Best practices in prediction for decision-making : lessons from the atmospheric and earth sciences

Predictions that result from scientific research hold great appeal for decision-makers who are grappling with complex and controversial environmental issues, by promising to enhance their ability to determine a need for and outcomes of alternative decisions. A problem exists in that decision-makers and scientists in the public and private sectors solicit, produce, and use such predictions with little understanding of their accuracy or utility, and often without systematic evaluation or mechanisms of accountability. In order to contribute to a more effective role for ecological science in support of decision-making, this paper discusses three ``best practices'' for quantitative ecosystem modeling and prediction gleaned from research on modeling, prediction, and decision-making in the atmospheric and earth sciences. The lessons are distilled from a series of case studies and placed into the specific context of examples from ecological science.

On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts—variation over and above that accounted for by the Poisson density. The extra-variation – or dispersion – is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models—tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31–40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

Accident prediction model for railway-highway interfaces

Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes. Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes.

Development of accident prediction models for rural highway intersections

A study was done to develop macrolevel crash prediction models that can be used to understand and identify effective countermeasures for improving signalized highway intersections and multilane stop-controlled highway intersections in rural areas. Poisson and negative binomial regression models were fit to intersection crash data from Georgia, California, and Michigan. To assess the suitability of the models, several goodness-of-fit measures were computed. The statistical models were then used to shed light on the relationships between crash occurrence and traffic and geometric features of the rural signalized intersections. The results revealed that traffic flow variables significantly affected the overall safety performance of the intersections regardless of intersection type and that the geometric features of intersections varied across intersection type and also influenced crash type.

Investigation on parameters used in warning systems for rain-induced embankment instability

A number of instrumented laboratory-scale soil embankment slopes were subjected to artificial rainfall until they failed. The factor of safety of the slope based on real-time measurements of pore-water pressure (suction) and laboratory measured soil properties were calculated as the rainfall progressed. Based on the experiment measurements and slope stability analysis, it was observed that slope displacement measurements can be used to warn the slope failure more accurately. Further, moisture content/pore-water pressure measurements near the toe of the slope and the real-time factor of safety can also be used for prediction of rainfall-induced embankment failures with adequate accuracy.

Franchisor failure : an assessment of the adequacy of regulatory response

Franchisor failure is one of the most problematic areas of the franchise relationship. It impacts negatively on landlords and other suppliers, but the contracting parties that are currently without legal rights to respond when a franchisor fails, and thus without consumer protection, are its franchisees. In this thesis I explore the current contractual, regulatory and commercial environment that franchisees inhabit, within the context of franchisor failure. I conclude that ex ante there are opportunities to level the playing field through consumer protection legislation. I also conclude that the task is not one solely for the consumer protection legislation; the problem should also be addressed ex post through the Corporations Act.

Remaining useful life prediction using elliptical basis function network and Markov Chain

This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.

Behavioral drivers of EIS failure

We argue that the sustained successful operation of an ES is determined by, and is dependent on, multiple organizational stakeholders. The single greatest organizational barrier to EIS success and achieving widespread organizational benefits can be attributed to the way in which different subcultures treat data critical to EIS operation. Building on Lee & Strong’s (2004) data roles we incorporate Schien’s (1996) cultural framework along with DeLone and McLean’s (2003) dimensions of IS success, unpacking the underlying drivers of behaviors as they relate to EIS data. Further, we explain the origins of data based conflict resulting in poor EIS data utilization.

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.

Which components of heart failure programs are effective? A meta-analysis of the outcomes of structured telephone support or telemonitoring as the primary component of heart failure management in 9,560 patients (Abridged Cochrane Review)

Aims--Telemonitoring (TM) and structured telephone support (STS) have the potential to deliver specialised management to more patients with chronic heart failure (CHF), but their efficacy is still to be proven. Objectives To review randomised controlled trials (RCTs) of TM or STS on all- cause mortality and all-cause and CHF-related hospitalisations in patients with CHF, as a non-invasive remote model of specialised disease-management intervention.--Methods and Results--Data sources:We searched 15 electronic databases and hand-searched bibliographies of relevant studies, systematic reviews, and meeting abstracts. Two reviewers independently extracted all data. Study eligibility and participants: We included any randomised controlled trials (RCT) comparing TM or STS to usual care of patients with CHF. Studies that included intensified management with additional home or clinic visits were excluded. Synthesis: Primary outcomes (mortality and hospitalisations) were analysed; secondary outcomes (cost, length of stay, quality of life) were tabulated.--Results: Thirty RCTs of STS and TM were identified (25 peer-reviewed publications (n=8,323) and five abstracts (n=1,482)). Of the 25 peer-reviewed studies, 11 evaluated TM (2,710 participants), 16 evaluated STS (5,613 participants) and two tested both interventions. TM reduced all-cause mortality (risk ratio (RR 0•66 [95% CI 0•54-0•81], p<0•0001) and STS showed similar trends (RR 0•88 [95% CI 0•76-1•01], p=0•08). Both TM (RR 0•79 [95% CI 0•67-0•94], p=0•008) and STS (RR 0•77 [95% CI 0•68-0•87], p<0•0001) reduced CHF-related hospitalisations. Both interventions improved quality of life, reduced costs, and were acceptable to patients. Improvements in prescribing, patient-knowledge and self-care, and functional class were observed.--Conclusion: TM and STS both appear effective interventions to improve outcomes in patients with CHF.

