Performing arts management in a climate of adjustment : case studies from Vietnam and Australia

This paper presents a series of empirical case studies to discuss impacts of economic globalisation on the development of performing arts organisations in Vietnam (Hanoi Youth Theatre and Vietnam National Symphony Orchestra) and Australia (Melbourne Theatre Company and Sydney Symphony Orchestra), and focuses on how Vietnamese organisations have adapted to these changes. The paper also identifies cultural policy implications for the development of the sector; for arts management training in Vietnam so that the sector (and more importantly, the artists) may fully benefit from the open market context. The findings indicate that Vietnamese performing arts organisations have attempted to adapt to the new market context while struggling to balance artistic quality, freedom and financial viability in the new socialist regime. The Australian case studies offered a relevant management model to Vietnamese arts management practice and training.

The J-curve : evidence from Fiji

This article provides new evidence on both long run and short-run determinants of trade balance for Fiji and investigates evidence of J-curve adjustment behaviour in the aftermath of a devaluation. We adopt a partial reduced form model that models the real trade balance directly as a function of the real exchange rate and real domestic and foreign incomes. Cointegration analysis is based on a recently developed autoregressive distributed lag approach—shown to provide robust results in finite samples. The long run elasticities are also estimated using a dynamic ordinary least squares approach and the Fully Modified Ordinary Least Squares (FM-OLS) approach. Amongst our key results we find that there is a long-run relationship between trade balance and its determinants. There is evidence of the J-curve pattern; growth in domestic income affects Fiji's trade balance adversely while foreign income improves it.

Policy-based lending, structural adjustment and economic growth in Pakistan

This paper provides an econometric analysis of Pakistan’s structural adjustment program. Introduced in 1980, this program has been supported by the World Bank and IMF under their policy-based lending regimes. Building on recent advances in modelling the impact of policy reform, the paper applies a smooth transitions model to Pakistani real GDP data for the period 1960–2000. Results of this analysis suggest that the adjustment program has not stimulated growth in Pakistan, and that the origins of Pakistan’s post-program growth performance well pre-date this program.

The effect of cocoa supplementation on hepatic steatosis, reactive oxygen species and LFABP in a rat model of NASH

Background: Non alcoholic steatohepatitis is hypothesised to develop via a mechanism involving fat accumulation and oxidative stress. The current study aimed to investigate if an increase in oxidative stress was associated with changes in the expression of liver fatty acid binding protein in a rat model of non alcoholic steatohepatitis and whether cocoa supplementation attenuated those changes.

Methods: Female Sprague Dawley rats were fed a high fat control diet, a high fat methionine choline deficient diet, or one of four 12.5% cocoa supplementation regimes in combination with the high fat methionine choline deficient diet.

Results: Liver fatty acid binding protein mRNA and protein levels were reduced in the liver of animals with fatty liver disease when compared to controls. Increased hepatic fat content was accompanied by higher levels of oxidative stress in animals with fatty liver disease when compared to controls. An inverse association was found between the levels of hepatic liver fatty acid binding protein and the level of hepatic oxidative stress in fatty liver disease. Elevated NADPH oxidase protein levels were detected in the liver of animals with increased severity in inflammation and fibrosis. Cocoa supplementation was associated with partial attenuation of these pathological changes, although the severity of liver disease induced by the methionine choline deficient diet prevented complete reversal of any disease associated changes. Red blood cell glutathione was increased by cocoa supplementation, whereas liver glutathione was reduced by cocoa compared to methionine choline deficient diet fed animals.

Conclusion: These findings suggest a potential role for liver fatty acid binding protein and NADPH oxidase in the development of non alcoholic steatohepatitis. Furthermore, cocoa supplementation may have be of therapeutic benefit in less sever forms of NASH.

The hidden permutation model and location-based activity recognition

Permutation modeling is challenging because of the combinatorial nature of the problem. However, such modeling is often required in many real-world applications, including activity recognition where subactivities are often permuted and partially ordered. This paper introduces a novel Hidden Permutation Model (HPM) that can learn the partial ordering constraints in permuted state sequences. The HPM is parameterized as an exponential family distribution and is flexible so that it can encode constraints via different feature functions. A chain-flipping Metropolis-Hastings Markov chain Monte Carlo (MCMC) is employed for inference to overcome the O(n!) complexity. Gradient-based maximum likelihood parameter learning is presented for two cases when the permutation is known and when it is hidden. The HPM is evaluated using both simulated and real data from a location-based activity recognition domain. Experimental results indicate that the HPM performs far better than other baseline models, including the naive Bayes classifier, the HMM classifier, and Kirshner's multinomial permutation model. Our presented HPM is generic and can potentially be utilized in any problem where the modeling of permuted states from noisy data is needed.

Childhood cruelty to animals in China: the relationship with psychological adjustment and family functioning

Background
The current study broadened the general scope of research conducted on childhood cruelty to animals by examining the association between psychological adjustment, family functioning and animal cruelty in an Eastern context, China.

