A new method for resolving uncertainty of energy requirements in large water breathers: The 'mega-flume' seagoing swim-tunnel respirometer

Body size is a key determinant of metabolic rate, but logistical constraints have led to a paucity of energetics measurements from large water-breathing animals. As a result, estimating energy requirements of large fish generally relies on extrapolation of metabolic rate from individuals of lower body mass using allometric relationships that are notoriously variable. Swim-tunnel respirometry is the 'gold standard' for measuring active metabolic rates in water-breathing animals, yet previous data are entirely derived from body masses <10 kg - at least one order of magnitude lower than the body masses of many top-order marine predators. Here, we describe the design and testing of a new method for measuring metabolic rates of large water-breathing animals: a c. 26 000 L seagoing 'mega-flume' swim-tunnel respirometer. We measured the swimming metabolic rate of a 2·1-m, 36-kg zebra shark Stegostoma fasciatum within this new mega-flume and compared the results to data we collected from other S. fasciatum (3·8-47·7 kg body mass) swimming in static respirometers and previously published measurements of active metabolic rate measurements from other shark species. The mega-flume performed well during initial tests, with intra- and interspecific comparisons suggesting accurate metabolic rate measurements can be obtained with this new tool. Inclusion of our data showed that the scaling exponent of active metabolic rate with mass for sharks ranging from 0·13 to 47·7 kg was 0·79; a similar value to previous estimates for resting metabolic rates in smaller fishes. We describe the operation and usefulness of this new method in the context of our current uncertainties surrounding energy requirements of large water-breathing animals. We also highlight the sensitivity of mass-extrapolated energetic estimates in large aquatic animals and discuss the consequences for predicting ecosystem impacts such as trophic cascades.

Improving load forecasting accuracy through combination of best forecasts

Neural network (NN) models have been widely used in the literature for short-term load forecasting. Their popularity is mainly due to their excellent learning and approximation capability. However, their forecasting performance significantly depends on several factors including initializing parameters, training algorithm, and NN structure. To minimize negative effects of these factors, this paper proposes a practically simple, yet effective and an efficient method to combine forecasts generated by NN models. The proposed method includes three main phases: (i) training NNs with different structures, (ii) selecting best NN models based on their forecasting performance for a validation set, and (iii) combination of forecasts for selected best NNs. Forecast combination is performed through calculating the mean of forecasts generated by best NN models. The performance of the proposed method is examined using real world data set. Comparative studies demonstrate that the accuracy of combined forecasts is significantly superior to those obtained from individual NN models.

Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming

Uncertainty of data affects decision making process as it increases the risk and the costs of the decision. One of the challenges in minimizing the impact of the bounded uncertainty on any scheduling algorithm is the lack of information, as only the upper bound and the lower bound are provided without any known probability or membership function. On the contrary, probabilistic uncertainty can use probability distributions and fuzzy uncertainty can use the membership function. McNaughton's algorithm is used to find the optimum schedule that minimizes the makespan taking into consideration the preemption of tasks. The challenge here is the bounded inaccuracy of the input parameters for the algorithm, namely known as bounded uncertain data. This research uses interval programming to minimise the impact of bounded uncertainty of input parameters on McNaughton’s algorithm, it minimises the uncertainty of the cost function estimate and increase its optimality. This research is based on the hypothesis that doing the calculations on interval values then approximate the end result will produce more accurate results than approximating each interval input then doing numerical calculations.

Construction of neural network-based prediction intervals for short-term electrical load forecasting

Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.

