A bayesian semiparametric analysis for additive Hazard models with censored observations

The hazard function, also called the risk function or intensity function, is usually used to model survival data or other waiting times, such as unemployment times. In contrast to the proportional hazard model, the additive risk model assumes that the hazard function is the sum of rather than the product of, the baseline hazard function and a non-negative function of covariates. We propose to introduce the covariates into the model through a Gamma hazard function, while the baseline hazard function is left unspecified. Following the Bayesian paradigm, we obtain an approximation to the posterior distribution using Markov Chain Monte Carlo techniques. The subject-specific survival estimation is also studied. A real example using unemployment data is provided. This work was partially supported by the Spanish Education and Science Council Project PB96-0776.     

Comparison of methods for analyzing recurrent events data: application to the Emergency Department Visits of Pediatric Firearm Victims

In many medical conditions subjects can experience recurrent incidents. A common feature for the recurrent events data and multi-stage failure time observations is that the events are naturally ordered and occur in a certain sequence over time. To analyze such data, conventional methods based on either the frequency of events or the time to the first event or overall survival time is often inefficient and unsophisticated. If data have repeated events over a period with censored failure time in longitudinal studies, more complex analytic approaches are needed to obtain accurate estimates and efficient inferences, because adjustment is necessary for existing correlation between recurrent failure times within a subject. For analyzing different kinds of recurrent event data we review the existing models-multiple failure time models and frailty models, which allow use of all the available information to accurately estimate the relative risks of recurrences in a given dataset. Using the Pediatric Firearm Victim's Emergency Department Visit Study, the results from the proposed models are compared, and applicability and appropriateness of each model are discussed.     

Headrest position during normal driving: Implication to neck injury risk in rear crashes

The gap and relative height of headrest behind drivers were determined for 1915 vehicles approaching an intersection on a two lane road. Vehicle type and headrest adjustment were also evaluated using film of normal driving taken by the Insurance Institute for Highway Safety. Only 10% of drivers had headrests in the most favorable position to prevent neck extension during a rearend crash. 73% of cars had adjustable headrests, but only a quarter were placed in the up position. 83% of the adjustable headrests could have been raised to better protect the driver. Hyge sled tests were run to determine biomechanical responses for the various conditions observed in normal driving. This included three headrest heights and three gaps behind the head. Neck extension from the Hybrid III dummy was normalized to the response for a high, close headrest, and injury risk was assumed to be proportional to neck extension. The current driving situation has a relative injury risk of 3.4 in rearend crashes, compared to 1.0 for the favorable condition. If all adjustable headrests were placed in the up position, the relative risk would be lowered to 2.4, a 28.3% reduction in whiplash injury risk. Public education and vehicle design should address the importance of proper headrest placement for driving safety.     

Using hierarchical Bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data

Severe crashes are causing serious social and economic loss, and because of this, reducing crash injury severity has become one of the key objectives of the high speed facilities’ (freeway and expressway) management. Traditional crash injury severity analysis utilized data mainly from crash reports concerning the crash occurrence information, drivers’ characteristics and roadway geometric related variables. In this study, real-time traffic and weather data were introduced to analyze the crash injury severity. The space mean speeds captured by the Automatic Vehicle Identification (AVI) system on the two roadways were used as explanatory variables in this study; and data from a mountainous freeway (I-70 in Colorado) and an urban expressway (State Road 408 in Orlando) have been used to identify the analysis result's consistence. Binary probit (BP) models were estimated to classify the non-severe (property damage only) crashes and severe (injury and fatality) crashes. Firstly, Bayesian BP models’ results were compared to the results from Maximum Likelihood Estimation BP models and it was concluded that Bayesian inference was superior with more significant variables. Then different levels of hierarchical Bayesian BP models were developed with random effects accounting for the unobserved heterogeneity at segment level and crash individual level, respectively. Modeling results from both studied locations demonstrate that large variations of speed prior to the crash occurrence would increase the likelihood of severe crash occurrence. Moreover, with considering unobserved heterogeneity in the Bayesian BP models, the model goodness-of-fit has improved substantially. Finally, possible future applications of the model results and the hierarchical Bayesian probit models were discussed.     

