Developing a theatre of the integrated actor : the application of the Suzuki actor training method within three theatrical contexts

This research project examines the application of the Suzuki Actor Training Method (the Suzuki Method) within the work ofTadashi Suzuki's company in Japan, the Shizuoka Performing Arts Complex (SPAC), within the work of Brisbane theatre company Frank:Austral Asian Performance Ensemble (Frank:AAPE), and as related to the development of the theatre performance Surfacing. These three theatrical contexts have been studied from the viewpoint of a "participant- observer". The researcher has trained in the Suzuki Method with Frank:AAPE and SP AC, performed with Frank:AAPE, and was the solo performer and collaborative developer in the performance Surfacing (directed by Leah Mercer). Observations of these three groups are based on a phenomenological definition of the "integrated actor", an actor who is able to achieve a totality or unity between the body and the mind, and between the body and the voice, through a powerful sense of intention. The term "integrated actor" has been informed by the philosophy of Merleau-Ponty and his concept of the "lived body". Three main hypotheses are presented in this study: that the Suzuki Method focuses on actors learning through their body; that the Suzuki Method presents an holistic approach to the body and the voice; and that the Suzuki Method develops actors with a strong sense of intention. These three aspects of the Suzuki Method are explored in relation to the stylistic features of the work of SPAC, Frank:AAPE and the performance Surfacing.

A Novel, Balanced, and Energy-Efficient Training Method for Receive Antenna Selection

In receive antenna selection (AS), only signals from a subset of the antennas are processed at any time by the limited number of radio frequency (RF) chains available at the receiver. Hence, the transmitter needs to send pilots multiple times to enable the receiver to estimate the channel state of all the antennas and select the best subset. Conventionally, the sensitivity of coherent reception to channel estimation errors has been tackled by boosting the energy allocated to all pilots to ensure accurate channel estimates for all antennas. Energy for pilots received by unselected antennas is mostly wasted, especially since the selection process is robust to estimation errors. In this paper, we propose a novel training method uniquely tailored for AS that transmits one extra pilot symbol that generates accurate channel estimates for the antenna subset that actually receives data. Consequently, the transmitter can selectively boost the energy allocated to the extra pilot. We derive closed-form expressions for the proposed scheme's symbol error probability for MPSK and MQAM, and optimize the energy allocated to pilot and data symbols. Through an insightful asymptotic analysis, we show that the optimal solution achieves full diversity and is better than the conventional method.

A hybrid training method for B-spline neural networks

Current and past research has brought up new views related to the optimization of neural networks. For a fixed structure, second order methods are seen as the most promising. From previous works we have shown how second order methods are of easy applicability to a neural network. Namely, we have proved how the Levenberg-Marquard possesses not only better convergence but how it can assure the convergence to a local minima. However, as any gradient-based method, the results obtained depend on the startup point. In this work, a reformulated Evolutionary algorithm - the Bacterial Programming for Levenberg-Marquardt is proposed, as an heuristic which can be used to determine the most suitable starting points, therefore achieving, in most cases, the global optimum.

A survey of the Queensland healthcare workforce : attitudes towards dementia care and training

Background Positive attitudes of healthcare staff towards people with dementia promote higher quality care, although little is known about important factors that underlie positive attitudes. Key aims of this project were to explore the relationships between staff attitudes towards dementia, self-confidence in caring for people with dementia, experience and dementia education and training. Method A brief online survey was developed and widely distributed to registered nurses and allied health professionals working in Queensland in 2012. Regression analyses were performed to identify important predictors of self-confidence in caring for people with dementia and positive attitudes towards people with dementia. Results Five hundred and twenty-four surveys were completed by respondents working in a range of care settings across Queensland. Respondents were predominantly female (94.1%), and most were registered nurses (60%), aged between 41 and 60 years (65.6%). Around 40% regularly worked with people with dementia and high levels of self-confidence in caring for this population and positive attitudes towards people with dementia were reported. The majority of respondents (67%) had participated in a dementia education/training activity in the past 12 months. More experience working with people with dementia predicted greater self-confidence while recent participation in a dementia education/training and higher self-confidence in caring for a person with dementia significantly predicted more positive attitudes towards people with dementia. Conclusion These results confirm the importance of self-confidence and dementia education in fostering positive attitudes and care practices towards people with dementia. Our results also indicate that the demand for ongoing dementia education is high amongst health care workers and it is recommended that regular dementia education/ training be provided and promoted for all healthcare personnel who work with people with dementia.

