Changes in the site distribution of common melanoma subtypes in Queensland, Australia over time: implications for public health campaigns

Abstract BACKGROUND: An examination of melanoma incidence according to anatomical region may be one method of monitoring the impact of public health initiatives. OBJECTIVES:   To examine melanoma incidence trends by body site, sex and age at diagnosis or body site and morphology in a population at high risk. MATERIALS AND METHODS:   Population-based data on invasive melanoma cases (n = 51473) diagnosed between 1982 and 2008 were extracted from the Queensland Cancer Registry. Age-standardized incidence rates were calculated using the direct method (2000 world standard population) and joinpoint regression models were used to fit trend lines. RESULTS:   Significantly decreasing trends for melanomas on the trunk and upper limbs/shoulders were observed during recent years for both sexes under the age of 40 years and among males aged 40-59years. However, in the 60 and over age group, the incidence of melanoma is continuing to increase at all sites (apart from the trunk) for males and on the scalp/neck and upper limbs/shoulders for females. Rates of nodular melanoma are currently decreasing on the trunk and lower limbs. In contrast, superficial spreading melanoma is significantly increasing on the scalp/neck and lower limbs, along with substantial increases in lentigo maligna melanoma since the late 1990s at all sites apart from the lower limbs. CONCLUSIONS:   In this large study we have observed significant decreases in rates of invasive melanoma in the younger age groups on less frequently exposed body sites. These results may provide some indirect evidence of the impact of long-running primary prevention campaigns.

A novel real time pricing scheme for demand response in residential distribution systems

Price based technique is one way to handle increase in peak demand and deal with voltage violations in residential distribution systems. This paper proposes an improved real time pricing scheme for residential customers with demand response option. Smart meters and in-home display units are used to broadcast the price and appropriate load adjustment signals. Customers are given an opportunity to respond to the signals and adjust the loads. This scheme helps distribution companies to deal with overloading problems and voltage issues in a more efficient way. Also, variations in wholesale electricity prices are passed on to electricity customers to take collective measure to reduce network peak demand. It is ensured that both customers and utility are benefitted by this scheme.

A time-based quantitative approach for selecting lean strategies for manufacturing organisations

Lean strategies have been developed to eliminate or reduce waste and thus improve operational efficiency in a manufacturing environment. However, in practice, manufacturers encounter difficulties to select appropriate lean strategies within their resource constraints and to quantitatively evaluate the perceived value of manufacturing waste reduction. This paper presents a methodology developed to quantitatively evaluate the contribution of lean strategies selected to reduce manufacturing wastes within the manufacturers’ resource (time) constraints. A mathematical model has been developed for evaluating the perceived value of lean strategies to manufacturing waste reduction and a step-by-step methodology is provided for selecting appropriate lean strategies to improve the manufacturing performance within their resource constraints. A computer program is developed in MATLAB for finding the optimum solution. With the help of a case study, the proposed methodology and developed model has been validated. A ‘lean strategy-wastes’ correlation matrix has been proposed to establish the relationship between the manufacturing wastes and lean strategies. Using the correlation matrix and applying the proposed methodology and developed mathematical model, authors came out with optimised perceived value of reduction of a manufacturer's wastes by implementing appropriate lean strategies within a manufacturer's resources constraints. Results also demonstrate that the perceived value of reduction of manufacturing wastes can significantly be changed based on policies and product strategy taken by a manufacturer. The proposed methodology can also be used in dynamic situations by changing the input in the programme developed in MATLAB. By identifying appropriate lean strategies for specific manufacturing wastes, a manufacturer can better prioritise implementation efforts and resources to maximise the success of implementing lean strategies in their organisation.

Copyright and computer generated materials : is it time to reboot the discussion about authorship?

Computer generated materials are ubiquitous and we encounter them on a daily basis, even though most people are unaware that this is the case. Blockbuster movies, television weather reports and telephone directories all include material that is produced by utilising computer technologies. Copyright protection for materials generated by a programmed computer was considered by the Federal Court and Full Court of the Federal Court in Telstra Corporation Limited v Phone Directories Company Pty Ltd. The court held that the White and Yellow pages telephone directories produced by Telstra and its subsidiary, Sensis, were not protected by copyright because they were computer-generated works which lacked the requisite human authorship. The Copyright Act 1968 (Cth) does not contain specific provisions on the subsistence of copyright in computer-generated materials. Although the issue of copyright protection for computer-generated materials has been examined in Australia on two separate occasions by independently-constituted Copyright Law Review Committees over a period of 10 years (1988 to 1998), the Committees’ recommendations for legislative clarification by the enactment of specific amendments to the Copyright Act have not yet been implemented and the legal position remains unclear. In the light of the decision of the Full Federal Court in Telstra v Phone Directories it is timely to consider whether specific provisions should be enacted to clarify the position of computer-generated works under copyright law and, in particular, whether the requirement of human authorship for original works protected under Part III of the Copyright Act should now be reconceptualised to align with the realities of how copyright materials are created in the digital era.

