High temperatures-related elderly mortality varied greatly from year to year : important information for heat-warning systems

We examined the variation in association between high temperatures and elderly mortality (age ≥ 75 years) from year to year in 83 US cities between 1987 and 2000. We used a Poisson regression model and decomposed the mortality risk for high temperatures into: a “main effect” due to high temperatures using lagged non-linear function, and an “added effect” due to consecutive high temperature days. We pooled yearly effects across both regional and national levels. The high temperature effects (both main and added effects) on elderly mortality varied greatly from year to year. In every city there was at least one year where higher temperatures were associated with lower mortality. Years with relatively high heat-related mortality were often followed by years with relatively low mortality. These year to year changes have important consequences for heat-warning systems and for predictions of heat-related mortality due to climate change.

Application of a digital elevation model for flood modeling for the Pagsangaan River in Leyte

Flood related scientific and community-based data are rarely systematically collected and analysed in the Philippines. Over the last decades the Pagsangaan River Basin, Leyte, has experienced several flood events. However, documentation describing flood characteristics such as extent, duration or height of these floods are close to non-existing. To address this issue, computerized flood modelling was used to reproduce past events where there was data available for at least partial calibration and validation. The model was also used to provide scenario-based predictions based on A1B climate change assumptions for the area. The most important input for flood modelling is a Digital Elevation Model (DEM) of the river basin. No accurate topographic maps or Light Detection And Ranging (LIDAR)-generated data are available for the Pagsangaan River. Therefore, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM), Version 1, was chosen as the DEM. Although the horizontal spatial resolution of 30 m is rather desirable, it contains substantial vertical errors. These were identified, different correction methods were tested and the resulting DEM was used for flood modelling. The above mentioned data were combined with cross-sections at various strategic locations of the river network, meteorological records, river water level, and current velocity to develop the 1D-2D flood model. SOBEK was used as modelling software to create different rainfall scenarios, including historic flooding events. Due to the lack of scientific data for the verification of the model quality, interviews with local stakeholders served as the gauge to judge the quality of the generated flood maps. According to interviewees, the model reflects reality more accurately than previously available flood maps. The resulting flood maps are now used by the operations centre of a local flood early warning system for warnings and evacuation alerts. Furthermore these maps can serve as a basis to identify flood hazard areas for spatial land use planning purposes.

Learning landscapes 2.0 : warning - assembly required

Twenty first century learners operate in organic, immersive environments. A pedagogy of student-centred learning is not a recipe for rooms. A contemporary learning environment is like a landscape that grows, morphs, and responds to the pressures of the context and micro-culture. There is no single adaptable solution, nor a suite of off-the-shelf answers; propositions must be customisable and infinitely variable. They must be indeterminate and changeable; based on the creation of learning places, not restrictive or constraining spaces. A sustainable solution will be un-fixed, responsive to the life cycle of the components and materials, able to be manipulated by the users; it will create and construct its own history. Learning occurs as formal education with situational knowledge structures, but also as informal learning, active learning, blended learning social learning, incidental learning, and unintended learning. These are not spatial concepts but socio-cultural patterns of discovery. Individual learning requirements must run free and need to be accommodated as the learner sees fit. The spatial solution must accommodate and enable a full array of learning situations. It is a system not an object. Three major components: 1. The determinate landscape: in-situ concrete 'plate' that is permanent. It predates the other components of the system and remains as a remnant/imprint/fossil after the other components of the system have been relocated. It is a functional learning landscape in its own right; enabling a variety of experiences and activities. 2. The indeterminate landscape: a kit of pre-fabricated 2-D panels assembled in a unique manner at each site to suit the client and context. Manufactured to the principles of design-for-disassembly. A symbiotic barnacle like system that attaches itself to the existing infrastructure through the determinate landscape which acts as a fast growth rhizome. A carapace of protective panels, infinitely variable to create enclosed, semi-enclosed, and open learning places. 3. The stations: pre-fabricated packages of highly-serviced space connected through the determinate landscape. Four main types of stations; wet-room learning centres, dry-room learning centres, ablutions, and low-impact building services. Entirely customised at the factory and delivered to site. The stations can be retro-fitted to suit a new context during relocation. Principles of design for disassembly: material principles • use recycled and recyclable materials • minimise the number of types of materials • no toxic materials • use lightweight materials • avoid secondary finishes • provide identification of material types component principles • minimise/standardise the number of types of components • use mechanical not chemical connections • design for use of common tools and equipment • provide easy access to all components • make component size to suite means of handling • provide built in means of handling • design to realistic tolerances • use a minimum number of connectors and a minimum number of types system principles • design for durability and repeated use • use prefabrication and mass production • provide spare components on site • sustain all assembly and material information

