Support for an auto-associative model of spoken cued recall: Evidence from fMRI

Cued recall and item recognition are considered the standard episodic memory retrieval tasks. However, only the neural correlates of the latter have been studied in detail with fMRI. Using an event-related fMRI experimental design that permits spoken responses, we tested hypotheses from an auto-associative model of cued recall and item recognition [Chappell, M., & Humphreys, M. S. (1994). An auto-associative neural network for sparse representations: Analysis and application to models of recognition and cued recall. Psychological Review, 101, 103-128]. In brief, the model assumes that cues elicit a network of phonological short term memory (STM) and semantic long term memory (LTM) representations distributed throughout the neocortex as patterns of sparse activations. This information is transferred to the hippocampus which converges upon the item closest to a stored pattern and outputs a response. Word pairs were learned from a study list, with one member of the pair serving as the cue at test. Unstudied words were also intermingled at test in order to provide an analogue of yes/no recognition tasks. Compared to incorrectly rejected studied items (misses) and correctly rejected (CR) unstudied items, correctly recalled items (hits) elicited increased responses in the left hippocampus and neocortical regions including the left inferior prefrontal cortex (LIPC), left mid lateral temporal cortex and inferior parietal cortex, consistent with predictions from the model. This network was very similar to that observed in yes/no recognition studies, supporting proposals that cued recall and item recognition involve common rather than separate mechanisms.

National Profiles of In-Country Capacity to Support Disability-Inclusive Education; Fiji, Samoa, Soloman Islands and Vanuatu

This publication emanates from the four-country research project entitled “Strengthening capacity for disability-inclusive education development policy formulation, implementation and monitoring in the South Pacific region” funded by the Australian Development Research Award Scheme (ADRAS) and conducted jointly by the academic staff from the Queensland University of Technology and the University of the South Pacific.

Shear tests of hollow flange channel beams with real support conditions

This paper presents the details of experimental studies on the effect of real support conditions on the shear strength of hollow flange channel beams, known as LiteSteel beams (LSB). The LSB has a unique shape of a channel beam with two rectangular hollow flanges, made using a unique manufacturing process. In many applications in the building industry LSBs are used with only one web side plate (WSP) at their supports. The WSPs are also often not full height plates. Past research studies showed that these real support connections did not provide the required simply supported conditions. Many studies have been carried out to evaluate the behaviour and design of LSBs with simply supported conditions subject to pure bending and predominant shear actions. To date, however, no investigation has been conducted into the effect of real support conditions on the shear strength of LSBs. Hence a detailed experimental study based on 25 shear tests was undertaken to investigate the shear behaviour and strength of LSBs with real support conditions. Simply supported test specimens of LSBs with aspect ratios of 1.0 and 1.5 were loaded at mid-span until failure. It was found that the effect of using one WSP on the shear behaviour of LSB is significant and there is about 25% shear capacity reduction due to the lateral movement of the bottom flange at the supports. Shear capacity of LSB was also found to decrease when full height WSPs were not used. Suitably improved support connections were developed to improve the shear capacity of LSBs based on test results. Details of the recommended support connections and shear capacity results are given in this paper.

Multiple sulfur isotope analyses support a magmatic model for the volcanogenic massive sulfide deposits of the Teutonic Bore volcanic complex, Yilgarn Craton, Western Australia

We report sensitive high mass resolution ion microprobe, stable isotopes (SHRIMP SI) multiple sulfur isotope analyses (32S, 33S, 34S) to constrain the sources of sulfur in three Archean VMS deposits—Teutonic Bore, Bentley, and Jaguar—from the Teutonic Bore volcanic complex of the Yilgarn Craton, Western Australia, together with sedimentary pyrites from associated black shales and interpillow pyrites. The pyrites from VMS mineralization are dominated by mantle sulfur but include a small amount of slightly negative mass-independent fractionation (MIF) anomalies, whereas sulfur from the pyrites in the sedimentary rocks has pronounced positive MIF, with ∆33S values that lie between 0.19 and 6.20‰ (with one outlier at −1.62‰). The wall rocks to the mineralization include sedimentary rocks that have contributed no detectable positive MIF sulfur to the VMS deposits, which is difficult to reconcile with the leaching model for the formation of these deposits. The sulfur isotope data are best explained by mixing between sulfur derived from a magmatic-hydrothermal fluid and seawater sulfur as represented by the interpillow pyrites. The massive sulfide lens pyrites have a weighted mean ∆33S value of −0.27 ± 0.05‰ (MSWD = 1.6) nearly identical with −0.31 ± 0.08‰ (MSWD = 2.4) for pyrites from the stringer zone, which requires mixing to have occurred below the sea floor. We employed a two-component mixing model to estimate the contribution of seawater sulfur to the total sulfur budget of the two Teutonic Bore volcanic complex VMS deposits. The results are 15 to 18% for both Teutonic Bore and Bentley, much higher than the 3% obtained by Jamieson et al. (2013) for the giant Kidd Creek deposit. Similar calculations, carried out for other Neoarchean VMS deposits give value between 2% and 30%, which are similar to modern hydrothermal VMS deposits. We suggest that multiple sulfur isotope analyses may be used to predict the size of Archean VMS deposits and to provide a vector to ore deposit but further studies are needed to test these suggestions.