Ryanodine receptor phosphorylation in human heart failure

Heart failure is a complex disorder, characterized by activation of the sympathetic nervous system, leading to dysregulated Ca2+ homeostasis in cardiac myocytes and tissue remodeling. In a variety of diseases, cardiac malfunction is associated with aberrant fluxes of Ca2+ across both the surface membrane and the internal Ca2+ store, the sarcoplasmic reticulum (SR). One prominent hypothesis residues is that in heart failure, the activity of the ryanodine receptor (RyR2) Ca2+ release channel in the SR is increased due to excess phosphorylation and that this contributes to excess SR Ca2+ leak in diastole, reduced SR Ca2+ load and decreased contractility (Huke & Bers, 2008). There is controversy over which serine residues in RyR2 are hyperphosphorylated in animal models of heart failure and whether this is via the CaMKII or the PKA-linked signaling pathway. S2808, S2814 and S2030 in RyR2 have been variously claimed to be hyperphosphorylated. Our aim was to examine the degree of phosphorylation of these residues in RyR2 from failing human hearts. The use of human tissue was approved by the Human Research Ethics Committee, The Prince Charles Hospital, EC28114. Left ventricular tissue samples were obtained from an explanted heart of a patient with endstage heart failure (Emery Dreifuss Muscular Dystrophy with cardiomyopathy) and non-failing tissue was from a patient with cystic fibrosis undergoing heart-lung transplantation with no history of heart disease. SR vesicles were prepared as described by Laver et al. (1995) and examined with SDS-Page and Western Blot. Transferred proteins were probed with antibodies to detect total protein phosphorylation, phosphorylation of RyR2 serine residues S2808, S2814, S2030 and for the key proteins calsequestrin, triadin, junctin and FKBP12.6. To avoid membrane stripping artifact, each membrane was exposed to one phosphorylation-specific antibody and signal densities quantified using Bio-Rad Quantity One software. We found no distinguishable difference between failing and healthy hearts in the protein expression levels of RyR2, triadin, junctin or calsequestrin. We found an expected upregulation of total RyR2 phosphorylation in the failing heart sample, compared to a matched amount of RyR2 (quantified using densiometry) in healthy heart. Probing with antibodies detecting only the phosphorylated form of the specific RyR2 residues showed that the increase in total RyR2 phosphorylation in the failing heart was due to hyperphosphorylation of S2808 and S2814. We found that S2030 phosphorylation levels were unchanged in human heart failure. Interestingly, we found that S2030 has a basal level of phosphorylation in the healthy human heart, different from the absence of basal phosphorylation recently reported in rodent heart (Huke & Bers, 2008). Finally, preliminary results indicate that less FKBP 12.6 is associated with RyR2 in the failing heart, possibly as a consequence of PKA activation. In conclusion, residues S2808 and S2814 are hyperphosphorylated in human heart failure, presumably due to upregulation of the CaMKII and/or PKA signaling pathway as a result of chronic activation of the sympathetic nervous system. Such changes in RyR2 phosphorylation are believed to contribute to the leaky RyR2 phenotype associated with heart failure, which increases the incidence of arrhythmia and contributes to the severely impaired contractile performance of the failing heart. Huke S & Bers DM. (2008). Ryanodine receptor phosphorylation at serine 2030, 2808 and 2814 in rat cardiomyocytes. Biochemical and Biophysical Research Communications 376, 80-85. Laver DR, Roden LD, Ahern GP, Eager KR, Junankar PR & Dulhunty AF. (1995). Cytoplasmic Ca2+ inhibits the ryanodine receptor from cardiac muscle. Journal of Membrane Biology 147, 7-22. Proceedings

Efficient control of an autonomous underwater vehicle while accounting for thruster failure

This paper is concerned with the design and implementation of control strategies onto a test-bed vehicle with six degrees-of-freedom. We design our trajectories to be efficient in time and in power consumption. Moreover, we also consider cases when actuator failure can arise and discuss alternate control strategies in this situation. Our calculations are supplemented by experimental results.

Asset health prediction using condition indicators and operating environment indicators : a case study

Prognostics and asset life prediction is one of research potentials in engineering asset health management. We previously developed the Explicit Hazard Model (EHM) to effectively and explicitly predict asset life using three types of information: population characteristics; condition indicators; and operating environment indicators. We have formerly studied the application of both the semi-parametric EHM and non-parametric EHM to the survival probability estimation in the reliability field. The survival time in these models is dependent not only upon the age of the asset monitored, but also upon the condition and operating environment information obtained. This paper is a further study of the semi-parametric and non-parametric EHMs to the hazard and residual life prediction of a set of resistance elements. The resistance elements were used as corrosion sensors for measuring the atmospheric corrosion rate in a laboratory experiment. In this paper, the estimated hazard of the resistance element using the semi-parametric EHM and the non-parametric EHM is compared to the traditional Weibull model and the Aalen Linear Regression Model (ALRM), respectively. Due to assuming a Weibull distribution in the baseline hazard of the semi-parametric EHM, the estimated hazard using this model is compared to the traditional Weibull model. The estimated hazard using the non-parametric EHM is compared to ALRM which is a well-known non-parametric covariate-based hazard model. At last, the predicted residual life of the resistance element using both EHMs is compared to the actual life data.

Flight testing of an augmented separation management system providing separation protection during failure of the central communication network

Future air traffic management concepts often involve the proposal of automated separation management algorithms that replaces human air traffic controllers. This paper proposes a new type of automated separation management algorithm (based on the satisficing approach) that utilizes inter-aircraft communication and a track file manager (or bank of Kalman filters) that is capable of resolving conflicts during periods of communication failure. The proposed separation management algorithm is tested in a range of flight scenarios involving during periods of communication failure, in both simulation and flight test (flight tests were conducted as part of the Smart Skies project). The intention of the conducted flight tests was to investigate the benefits of using inter-aircraft communication to provide an extra layer of safety protection in support air traffic management during periods of failure of the communication network. These benefits were confirmed.