Smoothing a Discrete Hazard Function for the Number of Patients Colonized with Methicillin-Resistance Staphylococcus Aureus in an Intensive Care Unit

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.

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.

Remaining useful life prediction of rotating equipment using covariate-based hazard models : industry applications

The ability to estimate the expected Remaining Useful Life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the Reliability Centred Maintenance (RCM) and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the Condition-Based Maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, Proportional Hazard Model (PHM) and Proportional Covariate Model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

Determination of preventive maintenance lead time using hybrid analysis

The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.

The impact of temperature variability on years of life lost

The relationship between temperature and mortality is generally found to be bathtub shaped (rising at both extremes). However, there are limited data on the potential health effects of temperature variability and on temperature itself...

A comparison of the hazard perception ability of matched groups of healthy drivers aged 35 to 55, 65 to 74, and 75 to 84 years

We examined differences in response latencies obtained during a validated video-based hazard perception driving test between three healthy, community-dwelling groups: 22 mid-aged (35-55 years), 34 young-old (65-74 years), and 23 old-old (75-84 years) current drivers, matched for gender, education level, and vocabulary. We found no significant difference in performance between mid-aged and young-old groups, but the old-old group was significantly slower than the other two groups. The differences between the old-old group and the other groups combined were independently mediated by useful field of view (UFOV), contrast sensitivity, and simple reaction time measures. Given that hazard perception latency has been linked with increased crash risk, these results are consistent with the idea that increased crash risk in older adults could be a function of poorer hazard perception, though this decline does not appear to manifest until age 75+ in healthy drivers.

The expression, regulation and function of kallikrein 4 in prostate cancer

Prostate cancer is a significant health problem faced by aging men. Currently, diagnostic strategies for the detection of prostate cancer are either unreliable, yielding high numbers of false positive results, or too invasive to be used widely as screening tests. Furthermore, the current therapeutic strategies for the treatment of the disease carry considerable side effects. Although organ confined prostate cancer can be curable, most detectable clinical symptoms occur in advanced disease when primary tumour cells have metastasised to distant sites - usually lymph nodes and bone. Many growth factors and steroids assist the continued growth and maintenance of prostatic tumour cells. Of these mitogens, androgens are important in the development of the normal prostate but are also required to sustain the growth of prostate cancer cells in the early stage of the disease. Not only are androgens required in the early stage of disease, but also many other growth factors and hormones interact to cause uncontrolled proliferation of malignant cells. The early, androgen sensitive phase of disease is followed by an androgen insensitive phase, whereby androgens are no longer required to stimulate the growth of the tumour cells. Growth factors such as transforming growth factor  and  (TGF/), epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), insulin-like growth factors (IGFs), Vitamin D and thyroid hormone have been suggested to be important at this stage of disease. Interestingly, some of the kallikrein family of genes, including prostate specific antigen (PSA), the current serum diagnostic marker for prostate cancer, are regulated by androgens and many of the aforementioned growth factors. The kallikrein gene family is a group of serine proteases that are involved in a diverse range of physiological processes: regulation of local blood flow, angiogenesis, tissue invasion and mitogenesis. The earliest members of the kallikrein gene family (KLK1-KLK3) have been strongly associated with general disease states, such as hypertension, inflammation, pancreatitis and renal disease, but are also linked to various cancers. Recently, this family was extended to include 15 genes (KLK1-15). Several newer members of the kallikrein family have been implicated in the carcinogenesis and tumour metastasis of hormone-dependent cancers such as prostate, breast, endometrial and ovarian cancer. The aims of this project were to investigate the expression of the newly identified kallikrein, KLK4, in benign and malignant prostate tissues, and prostate cancer cell lines. This thesis has demonstrated the elevated expression of KLK4 mRNA transcripts in malignant prostate tissue compared to benign prostates. Additionally, expression of the full length KLK4 transcript was detected in the androgen dependent prostate cancer cell line, LNCaP. Based on the above finding, the LNCaP cell line was chosen to assess the potential regulation of full length KLK4 by androgen, thyroid hormone and epidermal growth factor. KLK4 mRNA and protein was found to be up-regulated by androgen and a combination of androgen and thyroid hormone. Thyroid hormone alone produced no significant change in KLK4 mRNA or protein over the control. Epidermal growth factor treatment also resulted in elevated expression levels of KLK4 mRNA and protein. To assess the potential functional role(s) of KLK4/hK4 in processes associated with tumour progression, full length KLK4 was transfected into PC-3 cells - a prostate cancer cell line originally derived from a secondary bone lesion. The KLK4/hK4 over-expressing cells were assessed for their proliferation, migration, invasion and attachment properties. The KLK4 over-expressing clones exhibited a marked change in morphology, indicative of a more aggressive phenotype. The KLK4 clones were irregularly shaped with compromised adhesion to the growth surface. In contrast, the control cell lines (parent PC-3 and empty vector clones) retained a rounded morphology with obvious cell to cell adhesion, as well as significant adhesion to their growth surface. The KLK4 clones exhibited significantly greater attachment to Collagen I and IV than native PC-3s and empty vector controls. Over a 12 hour period, in comparison to the control cells, the KLK4 clones displayed an increase in migration towards PC-3 native conditioned media, a 3 fold increase towards conditioned media from an osteoblastic cell line (Saos-2) and no change in migration towards conditioned media from neonatal foreskin fibroblast cells or 20% foetal bovine serum. Furthermore, the increase in migration exhibited by the KLK4 clones was partially blocked by the serine protease inhibitor, aprotinin. The data presented in this thesis suggests that KLK4/hK4 is important in prostate carcinogenesis due to its over-expression in malignant prostate tissues, its regulation by hormones and growth factors associated with prostate disease and the functional consequences of over-expression of KLK4/hK4 in the PC-3 cell line. These results indicate that KLK4/hK4 may play an important role in tumour invasion and bone metastasis via increased attachment to the bone matrix protein, Collagen I, and enhanced migration due to soluble factors produced by osteoblast cells. This suggestion is further supported by the morphological changes displayed by the KLK4 over-expressing cells. Overall, this data suggests that KLK4/hK4 should be further studied to more fully investigate the potential value of KLK4/hK4 as a diagnostic/prognostic biomarker or in therapeutic applications.