Towards training-free appearance-based localization : probabilistic models for whole-image descriptors

Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.

Antenna Selection Training in MIMO-OFDM/OFDMA Cellular Systems

Antenna selection allows multiple-antenna systems to achieve most of their promised diversity gain, while keeping the number of RF chains and, thus, cost/complexity low. In this paper we investigate antenna selection for fourth-generation OFDMA- based cellular communications systems, in particular, 3GPP LTE (long-term evolution) systems. We propose a training method for antenna selection that is especially suitable for OFDMA. By means of simulation, we evaluate the SNR-gain that can be achieved with our design. We find that the performance depends on the bandwidth assigned to each user, the scheduling method (round-robin or frequency-domain scheduling), and the Doppler spread. Furthermore, the signal-to-noise ratio of the training sequence plays a critical role. Typical SNR gains are around 2 dB, with larger values obtainable in certain circumstances.

Energy-efficient training for antenna selection in time-varying channels

Training for receive antenna selection (AS) differs from that for conventional multiple antenna systems because of the limited hardware usage inherent in AS. We analyze and optimize the performance of a novel energy-efficient training method tailored for receive AS. In it, the transmitter sends not only pilots that enable the selection process, but also an extra pilot that leads to accurate channel estimates for the selected antenna that actually receives data. For time-varying channels, we propose a novel antenna selection rule and prove that it minimizes the symbol error probability (SEP). We also derive closed-form expressions for the SEP of MPSK, and show that the considered training method is significantly more energy-efficient than the conventional AS training method.

Effective measures to decrease air contaminants through risk and control visualization : a study of the effective use of QR codes to facilitate safety training

Woodworking industries still consists of wood dust problems. Young workers are especially vulnerable to safety risks. To reduce risks, it is important to change attitudes and increase knowledge about safety. Safety training have shown to establish positive attitudes towards safety among employees. The aim of current study is to analyze the effect of QR codes that link to Picture Mix EXposure (PIMEX) videos by analyzing attitudes to this safety training method and safety in student responses. Safety training videos were used in upper secondary school handicraft programs to demonstrate wood dust risks and methods to decrease exposure to wood dust. A preliminary study was conducted to investigate improvement of safety training in two schools in preparation for the main study that investigated a safety training method in three schools. In the preliminary study the PIMEX method was first used in which students were filmed while wood dust exposure was measured and subsequently displayed on a computer screen in real time. Before and after the filming, teachers, students, and researchers together analyzed wood dust risks and effective measures to reduce exposure to them. For the main study, QR codes linked to PIMEX videos were attached at wood processing machines. Subsequent interviews showed that this safety training method enables students in an early stage of their life to learn about risks and safety measures to control wood dust exposure. The new combination of methods can create awareness, change attitudes and motivation among students to work more frequently to reduce wood dust. 

Enhancing flexible business training through computer-mediated communication

A previous article in this journal, by the same author, reported on research that indicated that flexible training for business learners in the workplace needed to take account of their need for instructor guidance and direction, and of their preference for learning in affiliative environments with fellow learners and trainers. In this article the use of computer-mediated  communication (CMC) is explored as one training method that can assist with flexible training of this clientele. Some specific strategies for the successful use of CMC are suggested.

