The impact of Cool Roof applications on energy performance : results from Australian sub-tropical and tropical field studies

Cool roof coatings are identified by their solar reflectance index. They have been reported to have multiple benefits, the extent of which are strongly dependent on the peculiarities of the local climate, building stock and electricity network. This paper presents measured and simulated data from residential, educational and commercial buildings involved in recent field trials in Australia. The purpose of the field trials was to evaluate the impact of such coatings on electricity demand and load and to assess their potential application to improve comfort whilst avoiding the need for air conditioners. Measured reductions in temperature, power (kW) and energy (kWh) were used to develop a predictive model that correlates ambient temperature distribution profiles, building demand reduction profiles and electricity network peak demand times. Combined with simulated data, the study indicates the types of buildings that could be targeted in Demand Management programs for the mutual benefit of electricity networks and building occupants.

Emotional intelligence and job-demands resources model

Occupational stress research has consistently demonstrated negative effects for employees. Research also describes potential moderators of this relationship. While research has revealed some positive effects of emotional intelligence (EI) on employee adjustment, it has neglected investigation of their potential stress buffering effects. Based on the Job-Demand Resources model, it was predicted that higher trait emotional intelligence would act as a buffer to the potential negative effects of stressors on employee adjustment. Hierarchical multiple regression analyses with a sample of 306 nurses found no main effects of EI but revealed eight moderating effects. While some interactions support the buffering hypothesis, others revealed buffering for those with low EI. Findings are discussed in terms of theoretical and practical implications.

Assessing the significance of climate and community factors on urban water demand

Ensuring adequate water supply to urban areas is a challenging task due to factors such as rapid urban growth, increasing water demand and climate change. In developing a sustainable water supply system, it is important to identify the dominant water demand factors for any given water supply scheme. This paper applies principal components analysis to identify the factors that dominate residential water demand using the Blue Mountains Water Supply System in Australia as a case study. The results show that the influence of community intervention factors (e.g. use of water efficient appliances and rainwater tanks) on water demand are among the most significant. The result also confirmed that the community intervention programmes and water pricing policy together can play a noticeable role in reducing the overall water demand. On the other hand, the influence of rainfall on water demand is found to be very limited, while temperature shows some degree of correlation with water demand. The results of this study would help water authorities to plan for effective water demand management strategies and to develop a water demand forecasting model with appropriate climatic factors to achieve sustainable water resources management. The methodology developed in this paper can be adapted to other water supply systems to identify the influential factors in water demand modelling and to devise an effective demand management strategy.

Effect of building integrated photovoltaics on microclimate of urban canopy layer

Building integrated photovoltaics (BIPV) has potential of becoming the mainstream of renewable energy in the urban environment. BIPV has significant influence on the thermal performance of building envelope and changes radiation energy balance by adding or replacing conventional building elements in urban areas. PTEBU model was developed to evaluate the effect of photovoltaic (PV) system on the microclimate of urban canopy layer. PTEBU model consists of four sub-models: PV thermal model, PV electrical performance model, building energy consumption model, and urban canyon energy budget model. PTEBU model is forced with temperature, wind speed, and solar radiation above the roof level and incorporates detailed data of PV system and urban canyon in Tianjin, China. The simulation results show that PV roof and PV façade with ventilated air gap significantly change the building surface temperature and sensible heat flux density, but the air temperature of urban canyon with PV module varies little compared with the urban canyon of no PV. The PV module also changes the magnitude and pattern of diurnal variation of the storage heat flux and the net radiation for the urban canyon with PV increase slightly. The increase in the PV conversion efficiency not only improves the PV power output, but also reduces the urban canyon air temperature. © 2006.

Explanatory Model for the Use of Traditional Medicines in Kilimanjaro, Tanzania

Introduction: Traditional medicines are one of the most important means of achieving total health care coverage globally, and their importance in Tanzania extends beyond the impoverished rural areas. Their use remains high even in urban settings among the educated middle and upper classes. They are a critical component healthcare in Tanzania, but they also can have harmful side effects. Therefore we sought to understand the decision-making and reasoning processes by building an explanatory model for the use of traditional medicines in Tanzania.

Methods: We conducted a mixed-methods study between December 2013 and June 2014 in the Kilimanjaro Region of Tanzania. Using purposive sampling methods, we conducted focus group discussions (FGDs) and in-depth interviews of key informants, and the qualitative data were analyzed using an inductive Framework Method. A structured survey was created, piloted, and then administered it to a random sample of adults. We reported upon the reliability and validity of the structured survey, and we used triangulation from multiple sources to synthesize the qualitative and quantitative data.

