The Explicit Hazard Model – Part 2 : Applications

Hazard and reliability prediction of an engineering asset is one of the significant fields of research in Engineering Asset Health Management (EAHM). In real-life situations where an engineering asset operates under dynamic operational and environmental conditions, the lifetime of an engineering asset can be influenced and/or indicated by different factors that are termed as covariates. The Explicit Hazard Model (EHM) as a covariate-based hazard model is a new approach for hazard prediction which explicitly incorporates both internal and external covariates into one model. EHM is an appropriate model to use in the analysis of lifetime data in presence of both internal and external covariates in the reliability field. This paper presents applications of the methodology which is introduced and illustrated in the theory part of this study. In this paper, the semi-parametric EHM is applied to a case study so as to predict the hazard and reliability of resistance elements on a Resistance Corrosion Sensor Board (RCSB).

End-of-life care pathways for improving outcomes in caring for the dying (Review)

Background In many clinical areas, integrated care pathways are utilised as structured multidisciplinary care plans which detail essential steps in caring for patients with specific clinical problems. Particularly, care pathways for the dying have been developed as a model to improve the end-of-life care of all patients. They aim to ensure that the most appropriate management occurs at the most appropriate time and that it is provided by the most appropriate health professional. Clinical pathways for end-of-life care management are used widely around the world and have been regarded as the gold standard. Therefore, there is a significant need for clinicians to be informed about the utilisation of end-of-life care pathways with a systematic review. Objectives To assess the effects of end-of-life care pathways, compared with usual care (no pathway) or with care guided by another end-of-life care pathway across all healthcare settings (e.g. hospitals, residential aged care facilities, community). Search strategy The Cochrane Register of controlled Trials (CENTRAL), the Pain, Palliative and Supportive Care Review group specialised register,MEDLINE, EMBASE, review articles and reference lists of relevant articles were searched. The search was carried out in September 2009. Selection criteria All randomised controlled trials (RCTs), quasi-randomised trial or high quality controlled before and after studies comparing use versus non-use of an end-of-life care pathway in caring for the dying. Data collection and analysis Results of searches were reviewed against the pre-determined criteria for inclusion by two review authors. Main results The search identified 920 potentially relevant titles, but no studies met criteria for inclusion in the review. Authors’ conclusions Without further available evidence, recommendations for the use of end-of-life pathways in caring for the dying cannot be made. RCTs or other well designed controlled studies are needed for evaluating the use of end-of-life care pathways in caring for dying people.

Weather variability, sunspots, and the blooms of cyanobacteria

The roles of weather variability and sunspots in the occurrence of cyanobacteria blooms, were investigated using cyanobacteria cell data collected from the Fred Haigh Dam, Queensland, Australia. Time series generalized linear model and classification and regression (CART) model were used in the analysis. Data on notified cell numbers of cyanobacteria and weather variables over the periods 2001 and 2005 were provided by the Australian Department of Natural Resources and Water, and Australian Bureau of Meteorology, respectively. The results indicate that monthly minimum temperature (relative risk [RR]: 1.13, 95% confidence interval [CI]: 1.02-1.25) and rainfall (RR: 1.11; 95% CI: 1.03-1.20) had a positive association, but relative humidity (RR: 0.94; 95% CI: 0.91-0.98) and wind speed (RR:0.90; 95% CI: 0.82-0.98) were negatively associated with the cyanobacterial numbers, after adjustment for seasonality and auto-correlation. The CART model showed that the cyanobacteria numbers were best described by an interaction between minimum temperature, relative humidity, and sunspot numbers. When minimum temperature exceeded 18%C and relative humidity was under 66%, the number of cyanobacterial cells rose by 2.15-fold. We conclude that the weather variability and sunspot activity may affect cyanobacterial blooms in dams.