Intercontinental haptic teleoperation of a flying vehicle : a step towards real-time applications

This paper describes the theory and practice for a stable haptic teleoperation of a flying vehicle. It extends passivity-based control framework for haptic teleoperation of aerial vehicles in the longest intercontinental setting that presents great challenges. The practicality of the control architecture has been shown in maneuvering and obstacle-avoidance tasks over the internet with the presence of significant time-varying delays and packet losses. Experimental results are presented for teleoperation of a slave quadrotor in Australia from a master station in the Netherlands. The results show that the remote operator is able to safely maneuver the flying vehicle through a structure using haptic feedback of the state of the slave and the perceived obstacles.

Is the two hour recommended maximum driving time appropriate for both healthy older and treated obstructive sleep apnoea drivers?

Objectives The UK Department for Transport recommends taking a break from driving every 2 h. This study investigated: (i) if a 2 h drive time on a monotonous road is appropriate for OSA patients treated with CPAP, compared with healthy age matched controls, (ii) the impact of a night’s sleep restriction (with CPAP) and (iii) what happens if these patients miss one nights’ CPAP treatment. Methods About 19 healthy men aged 52–74 y (m = 66.2 y) and 19 OSA participants aged 50–75 y (m = 64.4 y) drove an interactive car simulator under monotonous motorway conditions for 2 h on two afternoons, in a counterbalanced design; (1) following a normal night’s sleep (8 h). (2) following a restricted night’s sleep (5 h), with normal CPAP use (3) following a night without CPAP treatment. (n = 11) Lane drifting incidents, indicative of falling asleep, were recorded for up to 2 h depending on competence to continue driving. Results Normal sleep: Controls drove for an average of 95.9 min (s.d. 37 min) and treated OSA drivers for 89.6 min (s.d. 29 min) without incident. 63.2% of controls and 42.1% of OSA drivers successfully completed the drive without an incident. Sleep restriction: 47.4% of controls and 26.3% OSA drivers finished without incident. Overall: controls drove for an average of 89.5 min (s.d. 39 min) and treated OSA drivers 65 min (s.d. 42 min) without incident. The effect of condition was significant [F(1.36) = 9.237, P < 0.05, eta2 = 0.204]. Stopping CPAP: 18.2% of drivers successfully completed the drive. Overall, participants drove for an average of 50.1 min (s.d. 38 min) without incident. The effect of condition was significant [F(2) = 8.8, P < 0.05, eta2 = 0.468]. Conclusion 52.6% of all drivers were able to complete a 2 hour drive under monotonous conditions after a full night’s sleep. Sleep restriction significantly affected both control and OSA drivers. We find evidence that treated OSA drivers are more impaired by sleep restriction than healthy control, as they were less able to sustain safely the 2 h drive without incidents. OSA drivers should be aware that non-compliance with CPAP can significantly impair driving performance. It may be appropriate to recommend older drivers take a break from driving every 90 min especially when undertaking a monotonous drive, as was the case here.

Locally weighted learning model predictive control for nonlinear and time varying dynamics

This paper proposes an online learning control system that uses the strategy of Model Predictive Control (MPC) in a model based locally weighted learning framework. The new approach, named Locally Weighted Learning Model Predictive Control (LWL-MPC), is proposed as a solution to learn to control robotic systems with nonlinear and time varying dynamics. This paper demonstrates the capability of LWL-MPC to perform online learning while controlling the joint trajectories of a low cost, three degree of freedom elastic joint robot. The learning performance is investigated in both an initial learning phase, and when the system dynamics change due to a heavy object added to the tool point. The experiment on the real elastic joint robot is presented and LWL-MPC is shown to successfully learn to control the system with and without the object. The results highlight the capability of the learning control system to accommodate the lack of mechanical consistency and linearity in a low cost robot arm.