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Hazard based models for freeway traffic incident duration

Assessing and prioritising cost-effective strategies to mitigate the impacts of traffic incidents and accidents on non-recurrent congestion on major roads represents a significant challenge for road network managers. This research examines the influence of numerous factors associated with incidents of various types on their duration. It presents a comprehensive traffic incident data mining and analysis by developing an incident duration model based on twelve months of incident data obtained from the Australian freeway network. Parametric accelerated failure time (AFT) survival models of incident duration were developed, including log-logistic, lognormal, and Weibul-considering both fixed and random parameters, as well as a Weibull model with gamma heterogeneity. The Weibull AFT models with random parameters were appropriate for modelling incident duration arising from crashes and hazards. A Weibull model with gamma heterogeneity was most suitable for modelling incident duration of stationary vehicles. Significant variables affecting incident duration include characteristics of the incidents (severity, type, towing requirements, etc.), and location, time of day, and traffic characteristics of the incident. Moreover, the findings reveal no significant effects of infrastructure and weather on incident duration. A significant and unique contribution of this paper is that the durations of each type of incident are uniquely different and respond to different factors. The results of this study are useful for traffic incident management agencies to implement strategies to reduce incident duration, leading to reduced congestion, secondary incidents, and the associated human and economic losses.

How safe is safe enough? : a socio-technical view of low-cost level crossing safety

Low-cost level crossings are often criticized as being unsafe. Does a SIL (safety integrity level) rating make the railway crossing any safer? This paper discusses how a supporting argument might be made for low-cost level crossing warning devices with lower levels of safety integrity and issues such as risk tolerability and derivation of tolerable hazard rates for system-level hazards. As part of the design of such systems according to fail-safe principles, the paper considers the assumptions around the pre-defined safe states of existing warning devices and how human factors issues around such states can give rise to additional hazards.

Evaluating the effectiveness of heat warning systems : a systematic review of epidemiological evidence

Objectives To review the existing research on the effectiveness of heat warning systems (HWSs) in saving lives and reducing harm. Methods A systematic search of major databases was conducted, using “heat, heatwave, high temperature, hot temperature, OR hot climate” AND “warning system”. Results Fifteen articles were retrieved. Six studies asserted that fewer people died of excessive heat after HWS implementation. HWS was associated with reduction in ambulance use. One study estimated the benefits of HWS to be 468millionforsaving117livescomparedto210,000 costs of running the system. Eight studies showed that mere availability of HWS did not lead to behavioral changes. Perceived threat of heat dangers to self/others was the main factor related to heeding warnings and taking proper actions. However, costs and barriers associated with taking protective actions, such as costs of running air conditioners, were of significant concern particularly to the poor. Conclusions Research in this area is limited. Prospective designs applying health behavior theories should establish whether HWS can produce the health benefits they are purported to achieve by identifying the target vulnerable groups.

Are heat warning systems effective?

Heatwaves are associated with significant health risks particularly among vulnerable groups. To minimize these risks, heat warning systems have been implemented. The question therefore is how effective these systems are in saving lives and reducing heat-related harm. We systematically searched and reviewed 15 studies which examined this. Six studies asserted that fewer people died of excessive heat after the implementation of heat warning systems. Demand for ambulance decreased following the implementation of these systems. One study also estimated the costs of running heat warning systems at US$210,000 compared to the US$468 million benefits of saving 117 lives. The remaining eight studies investigated people?s response to heat warning systems and taking appropriate actions against heat harms. Perceived threat of heat dangers emerged as the main factor related to heeding the warnings and taking proper actions. However, barriers, such as costs of running air-conditioners, were of significant concern, particularly to the poor. The weight of the evidence suggests that heat warning systems are effective in reducing mortality and, potentially, morbidity. However, their effectiveness may be mediated by cognitive, emotive and socio-demographic characteristics. More research is urgently required into the cost-effectiveness of heat warning systems? measures and improving the utilization of the services.