Incidence and risk factors of severe traumatic brain injury resulting from road accidents: a population-based study

A population-based study was carried out in 1996-2001 to provide the incidence and to identify the risk factors of severe traumatic brain injury (TBI) resulting from a road accident. The severe TBI was defined as an injury to the brain or the skull, excluding scalp injuries, with an abbreviated injury scale (AIS) severity score greater than 2. The severe TBI of 1238 patients were described. The annual incidence and mortality of severe TBI were, respectively, 13.7 per 100,000 and 5.3 per 100,000. The fatality rate increased from 20% in childhood to 71% over 75-year-old. Compared to restrained car occupants, the odds ratio for having a severe TBI was 18.1 (95% confidence interval, CI=12.8-25.5) for un-helmeted motorcyclists, 9.2 (95% CI=7.5-11.3) for pedestrians, 6.4 (95% CI=4.7-8.8) for un-helmeted cyclists, 3.9 (95% CI=3.1-4.8) for unrestrained car occupants and 2.8 (95% CI=2.2-3.5) for helmeted motorcyclists. Even after adjustment for several severity factors, male gender and age above 55 were both risk factors. Prevention programs aiming at improving the head protection should be promoted. The circumstances of the accident should be taken into account to predict a severe TBI.     

Appropriate analysis of CIREN data: Using NASS-CDS to reduce bias in estimation of injury risk factors in passenger vehicle crashes

The Crash Injury Research Engineering Network (CIREN) database contains detailed medical and crash information on a large number of severely injured occupants in motor vehicle crashes. CIREN's major limitation for stand-alone analyses to explore injury risk factors is that control subjects without a given injury type must have another severe injury to be included in the database. This leads to bias toward the null in the estimation of risk associations. One method to cope with this limitation is to obtain information about occupants without a given injury type from the National Automotive Sampling System's Crashworthiness Data System (NASS-CDS), which is a probability sample of towaway crashes, containing similar crash information, but less medical detail. Combining CIREN and NASS-CDS in this manner takes advantage of the increased sample size when outcomes are available in both datasets; otherwise NASS-CDS can serve as a sample of controls to be combined with CIREN cases, possibly under a sensitivity analysis that includes and excludes NASS-CDS subjects whose status as a control is uncertain. Because CIREN is not a probability sample of crashes that meet its inclusion criteria, we develop a method to estimate the probability of selection for the CIREN cases using data from NASS-CDS. These estimated probabilities are then used to compute “pseudo-weights” for the CIREN cases. These pseudo-weights not only allow for reduced bias in the estimation of risk associations, they allow direct prevalence estimates to be made using medical outcome data available only in CIREN. We illustrate the use of these methods with both simulation studies and application to estimation of prevalence and predictors of AIS 3+ injury risk to head, thorax, and lower extremity regions, as well as prevalence and predictors of acetabular pelvic fractures. Results of these analyses demonstrate combining NASS and CIREN data can yield improvements in mean square error and nominal confidence interval coverage over analyses that use either the NASS-CDS or the CIREN sample alone.     

High strength steels,stiffness of vehicle front-end structure,and risk of injury to rear seat occupants

Previous research has shown that rear seat occupant protection has decreased over model years, and front-end stiffness is a possible factor causing this trend. In this research, the effects of a change in stiffness on protection of rear seat occupants in frontal crashes were investigated. The stiffness was adjusted by using higher strength steels (DP and TRIP), or thicker metal sheets. Finite element simulations were performed, using an LS Dyna vehicle model coupled with a MADYMO dummy. Simulation results showed that an increase in stiffness, to the extent it happened in recent model years, can increase the risk of AIS3+ head injuries from 4.8% in the original model (with a stiffness of 1000 N/mm) to 24.2% in a modified model (with a stiffness of 2356 N/mm). The simulations also showed an increased risk of chest injury from 9.1% in the original model to 11.8% in the modified model. Distribution of injuries from real world accident data confirms the findings of the simulations.     

