Using GPS technology to (re)-examine operational definitions of 'neighbourhood' in place-based health research

BACKGROUND Inconsistencies in research findings on the impact of the built environment on walking across the life course may be methodologically driven. Commonly used methods to define 'neighbourhood', from which built environment variables are measured, may not accurately represent the spatial extent to which the behaviour in question occurs. This paper aims to provide new methods for spatially defining 'neighbourhood' based on how people use their surrounding environment. RESULTS Informed by Global Positioning Systems (GPS) tracking data, several alternative neighbourhood delineation techniques were examined (i.e., variable width, convex hull and standard deviation buffers). Compared with traditionally used buffers (i.e., circular and polygon network), differences were found in built environment characteristics within the newly created 'neighbourhoods'. Model fit statistics indicated that exposure measures derived from alternative buffering techniques provided a better fit when examining the relationship between land-use and walking for transport or leisure. CONCLUSIONS This research identifies how changes in the spatial extent from which built environment measures are derived may influence walking behaviour. Buffer size and orientation influences the relationship between built environment measures and walking for leisure in older adults. The use of GPS data proved suitable for re-examining operational definitions of neighbourhood.

Tensile properties of graphene nanotube hybrid structures : a molecular dynamics study

Graphene has been reported with record-breaking properties which have opened up huge potential applications. A considerable research has been devoted to manipulate or modify the properties of graphene to target a more smart nanoscale device. Graphene and carbon nanotube hybrid structure (GNHS) is one of the promising graphene derivates, while their mechanical properties have been rarely discussed in literature. Therefore, such a studied is conducted in this paper basing on the large-scale molecular dynamics simulation. The target GNHS is constructed by considering two separate graphene layers that being connected by single-wall carbon nanotubes (SWCNTs) according to the experimental observations. It is found that the GNHSs exhibit a much lower yield strength, Young’s modulus, and earlier yielding comparing with a bilayer graphene sheet. Fracture of studied GNHSs is found to fracture located at the connecting region between carbon nanotubes (CNTs) and graphene. After failure, monatomic chains are normally observed at the front of the failure region, and the two graphene layers at the failure region without connecting CNTs will adhere to each other, generating a bilayer graphene sheet scheme (with a layer distance about 3.4 Å). This study will enrich the current understanding of the mechanical performance of GNHS, which will guide the design of GNHS and shed lights on its various applications.

Pathway to sustainability : the Lady Elliot Island hybrid solar power station

The Lady Elliot Island eco-resort, on the Great Barrier Reef, operates with a strong sustainability ethic, and has broken away from its reliance on diesel generators, an initiative which has ongoing and substantial economic benefit. The first step was an energy audit that led to a 35% reduction in energy usage, to an average of 575 kWh per day. The eco-resort then commissioned a hybrid solar power station, in 2008, with energy storage in battery banks. Solar power is currently (2013) providing about 160 kWh of energy per day, and the eco-resort’s diesel fuel usage has decreased from 550 to 100 litres per day, enabling the power station to pay for itself in 3 years. The eco-resort plans to complete its transition to renewable energy by 2015, by installing additional solar panels, and a 10-15 kW wind turbine. This paper starts by discussing why the eco-resort chose a hybrid solar power station to transition to renewable energy, and the barriers to change. It then describes the power station, upgrades through to 2013, the power control system, the problems that were solved to realise the potential of a facility operating in a harsh and remote environment, and its performance. The paper concludes by outlining other eco-resort sustainability practices, including education and knowledge-sharing initiatives, and monitoring the island’s environmental and ecological condition.

Hybrid model for motorway travel time estimation considering increased detector spacing

Travel time estimation and prediction on motorways has long been a topic of research. Prediction modeling generally assumes that the estimation is perfect. No matter how good is the prediction modeling- the errors in estimation can significantly deteriorate the accuracy and reliability of the prediction. Models have been proposed to estimate travel time from loop detector data. Generally, detectors are closely spaced (say 500m) and travel time can be estimated accurately. However, detectors are not always perfect, and even during normal running conditions few detectors malfunction, resulting in increase in the spacing between the functional detectors. Under such conditions, error in the travel time estimation is significantly large and generally unacceptable. This research evaluates the in-practice travel time estimation model during different traffic conditions. It is observed that the existing models fail to accurately estimate travel time during large detector spacing and congestion shoulder periods. Addressing this issue, an innovative Hybrid model that only considers loop data for travel time estimation is proposed. The model is tested using simulation and is validated with real Bluetooth data from Pacific Motorway Brisbane. Results indicate that during non free flow conditions and larger detector spacing Hybrid model provides significant improvement in the accuracy of travel time estimation.

From “anytime, anywhere” to “here and now”: place and time restrictions in mobile narratives to enhance situated engagement of mobile users

The usage of the mobile Internet has increased tremendously within the last couple of years, and thereby the vision of accessing information anytime, anywhere has become more realistic and a dominant design principle for providing content. However, this study challenges this paradigm of unlimited and unrestricted access, and explores the question whether constraints and restrictions can positively influence the motivation and enticement of mobile users to engage with location-specific content. Restrictions, such as a particular time or location that gives a user access to content, may be used to foster participation and engagement, as well as to support content production and to enhance the user’s experience. In order to explore this, a Mobile Narrative and a Narrative Map have been created. For the former, the access to individual chapters of the story was restricted. Authors can specify constraints, such as a location or time, which need to be met by the reader if they want to read the story. This concept allows creative writers of the story to exploit the fact that the reader’s context is known, by intensifying the user experience and integrating this knowledge into the writing process. The latter, the Narrative Map, provides users with extracts from stories or information snippets about authors at relevant locations. In both concepts, a feedback channel was also integrated, on which location, time, and size constraints were imposed. In a user-centred design process involving authors and potential readers, those concepts have been implemented, followed by an evaluation comprising four user studies. The results show that restrictions and constraints can indeed lead to more enticing and engaging user experiences, and restricted contribution opportunities can lead to a higher motivation to participate as well as to an improved quality of submissions. These findings are relevant for future developments in the area of mobile narratives and creative writing, as well as for common mobile services that aim for enticing user experiences.

