Listening to nature : acoustic monitoring of the environment

The requirement to monitor the rapid pace of environmental change due to global warming and to human development is producing large volumes of data but placing much stress on the capacity of ecologists to store, analyse and visualise that data. To date, much of the data has been provided by low level sensors monitoring soil moisture, dissolved nutrients, light intensity, gas composition and the like. However, a significant part of an ecologist’s work is to obtain information about species diversity, distributions and relationships. This task typically requires the physical presence of an ecologist in the field, listening and watching for species of interest. It is an extremely difficult task to automate because of the higher order difficulties in bandwidth, data management and intelligent analysis if one wishes to emulate the highly trained eyes and ears of an ecologist. This paper is concerned with just one part of the bigger challenge of environmental monitoring – the acquisition and analysis of acoustic recordings of the environment. Our intention is to provide helpful tools to ecologists – tools that apply information technologies and computational technologies to all aspects of the acoustic environment. The on-line system which we are building in conjunction with ecologists offers an integrated approach to recording, data management and analysis. The ecologists we work with have different requirements and therefore we have adopted the toolbox approach, that is, we offer a number of different web services that can be concatenated according to need. In particular, one group of ecologists is concerned with identifying the presence or absence of species and their distributions in time and space. Another group, motivated by legislative requirements for measuring habitat condition, are interested in summary indices of environmental health. In both case, the key issues are scalability and automation.

Way-Finding in the Built Environment

The Way-fi nding in the Built Environment project is a worldwide review identifying those way-fi nding systems and technologies that could be used to make it easier and safer for people with a sensory impairment (and in particular a vision impairment) to fi nd their way around buildings and large public spaces. The project makes recommendations on how these technologies and systems may be incorporated, by law or otherwise, into Australia’s building and construction practice. Way-fi nding aims to ensure that people with a sensory impairment know where they are in a building or an environment, where their desired location is, and how to get there from their present location. It is unlawful to discriminate against people with a disability under the Disability Discrimination Act 1992.

Comparative study : health research on traffic noise pollution vs. Technical research on traffic noise mitigation

Community awareness and the perception on the traffic noise related health impacts have increased significantly over the last decade resulting in a large volume of public inquiries flowing to Road Authorities for planning advice. Traffic noise management in the urban environment is therefore becoming a “social obligation”, essentially due to noise related health concerns. Although various aspects of urban noise pollution and mitigation have been researched independently, an integrated approach by stakeholders has not been attempted. Although the current treatment and mitigation strategies are predominantly handled by the Road Agencies, a concerted effort by all stakeholders is becoming mandatory for effective and tangible outcomes in the future. A research project is underway a RMIT University, Australia, led by the second author to consider the use of “hedonic pricing” for alternative noise amelioration treatments within the road reserve and outside the road reserve. The project aims to foster a full range noise abatement strategy encompassing source, path and noise receiver. The benefit of such a study would be to mitigate the problem where it is most effective and would defuse traditional “authority” boundaries to produce the optimum outcome. The project is conducted in collaboration with the Department of Main Roads Queensland, Australia and funded by the CRC for Construction Innovation. As part of this study, a comprehensive literature search is currently underway to investigate the advancements in community health research, related to environmental noise pollution, and the advancements in technical and engineering research in mitigating the issue. This paper presents the outcomes of this work outlining state of the art, national and international good practices and gap analysis to identify major anomalies and developments.