Marketing across cultures : an investigation into place specific dimensions of service quality in sport

Abstract The purpose of this study was to identify place-specific dimensions of service quality in spectator sport settings and determine if the importance of these dimensions differed across cultures. The study was limited to the soccer industry and involved the collection of responses from identified soccer spectators to a range of items presented in a survey instrument. The survey was distributed to respondents face-to-face on a match day of the club they supported, or mailed to their home address. Responses were obtained from spectators from two clubs from Australia (n=277), one club from the USA (n=199), one club from the Netherlands (n=245) and one federation from Malaysia (n= 100). Based on the findings of a number of authors, six categories of potential place-specific dimensions of service quality in spectator sport settings were created and the research instrument contained a number of items that could be categorised under one of these headings. These categories were Home, Religion, Social facilitation, Sensory, Uncertainty of outcome, and Personal attention. In this thesis it was assumed that place-specific service quality issues are similar for sport spectators of different cultures, although differences in degree of importance of these dimensions (etic approach) were likely to emerge. In other words, although it was expected dimensions per country to be similar, differences in degree of importance of these dimensions were expected. Given the lack of confirmatory statistical evidence pertaining to the specific country samples, it was concluded that differences per country are likely to be more than just differences in degree. Both the overall structure and structures per country could not be confirmed, and hence the conclusion was drawn that differences in nature between the countries were present. In other words, what is a dimension of place-specific service quality in one country is not necessarily a dimension in another country. The results of a content analysis of ‘core component’ structures per country compared with a (full sample) core component structure delivered six components (referred to as place-specific dimensions of service quality) that were defined as Home, Hedonist, Religious follower, Safe atmosphere, Hospitality and Personal Attention. It was found that in most cases the cultural orientation of soccer spectators reflects the cultural orientation of the country as a whole as proposed by Hofstede (1991). However, in line with Huntington (1997), it was also argued that grouping people based on their country of origin as a proxy for their cultural orientation, will increasingly lead to flawed and incomplete research findings. As noted by Yoo etal. (1999), the identification of a person's cultural orientation is likely to deliver more direct results when measured at the individual level In that regard it is concluded that it may seem prudent to view Hofstede's dimensions of culture with increased conceptual scrutiny. Although having been replicated in multiple studies, it becomes increasingly unlikely that Hofstede's dimensions cover the complete spectrum of an individual person's cultural orientation. In conclusion, this study identified that soccer spectators (from a number of clubs) from Australia, the USA, the Netherlands and to a lesser extent Malaysia, perceive a range of place-specific service quality dimensions in spectator spoil settings to be important when visiting a soccer match. Before research into satisfaction with and value of place-specific dimensions of the spectator sport service product is initiated, it is pertinent the identified dimensions are further explored and confirmed in different country (culture) settings. The confusion that still exists about the place of the value concept (in relation to quality and satisfaction), where Holbrook (1994) defines quality as a type of value and Chelladurai and Chang (2000) argue that value is a type of quality, further underpins this necessity. It needs to be clear what are the targets of service quality before this information is integrated in larger holistic research frameworks. In the final section of the thesis a conceptual model for international services marketing research in the sport industry was presented as a first attempt to integrate the findings of this research and other researchers.

Changing the future of obesity : science, policy, and action

The global obesity epidemic has been escalating for four decades, yet sustained prevention efforts have barely begun. An emerging science that uses quantitative models has provided key insights into the dynamics of this epidemic, and enabled researchers to combine evidence and to calculate the effect of behaviours, interventions, and policies at several levels—from individual to population. Forecasts suggest that high rates of obesity will affect future population health and economics. Energy gap models have quantified the association of changes in energy intake and expenditure with weight change, and have documented the effect of higher intake on obesity prevalence. Empirical evidence that shows interventions are effective is limited but expanding. We identify several cost-effective policies that governments should prioritise for implementation. Systems science provides a framework for organising the complexity of forces driving the obesity epidemic and has important implications for policy makers. Many parties (such as governments, international organisations, the private sector, and civil society) need to contribute complementary actions in a coordinated approach. Priority actions include policies to improve the food and built environments, cross-cutting actions (such as leadership, healthy public policies, and monitoring), and much greater funding for prevention programmes. Increased investment in population obesity monitoring would improve the accuracy of forecasts and evaluations. The integration of actions within existing systems into both health and non-health sectors (trade, agriculture, transport, urban planning, and development) can greatly increase the influence and sustainability of policies. We call for a sustained worldwide effort to monitor, prevent, and control obesity.