Visual guidance during competition performance and run-through training in long jumping

To ensure precise foot placement on the take-off board, long jumpers visually regulate their stride pattern during their run-up. A relationship between how much visual guidance they use and the horizontal distance they jump has not, however, been quantified. Run-up precision is often practiced using run-throughs, which exclude the take-off and, therefore, the high physical stress of the complete long jump. The validity with which this common training method simulates the long jump approach remains, however, to be verified. Four state-standard long jumpers and two heptathletes completed two sessions, each comprising six run-throughs and six competition long jumps. A 50 Hz video camera was manually panned from an elevated platform to film each trial, to enable subsequent gait characteristic evaluations. Linear regression analyses identified that a longer visual regulation phase, measured in time, distance or number of strides, was a key predictor of long jump distance. The number of strides that were visually regulated during the long jump approach was, accordingly, positively correlated with long jump distance (r = 0.67, p = 0.001). The amount of visual regulation used during run-throughs was, however, less than half (p = 0.001) of that observed during long jump approaches. Our results should compel long jump coaches to supplement run-through training with additional visual guidance exercises, to encourage their athletes to visually regulate more of their long jump approach.

The effects of general strength, maximal strength and plyometric training on leg strength and power in soccer players

The study compared the effects of a maximal strength training method with a form of explosive jump training (plyometrics) on soccer players. Overall, the results showed that weight training and jumping groups improved strength and power better than just regular soccer training. There was no difference between the weight lifting and the jumping groups on the strength and power tests.

Wind farm power uncertainty quantification using a mean-variance estimation method

This paper proposes an innovative optimized parametric method for construction of prediction intervals (PIs) for uncertainty quantification. The mean-variance estimation (MVE) method employs two separate neural network (NN) models to estimate the mean and variance of targets. A new training method is developed in this study that adjusts parameters of NN models through minimization of a PI-based cost functions. A simulated annealing method is applied for minimization of the nonlinear non-differentiable cost function. The performance of the proposed method for PI construction is examined using monthly data sets taken from a wind farm in Australia. PIs for the wind farm power generation are constructed with five confidence levels between 50% and 90%. Demonstrated results indicate that valid PIs constructed using the optimized MVE method have a quality much better than the traditional MVE-based PIs.

An interval type-2 fuzzy logic system-based method for prediction interval construction

This paper introduces a new non-parametric method for uncertainty quantification through construction of prediction intervals (PIs). The method takes the left and right end points of the type-reduced set of an interval type-2 fuzzy logic system (IT2FLS) model as the lower and upper bounds of a PI. No assumption is made in regard to the data distribution, behaviour, and patterns when developing intervals. A training method is proposed to link the confidence level (CL) concept of PIs to the intervals generated by IT2FLS models. The new PI-based training algorithm not only ensures that PIs constructed using IT2FLS models satisfy the CL requirements, but also reduces widths of PIs and generates practically informative PIs. Proper adjustment of parameters of IT2FLSs is performed through the minimization of a PI-based objective function. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Performance of the proposed method is examined for seven synthetic and real world benchmark case studies with homogenous and heterogeneous noise. The demonstrated results indicate that the proposed method is capable of generating high quality PIs. Comparative studies also show that the performance of the proposed method is equal to or better than traditional neural network-based methods for construction of PIs in more than 90% of cases. The superiority is more evident for the case of data with a heterogeneous noise. © 2014 Elsevier B.V.

The application of biomechanics to penalty corner drag-flick training: a case of Study

The penalty corner is one of the most important game situations in field hockey with one third of all goals resulting from this tactical situation. The aim of this study was to develop and apply a training method, based on previous studies, to improve the drag- flick skill on a young top-class field hockey player. A young top-class player exercised three times per week using specific drills over a four week period. A VICON optoelectronic system (Oxford Metrics, Oxford, UK) was employed to capture twenty drag-flicks, with six cameras sampling at 250 Hz, prior and after the training period. In order to analyze pre- and post-test differences a dependent t-test was carried out. Angular velocities and the kinematic sequence were similar to previous studies. The player improved (albeit not significantly) the angular velocity of the stick. The player increased front foot to the ball at T1 (p < 0.01) and the drag-flick distances. The range of motion from the front leg decreased from T1 to T6 after the training period (p < 0.01). The specific training sessions conducted with the player improved some features of this particular skill. This article shows how technical knowledge can help with the design of training programs and whether some drills are more effective than others.

Interactive Video Instruction: A Training Tool Whose Time Has Come

Training employees in the hospitality industry to meet guests' expectations is a critical element to success. Unfortunately, this element is often ignored. This article explores an exciting training method (interactive video) and its advantages over other training approaches.