A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

Development of an integrated GIS and land use planning course : impacts of hybrid instructional methods

This study reports an action research undertaken at Queensland University of Technology. It evaluates the effectiveness of the integration of GIS within the substantive domains of an existing land use planning course in 2011. Using student performance, learning experience survey, and questionnaire survey data, it also evaluates the impacts of incorporating hybrid instructional methods (e.g., in-class and online instructional videos) in 2012 and 2013. Results show that: students (re)iterated the importance of GIS in the course justifying the integration; the hybrid methods significantly increased student performance; and unlike replacement, the videos are more suitable as a complement to in-class activity.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Exploring pedestrian accessibility and walkability : insight from public place observations in inner-urban higher density Brisbane, Australia

As planners work to create more sustainable and liveable urban environments, a priority is to transition away from prioritising the automobile and towards enhancing the pedestrian experience. Thus, this research explores the experience of pedestrian accessibility in inner-urban higher-density Brisbane in Australia, drawing on findings from semi-structured in-depth interviews with 24 residents and over 100 hours of public place observations in three case-study neighbourhoods. The interviews took place in residents homes and explored their experience of higher density living and their neighbourhood, whilst observations were recorded through a combination of methods including photographs, sketch maps, recordings and field journals. Observation locations included retail and commercial space, roads, parkland and open space, with multiple observations at each location. A thematic analysis identified common themes in both interviews and the observations, with this paper focusing on residents’ lived experience in urban built environments. This analysis revealed that pedestrian accessibility is linked to access to local amenities and direct routes, aesthetics, sense of community, ownership of space and safety. In particular, observations revealed how pedestrian accessibility and route-taking works with, against or in spite of the design features of urban environments, as well as the importance of the social use of the built environment. Residents spoke about although walking quick and preferred for local amenities, the decision to walk was moderated by factors such as time of day and perceived safety. Measures to ensure and improve the pedestrian accessibility of urban areas needs to take into account the propensity for people to prefer and improvise direct routes (often to the detriment of traffic safety considerations), the importance of ongoing maintenance and upgrading of walking infrastructure and the importance of aesthetically pleasing and safe walking environments. By combining interviews and observations, this research highlights the current dominance of the automobile culture in Brisbane and the layers of meaning, experiences and complexity hidden within the pedestrian experience.

Power sharing control with frequency droop in a hybrid microgrid

A microgrid may contain a large number of distributed generators (DGs). These DGs can be either inertial or non-inertial, either dispatchable or non-dispatchable. Moreover, the DGs may operate in plug and play fashion. The combination of these various types of operation makes the microgrid control a challenging task, especially when the microgrid operates in an autonomous mode. In this paper, a new control algorithm for converter interfaced (dispatchable) DG is proposed which facilitates smooth operation in a hybrid microgrid containing inertial and non-inertial DGs. The control algorithm works satisfactorily even when some of the DGs operate in plug and play mode. The proposed strategy is validated through PSCAD simulation studies.

Residential voltage dip and swell mitigation using Plug-in Hybrid Electric Vehicle in smart grid

Solutions to remedy the voltage disturbances have been mostly suggested only for industrial customers. However, not much research has been done on the impact of the voltage problems on residential facilities. This paper proposes a new method to reduce the effect of voltage dip and swell in smart grids equipped by communication systems. To reach this purpose, a voltage source inverter and the corresponding control system are employed. The behavior of a power system during voltage dip and swell are analyzed. The results demonstrate reasonable improvement in terms of voltage dip and swell mitigation. All simulations are implemented in MATLAB/Simulink environment.

A phase-locked-loop design for the smooth operation of a hybrid microgrid

A microgrid contains both distributed generators (DGs) and loads and can be viewed by a controllable load by utilities. The DGs can be either inertial synchronous generators or non-inertial converter interfaced. Moreover, some of them can come online or go offline in plug and play fashion. The combination of these various types of operation makes the microgrid control a challenging task, especially when the microgrid operates in an autonomous mode. In this paper, a new phase locked loop (PLL) algorithm is proposed for smooth synchronization of plug and play DGs. A frequency droop for power sharing is used and a pseudo inertia has been introduced to non-inertial DGs in order to match their response with inertial DGs. The proposed strategy is validated through PSCAD simulation studies.

Hybrid multi-channel cooperative spectrum sensing to satisfy channel target

Spectrum sensing of multiple primary user channels is a crucial function in cognitive radio networks. In this paper we propose an optimal, sensing resource allocation algorithm for multi-channel cooperative spectrum sensing. The channel target is implemented as an objective and constraint to ensure a pre-determined number of empty channels are detected for secondary user network operations. Based on primary user traffic parameters, we calculate the minimum number of primary user channels that must be sensed to satisfy the channel target. We implement a hybrid sensing structure by grouping secondary user nodes into clusters and assign each cluster to sense a different primary user channels. We then solve the resource allocation problem to find the optimal sensing configuration and node allocation to minimise sensing duration. Simulation results show that the proposed algorithm requires the shortest sensing duration to achieve the channel target compared to existing studies that require long sensing and cannot guarantee the target.

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.