Patient use of smartphones to communicate subjective data in clinical trials

Purpose. Various methods have been used in clinical trials to collect time-sensitive subjective responses, including study diaries, telephone interviews, and use of text messaging. However, all of these methods are limited by the uncertainty of when the participants enrolled in the study actually record their responses. This technical note reports on the utility of the BlackBerry smartphone to collect such data and why such a system provides advantages over other methods to report subjective ratings in clinical studies.

Methods. The Centre for Contact Lens Research developed an on-line web-enabled system that permits participants to record and immediately transmit subjective rating scores in numerical form directly into a web-enabled database. This, combined with the utility of BlackBerrys, enabled time-specific e-mail requests to be sent to the study participants and then for that data to be simultaneously transmitted to the web-enabled database. This system has been used in several clinical trials conducted at the Centre for Contact Lens Research, in which data were collected at various times and in several specific locations or environments.

Results. In the clinical trials conducted using this system, participants provided responses on 97.5% of occasions to the requests for data generated by the automated system. When the request was for data on a set date, this method resulted in responses of 84.1% of the time.

Conclusions. The series of clinical trials reported here show the benefits of the utilization of the BlackBerry to collect time- or environment-sensitive data via a web-enabled system.

Minimizing bounded uncertainty impact on scheduling with earliest start and due-date constraints via interval computation

Bounded uncertainty is a major challenge to real life scheduling as it increases the risk and cost depending on the objective function. Bounded uncertainty provides limited information about its nature. It provides only the upper and the lower bounds without information in between, in contrast to probability distributions and fuzzymembership functions. Bratley algorithm is usually used for scheduling with the constraints of earliest start and due-date. It is formulated as . The proposed research uses interval computation to minimize the impact of bounded uncertainty of processing times on Bratley’s algorithm. It minimizes the uncertainty of the estimate of the objective function. The proposed concept is to do the calculations on the interval values and approximate the end result instead of approximating each interval then doing numerical calculations. This methodology gives a more certain estimate of the objective function.

Neural network ensemble : evaluation of aggregation algorithms for electricity demand forecasting

This paper examines and analyzes different aggregation algorithms to improve accuracy of forecasts obtained using neural network (NN) ensembles. These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). The predictive performance of these algorithms are evaluated using Australian electricity demand data. The output of the aggregation algorithms of NN ensembles are compared with a Naive approach. Mean absolute percentage error is applied as the performance index for assessing the quality of aggregated forecasts. Through comprehensive simulations, it is found that the aggregation algorithms can significantly improve the forecasting accuracies. The BMA algorithm also demonstrates the best performance amongst aggregation algorithms investigated in this study.

The four national taps of Singapore: a holistic approach to water resources management from drainage to drinking water