Visual motion perception predicts driving hazard perception ability

PURPOSE: To examine the basis of previous findings of an association between indices of driving safety and visual motion sensitivity and to examine whether this association could be explained by low-level changes in visual function. METHODS: 36 visually normal participants (aged 19 – 80 years), completed a battery of standard vision tests including visual acuity, contrast sensitivity and automated visual fields. and two tests of motion perception including sensitivity for movement of a drifting Gabor stimulus, and sensitivity for displacement in a random-dot kinematogram (Dmin). Participants also completed a hazard perception test (HPT) which measured participants’ response times to hazards embedded in video recordings of real world driving which has been shown to be linked to crash risk. RESULTS: Dmin for the random-dot stimulus ranged from -0.88 to -0.12 log minutes of arc, and the minimum drift rate for the Gabor stimulus ranged from 0.01 to 0.35 cycles per second. Both measures of motion sensitivity significantly predicted response times on the HPT. In addition, while the relationship involving the HPT and motion sensitivity for the random-dot kinematogram was partially explained by the other visual function measures, the relationship with sensitivity for detection of the drifting Gabor stimulus remained significant even after controlling for these variables. CONCLUSION: These findings suggest that motion perception plays an important role in the visual perception of driving-relevant hazards independent of other areas of visual function and should be further explored as a predictive test of driving safety. Future research should explore the causes of reduced motion perception in order to develop better interventions to improve road safety.

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.

Natural convection due to differential heating of inclined walls and heat source placed on bottom wall of an attic shaped space

Numerical investigation of free convection heat transfer in an attic shaped enclosure with differentially heated two inclined walls and filled with air is performed in this study. The left inclined surface is uniformly heated whereas the right inclined surface is uniformly cooled. There is a heat source placed on the right side of the bottom surface. Rest of the bottom surface is kept as adiabatic. Finite volume based commercial software ANSYS 15 (Fluent) is used to solve the governing equations. Dependency of various flow parameters of fluid flow and heat transfer is analyzed including Rayleigh number, Ra ranging from 103 to 106, heater size from 0.2 to 0.6, heater position from 0.3 to 0.7 and aspect ratio from 0.2 to 1.0 with a fixed Prandtl number of 0.72. Outcomes have been reported in terms of temperature and stream function contours and local Nusselt number for various Ra, heater size, heater position, and aspect ratio. Grid sensitivity analysis is performed and numerically obtained results have been compared with those results available in the literature and found good agreement.

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.