Malnutrition and pressure ulcer risk in adults in Australian health care facilities

Aims To determine the effect of nutritional status on the presence and severity of pressure ulcers in statewide? public healthcare facilities, in Queensland, Australia. Research Methods A multicentre, cross sectional audit of nutritional status of a convenience sample of subjects was carried out as part of a large audit of pressure ulcers in a sample of state based public healthcare facilities in 2002 and 2003. Dietitians in 20 hospitals and six residential aged care facilities conducted single day nutritional status audits of 2208 acute and 839 aged care subjects using the Subjective Global Assessment. The effect of nutritional status on the presence, highest stage and number of pressure ulcers was determined by logistic regression in a model controlling for age, gender, medical specialty and facility location. The potential clustering effect of facility was accounted for in the model using an analysis of correlated data approach. Results Subjects with malnutrition had an adjusted odds risk of 2.6 (95% CI 1.8-3.5, p<0.001) of having a pressure ulcer in acute facilities and 2.0 (95% CI 1.5-2.7, p<0.001) for residential aged care facilities. There was also increased odds risk of having a pressure ulcer, having a higher stage pressure ulcer and a higher number of pressure ulcers with increased severity of malnutrition. Conclusion Malnutrition was associated with at least twice the odds risk of having a pressure ulcer of in public healthcare facilities in Queensland. Action must be taken to identify, prevent and treat malnutrition, especially in patients at risk of pressure ulcer.

Towards a new employment relationship model

Purpose – The purpose of this study is to examine and extend Noer’s theoretical model of the new employment relationship. Design/methodology/approach – Case study methodology is used to scrutinise the model. The results of a literature-based survey on the elements underpinning the five values in the model are analysed from dual perspectives of individual and organization using a multi-source assessment instrument. A schema is developed to guide and inform a series of focus group discussions from an analysis of the survey data. Using content analysis, the transcripts from the focus group discussions are evaluated using the model���s values and their elements. The transcripts are also reviewed for implicit themes. The case studied is Flight Centre Limited, an Australian-based international retail travel company. Findings – Using this approach, some elements of the five values in Noer’s model are identified as characteristic of the company’s psychological contract. Specifically, to some extent, the model���s values of flexible deployment, customer focus, performance focus, project-based work, and human spirit and work can be applied in this case. A further analysis of the transcripts validates three additional values in the psychological contract literature: commitment; learning and development; and open information. As a result of the findings, Noer’s model is extended to eight values. Research limitations/implications – The study offers a research-based model of the new employment relationship. Since generalisations from the case study findings cannot be applied directly to other settings, the opportunity to test this model in a variety of contexts is open to other researchers. Originality/value – In practice, the methodology used is a unique process for benchmarking the psychological contract. The process may be applied in other business settings. By doing so, organization development professionals have a consulting framework for comparing an organization’s dominant psychological contract with the extended model presented here.

How can we exploit mobile data to customise learning based on a student’s environmental surroundings?

User-Based intelligent systems are already commonplace in a student’s online digital life. Each time they browse, search, buy, join, comment, play, travel, upload, download, a system collects, analyses and processes data in an effort to customise content and further improve services. This panel session will explore how intelligent systems, particularly those that gather data from mobile devices, can offer new possibilities to assist in the delivery of customised, personal and engaging learning experiences. The value of intelligent systems for education lies in their ability to formulate authentic and complex learner profiles that bring together and systematically integrate a student’s personal world with a formal curriculum framework. As we well know, a mobile device can collect data relating to a student’s interests (gathered from search history, applications and communications), location, surroundings and proximity to others (GPS, Bluetooth). However, what has been less explored is the opportunity for a mobile device to map the movements and activities of a student from moment to moment and over time. This longitudinal data provides a holistic profile of a student, their state and surroundings. Analysing this data may allow us to identify patterns that reveal a student’s learning processes; when and where they work best and for how long. Through revealing a student’s state and surroundings outside of schools hour, this longitudinal data may also highlight opportunities to transform a student’s everyday world into an inventory for learning, punctuating their surroundings with learning recommendations. This would in turn lead to new ways to acknowledge and validate and foster informal learning, making it legitimate within a formal curriculum.

IT-enhanced communication protocols for building project management

Purpose – Building project management requires real time flow of information between all the project team members or the supply chain members. In the present scenario, when project participants are geographically separated, adoption of Information Communication Technology (ICT) enables such effective communication. But strategic adoption of ICT requires that all the supply chain members follow the accepted methods of communication or the communication protocols. The majority of the construction organizations are small and medium enterprises (SMEs). This research, therefore, proposes to focus on developing IT-enhanced communication protocols for building project management by SMEs. Design/methodology/approach – The research adopts a sequential mixed methods approach, where data collection and analysis are conducted in both the quantitative and qualitative phases of research. Findings – The protocols are proposed as a “Strategic Model for Enhancing ICT Diffusion in Building Projects”. The framework for the model is discussed at three levels of study, i.e industry, organization, and people. Practical implications – While the research was conducted in an Indian context, the research outcome is envisaged to be widely applicable in other countries with due considerations. Originality/value – The developed framework has implications for national level bodies and academic institutions, organizations, people or project managers and is applicable at the international level after due considerations.