The impact of legislation on divorce : a hazard function approach

The paper examines the impact of the introduction of no-fault divorce legislation in Australia. The approach used is rather novel, a hazard model of the divorce rate is estimated with the role of legislation captured via a time-varying covariate. The paper concludes that contrary to US empirical evidence, no-fault divorce legislation appears to have had a positive impact upon the divorce rate in Australia.

Video-based road commentary training improves hazard perception of young drivers in a dual task

This study used a video-based hazard perception dual task to compare the hazard perception skills of young drivers with middle aged, more experienced drivers and to determine if these skills can be improved with video-based road commentary training. The primary task required the participants to detect and verbally identify immediate hazard on video-based traffic scenarios while concurrently performing a secondary tracking task, simulating the steering of real driving. The results showed that the young drivers perceived fewer immediate hazards (mean = 75.2%, n = 24, 19 females) than the more experienced drivers (mean = 87.5%, n = 8, all females), and had longer hazard perception times, but performed better in the secondary tracking task. After the road commentary training, the mean percentage of hazards detected and identified by the young drivers improved to the level of the experienced drivers and was significantly higher than that of an age and driving experience matched control group. The results will be discussed in the context of psychological theories of hazard perception and in relation to road commentary as an evidence-based training intervention that seems to improve many aspects of unsafe driving behaviour in young drivers.

Simulation architecture for the design of cooperative collision warning systems

Simulation has been widely used to estimate the benefits of Cooperative Systems (CS) based on Inter-Vehicular Communications (IVC). This paper presents a new architecture built with the SiVIC simulator and the RTMaps™ multisensors prototyping platform. We introduce several improvements from a previous similar architecture, regarding IVC modelisation and vehicles’ control. It has been tuned with on-road measurements to improve fidelity. We discuss the results of a freeway emergency braking scenario (EEBL) implemented to validate our architecture’s capabilities.

A method for predicting rain-induced instability of an individual slope

Awareness to avoid losses and casualties due to rain-induced landslide is increasing in regions that routinely experience heavy rainfall. Improvements in early warning systems against rain-induced landslide such as prediction modelling using rainfall records, is urgently needed in vulnerable regions. The existing warning systems have been applied using stability chart development and real-time displacement measurement on slope surfaces. However, there are still some drawbacks such as: ignorance of rain-induced instability mechanism, mislead prediction due to the probabilistic prediction and short time for evacuation. In this research, a real-time predictive method was proposed to alleviate the drawbacks mentioned above. A case-study soil slope in Indonesia that failed in 2010 during rainfall was used to verify the proposed predictive method. Using the results from the field and laboratory characterizations, numerical analyses can be applied to develop a model of unsaturated residual soils slope with deep cracks and subject to rainwater infiltration. Real-time rainfall measurement in the slope and the prediction of future rainfall are needed. By coupling transient seepage and stability analysis, the variation of safety factor of the slope with time were provided as a basis to develop method for the real-time prediction of the rain-induced instability of slopes. This study shows the proposed prediction method has the potential to be used in an early warning system against landslide hazard, since the FOS value and the timing of the end-result of the prediction can be provided before the actual failure of the case study slope.

Modelling risk at low exposure railway level crossings : supporting an argument for low-cost level crossing warning devices with lower levels of safety integrity

This paper describes a risk model for estimating the likelihood of collisions at low-exposure railway level crossings, demonstrating the effect that differences in safety integrity can have on the likelihood of a collision. The model facilitates the comparison of safety benefits between level crossings with passive controls (stop or give-way signs) and level crossings that have been hypothetically upgraded with conventional or low-cost warning devices. The scenario presented illustrates how treatment of a cross-section of level crossings with low cost devices can provide a greater safety benefit compared to treatment with conventional warning devices for the same budget.

Waiting for warning: Driver Situation awareness at rural rail level crossings

Driver behaviour at rail level crossings represents a key area for further research. This paper describes an on-road study comparing novice and experienced driver situation awareness at rural rail level crossings. Participants provided verbal protocols while driving a pre-determined rural route incorporating ten rail level crossings. Driver situation awareness was assessed using a network analysis approach. The analysis revealed key differences between novice and experienced drivers' situation awareness. In particular, the novice drivers seemed to be more reliant on rail level crossing warnings and their situation awareness was less focussed on the environment outside of the rail level crossing. In closing, the implications for rail level crossing safety are discussed.