Decision - Support Software Tool Handbook

The road and transport industry in Australia and overseas has come a long way to understanding the impact of road traffic noise on the urban environment. Most road authorities now have guidelines to help assess and manage the impact of road traffic noise on noise-sensitive areas and development. While several economic studies across Australia and overseas have tried to value the impact of noise on property prices, decision-makers investing in road traffic noise management strategies have relatively limited historic data and case studies to go on. The perceived success of a noise management strategy currently relies largely on community expectations at a given time, and is not necessarily based on the analysis of the costs and benefits, or the long-term viability and value to the community of the proposed treatment options. With changing trends in urban design, it is essential that the 'whole-of-life' costs and benefits of noise ameliorative treatment options and strategies be identified and made available for decisionmakers in future investment considerations. For this reason, CRC for Construction Innovation Australia funded a research project, Noise Management in Urban Environments to help decision-makers with future road traffic noise management investment decisions. RMIT University and the Queensland Department of Main Roads (QDMR) have conducted the research work, in collaboration with the Queensland Department of Public Works, ARUP Pty Ltd, and the Queensland University of Technology. The research has formed the basis for the development of a decision-support software tool, and helped collate technical and costing data for known noise amelioration treatment options. We intend that the decision support software tool (DST) should help an investment decision-maker to be better informed of suitable noise ameliorative treatment options on a project-by-project basis and identify likely costs and benefits associated with each of those options. This handbook has been prepared as a procedural guide for conducting a comparative assessment of noise ameliorative options. The handbook outlines the methodology and assumptions adopted in the decision-support framework for the investment decision-maker and user of the DST. The DST has been developed to provide an integrated user-friendly interface between road traffic noise modelling software, the relevant assessment criteria and the options analysis process. A user guide for the DST is incorporated in this handbook.

Road Asset Management Investment

Australias civil infrastructure assets of roads, bridges, railways, buildings and other structures are worth billions of dollars. To effectively manage road infrastructures, road agencies firstly need to optimise the expenditure for data collection whilst not jeopardising the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates can be accurately calculated. Finally, the prediction of budgets for maintenance and rehabilitation must be reasonably reliable.

Variations in MIMO-OFDM channel capacity due to random human movement in an indoor environment

Channel measurements and simulations have been carried out to observe the effects of pedestrian movement on multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) channel capacity. An in-house built MIMO-OFDM packet transmission demonstrator equipped with four transmitters and four receivers has been utilized to perform channel measurements at 5.2 GHz. Variations in the channel capacity dynamic range have been analysed for 1 to 10 pedestrians and different antenna arrays (2 × 2, 3 × 3 and 4 × 4). Results show a predicted 5.5 bits/s/Hz and a measured 1.5 bits/s/Hz increment in the capacity dynamic range with the number of pedestrian and the number of antennas in the transmitter and receiver array.

Increase in particle number emissions from motor vehicles due to interruption of steady traffic flow

We assess the increase in particle number emissions from motor vehicles driving at steady speed when forced to stop and accelerate from rest. Considering the example of a signalized pedestrian crossing on a two-way single-lane urban road, we use a complex line source method to calculate the total emissions produced by a specific number and mix of light petrol cars and diesel passenger buses and show that the total emissions during a red light is significantly higher than during the time when the light remains green. Replacing two cars with one bus increased the emissions by over an order of magnitude. Considering these large differences, we conclude that the importance attached to particle number emissions in traffic management policies be reassessed in the future.

Submission to the House of Representatives Standing Committee on Environment and Heritage : Inquiry into a Sustainability Charter by the Cooperative Research Centre for Construction Innovation

The Cooperative Research Centre for Construction Innovation1 (hereafter called Construction Innovation) supports the notion of the establishment of a Sustainability Charter for Australia and is interested in working collaboratively to achieve this outcome. A number of challenges need to be addressed to develop this Charter. This submission outlines these challenges and possible responses to them by a Sustainability Commission.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.

Simulations of pollutant dispersion at toll plazas using three-dimensional CFD models

Toll plazas are particularly susceptible to build-ups of vehicle-emitted pollutants because vehicles pass through in low gear. To look at this, three-dimensional computational fluid dynamics simulations of pollutant dispersion are used on the standard k e turbulence model. The effects of wind speed, wind direction and topography on pollutant dispersion were discussed. The Wuzhuang toll plaza on the Hefei-Nanjing expressway is considered, and the effects of the retaining walls along both sides of the plaza on pollutant dispersion is analysed. There are greater pollutant concentrations near the tollbooths as the angle between the direction of the wind and traffic increases implying that retaining walls impede dispersion. The slope of the walls has little influence on the variations in pollutant concentration.