Results: A total of five FGDs composed of 59 participants and 27 in-depth interviews were conducted in total. 16 of the in-depth interviews were with self-described traditional practitioners or herbal vendors. We identified five major thematic categories that relate to the decision to use traditional medicines in Kilimanjaro: healthcare delivery, disease understanding, credibility of the traditional practices, health status, and strong cultural beliefs.

A total of 473 participants (24.1% male) completed the structured survey. The most common reasons for taking traditional medicines were that they are more affordable (14%, 12.0-16.0), failure of hospital medicines (13%, 11.1-15.0), they work better (12%, 10.7-14.4), they are easier

to obtain (11%, 9.48-13.1), they are found naturally or free (8%, 6.56-9.68), hospital medicines have too many chemical (8%, 6.33-9.40), and they have fewer side effects (8%, 6.25-9.30). The most common uses of traditional medicines were for symptomatic conditions (42%), chronic diseases (14%), reproductive problems (11%), and malaria and febrile illnesses (10%). Participants currently taking hospital medicines for chronic conditions were nearly twice as likely to report traditional medicines usage in the past year (RR 1.97, p=0.05).

Conclusions: We built broad explanatory model for the use of traditional medicines in Kilimanjaro. The use of traditional medicines is not limited to rural or low socioeconomic populations and concurrent use of traditional medicines and biomedicine is high with frequent ethnomedical doctor shopping. Our model provides a working framework for understanding the complex interactions between biomedicine and traditional medicine. Future disease management and treatment programs will benefit from this understanding, and it can lead to synergistic policies with more effective implementation.

Extending the capabilities of the buildingEXODUS evacuation model to cater for hospital evacuations

This paper describes the extension of the building EXODUS evacuation model in order to: allow occupants to be assigned a limited set of tasks, display co-operation

Robust Dynamic Pricing Over Infinite Horizon in The Presence of Model Uncertainty( 2* Journal)

This paper studies a problem of dynamic pricing faced by a retailer with limited inventory, uncertain about the demand rate model, aiming to maximize expected discounted revenue over an infinite time horizon. The retailer doubts his demand model which is generated by historical data and views it as an approximation. Uncertainty in the demand rate model is represented by a notion of generalized relative entropy process, and the robust pricing problem is formulated as a two-player zero-sum stochastic differential game. The pricing policy is obtained through the Hamilton-Jacobi-Isaacs (HJI) equation. The existence and uniqueness of the solution of the HJI equation is shown and a verification theorem is proved to show that the solution of the HJI equation is indeed the value function of the pricing problem. The results are illustrated by an example with exponential nominal demand rate.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Aplicação da filosofia Lean management:caso de estudo, aplicado à manutenção de edifícios

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia da Manutenção

The hotelling model with multiple demands

The purpose of this chapter is to provide an elementary introduction to the non-renewable resource model with multiple demand curves. The theoretical literature following Hotelling (1931) assumed that all energy needs are satisfied by one type of resource (e.g. ‘oil’), extractible at different per-unit costs. This formulation implicitly assumes that all users are the same distance from each resource pool, that all users are subject to the same regulations, and that motorist users can switch as easily from liquid fossil fuels to coal as electric utilities can. These assumptions imply, as Herfindahl (1967) showed, that in competitive equilibrium all users will exhaust a lower cost resource completely before beginning to extract a higher cost resource: simultaneous extraction of different grades of oil or of oil and coal should never occur. In trying to apply the single-demand curve model during the last twenty years, several teams of authors have independently found a need to generalize it to account for users differing in their (1) location, (2) regulatory environment, or (3) resource needs. Each research team found that Herfindahl's strong, unrealistic conclusion disappears in the generalized model; in its place, a weaker Herfindahl result emerges. Since each research team focussed on a different application, however, it has not always been clear that everyone has been describing the same generalized model. Our goal is to integrate the findings of these teams and to exposit the generalized model in a form which is easily accessible.

