Development of a switch mode assisted linear amplifier for use as a high fidelity voltage source

Frequency Domain Spectroscopy (FDS) is one of the major techniques used for determining the condition of the cellulose based paper and pressboard components in large oil/paper insulated power transformers. This technique typically makes use of a sinusoidal voltage source swept from 0.1 mHz to 1 kHz. The excitation test voltage source used must meet certain characteristics, such as high output voltage, high fidelity, low noise and low harmonic content. The amplifier used; in the test voltage source; must be able to drive highly capacitive loads. This paper proposes that a switch-mode assisted linear amplifier (SMALA) can be used in the test voltage source to meet these criteria. A three level SMALA prototype amplifier was built to experimentally demonstrate the effectiveness of this proposal. The developed SMALA prototype shows no discernable harmonic distortion in the output voltage waveform, or the need for output filters, and is therefore seen as a preferable option to pulse width modulated digital amplifiers. The lack of harmonic distortion and high frequency switching noise in the output voltage of this SMALA prototype demonstrates its feasibility for applications in FDS, particularly on highly capacitive test objects such as transformer insulation systems.

Design and fabrication of linear pixel arrays for organic photovoltaic modules to achieve scalable power output

We describe a design and fabrication method to enable simpler manufacturing of more efficient organic solar cell modules using a modified flat panel deposition technique. Many mini-cell pixels are individually connected to each other in parallel forming a macro-scale solar cell array. The pixel size of each array is optimized through experimentation to maximize the efficiency of the whole array. We demonstrate that integrated organic solar cell modules with a scalable current output can be fabricated in this fashion and can also be connected in series to generate a scalable voltage output.

General motor vehicle drivers' knowledge and practices regarding drink driving in Yinchuan and Guangzhou, China

Objective: Drink driving contributes to significant levels of injury and economic loss in China but is not well researched. This study examined knowledge, drink-driving practices, and alcohol misuse problems among general drivers in Yinchuan. The objectives were to gain a better understanding of drink driving in Yinchuan, identify areas that need to be addressed, and compare the results with a similar study in Guangzhou. Methods: This was a cross-sectional study with a survey designed to collect information on participants’ demographic characteristics and their knowledge and practices in relation to drinking and driving. The survey was composed of questions on knowledge and practices in relation to drink driving and was administered to a convenience sample of 406 drivers. Alcohol misuse problems were assessed by using the Alcohol Use Disorders Identification Test (AUDIT). Results: Males accounted for the main proportion of drivers sampled from the general population (“general drivers”). A majority of general drivers in both cities knew that drunk driving had become a criminal offense in 2011; however, knowledge of 2 legal blood alcohol concentration (BAC) limits was quite low. Fewer drivers in Yinchuan (22.6%) than in Guangzhou (27.9) reported having been stopped by police conducting breath alcohol testing at least once in the last 12 months. The mean AUDIT score in Yinchuan (M = 8.2) was higher than that in Guangzhou (M = 7.4), and the proportion of Yinchuan drivers with medium or higher alcohol misuse problems (31.2%) was correspondingly higher than in Guangzhou (23.1%). In Yinchuan, males had a significantly higher AUDIT score than females (t = 3.454, P < .001), similar to Guangzhou. Multiple regression analyses were conducted on potential predictors of the AUDIT score (age, gender, monthly income, education level, years licensed, and age started drinking). There were significant individual contributions of gender (beta = 0.173, P = .09) and age at which drinking started (beta = 0.141, P = .033), but the overall model for Yinchuan was not significant, unlike Guangzhou. Conclusions: The results show that there are shortfalls in knowledge of the legislation and how to comply with it and deficiencies in police enforcement. In addition, there was evidence of drink driving and drink riding at high levels in both cities. Recommendations are made to address these issues.

Nursing care of children in general practice settings: roles and responsibilities

Aims. To examine roles and responsibilities of Practice Nurses in the area of child health and development and in advising parents about child health issues. Background. As the focus of Australia’s health care system shifts further towards the primary health care sector, governmental initiatives require that Practice Nurses are knowledgeable, confident and competent in providing care in the area of child health and development. Little is known about roles and responsibilities of Practice Nurses in this area. Design. Cross-sectional survey design. Methods. Practice Nurses completed a national online survey examining the roles and responsibilities in child health and development, professional development needs and role satisfaction. Data were collected from June 2010–April 2011. Results. Respondents (N = 159) reported having a significant role in well and sick child care and were interested in extending their role. Frequent activities included immunization, phone triage/advice, child health/development advice, wound care and Healthy Kids Checks. However, few had paediatric/child nursing backgrounds or postgraduate qualifications in paediatric nursing and they reported limited preparation for the role. Practice Nurses reported difficulties with keeping up-to-date with child health information and advising parents confidently. Satisfaction was relatively low regarding opportunities and encouragement to undertake professional development and expand scope of practice. Conclusion. Practice Nurses are largely unprepared to meet the demands of their child health role and need support to develop and maintain the skills and knowledge base necessary for high-quality, evidence-based practice. Both financial and time support is needed to enable Practice Nurses to access child health professional development.

MegaVoltage Cone Beam Computed Tomography with a standard medical linear accelerator

Accurate patient positioning is vital for improved clinical outcomes for cancer treatments using radiotherapy. This project has developed Mega Voltage Cone Beam CT using a standard medical linear accelerator to allow 3D imaging of the patient position at treatment time with no additional hardware required. Providing 3D imaging functionality at no further cost allows enhanced patient position verification on older linear accelerators and in developing countries where access to new technology is limited.