Creation of a validated 3D finite element model of an ovine tibia

Introduction Ovine models are widely used in orthopaedic research. To better understand the impact of orthopaedic procedures computer simulations are necessary. 3D finite element (FE) models of bones allow implant designs to be investigated mechanically, thereby reducing mechanical testing. Hypothesis We present the development and validation of an ovine tibia FE model for use in the analysis of tibia fracture fixation plates. Material & Methods Mechanical testing of the tibia consisted of an offset 3-pt bend test with three repetitions of loading to 350N and return to 50N. Tri-axial stacked strain gauges were applied to the anterior and posterior surfaces of the bone and two rigid bodies – consisting of eight infrared active markers, were attached to the ends of the tibia. Positional measurements were taken with a FARO arm 3D digitiser. The FE model was constructed with both geometry and material properties derived from CT images of the bone. The elasticity-density relationship used for material property determination was validated separately using mechanical testing. This model was then transformed to the same coordinate system as the in vitro mechanical test and loads applied. Results Comparison between the mechanical testing and the FE model showed good correlation in surface strains (difference: anterior 2.3%, posterior 3.2%). Discussion & Conclusion This method of model creation provides a simple method for generating subject specific FE models from CT scans. The use of the CT data set for both the geometry and the material properties ensures a more accurate representation of the specific bone. This is reflected in the similarity of the surface strain results.

Generic one round group key exchange in the standard model

Minimizing complexity of group key exchange (GKE) protocols is an important milestone towards their practical deployment. An interesting approach to achieve this goal is to simplify the design of GKE protocols by using generic building blocks. In this paper we investigate the possibility of founding GKE protocols based on a primitive called multi key encapsulation mechanism (mKEM) and describe advantages and limitations of this approach. In particular, we show how to design a one-round GKE protocol which satisfies the classical requirement of authenticated key exchange (AKE) security, yet without forward secrecy. As a result, we obtain the first one-round GKE protocol secure in the standard model. We also conduct our analysis using recent formal models that take into account both outsider and insider attacks as well as the notion of key compromise impersonation resilience (KCIR). In contrast to previous models we show how to model both outsider and insider KCIR within the definition of mutual authentication. Our analysis additionally implies that the insider security compiler by Katz and Shin from ACM CCS 2005 can be used to achieve more than what is shown in the original work, namely both outsider and insider KCIR.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Caveat Anicula! Beware of the Quiet Little Old Ladies : Demographics, Pharmacotherapy, Survival and Readmissions in a 10 Year Cohort of Heart Failure Patients with Preserved Systolic Function

Objective--To determine whether heart failure with preserved systolic function (HFPSF) has different natural history from left ventricular systolic dysfunction (LVSD). Design and setting--A retrospective analysis of 10 years of data (for patients admitted between 1 July 1994 and 30 June 2004, and with a study census date of 30 June 2005) routinely collected as part of clinical practice in a large tertiary referral hospital.Main outcome measures-- Sociodemographic characteristics, diagnostic features, comorbid conditions, pharmacotherapies, readmission rates and survival.Results--Of the 2961 patients admitted with chronic heart failure, 753 had echocardiograms available for this analysis. Of these, 189 (25%) had normal left ventricular size and systolic function. In comparison to patients with LVSD, those with HFPSF were more often female (62.4% v 38.5%; P = 0.001), had less social support, and were more likely to live in nursing homes (17.9% v 7.6%; P < 0.001), and had a greater prevalence of renal impairment (86.7% v 6.2%; P = 0.004), anaemia (34.3% v 6.3%; P = 0.013) and atrial fibrillation (51.3% v 47.1%; P = 0.008), but significantly less ischaemic heart disease (53.4% v 81.2%; P = 0.001). Patients with HFPSF were less likely to be prescribed an angiotensin-converting enzyme inhibitor (61.9% v 72.5%; P = 0.008); carvedilol was used more frequently in LVSD (1.5% v 8.8%; P < 0.001). Readmission rates were higher in the HFPSF group (median, 2 v 1.5 admissions; P = 0.032), particularly for malignancy (4.2% v 1.8%; P < 0.001) and anaemia (3.9% v 2.3%; P < 0.001). Both groups had the same poor survival rate (P = 0.912). Conclusions--Patients with HFPSF were predominantly older women with less social support and higher readmission rates for associated comorbid illnesses. We therefore propose that reduced survival in HFPSF may relate more to comorbid conditions than suboptimal cardiac management.