Bayesian inference for a skew-normal IRT model under the centred parameterization

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is interest in studying latent variables. These latent variables are directly considered in the Item Response Models (IRM) and they are usually called latent traits. A usual assumption for parameter estimation of the IRM, considering one group of examinees, is to assume that the latent traits are random variables which follow a standard normal distribution. However, many works suggest that this assumption does not apply in many cases. Furthermore, when this assumption does not hold, the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, it is important to model the distribution of the latent traits properly. In this paper we present an alternative latent traits modeling based on the so-called skew-normal distribution; see Genton (2004). We used the centred parameterization, which was proposed by Azzalini (1985). This approach ensures the model identifiability as pointed out by Azevedo et al. (2009b). Also, a Metropolis Hastings within Gibbs sampling (MHWGS) algorithm was built for parameter estimation by using an augmented data approach. A simulation study was performed in order to assess the parameter recovery in the proposed model and the estimation method, and the effect of the asymmetry level of the latent traits distribution on the parameter estimation. Also, a comparison of our approach with other estimation methods (which consider the assumption of symmetric normality for the latent traits distribution) was considered. The results indicated that our proposed algorithm recovers properly all parameters. Specifically, the greater the asymmetry level, the better the performance of our approach compared with other approaches, mainly in the presence of small sample sizes (number of examinees). Furthermore, we analyzed a real data set which presents indication of asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of strong negative asymmetry of the latent traits distribution. (C) 2010 Elsevier B.V. All rights reserved.

Optimising the life cycle energy performance of residential buildings

A holistic approach to low-energy building design is essential to ensure that any efficiency improvement strategies provide a net energy benefit over the life of the building. Previous work by the authors has established a model for informing low-energy building design based on a comparison of the life cycle energy demand associated with a broad range of building assemblies. This model ranks assemblies based on their combined initial and recurrent embodied energy and operational energy demand. The current study applies this model to an actual residential building in order to demonstrate the application of the model for optimising a building’s life cycle energy performance. The aim of this study was to demonstrate how the availability of comparable energy performance information at the building design stage can be used to better optimise a building’s energy performance. The life cycle energy demand of the case study building, located in the temperate climate of Melbourne, Australia, was quantified using a comprehensive embodied energy assessment technique and TRNSYS thermal energy simulation software. The building was then modelled with variations to its external assemblies in an attempt to optimise its life cycle energy performance. The alternative assemblies chosen were those shown through the author’s previous modelling to result in the lowest life cycle energy demand for each building element. The best performing assemblies for each of the main external building elements were then combined into a best-case scenario to quantify the potential life cycle energy savings possible compared to the original building. The study showed that significant life cycle energy savings are possible through the modelling of individual building elements for the case study building. While these findings relate to a very specific case, this study demonstrates the application of a model for optimising building life cycle energy performance that may be applied more broadly during early-stage building design to optimise life cycle energy performance.

Food demand elasticities for Australia

There is renewed interest in robust estimates of food demand elasticities at a disaggregated level not only to analyse the impact of changing food preferences on the agricultural sector, but also to establish the likely impact of pricing incentives on households. Using data drawn from two national Household Expenditure Surveys covering the periods 1998/1999 and 2003/2004, and adopting an Almost Ideal Demand System approach that addresses the zero observations problem, this paper estimates a food demand system for 15 food categories for Australia. The categories cover the standard food items that Australian households demand routinely. Own-price, cross-price and expenditure elasticity estimates of the Marshallian and Hicksian types have been derived for all categories. The parameter estimates obtained in this study represent the first integrated set of food demand elasticities based on a highly disaggregated food demand system for Australia, and all accord with economic intuition.

The work-based predictors of job engagement and job satisfaction experienced by community health professionals

Background: Job engagement represents a critical resource for community-based health care agencies to achieve high levels of effectiveness. However, studies examining the organisational sources of job engagement among health care professionals have generally overlooked those workers based in community settings.
Purpose: This study drew on the demand-control model, in addition to stressors that are more specific to community health services (e.g., unrewarding management practices), to identify conditions that are closely associated with the engagement experienced by a community health workforce. Job satisfaction was also included as a way of assessing how the predictors of job engagement differ from those associated with other job attitudes.
Methodology/Approach: Health and allied health care professionals (n = 516) from two
Australian community health services took part in the current investigation. Responses from the two organisations were pooled and analysed using linear multiple regression.
Findings: The analyses revealed that three working conditions were predictive of both job engagement and job satisfaction (i.e., job control, quantitative demands and unrewarding management practices). There was some evidence of differential effects with cognitive demands being associated with job engagement, but not job satisfaction.
Practice Implications: The results provide important insights into the working conditions that, if addressed, could play key roles in building a more engaged and satisfied community health workforce. Further, working conditions like job control and management practices are amenable to change and thus represent important areas where community health services could enhance the energetic and motivational resources of their employees.