 Water resources in Singapore are managed following the principles of a closed loop hydrologic cycle by one agency, the Public Utility Board (PUB), which promotes its management philosophy through the Four National Taps of Singapore program. The four national taps are: water from local catchment areas; imported water (from Malaysia); reused water (known as NEWater); and desalinated water. Given the uncertainty of water imports, the remaining three national taps have become increasingly important and this paper begins with a general overview of the innovative programs implemented by PUB in support of these three taps. Stormwater runoff is captured from two-thirds of Singapore’s land area and stored in reservoirs for subsequent use. Stormwater management is an important component of the catchment area tap and extensive low impact development (LID) implementation has become a priority through the ABC (Active, Beautiful, Clean) Waters Program. Examples of several ABC Waters projects are discussed. NEWater currently supplies 30% of the country’s demand and this is projected to increase to 50% by 2060. NEWater plants take treated wastewater through the additional steps of microfiltration, reverse osmosis and ultraviolet treatment for use primarily in industry, although a portion also is blended into the municipal reservoirs. Singapore’s single desalination plant currently meets 10% of its demand, with a second plant to be completed in 2013 that will more than double production. Also discussed are the results of recently completed pilot projects related to stormwater management including testing of E. coli in runoff from high density residential areas, a blind taste test and survey on acceptance of NEWater, and a survey of Singaporean understanding about stormwater management issues.Water resources in Singapore are managed following the principles of a closed loop hydrologic cycle by one agency, the Public Utility Board (PUB), which promotes its management philosophy through the Four National Taps of Singapore program. The four national taps are: water from local catchment areas; imported water (from Malaysia); reused water (known as NEWater); and desalinated water. Given the uncertainty of water imports, the remaining three national taps have become increasingly important and this paper begins with a general overview of the innovative programs implemented by PUB in support of these three taps. Stormwater runoff is captured from two-thirds of Singapore’s land area and stored in reservoirs for subsequent use. Stormwater management is an important component of the catchment area tap and extensive low impact development (LID) implementation has become a priority through the ABC (Active, Beautiful, Clean) Waters Program. Examples of several ABC Waters projects are discussed. NEWater currently supplies 30% of the country’s demand and this is projected to increase to 50% by 2060. NEWater plants take treated wastewater through the additional steps of microfiltration, reverse osmosis and ultraviolet treatment for use primarily in industry, although a portion also is blended into the municipal reservoirs. Singapore’s single desalination plant currently meets 10% of its demand, with a second plant to be completed in 2013 that will more than double production. Also discussed are the results of recently completed pilot projects related to stormwater management including testing of E. coli in runoff from high density residential areas, a blind taste test and survey on acceptance of NEWater, and a survey of Singaporean understanding about stormwater management issues.

A Bayesian nonparametric model for hierarchical sequence of images

 Scale features are useful for a great number of applications in computer vision. However, it is difficult to tolerate diversities of features in natural scenes by parametric methods. Empirical studies show that object frequencies and segment sizes follow the power law distributions which are well generated by Pitman-Yor (PY) processes. Based on mid-level segments, we propose a hierarchical sequence of images to obtain scale information stored in a hierarchical structure through the hierarchical Pitman-Yor (HPY) model which is expected to tolerate uncertainty of natural images. We also evaluate our representation by the application of segmentation.

Economic Bias of Weather Forecasting: A Spatial Modeling Approach

The value of accurate weather forecast information is substantial. In this paper we examine competition among forecast providers and its implications for the quality of forecasts. A simple economic model shows that an economic bias geographical inequality in forecast accuracy arises due to the extent of the market. Using the unique data on daily high temperature forecasts for 704 U.S. cities, we find that forecast accuracy increases with population and income. Furthermore, the economic bias gets larger when the day of forecasting is closer to the target day; i.e. when people are more concerned about the quality of forecasts. The results hold even after we control for location-specific heterogeneity and difficulty of forecasting.

A robust adaptive hybrid force/position control scheme of two planar manipulators handling an unknown object interacting with an environment

This paper presents a robust hybrid force/position control scheme of two cooperative manipulators handling an unknown object interacting with an unknown environment. The uncertainty of the object is considered in the weight, length, and the position of centre of mass (COM). The environment is assumed to have an unknown but high stiffness. A hybrid force/position control algorithm is designed for the known system and environment case. The exponential convergence of the position and the interaction force with the environment is proved using the Lyapunov direct method. Similarly, in the unknown object and environment case, and in the presence of bounded disturbances on the robots and the object, an adaptive sliding mode hybrid force/position control scheme is designed. The asymptotic convergence of the object's position and the constraint force is guaranteed using the proposed control methodology. The internal forces and moments between the object and robots are controlled independently of the object's motion and environmental interaction forces. Simulation results confirm the performance and effectiveness of the suggested control methodologies.