Incentivizing innovation: A review of the Brazilian federal innovation support programs

Innovation is the transformation of knowledge of any kind into new products or services in the market. Its importance as a production factor is widely acknowledged. In the age of the knowledge-based economy innovation became critical for any company or even country to compete globally. Many countries are encouraging innovation through various mechanisms, and one of the most widely used is the provision of special incentives for innovation. This paper investigates incentive systems for the growth of technology companies as a strategy to promote knowledge-based economic development. As for the case investigations the study focuses on an emerging economy, Brazil. The research is based upon the available literature, best practices, government policy and review of incentive systems. The findings provide insights from the case study in a country context and some lessons learned for other countries using incentive systems to boost the innovation capabilities of their technology companies.

Multidimensional classroom support to inclusive education teachers in Beijing, China

Classroom support plays a salient role in successful inclusive education, hence it has been widely debated in the literature. Much extant work has only focused on a particular aspect of classroom support. A comprehensive, systematic discussion of classroom support is sporadic in the literature. Relevant research concerning the Chinese context is even more limited. To address this gap, our study developed and validated a multidimensional classroom support model conducive to teachers’ inclusive education practices. Data were drawn from our large-scale survey with inclusive education teachers in Beijing. Further analyses were conducted to compare different dimensions within the classroom support model. Drawing insights from the results, we provide some recommendations for practice and research.

A decision support framework for prioritization of engineering asset management activities under uncertainty

Engineers and asset managers must often make decisions on how to best allocate limited resources amongst different interrelated activities, including repair, renewal, inspection, and procurement of new assets. The presence of project interdependencies and the lack of sufficient information on the true value of an activity often produce complex problems and leave the decision maker guessing about the quality and robustness of their decision. In this paper, a decision support framework for uncertain interrelated activities is presented. The framework employs a methodology for multi-criteria ranking in the presence of uncertainty, detailing the effect that uncertain valuations may have on the priority of a particular activity. The framework employs employing semi-quantitative risk measures that can be tailored to an organisation and enable a transparent and simple-to-use uncertainty specification by the decision maker. The framework is then demonstrated on a real world project set from a major Australian utility provider.

Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

A Strength Pareto Evolutionary Algorithm for live migration of multiple interdependent virtual machines in data centers

Although live VM migration has been intensively studied, the problem of live migration of multiple interdependent VMs has hardly been investigated. The most important problem in the live migration of multiple interdependent VMs is how to schedule VM migrations as the schedule will directly affect the total migration time and the total downtime of those VMs. Aiming at minimizing both the total migration time and the total downtime simultaneously, this paper presents a Strength Pareto Evolutionary Algorithm 2 (SPEA2) for the multi-VM migration scheduling problem. The SPEA2 has been evaluated by experiments, and the experimental results show that the SPEA2 can generate a set of VM migration schedules with a shorter total migration time and a shorter total downtime than an existing genetic algorithm, namely Random Key Genetic Algorithm (RKGA). This paper also studies the scalability of the SPEA2.