Construction and evaluation of thoracic injury risk curves for a finite element human body model in frontal car crashes

There is a need to improve the protection to the thorax of occupants in frontal car crashes. Finite element human body models are a more detailed representation of humans than anthropomorphic test devices (ATDs). On the other hand, there is no clear consensus on the injury criteria and the thresholds to use with finite element human body models to predict rib fractures. The objective of this study was to establish a set of injury risk curves to predict rib fractures using a modified Total HUman Model for Safety (THUMS). Injury criteria at the global, structural and material levels were computed with a modified THUMS in matched Post Mortem Human Subjects (PMHSs) tests. Finally, the quality of each injury risk curve was determined. For the included PMHS tests and the modified THUMS, DcTHOR and shear stress were the criteria at the global and material levels that reached an acceptable quality. The injury risk curves at the structural level did not reach an acceptable quality.     

Overall injury risk to different drivers: combining exposure,frequency, and severity models

Traffic crash risk assessments should incorporate appropriate exposure data. However, existing US nationwide crash data sets, the NASS General Estimates System (GES) and the Fatality Analysis Reporting System (FARS), do not contain information on driver or vehicle exposure. In order to obtain appropriate exposure data, this work estimates vehicle miles driven (VMD) by different drivers using the Nationwide Personal Transportation Survey (NPTS). These results are combined with annual crash rates and injury severity information from the GES for a comprehensive assessment of overall risk to different drivers across vehicle classes.Data are distinguished by driver age, gender, vehicle type, crash type (rollover versus non-rollover), and injury severity. After correcting for drivers' crash exposure, results indicate that young drivers are far more crash prone than other drivers (per VMD) and that drivers of sports utility vehicles (SUVs) and pickups (PUs) are more likely to be involved in rollover crashes than those driving passenger cars. Although, the results suggest that drivers of SUVs are generally much less crash prone than drivers of passenger cars, the rollover propensity of SUVs and the severity of that crash type offset many of the incident benefits for SUV drivers.     

Fuzzy-logic-based network for complex systems risk assessment: Application to ship performance analysis

In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster–Shafer theory of evidence is extended to monitor safety-critical systems’ performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi–Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input–output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex.     

An approach to verifying and debugging simulation models governed by ordinary differential equations: Part 2. Residuals analysis and a case study

In Part 1 of this paper a methodology for back‐to‐back testing of simulation software was described. Residuals with error‐dependent geometric properties were generated. A set of potential coding errors was enumerated, along with a corresponding set of feature matrices, which describe the geometric properties imposed on the residuals by each of the errors. In this part of the paper, an algorithm is developed to isolate the coding errors present by analysing the residuals. A set of errors is isolated when the subspace spanned by their combined feature matrices corresponds to that of the residuals. Individual feature matrices are compared to the residuals and classified as ‘definite’, ‘possible’ or ‘impossible’. The status of ‘possible’ errors is resolved using a dynamic subset testing algorithm. To demonstrate and validate the testing methodology presented in Part 1 and the isolation algorithm presented in Part 2, a case study is presented using a model for biological wastewater treatment. Both single and simultaneous errors that are deliberately introduced into the simulation code are correctly detected and isolated. Copyright © 2003 John Wiley & Sons, Ltd.     

How to Perform Continuous Sampling (CSP), Vol. 2: ASQC Basic Reference Series

Modeling recurrent event data is of current interest in statistics and engineering. This article proposes a framework for incorporating covariates in flowgraph models, with application to recurrent event data in systems reliability settings. A flowgraph is a generalized transition graph (GTG) originally developed to model total system waiting times for semi-Markov processes. The focus of flowgraph models is expanded by linking covariates into branch transition models, enriching the toolkit of available data analysis methods for complex stochastic systems. This article takes a Bayesian approach to the analysis of flowgraph models. Potential applications are not limited to engineering systems, but also extend to survival analysis.     