A study of Chinese private passenger cars based on BP neural network

This study employs BP neural network to simulate the development of Chinese private passenger cars. Considering the uncertain and complex environment for the development of private passenger cars, indicators of economy, population, price, infrastructure, income, energy and some other fields which have major impacts on it are selected at first. The network is proved to be operable to simulate the progress of chinese private passenger cars after modeling, training and generalization test. Based on the BP neural network model, sensitivity analysis of each indicator is carried on and shows that the sensitivity coefficients of fuel price change suddenly. This special phenomenon reveals that the development of Chinese private passenger cars may be seriously affected by the recent high fuel price. This finding is also consistent with facts and figures

Innovative Practices in the Australian Built Environment Sector : An Information Resource for Industry

PROJECT BRIEF Information provided by the Built Environment Industry Innovation Council as background to this project includes the following information on construction and innovation within the industry. • The construction industry contributes around $67 billion to GDP and employs around 970,000 and generates exports of nearly $150 million. • The industry has one of the lowest innovation rates of any industry in Australia, ranking third last across all Australian industries in terms of its proportion of business expenditure on innovation, and second last in terms of the proportion of income generated from innovation (ABS, 2006). • Key innovation challenges include addressing energy and water use efficiency, and housing costs in preparing for the implementation of the Carbon Pollution Reduction Scheme. The sector will need to build its capability and capacity to deliver the technical and operational expertise required.The broader Built Environment Innovation Project aims to address the following two objectives: 1. Identify current innovative practice across the Built Environment industry. 2. Develop a knowledge exchange strategy for this information to be disseminated to all industry stakeholders. Industry practice issues are critical to the built environment industry’s ability to innovate, and the BRITE project from the CRC for Construction Innovation has previously undertaken work to identify the key factors that drive innovation. Part 1 of the current project aims to extend this work by conducting a stocktake of current and emerging innovative practices within the built environment industry. Part 2 of the project addresses the second of these objectives, that is, to recommend a knowledge exchange strategy for promoting the wider uptake of innovative practices that makes the information identified in Part 1 of the study (on emerging innovative practices) accessible to Australian built environment industry stakeholders. The project brief was for the strategy to include a mechanism to enable this information resource to be updated as new initiatives/practices are developed. A better understanding of the built environment industry’s own knowledge infrastructure also has the potential to enhance innovation outcomes for the industry. This project will develop a coordinated knowledge exchange strategy, informed by the best available information on current innovation practices within the industry and suggest directions for gaining a better understanding of: the industry contexts that lead to innovative practices; the industry (including enterprise and individual) drivers for innovation; and appropriate knowledge exchange pathways for delivering future industry innovation. A deliverable of Part 2 will be a recommendation for a knowledge exchange strategy to accelerate adoption of innovative practices in the built environment industry, including resource implications and how such a recommendation could be taken forward as an ongoing resource.

The development of a clinical learning environment scale

Within nursing, there is a strong demand for high-quality, cost-effective clinical education experiences that facilitate student learning in the clinical setting The clinical learning environment (CLE) is the interactive network of forces within the clinical setting that influence the students'clinical learning outcomes The identification of factors that characterize CLE could lead to strategies that foster the factors most predictive of desirable student learning outcomes and ameliorate those which may have a negative impact on student outcomes The CLE scale is a 23-item instrument with five subscales staff–student relationships, nurse manager commitment, patient relationships, interpersonal relationships, and student satisfaction These factors have strong substantive face validity and construct validity, as determined by confirmatory factor analysis Reliability coefficients range from high (0 85) to marginal (0 63) The CLE scale provides the educator with a valid and reliable instrument to evaluate affectively relevant factors in the CLE, direct resources to areas where improvement may be required, and nurture those areas functioning well It will assist in the application of resources in a cost-effective, efficient, productive manner, and will ensure that the clinical learning experience offers the nursing student the best possible learning outcomes