Method
The mothers and fathers of 729 children attending primary school in Chengdu, China participated in this study. Each parent completed the Strengths and Difficulties Questionnaire, the Chinese Family Assessment Instrument, and the Children's Attitudes and Behaviours towards Animals questionnaire.

Results
Findings from an actor partner interdependence model demonstrated that parents' ratings of family functioning and of their child's externalizing coping style predicted only modest amounts of variance in animal cruelty. In particular, parents' ratings of their child's externalizing coping style most consistently predicted animal cruelty. Family functioning, fathers' ratings in particular, played a minor role, more so for boys compared with girls.

Conclusion
This study provided the first insight into childhood animal cruelty in China, and suggests that further research may enhance our understanding of these phenomena.

Rapid determination of protein contents in microencapsulated fish oil supplements by ATR-FTIR spectroscopy and partial least square regression (PLSR) analysis

Following the recent success in quantitative analysis of essential fatty acid compositions in a commercial microencapsulated fish oil (?EFO) supplement, we extended the application of portable attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis for rapid determination of total protein contents-the other major component in most commercial ?EFO powders. In contrast to the traditional chromatographic methodology used in a routine amino acid analysis (AAA), the ATR-FTIR spectra of the ?EFO powder can be acquired directly from its original powder form with no requirement of any sample preparation, making the technique exceptionally fast, noninvasive, and environmentally friendly as well as being cost effective and hence eminently suitable for routine use by industry. By optimizing the spectral region of interest and number of latent factors through the developed PLSR strategy, a good linear calibration model was produced as indicated by an excellent value of coefficient of determination R2 = 0.9975, using standard ?EFO powders with total protein contents in the range of 140-450 mg/g. The prediction of the protein contents acquired from an independent validation set through the optimized PLSR model was highly accurate as evidenced through (1) a good linear fitting (R2 = 0.9759) in the plot of predicted versus reference values, which were obtained from a standard AAA method, (2) lowest root mean square error of prediction (11.64 mg/g), and (3) high residual predictive deviation (6.83) ranked in very good level of predictive quality indicating high robustness and good predictive performance of the achieved PLSR calibration model. The study therefore demonstrated the potential application of the portable ATR-FTIR technique when used together with PLSR analysis for rapid online monitoring of the two major components (i.e., oil and protein contents) in finished ?EFO powders in the actual manufacturing setting.

Integrated depression management: a proposed trial of a new model of care in a low vision rehabilitation setting

Purpose:
Depression is a common problem among people with visual impairment and contributes to functional decline. This article presents a study protocol to evaluate a new model of care for those patients with depressive symptoms in which psychological treatment is integrated into low vision rehabilitation services. Low vision staff will be trained to deliver "problem solving therapy for primary care" (PST-PC), an effective psychological treatment developed specifically for delivery by non-mental health care staff. PST-PC is delivered in 8 weekly telephone sessions of 30-45 minutes duration and 4 monthly maintenance sessions. We predict this new integrated model of care will significantly reduce depressive symptoms and improve the quality of life for people with visual impairment.

Methods and Design:
A randomized controlled trial of PST-PC will be implemented nationally across low vision rehabilitation services provided by Vision Australia. Clients who screen positive for depressive symptoms and meet study criteria will be randomized to receive PST-PC or usual care, consisting of a referral to their general practitioner for more detailed assessment and treatment. Outcome measures include depressive symptoms and behaviors, quality of life, coping and psychological adjustment to visual impairment. Masked assessments will take place pre- and post-intervention as well as at 6- and 12-month follow-up.

Conclusion:
We anticipate that this innovative service delivery model will lead to sustained improvements in clients' quality of life in a cost effective manner and provide an innovative service delivery model suitable for other health care areas in which depression is co-morbid.

Optimal effort investment for overcoming the weakest point: new insights from a computational model of neuromuscular adaptation

The occurrence of so-called sticking points in a lift is pervasive in weight training practice. Biomechanically complex exercises often exhibit multi modal variation of effective force exerted against the load as a function of the elevation and velocity of the load. This results in a variety of possible loci for the occurrence of sticking points and makes the problem of designing the optimal training strategy to overcome them challenging. In this article a case founded on theoretical grounds is made against a purely empirical method. It is argued that the nature of the problem considered and the wide range of variables involved limit the generality of conclusions which can be drawn from experimental studies alone. Instead an alternative is described, whereby a recently proposed mathematical model of neuromuscular adaptation is employed in a series of computer simulations. These are used to examine quantitatively the effects of differently targeted partial range of motion (ROM) training approaches. Counter-intuitively and in contrast to common training practices, the key novel insight inferred from the obtained results is that in some cases the most effective approach for improving performance in an exercise with a sticking point at a particular point in the ROM is to improve force production capability at a different and possibly remote position in the lift. In the context of the employed model, this result is explained by changes in the neuromuscular and biomechanical environment for force production.

