the similarity between different Web services can be quantitatively evaluated. Furthermore, a new service redundancy detection approach is proposed based on this similarity. Finally, the case study and experimental analysis illustrate the applicability and capability of the proposed service redundancy detection approach.