Convex optimization and limit analysis: Application to Gurson and porous Drucker-Prager materials

First, we summarize our convex optimization method to solve the static approach of limit analysis. Then, we present the main features of a quadratic extension of a recently proposed mixed finite element method of the kinematic approach. Both methods are applied to obtain precise solutions to a forming problem with Gurson and Drucker-Prager materials. Finally, in order to analyze the criterion of “Porous Drucker-Prager” materials, the Gurson micro-macro model involving a Drucker-Prager matrix containing cylindrical cavities is investigated. Comparing previous results shows, among other things, a similarity in the compression case not always observed for the “Porous von Mises” material between cylindrical and spherical cases.     

Assessing the risk of bluetongue to UK livestock: uncertainty and sensitivity analyses of a temperature-dependent model for the basic reproduction number.

Since 1998 bluetongue virus (BTV), which causes bluetongue, a non-contagious, insect-borne infectious disease of ruminants, has expanded northwards in Europe in an unprecedented series of incursions, suggesting that there is a risk to the large and valuable British livestock industry. The basic reproduction number, R(0), provides a powerful tool with which to assess the level of risk posed by a disease. In this paper, we compute R(0) for BTV in a population comprising two host species, cattle and sheep. Estimates for each parameter which influences R(0) were obtained from the published literature, using those applicable to the UK situation wherever possible. Moreover, explicit temperature dependence was included for those parameters for which it had been quantified. Uncertainty and sensitivity analyses based on Latin hypercube sampling and partial rank correlation coefficients identified temperature, the probability of transmission from host to vector and the vector to host ratio as being most important in determining the magnitude of R(0). The importance of temperature reflects the fact that it influences many processes involved in the transmission of BTV and, in particular, the biting rate, the extrinsic incubation period and the vector mortality rate.     

Blockchain applied to the construction supply chain: A case study with threat model

The construction industry has long faced the challenge of introducing collaborative systems among multiple stakeholders. This challenge creates a high level of rigidity in terms of processing shared information related to different processes, robust holistic regulations, payment actualizations, and resource utilization across different nodes. The need for a digital platform to cross-connect all stakeholders is necessary. A blockchain-based platform is a prime candidate to improve the industry in general and the construction supply chain (CSC) in particular. In this paper, a literature review is presented to establish the main challenges that CSC faces in terms of its effects on productivity and efficiency. In addition, the effect of applying blockchain platforms on a case study is presented and analyzed from performance and security level. The analysis aims to emphasize that blockchain, as presented in this paper, is a viable solution to the challenges in the CSC regardless of the risks associated with the security and robustness of the flow of information and data protection. Moreover, a threat analysis of applying a blockchain model on the CSC industry is introduced. This model indicates potential attacks and possible countermeasures to prevent the attacks. Future work is needed to expand, quantify, and optimize the threat model and conduct simulations considering proposed countermeasures for the different blockchain attacks outlined in this study.     

A dynamic dose–response model to account for exposure patterns in risk assessment: a case study in inhalation anthrax

The most commonly used dose–response models implicitly assume that accumulation of dose is a time-independent process where each pathogen has a fixed risk of initiating infection. Immune particle neutralization of pathogens, however, may create strong time dependence; i.e. temporally clustered pathogens have a better chance of overwhelming the immune particles than pathogen exposures that occur at lower levels for longer periods of time. In environmental transmission systems, we expect different routes of transmission to elicit different dose–timing patterns and thus potentially different realizations of risk. We present a dose–response model that captures time dependence in a manner that incorporates the dynamics of initial immune response. We then demonstrate the parameter estimation of our model in a dose–response survival analysis using empirical time-series data of inhalational anthrax in monkeys in which we find slight dose–timing effects. Future dose–response experiments should include varying the time pattern of exposure in addition to varying the total doses delivered. Ultimately, the dynamic dose–response paradigm presented here will improve modelling of environmental transmission systems where different systems have different time patterns of exposure.     