Stochastic linear multistep methods for the simulation of chemical kinetics

In this paper, we introduce the Stochastic Adams-Bashforth (SAB) and Stochastic Adams-Moulton (SAM) methods as an extension of the tau-leaping framework to past information. Using the theta-trapezoidal tau-leap method of weak order two as a starting procedure, we show that the k-step SAB method with k >= 3 is order three in the mean and correlation, while a predictor-corrector implementation of the SAM method is weak order three in the mean but only order one in the correlation. These convergence results have been derived analytically for linear problems and successfully tested numerically for both linear and non-linear systems. A series of additional examples have been implemented in order to demonstrate the efficacy of this approach.

The Australian litigation landscape - Oral and Maxillofacial Surgery and general dentistry (oral surgery procedures): An analysis of litigation cases

Background: There are persistent concerns about litigation in the dental and medical professions. These concerns arise in a setting where general dentists are more frequently undertaking a wider range of oral surgery procedures, potentially increasing legal risk. Methods: Judicial cases dealing with medical negligence in the fields of general dentistry (oral surgery procedure) and Oral and Maxillofacial Surgery were located using the three main legal databases. Relevant cases were analysed to determine the procedures involved, the patients’ claims of injury, findings of negligence, and damages awarded. A thematic analysis of the cases was undertaken to determine trends. Results: Fifteen cases over a twenty-year period were located across almost all Australian jurisdictions (eight cases involved general dentists; seven cases involved Oral and Maxillofacial Surgeons). Eleven of the fifteen cases involved determinations of whether or not the practitioner had failed in their duty of care; negligence was found in six cases. Eleven of the fifteen cases related to molar extractions (eight specifically to third molar). Conclusions: Dental and medical practitioners wanting to manage legal risk should have regard to circumstances arising in judicial cases. Adequate warning of risks is critical, as is offering referral in appropriate cases. Pre-operative radiographs, good medical records, and processes to ensure appropriate follow-up are also important.

Quantification of stability in an agility drill using linear and nonlinear measures of variability

This study implemented linear and nonlinear methods of measuring variability to determine differences in stability of two groups of skilled (n = 10) and unskilled (n = 10) participants performing 3m forward/backward shuttle agility drill. We also determined whether stability measures differed between the forward and backward segments of the drill. Finally, we sought to investigate whether local dynamic stability, measured using largest finite-time Lyapunov exponents, changed from distal to proximal lower extremity segments. Three-dimensional coordinates of five lower extremity markers data were recorded. Results revealed that the Lyapunov exponents were lower (P < 0.05) for skilled participants at all joint markers indicative of higher levels of local dynamic stability. Additionally, stability of motion did not differ between forward and backward segments of the drill (P > 0.05), signifying that almost the same control strategy was used in forward and backward directions by all participants, regardless of skill level. Furthermore, local dynamic stability increased from distal to proximal joints (P < 0.05) indicating that stability of proximal segments are prioritized by the neuromuscular control system. Finally, skilled participants displayed greater foot placement standard deviation values (P < 0.05), indicative of adaptation to task constraints. The results of this study provide new methods for sport scientists, coaches to characterize stability in agility drill performance.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php

The root locus method: application to linear stability analysis and design of cooperative car-following models

A global framework for linear stability analyses of traffic models, based on the dispersion relation root locus method, is presented and is applied taking the example of a broad class of car-following (CF) models. This approach is able to analyse all aspects of the dynamics: long waves and short wave behaviours, phase velocities and stability features. The methodology is applied to investigate the potential benefits of connected vehicles, i.e. V2V communication enabling a vehicle to send and receive information to and from surrounding vehicles. We choose to focus on the design of the coefficients of cooperation which weights the information from downstream vehicles. The coefficients tuning is performed and different ways of implementing an efficient cooperative strategy are discussed. Hence, this paper brings design methods in order to obtain robust stability of traffic models, with application on cooperative CF models

Temperature variation between neighboring days and mortality: a distributed lag non-linear analysis

Objectives To investigate whether a sudden temperature change between neighboring days has significant impact on mortality. Methods A Poisson generalized linear regression model combined with a distributed lag non-linear models was used to estimate the association of temperature change between neighboring days with mortality in a subtropical Chinese city during 2008–2012. Temperature change was calculated as the current day’s temperature minus the previous day’s temperature. Results A significant effect of temperature change between neighboring days on mortality was observed. Temperature increase was significantly associated with elevated mortality from non-accidental and cardiovascular diseases, while temperature decrease had a protective effect on non-accidental mortality and cardiovascular mortality. Males and people aged 65 years or older appeared to be more vulnerable to the impact of temperature change. Conclusions Temperature increase between neighboring days has a significant adverse impact on mortality. Further health mitigation strategies as a response to climate change should take into account temperature variation between neighboring days.

Estimation of the time of a linear trend in monitoring survival time

Change point estimation is recognized as an essential tool of root cause analyses within quality control programs as it enables clinical experts to search for potential causes of change in hospital outcomes more effectively. In this paper, we consider estimation of the time when a linear trend disturbance has occurred in survival time following an in-control clinical intervention in the presence of variable patient mix. To model the process and change point, a linear trend in the survival time of patients who underwent cardiac surgery is formulated using hierarchical models in a Bayesian framework. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. We use Markov Chain Monte Carlo to obtain posterior distributions of the change point parameters including the location and the slope size of the trend and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time cumulative sum control chart (CUSUM) control charts for different trend scenarios. In comparison with the alternatives, step change point model and built-in CUSUM estimator, more accurate and precise estimates are obtained by the proposed Bayesian estimator over linear trends. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

A novel near-infrared spectroscopy and chemometrics method for rapid analysis of several chemical components and antioxidant activity of mint (mentha haplocalyx briq.) samples

A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint (Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.

Linear programming for large-scale Markov decision problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose a technique based on stochastic convex optimization and give bounds that show that the performance of our algorithm approaches the best achievable by any policy in the comparison class. Most importantly, this result depends on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application.