Design and implementation of sensor networks for the observation and research of harmful algal blooms in southern California coastal waters

Harmful Algal Blooms (HABs) have become an important environmental concern along the western coast of the United States. Toxic and noxious blooms adversely impact the economies of coastal communities in the region, pose risks to human health, and cause mortality events that have resulted in the deaths of thousands of fish, marine mammals and seabirds. One goal of field-based research efforts on this topic is the development of predictive models of HABs that would enable rapid response, mitigation and ultimately prevention of these events. In turn, these objectives are predicated on understanding the environmental conditions that stimulate these transient phenomena. An embedded sensor network (Fig. 1), under development in the San Pedro Shelf region off the Southern California coast, is providing tools for acquiring chemical, physical and biological data at high temporal and spatial resolution to help document the emergence and persistence of HAB events, supporting the design and testing of predictive models, and providing contextual information for experimental studies designed to reveal the environmental conditions promoting HABs. The sensor platforms contained within this network include pier-based sensor arrays, ocean moorings, HF radar stations, along with mobile sensor nodes in the form of surface and subsurface autonomous vehicles. FreewaveTM radio modems facilitate network communication and form a minimally-intrusive, wireless communication infrastructure throughout the Southern California coastal region, allowing rapid and cost-effective data transfer. An emerging focus of this project is the incorporation of a predictive ocean model that assimilates near-real time, in situ data from deployed Autonomous Underwater Vehicles (AUVs). The model then assimilates the data to increase the skill of both nowcasts and forecasts, thus providing insight into bloom initiation as well as the movement of blooms or other oceanic features of interest (e.g., thermoclines, fronts, river discharge, etc.). From these predictions, deployed mobile sensors can be tasked to track a designated feature. This focus has led to the creation of a technology chain in which algorithms are being implemented for the innovative trajectory design for AUVs. Such intelligent mission planning is required to maneuver a vehicle to precise depths and locations that are the sites of active blooms, or physical/chemical features that might be sources of bloom initiation or persistence. The embedded network yields high-resolution, temporal and spatial measurements of pertinent environmental parameters and resulting biology (see Fig. 1). Supplementing this with ocean current information and remotely sensed imagery and meteorological data, we obtain a comprehensive foundation for developing a fundamental understanding of HAB events. This then directs labor- intensive and costly sampling efforts and analyses. Additionally, we provide coastal municipalities, managers and state agencies with detailed information to aid their efforts in providing responsible environmental stewardship of their coastal waters.

Modelling bus lost time : an additional parameter influencing bus dwell time and station platform capacity at a BRT station platform

The common approach to estimate bus dwell time at a BRT station is to apply the traditional dwell time methodology derived for suburban bus stops. In spite of being sensitive to boarding and alighting passenger numbers and to some extent towards fare collection media, these traditional dwell time models do not account for the platform crowding. Moreover, they fall short in accounting for the effects of passenger/s walking along a relatively longer BRT platform. Using the experience from Brisbane busway (BRT) stations, a new variable, Bus Lost Time (LT), is introduced in traditional dwell time model. The bus lost time variable captures the impact of passenger walking and platform crowding on bus dwell time. These are two characteristics which differentiate a BRT station from a bus stop. This paper reports the development of a methodology to estimate bus lost time experienced by buses at a BRT platform. Results were compared with the Transit Capacity and Quality of Servce Manual (TCQSM) approach of dwell time and station capacity estimation. When the bus lost time was used in dwell time calculations it was found that the BRT station platform capacity reduced by 10.1%.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.