Mixed-method pre-cooling reduces physiological demand without improving performance of medium-fast bowling in the heat

This study examined physiological and performance effects of pre-cooling on medium-fast bowling in the heat. Ten, medium-fast bowlers completed two randomised trials involving either cooling (mixed-methods) or control (no cooling) interventions before a 6-over bowling spell in 31.9±2.1°C and 63.5±9.3% relative humidity. Measures included bowling performance (ball speed, accuracy and run-up speeds), physical characteristics (global positioning system monitoring and counter-movement jump height), physiological (heart rate, core temperature, skin temperature and sweat loss), biochemical (serum concentrations of damage, stress and inflammation) and perceptual variables (perceived exertion and thermal sensation). Mean ball speed (114.5±7.1 vs. 114.1±7.2 km · h−1; P = 0.63; d = 0.09), accuracy (43.1±10.6 vs. 44.2±12.5 AU; P = 0.76; d = 0.14) and total run-up speed (19.1±4.1 vs. 19.3±3.8 km · h−1; P = 0.66; d = 0.06) did not differ between pre-cooling and control respectively; however 20-m sprint speed between overs was 5.9±7.3% greater at Over 4 after pre-cooling (P = 0.03; d = 0.75). Pre-cooling reduced skin temperature after the intervention period (P = 0.006; d = 2.28), core temperature and pre-over heart rates throughout (P = 0.01−0.04; d = 0.96−1.74) and sweat loss by 0.4±0.3 kg (P = 0.01; d = 0.34). Mean rating of perceived exertion and thermal sensation were lower during pre-cooling trials (P = 0.004−0.03; d = 0.77−3.13). Despite no observed improvement in bowling performance, pre-cooling maintained between-over sprint speeds and blunted physiological and perceptual demands to ease the thermoregulatory demands of medium-fast bowling in hot conditions.

Effects of mixed-method cooling on recovery of medium-fast bowling performance in hot conditions on consecutive days

This investigation examined physiological and performance effects of cooling on recovery of medium-fast bowlers in the heat. Eight, medium-fast bowlers completed two randomised trials, involving two sessions completed on consecutive days (Session 1: 10-overs and Session 2: 4-overs) in 31 ± 3°C and 55 ± 17% relative humidity. Recovery interventions were administered for 20 min (mixed-method cooling vs. control) after Session 1. Measures included bowling performance (ball speed, accuracy, run-up speeds), physical demands (global positioning system, counter-movement jump), physiological (heart rate, core temperature, skin temperature, sweat loss), biochemical (creatine kinase, C-reactive protein) and perceptual variables (perceived exertion, thermal sensation, muscle soreness). Mean ball speed was higher after cooling in Session 2 (118.9 ± 8.1 vs. 115.5 ± 8.6 km · h−1; P = 0.001; d = 0.67), reducing declines in ball speed between sessions (0.24 vs. −3.18 km · h−1; P = 0.03; d = 1.80). Large effects indicated higher accuracy in Session 2 after cooling (46.0 ± 11.2 vs. 39.4 ± 8.6 arbitrary units [AU]; P = 0.13; d = 0.93) without affecting total run-up speed (19.0 ± 3.1 vs. 19.0 ± 2.5 km · h−1; P = 0.97; d = 0.01). Cooling reduced core temperature, skin temperature and thermal sensation throughout the intervention (P = 0.001–0.05; d = 1.31–5.78) and attenuated creatine kinase (P = 0.04; d = 0.56) and muscle soreness at 24-h (P = 0.03; d = 2.05). Accordingly, mixed-method cooling can reduce thermal strain after a 10-over spell and improve markers of muscular damage and discomfort alongside maintained medium-fast bowling performance on consecutive days in hot conditions.

Physiological, perceptual and technical responses to on-court tennis training on hard and clay courts

The aim of this study was to investigate the effect of court surface (clay v hard-court) on technical, physiological and perceptual responses to on-court training. Four high-performance junior male players performed two identical training sessions on hard and clay courts, respectively. Sessions included both physical conditioning and technical elements as led by the coach. Each session was filmed for later notational analysis of stroke count and error rates. Further, players wore a global positioning satellite device to measure distance covered during each session; whilst heart rate, countermovement jump distance and capillary blood measures of metabolites were measured before, during and following each session. Additionally a respective coach and athlete rating of perceived exertion (RPE) were measured following each session. Total duration and distance covered during of each session were comparable (P>0.05; d<0.20). While forehand and backhands stroke volume did not differ between sessions (P>0.05; d<0.30); large effects for increased unforced and forced errors were present on the hard court (P>0.05; d>0.90). Furthermore, large effects for increased heart rate, blood lactate and RPE values were evident on clay compared to hard courts (P>0.05; d>0.90). Additionally, while player and coach RPE on hard courts were similar, there were large effects for coaches to underrate the RPE of players on clay courts (P>0.05; d>0.90). In conclusion, training on clay courts results in trends for increased heart rate, lactate and RPE values, suggesting sessions on clay tend towards higher physiological and perceptual loads than hard courts. Further, coaches appear effective at rating player RPE on hard courts, but may underrate the perceived exertion of sessions on clay courts.

RcppArmadillo : accelerating R with high-performance C++ linear algebra

The R statistical environment and language has demonstrated particular strengths for interactive development of statistical algorithms, as well as data modelling and visualisation. Its current implementation has an interpreter at its core which may result in a performance penalty in comparison to directly executing user algorithms in the native machine code of the host CPU. In contrast, the C++ language has no built-in visualisation capabilities, handling of linear algebra or even basic statistical algorithms; however, user programs are converted to high-performance machine code, ahead of execution. A new method avoids possible speed penalties in R by using the Rcpp extension package in conjunction with the Armadillo C++ matrix library. In addition to the inherent performance advantages of compiled code, Armadillo provides an easy-to-use template-based meta-programming framework, allowing the automatic pooling of several linear algebra operations into one, which in turn can lead to further speedups. With the aid of Rcpp and Armadillo, conversion of linear algebra centered algorithms from R to C++ becomes straightforward. The algorithms retains the overall structure as well as readability, all while maintaining a bidirectional link with the host R environment. Empirical timing comparisons of R and C++ implementations of a Kalman filtering algorithm indicate a speedup of several orders of magnitude.

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).

The effect of overnight sleep deprivation after competitive rugby league matches on postmatch physiological and perceptual recovery

PURPOSE: This study examined the effects of overnight sleep deprivation on recovery following competitive rugby league matches. METHODS: Eleven male, amateur rugby league players performed two competitive matches, followed by either a normal night's sleep (~8h; CONT) or a sleep deprived night (~0h; SDEP) in a randomised fashion. Testing was conducted the morning of the match, and immediately post-match, 2h post and the next morning (16h post-match). Measures included counter-movement jump (CMJ) distance, knee extensor maximal voluntary contraction (MVC), voluntary activation (VA), venous blood creatine kinase (CK) and C-reactive protein (CRP), perceived muscle soreness and a word-colour recognition cognitive function test. Percent change between post- and 16h post-match was reported to determine the effect of the intervention the next morning. RESULTS: Large effects indicated a greater post- to 16h post-match percentage decline in CMJ distance following SDEP compared to CONT (P=0.10-0.16; d=0.95-1.05). Similarly, the percentage decline in incongruent word-colour reaction times were increased in SDEP trials (P=0.007; d=1.75). Measures of MVC did not differ between conditions (P=0.40-0.75; d=0.13-0.33), though trends for larger percentage decline in VA were detected in SDEP (P=0.19; d=0.84). Further, large effects indicated higher CK and CRP responses 16h post-match during SDEP compared to CONT (P=0.11-0.87; d=0.80-0.88). CONCLUSIONS: Sleep deprivation negatively affected recovery following a rugby league match, specifically impairing CMJ distance and cognitive function. Practitioners should promote adequate post-match sleep patterns or adjust training demands the next day to accommodate the altered physical and cognitive state following sleep deprivation.

The influence of digital technology on the performance space

Through practice-led research, TESSA SMALLHORN examines the influence of digital technology on the performance space. From the mechanisation of modernist culture to the digitalisation of present day, technology acts as response material for scenographers investigating the stage as machine. The interactive, real-time tools of digital culture encourage a systems-orientated approach that challenges user and operator alike. This article explores the studio practice and critical theory that was combined to offer a functional model of a digital stage machine.

A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Towards the development of 1-to-n human machine interfaces for unmanned aerial vehicles

Multi-touch interfaces across a wide range of hardware platforms are becoming pervasive. This is due to the adoption of smart phones and tablets in both the consumer and corporate market place. This paper proposes a human-machine interface to interact with unmanned aerial systems based on the philosophy of multi-touch hardware-independent high-level interaction with multiple systems simultaneously. Our approach incorporates emerging development methods for multi-touch interfaces on mobile platforms. A framework is defined for supporting multiple protocols. An open source solution is presented that demonstrates: architecture supporting different communications hardware; an extensible approach for supporting multiple protocols; and the ability to monitor and interact with multiple UAVs from multiple clients simultaneously. Validation tests were conducted to assess the performance, scalability and impact on packet latency under different client configurations.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.