Establishing definitions and modeling public transport travel time variability

Public transport travel time variability (PTTV) is essential for understanding deteriorations in the reliability of travel time, optimizing transit schedules and route choices. This paper establishes key definitions of PTTV in which firstly include all buses, and secondly include only a single service from a bus route. The paper then analyses the day-to-day distribution of public transport travel time by using Transit Signal Priority data. A comprehensive approach using both parametric bootstrapping Kolmogorov-Smirnov test and Bayesian Information Creation technique is developed, recommends Lognormal distribution as the best descriptor of bus travel time on urban corridors. The probability density function of Lognormal distribution is finally used for calculating probability indicators of PTTV. The findings of this study are useful for both traffic managers and statisticians for planning and researching the transit systems.

Real-time mobile 3D temperature mapping

The ability to measure surface temperature and represent it on a metrically accurate 3D model has proven applications in many areas such as medical imaging, building energy auditing, and search and rescue. A system is proposed that enables this task to be performed with a handheld sensor, and for the first time with results able to be visualized and analyzed in real-time. A device comprising a thermal-infrared camera and range sensor is calibrated geometrically and used for data capture. The device is localized using a combination of ICP and video-based pose estimation from the thermal-infrared video footage which is shown to reduce the occurrence of failure modes. Furthermore, the problem of misregistration which can introduce severe distortions in assigned surface temperatures is avoided through the use of a risk-averse neighborhood weighting mechanism. Results demonstrate that the system is more stable and accurate than previous approaches, and can be used to accurately model complex objects and environments for practical tasks.

Developing a dashboard for real-time data stream composition and visualization

Technological advances have led to an influx of affordable hardware that supports sensing, computation and communication. This hardware is increasingly deployed in public and private spaces, tracking and aggregating a wealth of real-time environmental data. Although these technologies are the focus of several research areas, there is a lack of research dealing with the problem of making these capabilities accessible to everyday users. This thesis represents a first step towards developing systems that will allow users to leverage the available infrastructure and create custom tailored solutions. It explores how this notion can be utilized in the context of energy monitoring to improve conventional approaches. The project adopted a user-centered design process to inform the development of a flexible system for real-time data stream composition and visualization. This system features an extensible architecture and defines a unified API for heterogeneous data streams. Rather than displaying the data in a predetermined fashion, it makes this information available as building blocks that can be combined and shared. It is based on the insight that individual users have diverse information needs and presentation preferences. Therefore, it allows users to compose rich information displays, incorporating personally relevant data from an extensive information ecosystem. The prototype was evaluated in an exploratory study to observe its natural use in a real-world setting, gathering empirical usage statistics and conducting semi-structured interviews. The results show that a high degree of customization does not warrant sustained usage. Other factors were identified, yielding recommendations for increasing the impact on energy consumption.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Part-time working by students : is it a policy issue, and for whom?

This paper uses data from interviews with representatives of national and state organisations that have a policy interest in student-working in Australia. The interviewees included representatives from employer bodies and trade unions as well as government organisations. The data are used to discuss these stakeholders’ perceptions of the main advantages and disadvantages of working by young full-time students and the ways in which organisations in the business and educational sectors have adapted their policies and practices for student-working. The analysis is then used to inform a discussion about whether this is a legitimate area for public policy formulation and if so, what principles might underpin such policy and what some policies might look like.

Plasmodium vivax malaria incidence over time and its association with temperature and rainfall in four counties of Yunnan Province, China

Background Transmission of Plasmodium vivax malaria is dependent on vector availability, biting rates and parasite development. In turn, each of these is influenced by climatic conditions. Correlations have previously been detected between seasonal rainfall, temperature and malaria incidence patterns in various settings. An understanding of seasonal patterns of malaria, and their weather drivers, can provide vital information for control and elimination activities. This research aimed to describe temporal patterns in malaria, rainfall and temperature, and to examine the relationships between these variables within four counties of Yunnan Province, China. Methods Plasmodium vivax malaria surveillance data (1991–2006), and average monthly temperature and rainfall were acquired. Seasonal trend decomposition was used to examine secular trends and seasonal patterns in malaria. Distributed lag non-linear models were used to estimate the weather drivers of malaria seasonality, including the lag periods between weather conditions and malaria incidence. Results There was a declining trend in malaria incidence in all four counties. Increasing temperature resulted in increased malaria risk in all four areas and increasing rainfall resulted in increased malaria risk in one area and decreased malaria risk in one area. The lag times for these associations varied between areas. Conclusions The differences detected between the four counties highlight the need for local understanding of seasonal patterns of malaria and its climatic drivers.