A refined diffuse cohesive approach for the failure analysis in quasibrittle materials—part II: Application to plain and reinforced concrete structures

This work presents the numerical application of the diffuse cohesive interface model introduced in the Part I paper to the failure analysis of plain and reinforced concrete structures, subjected to complex loading conditions, inducing mixed‐mode fracture initiation and propagation. With the aim of capturing the interaction between concrete and steel reinforcements, the adopted fracture model is incorporated in a novel, more general numerical framework for the nonlinear analysis of reinforced concrete structures. Such a framework includes a newly proposed embedded truss model for the reinforcing bars, allowing them to be crossed by the neighboring propagating cracks. Comparisons with available experimental results are provided, assessing the reliability and the numerical accuracy of the proposed concrete model, with reference to plain specimens subjected to single‐crack propagation as well as to reinforced elements subjected to multiple cracking.     

3D‐based hp‐adaptive first‐order shell finite element for modelling and analysis of complex structures—Part 2: Application to structural analysis

The paper presents a 3D‐based adaptive first‐order shell finite element to be applied to hierarchical modelling and adaptive analysis of complex structures. The main feature of the element is that it is equipped with 3D degrees of freedom, while its mechanical model corresponds to classical first‐order shell theory. Other useful features of the element are its modelling and adaptive capabilities. The element is assigned to hierarchical modelling and hpq‐adaptive analysis of shell parts of complex structures consisting of solid, thick‐ and thin‐shell parts, as well as of transition zones, where h, p and q denote the mesh density parameter and the longitudinal and transverse orders of approximation, respectively. The proposed hp‐adaptive first‐order shell element can be joined with 3D‐based hpq‐adaptive hierarchical shell elements or 3D hpp‐adaptive solid elements by means of the family of 3D‐based hpq/hp‐ or hpp/hp‐adaptive transition elements. The main objective of the first part of our research, presented in the first part of the paper, was to provide non‐standard information on the original parts of the element algorithm. Here we describe the second part of the research, devoted to the methodology and results of the application of the element to various plate and shell problems. The main objective of this part is to verify algorithms of the element and to show its usefulness in modelling and adaptive analysis of shell and plate parts of complex structures. In order to do that, there is a presentation of the results of a comparative analysis of model plate and shell problems using the classical and our elements, and equidistributed and integrated Legendre shape functions. For the plate problem a comparison of the results obtained from the adaptive and non‐adaptive analysis is also included. Additionally, some advantages of the application of our element are shown through a comparative analysis of p‐convergence of the thin plate problem and an adaptive analysis of the exemplary complex structure. Copyright © 2006 John Wiley & Sons, Ltd.     

Text mining as a tool for real-time technology assessment: Application to the cross-national comparative study on artificial organ technology

This study proposed a methodology that integrate sociotechnical systems (STS) and media big data analysis using text mining for the new, real-time technology assessment (TA). The essential steps of this method are composed of data collection using a cultural map, analysis with trends and patents, and synthesis using media big data. By applying this methodology to artificial organs, first, we have shown that STS can be apply to biosocial technical systems beyond the sustainability transition. The result reveals that a media discourse structures, in which eight countries began to form socio-technical regimes around technologies with their respective strengths, in an objective way. Each technology corresponded to the vested interests in each country's socio-technical regimes. These discourse structures helped us to identify substitution, two types of transformation, and reconfiguration as transition pathways. More importantly, our analysis results have also shown that the methodology helps to overcome the anticipation dilemma, saving the time and resources required for TA. Our integrated methodology has achieved similar results by using 23% of the budget, 25% of the time, and 14% of the work hours used for official TA. Lastly, the “objectivity” and “agenda setting” of this methodology can provide a breakthrough in overcoming the control dilemma.