A physically based phenomenological model for deformation twinning in magnesium alloy

A partial differential equation is developed that captures the evolution of key characteristics of tensile twinning in magnesium base alloys. The objective is to provide a framework for ascertaining the effects of hardening – due to grain refinement, precipitation and dislocation substructure – on twin volume fraction, thickness and length. The model is developed with the help of observations made on alloy AZ31. It is shown that it is necessary to consider the nucleation of twins at locations where neighbouring twins impinge on the grain boundary. The model provides a reasonable approximation for the role of grain size on twinning. It predicts a period of low apparent work hardening following yielding and shows that this should be more extensive for finer grain sizes, in agreement with experiment. Finally, some predictions are made on the effect of changing the resistance to twinning.

Anisotropic hardening model based on non-associated flow rule and combined nonlinear kinematic hardening for sheet materials

 A material model for more effective analysis of plastic deformation of sheet materials is presented in this paper. The model is capable of considering the following aspects of plastic deformation behavior of sheet materials: the anisotropy in yielding stresses in different directions by using a quadratic yield function (based on Hill’s 1948 model and stress ratios), the anisotropy in work hardening by introducing non-constant flow stress hardening in different directions, the anisotropy in plastic strains in different directions by using a quadratic plastic potential function and non-associated flow rule (based on Hill’s 1948 model and plastic strain ratios, r-values), and finally some of the cyclic hardening phenomena such as Bauschinger’s effect and transient behavior for reverse loading by using a coupled nonlinear kinematic hardening (so-called Armstrong-Frederick-Chaboche model). Basic fundamentals of the plasticity of the model are presented in a general framework. Then, the model adjustment procedure is derived for the plasticity formulations. Also, a generic numerical stress integration procedure is developed based on backward-Euler method (so-called multistage return mapping algorithm). Different aspects of the model are verified for DP600 steel sheet. Results show that the new model is able to predict the sheet material behavior in both anisotropic hardening and cyclic hardening regimes more accurately. By featuring the above-mentioned facts in the presented constitutive model, it is expected that more accurate results can be obtained by implementing this model in computational simulations of sheet material forming processes. For instance, more precise results of springback prediction of the parts formed from highly anisotropic hardened materials or that of determining the forming limit diagrams is highly expected by using the developed material model.

Predicting migratory flight altitudes by physiological migration models

Using the altitudinal profiles of wind, temperature, pressure, and humidity in three flight models, we tried to explain the altitudinal distributions of nocturnal migrants recorded by radar above a desert in southern Israel. In the simplest model, only the tailwind component was used as a predictor of the most preferred flight altitude (T model). The energy model (E model) predicted flight ranges according to mechanical power consumption in flapping flight depending on air density and wind conditions, assuming optimal adjustment of airspeed and compensation of crosswinds, and including the influence of mass loss during flight. The energy-water model (EW model) used the same assumptions and parameters as the E model but also included restrictions caused by dehydration. Because wind was by far the most important factor governing altitudinal distribution of nocturnal migrants, differences in predictions of the three models were small. In a first approach, the EW model performed slightly better than the E model, and both performed slightly better than the T model. Differences were most pronounced in spring, when migrants should fly high according to wind conditions, but when climbing and descending they must cross lower altitudes where conditions are better with respect to dehydration. A simplified energy model (Es model) that omits the effect of air density on flight costs explained the same amount of variance in flight altitude as the more complicated E and EW models. By omitting the effect of air density, the Es model predicted lower flight altitudes and thus compensated for factors that generally bias height distributions downward but are not considered in the models (i.e. climb and descent through lower air layers, cost of ascent, and decrease of oxygen partial pressure with altitude). Our results confirm that wind profiles, and thus energy rather than water limitations, govern the altitudinal distribution of nocturnal migrants, even under the extreme humidity and temperature conditions in the trade wind zone.

High breakdown bundle adjustment

Identifying the parameters of a model such that it best fits an observed set of data points is fundamental to the majority of problems in computer vision. This task is particularly demanding when portions of the data has been corrupted by gross outliers, measurements that are not explained by the assumed distributions. In this paper we present a novel method that uses the Least Quantile of Squares (LQS) estimator, a well known but computationally demanding high-breakdown estimator with several appealing theoretical properties. The proposed method is a meta-algorithm, based on the well established principles of proximal splitting, that allows for the use of LQS estimators while still retaining computational efficiency. Implementing the method is straight-forward as the majority of the resulting sub-problems can be solved using existing standard bundle-adjustment packages. Preliminary experiments on synthetic and real image data demonstrate the impressive practical performance of our method as compared to existing robust estimators used in computer vision.

Construction and aggregation of preference relations based on fuzzy partial orders

In group decision-making problems it is common to elicit preferences from human experts in the form of pairwise preference relations. When this is extended to a fuzzy setting, entries in the pairwise preference matrix are interpreted to denote strength of preference, however once logical properties such as consistency and transitivity are enforced, the resulting preference relation requires almost as much information as providing raw scores or a complete order over the alternatives. Here we instead interpret fuzzy degrees of preference to only apply where the preference over two alternatives is genuinely fuzzy and then suggest an aggregation procedure that minimizes a generalized Kemeny distance to the nearest complete or partial order. By focusing on the fuzzy partial order, the method is less affected by differences in the natural scale over which an expert expresses their preference, and can also limit the influence of extreme scores.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.