An examination of the factors that influence drivers’ willingness to use hand-held mobile phones

Hand-held mobile phone use while driving is illegal throughout Australia yet many drivers persist with this behaviour. This study aims to understand the internal, driver-related and external, situational-related factors influencing drivers’ willingness to use a hand-held mobile phone while driving. Sampling 160 university students, this study utilised the Theory of Planned Behaviour (TPB) to examine a range of belief-based constructs. Additionally, drivers’ personality traits of neuroticism and extroversion were measured with the Neuroticism Extroversion Openness-Five Factor Inventory (NEO-FFI). In relation to the external, situational-related factors, four different driving-related scenarios, which were intended to evoke differing levels of drivers’ reported stress, were devised for the study and manipulated drivers’ time urgency (low versus high) and passenger presence (alone versus with friends). In these scenarios, drivers’ willingness to use a mobile phone in general was measured. Hierarchical regression analyses across the four different driving scenarios found that, overall, the TPB components significantly accounted for drivers’ willingness to use a mobile phone above and beyond the demographic variables. Subjective norms, however, was only a significant predictor of drivers’ willingness in situations where the drivers were driving alone. Generally, neuroticism and extroversion did not significantly predict drivers’ willingness above and beyond the TPB and demographic variables. Overall, the findings broaden our understanding of the internal and external factors influencing drivers’ willingness to use a hand-held mobile phone while driving despite the illegality of this behaviour. The findings may have important practical implications in terms of better informing road safety campaigns targeting drivers’ mobile phone use which, in turn, may contribute to a reduction in the extent that mobile phone use contributes to road crashes.

Development and validation of a novel approach to work sampling : a study of nurse practitioner work patterns

Objectives: This methodological paper reports on the development and validation of a work sampling instrument and data collection processes to conduct a national study of nurse practitioners’ work patterns. ---------- Design: Published work sampling instruments provided the basis for development and validation of a tool for use in a national study of nurse practitioner work activities across diverse contextual and clinical service models. Steps taken in the approach included design of a nurse practitioner-specific data collection tool and development of an innovative web-based program to train and establish inter rater reliability of a team of data collectors who were geographically dispersed across metropolitan, rural and remote health care settings. ---------- Setting: The study is part of a large funded study into nurse practitioner service. The Australian Nurse Practitioner Study is a national study phased over three years and was designed to provide essential information for Australian health service planners, regulators and consumer groups on the profile, process and outcome of nurse practitioner service. ---------- Results: The outcome if this phase of the study is empirically tested instruments, process and training materials for use in an international context by investigators interested in conducting a national study of nurse practitioner work practices. ---------- Conclusion: Development and preparation of a new approach to describing nurse practitioner practices using work sampling methods provides the groundwork for international collaboration in evaluation of nurse practitioner service.

The work of nurse practitioners : a national work sampling study

Aim: This paper is a report of a study of variations in the pattern of nurse practitioner work in a range of service fields and geographical locations, across direct patient care, indirect patient care and service-related activities. Background. The nurse practitioner role has been implemented internationally as a service reform model to improve the access and timeliness of health care. There is a substantial body of research into the nurse practitioner role and service outcomes, but scant information on the pattern of nurse practitioner work and how this is influenced by different service models. --------- Methods: We used work sampling methods. Data were collected between July 2008 and January 2009. Observations were recorded from a random sample of 30 nurse practitioners at 10-minute intervals in 2-hour blocks randomly generated to cover two weeks of work time from a sampling frame of six weeks. --------- Results: A total of 12,189 individual observations were conducted with nurse practitioners across Australia. Thirty individual activities were identified as describing nurse practitioner work, and these were distributed across three categories. Direct care accounted for 36.1% of how nurse practitioners spend their time, indirect care accounted for 32.2% and service-related activities made up 31.9%. --------- Conclusion. These findings provide useful baseline data for evaluation of nurse practitioner positions and the service effect of these positions. However, the study also raises questions about the best use of nurse practitioner time and the influences of barriers to and facilitators of this model of service innovation.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Hierarchical fuzzy logic traffic control at a road junction

Traffic control at road junctions is one of the major concerns in most metropolitan cities. Controllers of various approaches are available and the required control action is the effective green-time assigned to each traffic stream within a traffic-light cycle. The application of fuzzy logic provides the controller with the capability to handle uncertain natures of the system, such as drivers’ behaviour and random arrivals of vehicles. When turning traffic is allowed at the junction, the number of phases in the traffic-light cycle increases. The additional input variables inevitably complicate the controller and hence slow down the decision-making process, which is critical in this real-time control problem. In this paper, a hierarchical fuzzy logic controller is proposed to tackle this traffic control problem at a 2-way road junction with turning traffic. The two levels of fuzzy logic controllers devise the minimum effective green-time and fine-tune it respectively at each phase of a traffic-light cycle. The complexity of the controller at each level is reduced with smaller rule-set. The performance of this hierarchical controller is examined by comparison with a fixed-time controller under various traffic conditions. Substantial delay reduction has been achieved as a result and the performance and limitation of the controller will be discussed.

Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Traffic control at a road junction by a complex fuzzy logic controller is investigated. The increase in the complexity of junction means more number of input variables must be taken into account, which will increase the number of fuzzy rules in the system. A hierarchical fuzzy logic controller is introduced to reduce the number of rules. Besides, the increase in the complexity of the controller makes formulation of the fuzzy rules difficult. A genetic algorithm based off-line leaning algorithm is employed to generate the fuzzy rules. The learning algorithm uses constant flow-rates as training sets. The system is tested by both constant and time-varying flow-rates. Simulation results show that the proposed controller produces lower average delay than a fixed-time controller does under various traffic conditions.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.

Three-dimensional printing of hierarchical and tough mesoporous bioactive glass scaffolds with a controllable pore architecture, excellent mechanical strength and mineralization ability

New-generation biomaterials for bone regenerations should be highly bioactive, resorbable and mechanically strong. Mesoporous bioactive glass (MBG), as a novel bioactive material, has been used for the study of bone regeneration due to its excellent bioactivity, degradation and drug-delivery ability; however, how to construct a 3D MBG scaffold (including other bioactive inorganic scaffolds) for bone regeneration still maintains a significant challenge due to its/their inherit brittleness and low strength. In this brief communication, we reported a new facile method to prepare hierarchical and multifunctional MBG scaffolds with controllable pore architecture, excellent mechanical strength and mineralization ability for bone regeneration application by a modified 3D-printing technique using polyvinylalcohol (PVA), as a binder. The method provides a new way to solve the commonly existing issues for inorganic scaffold materials, for example, uncontrollable pore architecture, low strength, high brittleness and the requirement for the second sintering at high temperature. The obtained 3D-printing MBG scaffolds possess a high mechanical strength which is about 200 times for that of traditional polyurethane foam template-resulted MBG scaffolds. They have highly controllable pore architecture, excellent apatite-mineralization ability and sustained drug-delivery property. Our study indicates that the 3D-printed MBG scaffolds may be an excellent candidate for bone regeneration.

A hierarchical security assessment model for object-oriented programs

We present a hierarchical model for assessing an object-oriented program's security. Security is quantified using structural properties of the program code to identify the ways in which `classified' data values may be transferred between objects. The model begins with a set of low-level security metrics based on traditional design characteristics of object-oriented classes, such as data encapsulation, cohesion and coupling. These metrics are then used to characterise higher-level properties concerning the overall readability and writability of classified data throughout the program. In turn, these metrics are then mapped to well-known security design principles such as `assigning the least privilege' and `reducing the size of the attack surface'. Finally, the entire program's security is summarised as a single security index value. These metrics allow different versions of the same program, or different programs intended to perform the same task, to be compared for their relative security at a number of different abstraction levels. The model is validated via an experiment involving five open source Java programs, using a static analysis tool we have developed to automatically extract the security metrics from compiled Java bytecode.

Multifunctional magnetic mesoporous bioactive glass scaffolds with a hierarchical pore structure

Hyperthermia and local drug delivery have been proposed the potential therapeutic approaches for bone defects resulting from malignant bone tumors. Development of bioactive materials with magnetic and drug-delivery properties may potentially meet this target. The aim of this study is to develop a multifunctional mesoporous bioactive glass (MBG) scaffold system for both hyperthermia and local-drug delivery application potentially. For this aim, Iron (Fe) containing MBG (Fe-MBG) scaffolds with hierarchically large pores (300-500 µm) and fingerprint-like mesopores (4.5 nm) have been successfully prepared. The effect of Fe on the mesopore structure, physiochemical, magnetism, drug delivery and biological properties of MBG scaffolds has been systematically investigated. The results showed that the morphology of the mesopore varied from straight channels to curved fingerprint-like channels after incorporated parts of Fe into MBG scaffolds. The magnetism magnitude of MBG scaffolds can be tailored by controlling Fe contents. Furthermore, the incorporating of Fe into mesoporous MBG glass scaffolds enhanced the mitochondrial activity and bone-relative gene (ALP and OCN) expression of human bone marrow mesenchymal stem cells (BMSCs) on the scaffolds. The obtained Fe-MBG scaffolds also possessed high specific surface areas and sustained drug delivery. Therefore, Fe-MBG scaffolds are magnetic, degradable and bioactive. The multifunction of Fe-MBG scaffolds indicates that there is a great potential for Fe-MBG scaffolds to be used for the therapy and regeneration of large-bone defects caused by malignant bone tumors through the combination of hyperthermia, local drug delivery and their osteoconductivity.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Persistent ocean monitoring with underwater gliders : adapting sampling resolution

Ocean processes are dynamic, complex, and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high-value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path, while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long-term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show significant improvements in data resolution and path reliability compared to previously executed sampling paths used in the respective regions.