Social media in higher education: Strategy and support for staff and students

In 2013, social networking was the second most popular online activity after internet banking for Australians (ABS, 2014). The popularity and apparent ubiquity of social media is one of the most obvious and compelling arguments for integrating such technologies into higher education. Already, social media impacts a wide range of activities ranging in scope from marketing and communication to teaching and learning in higher education (Hrastinski & Dennen, 2012). Social media presents many exciting possibilities and opportunities for higher education. This session will focus on one staff focussed and one student focussed social media innovation currently underway at QUT. First, it will focus on the actions of QUT’s social media working group. The working group’s aim is to ensure an overarching social media policy for the university is developed and implemented that supports staff in the use of social media across a range of activities. Second, it will discuss the eResponsible and eProfessional Online resources for students project. The focus of this project is to develop a suite of online resources targeted at the devel opment of social media skills for undergraduate students at QUT. These initiatives are complementary and both aim to minimise risk while maximising opportuniti es for the university

The role of institutional pressures and top management support in the intention to adopt cloud computing solutions

Purpose – While many studies have predominantly looked at the benefits and risks of cloud computing, little is known whether and to what extent institutional forces play a role in cloud computing adoption. The purpose of this paper is to explore the role of institutional factors in top management team’s (TMT’s) decision to adopt cloud computing services. Design/methodology/approach – A model is developed and tested with data from an Australian survey using the partial least squares modeling technique. Findings – The results suggest that mimetic and coercive pressures influence TMT’s beliefs in the benefits of cloud computing. The results also show that TMT’s beliefs drive TMT’s participation, which in turn affects the intention to increase the adoption of cloud computing solutions. Research limitations/implications – Future studies could incorporate the influences of local actors who might also press for innovation. Practical implications – Given the influence of institutional forces and the plethora of cloud-based solutions on the market, it is recommended that TMTs exercise a high degree of caution when deciding for the types of applications to be outsourced as organizational requirements in terms of performance and security will differ. Originality/value – The paper contributes to the growing empirical literature on cloud computing adoption and offers the institutional framework as an alternative lens with which to interpret cloud-based information technology outsourcing.

Is there evidence to support the use of the angle of peak torque as a marker of hamstring injury and re-injury risk?

Hamstring strain injuries are the predominant injury in many sports, costing athletes and clubs a significant financial and performance burden. Therefore the ability to identify and intervene with individuals who are considered at a high risk of injury is important. One measure which has grown in popularity as an outcome variable following hamstring intervention/prevention studies and rehabilitation is the angle of peak knee flexor torque. This current opinion article will firstly introduce the measure and the processes behind it. Secondly, this article will summarise how the angle of peak knee flexor torque has been suggested to measure hamstring strain injury risk. Finally various limitations will be presented and outlined as to how they may influence the measure. These include the lack of muscle specificity, the common concentric contraction mode of assessment, reliability of the measure, various neural contributions (such as rate of force development and neuromuscular inhibition) as well as the lack of prospective data showing any predictive value in the measure.

Pedagogical practices and support systems of inclusion: Empirical evidence from Chinese societies

China has a massive population of children with disabilities. To address the special needs of these children, special/inclusive education in China has developed dramatically since the early 1980s onwards. This Special Issue puts together seven empirical studies emerging from the Chinese societies. These studies analyse inclusive discourses embedded in the education policy documents; scrutinise professional competence of inclusive education teachers; evaluate inclusive education practices in physical education, mathematics education, and job-related social skills education provided to students with disabilities; debate the required in-class support for inclusive education teachers; and discuss the social attitudes towards people with disabilities. The foci, methods and theories vary across the seven studies, while their aims converge. These studies are seeking best possible approaches and best available resources that facilitate inclusion. Knowledge built and lessons learned from these studies will provide implications for future inclusive education practices in China and beyond.

Copper nanoparticles on graphene support: An efficient photocatalyst for coupling of nitroaromatics in visible light

Copper is a low-cost plasmonic metal. Efficient photocatalysts of copper nanoparticles on graphene support are successfully developed for controllably catalyzing the coupling reactions of aromatic nitro compounds to the corresponding azoxy or azo compounds under visible-light irradiation. The coupling of nitrobenzene produces azoxybenzene with a yield of 90 % at 60 °C, but azobenzene with a yield of 96 % at 90 °C. When irradiated with natural sunlight (mean light intensity of 0.044 W cm−2) at about 35 °C, 70 % of the nitrobenzene is converted and 57 % of the product is azobenzene. The electrons of the copper nanoparticles gain the energy of the incident light through a localized surface plasmon resonance effect and photoexcitation of the bound electrons. The excited energetic electrons at the surface of the copper nanoparticles facilitate the cleavage of the NO bonds in the aromatic nitro compounds. Hence, the catalyzed coupling reaction can proceed under light irradiation and moderate conditions. This study provides a green photocatalytic route for the production of azo compounds and highlights a potential application for graphene.

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations