Comparison of high-speed videokeratoscopy and ultrasound distance sensing for measuring the longitudinal corneal apex movements

Two different methods to measure binocular longitudinal corneal apex movements were synchronously applied. High-speed videokeratoscopy at a sampling frequency of 15 Hz and a customdesigned ultrasound distance sensor at 100 Hz were used for the left and the right eye, respectively. Four healthy subjects participated in the study. Simultaneously, cardiac electric cycle (ECG) was registered for each subject at 100 Hz. Each measurement took 20 s. Subjects were asked to suppress blinking during the measurements. A rigid headrest and a bite-bar were used to minimize undesirable head movements. Time, frequency and time-frequency representations of the acquired signals were obtained to establish their temporal and spectral contents. Coherence analysis was used to estimate the correlation between the measured signals. The results showed close correlation between both corneal apex movements and the cardiopulmonary system. Unraveling these relationships could lead to better understanding of interactions between ocular biomechanics and vision. The advantages and disadvantages of the two methods in the context of measuring longitudinal movements of the corneal apex are outlined.

A ∑GIi/D/1/∞ queue with heterogeneous input/output slot times

In this paper, we present a ∑GIi/D/1/∞ queue with heterogeneous input/output slot times. This queueing model can be regarded as an extension of the ordinary GI/D/1/∞ model. For this ∑GIi/D/1/∞ queue, we assume that several input streams arrive at the system according to different slot times. In other words, there are different slot times for different input/output processes in the queueing model. The queueing model can therefore be used for an ATM multiplexer with heterogeneous input/output link capacities. Several cases of the queueing model are discussed to reflect different relationships among the input/output link capacities of an ATM multiplexer. In the queueing analysis, two approaches: the Markov model and the probability generating function technique, are adopted to develop the queue length distributions observed at different epochs. This model is particularly useful in the performance analysis of ATM multiplexers with heterogeneous input/output link capacities.

Simulated cataract and low contrast stimuli impair cognitive performance in older adults : implications for neuropsychological assessment and everyday function

Objective: To investigate how age-related declines in vision (particularly contrast sensitivity), simulated using cataract-goggles and low-contrast stimuli, influence the accuracy and speed of cognitive test performance in older adults. An additional aim was to investigate whether declines in vision differentially affect secondary more than primary memory. Method: Using a fully within-subjects design, 50 older drivers aged 66-87 years completed two tests of cognitive performance - letter matching (perceptual speed) and symbol recall (short-term memory) - under different viewing conditions that degraded visual input (low-contrast stimuli, cataract-goggles, and low-contrast stimuli combined with cataract-goggles, compared with normal viewing). However, presentation time was also manipulated for letter matching. Visual function, as measured using standard charts, was taken into account in statistical analyses. Results: Accuracy and speed for cognitive tasks were significantly impaired when visual input was degraded. Furthermore, cognitive performance was positively associated with contrast sensitivity. Presentation time did not influence cognitive performance, and visual gradation did not differentially influence primary and secondary memory. Conclusion: Age-related declines in visual function can impact on the accuracy and speed of cognitive performance, and therefore the cognitive abilities of older adults may be underestimated in neuropsychological testing. It is thus critical that visual function be assessed prior to testing, and that stimuli be adapted to older adults' sensory capabilities (e.g., by maximising stimuli contrast).

Executive function effects and numerical development in children : behavioural and ERP evidence from a numerical Stroop paradigm

Most research on numerical development in children is behavioural, focusing on accuracy and response time in different problem formats. However, Temple and Posner (1998) used ERPs and the numerical distance task with 5-year-olds to show that the development of numerical representations is difficult to disentangle from the development of the executive components of response organization and execution. Here we use the numerical Stroop paradigm (NSP) and ERPs to study possible executive interference in numerical processing tasks in 6–8-year-old children. In the NSP, the numerical magnitude of the digits is task-relevant and the physical size of the digits is task-irrelevant. We show that younger children are highly susceptible to interference from irrelevant physical information such as digit size, but that access to the numerical representation is almost as fast in young children as in adults. We argue that the developmental trajectories for executive function and numerical processing may act together to determine numerical development in young children.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Post-match changes in neuromuscular function and the relationship to match demands in amateur rugby league matches

Objectives: The current study investigated the change in neuromuscular contractile properties following competitive rugby league matches and the relationship with physical match demands. Design: Eleven trained, male rugby league players participated in 2–3 amateur, competitive matches (n = 30). Methods: Prior to, immediately (within 15-min) and 2 h post-match, players performed repeated counter-movement jumps (CMJ) followed by isometric tests on the right knee extensors for maximal voluntary contraction (MVC), voluntary activation (VA) and evoked twitch contractile properties of peak twitch force (Pt), rate of torque development (RTD), contraction duration (CD) and relaxation rate (RR). During each match, players wore 1 Hz Global Positioning Satellite devices to record distance and speeds of matches. Further, matches were filmed and underwent notational analysis for number of total body collisions. Results: Total, high-intensity, very-high intensity distances covered and mean speed were 5585 ± 1078 m, 661 ± 265, 216 ± 121 m and 75 ± 14 m min−1, respectively. MVC was significantly reduced immediately and 2 h post-match by 8 ± 11 and 12 ± 13% from pre-match (p < 0.05). Moreover, twitch contractile properties indicated a suppression of Pt, RTD and RR immediately post-match (p < 0.05). However, VA was not significantly altered from pre-match (90 ± 9%), immediately-post (89 ± 9%) or 2 h post (89 ± 8%), (p > 0.05). Correlation analyses indicated that total playing time (r = −0.50) and mean speed (r = −0.40) were moderately associated to the change in post-match MVC, while mean speed (r = 0.35) was moderately associated to VA. Conclusions: The present study highlights the physical demands of competitive amateur rugby league result in interruption of peripheral contractile function, and post-match voluntary torque suppression may be associated with match playing time and mean speeds.

A distance too far away : a novel and exegesis

This practice-led research examines the generative function of loss in fiction that explores themes of grief and longing. This research considers how loss may be understood as a structuring mechanism through which characters evaluate time, resolve loss and affect future change. The creative work is a work of literary fiction titled A Distance Too Far Away. Aubrey, the story’s protagonist, is a woman in her twenties living in Brisbane in the early 1980s, carving out an independent life for herself away from her family. Through a flashback narrative sequence, told from the perspective of the twelve year narrator, Aubrey retraces a significant point of rupture in her life following a series of family tragedies. A Distance Too Far Away explores the tension between belonging and freedom, and considers how the past provides a malleable space for illuminating desire in order to traverse the gap between the world as it is and the world as we want it to be. The exegetical component of this research considers an alternative critical frame for interpreting the work of American author Anne Tyler, a writer who has had a significant influence on my own practice. Frequently criticised for creating sentimental and inert characters, many critics observe that nothing happens in Tyler’s circular plots. This research challenges these assertions, and through a contextual analysis of Tyler’s Ladder of Years (1995) investigates how Tyler engages with memory and nostalgia in order to move across time and resolve loss.

Key derivation function : the SCKDF scheme

A key derivation function is used to generate one or more cryptographic keys from a private (secret) input value. This paper proposes a new method for constructing a generic stream cipher based key derivation function. We show that our proposed key derivation function based on stream ciphers is secure if the underlying stream cipher is secure. We simulate instances of this stream cipher based key derivation function using three eStream finalist: Trivium, Sosemanuk and Rabbit. The simulation results show these stream cipher based key derivation functions offer efficiency advantages over the more commonly used key derivation functions based on block ciphers and hash functions.

Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

The suffix-free-prefix-free hash function construction and its indifferentiability security analysis

In this paper, we observe that in the seminal work on indifferentiability analysis of iterated hash functions by Coron et al. and in subsequent works, the initial value (IV) of hash functions is fixed. In addition, these indifferentiability results do not depend on the Merkle–Damgård (MD) strengthening in the padding functionality of the hash functions. We propose a generic n -bit-iterated hash function framework based on an n -bit compression function called suffix-free-prefix-free (SFPF) that works for arbitrary IV s and does not possess MD strengthening. We formally prove that SFPF is indifferentiable from a random oracle (RO) when the compression function is viewed as a fixed input-length random oracle (FIL-RO). We show that some hash function constructions proposed in the literature fit in the SFPF framework while others that do not fit in this framework are not indifferentiable from a RO. We also show that the SFPF hash function framework with the provision of MD strengthening generalizes any n -bit-iterated hash function based on an n -bit compression function and with an n -bit chaining value that is proven indifferentiable from a RO.

EPC: a provably secure permutation based compression function

The security of permutation-based hash functions in the ideal permutation model has been studied when the input-length of compression function is larger than the input-length of the permutation function. In this paper, we consider permutation based compression functions that have input lengths shorter than that of the permutation. Under this assumption, we propose a permutation based compression function and prove its security with respect to collision and (second) preimage attacks in the ideal permutation model. The proposed compression function can be seen as a generalization of the compression function of MD6 hash function.