A data fusion approach of multiple maintenance data sources for real-world reliability modelling

A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.

How childcare providers interpret "reasonable suspicion" of child abuse

- Background Childcare providers are often “first responders” for suspected child abuse, and how they understand the concept of “reasonable suspicion” will influence their decisions regarding which warning signs warrant reporting. - Objective The purpose of this study was to investigate how childcare providers interpret the threshold for reporting suspected abuse, and to consider the implications of these findings for professional training and development. - Method A convenience sample of 355 childcare providers completed the Reasonable Suspicion of Child Abuse survey to quantify what likelihood of child abuse constitutes “reasonable suspicion.” Responses were examined for internal consistency, evidence of a group standard, and associations with professional and personal demographics. - Results On a Rank Order Scale, responses for what constitutes “reasonable suspicion” ranged from requiring that abuse be “the” most likely cause (8 %) of an injury, to the second most likely (9 %), third (18 %), fourth (18 %), to even the seventh (8 %) or eighth (5 %) most likely cause of an injury. On a numerical probability scale, 21 % of respondents indicated that “abuse” would need to be ≥83 % likely before reasonable suspicion existed; 40 % stated that a likelihood between 53–82 % was needed; 27 % identified the necessary likelihood between 33–52 %; and 12 % set a threshold between 1–32 %. - Conclusions The present finding that no consensus exists for interpreting “reasonable suspicion” suggests that a broadly accepted interpretive framework is needed in order to help prepare childcare providers to know when to report suspected abuse.

Acute fluid shifts influence the assessment of serum vitamin D status in critically ill patients

Introduction Recent reports have highlighted the prevalence of vitamin D deficiency and suggested an association with excess mortality in critically ill patients. Serum vitamin D concentrations in these studies were measured following resuscitation. It is unclear whether aggressive fluid resuscitation independently influences serum vitamin D. Methods Nineteen patients undergoing cardiopulmonary bypass were studied. Serum 25(OH)D3, 1α,25(OH)2D3, parathyroid hormone, C-reactive protein (CRP), and ionised calcium were measured at five defined timepoints: T1 - baseline, T2 - 5 minutes after onset of cardiopulmonary bypass (CPB) (time of maximal fluid effect), T3 - on return to the intensive care unit, T4 - 24 hrs after surgery and T5 - 5 days after surgery. Linear mixed models were used to compare measures at T2-T5 with baseline measures. Results Acute fluid loading resulted in a 35% reduction in 25(OH)D3 (59 ± 16 to 38 ± 14 nmol/L, P < 0.0001) and a 45% reduction in 1α,25(OH)2D3 (99 ± 40 to 54 ± 22 pmol/L P < 0.0001) and i(Ca) (P < 0.01), with elevation in parathyroid hormone (P < 0.0001). Serum 25(OH)D3 returned to baseline only at T5 while 1α,25(OH)2D3 demonstrated an overshoot above baseline at T5 (P < 0.0001). There was a delayed rise in CRP at T4 and T5; this was not associated with a reduction in vitamin D levels at these time points. Conclusions Hemodilution significantly lowers serum 25(OH)D3 and 1α,25(OH)2D3, which may take up to 24 hours to resolve. Moreover, delayed overshoot of 1α,25(OH)2D3 needs consideration. We urge caution in interpreting serum vitamin D in critically ill patients in the context of major resuscitation, and would advocate repeating the measurement once the effects of the resuscitation have abated.

Bayesian estimation of small effects in exercise and sports science

The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a ‘magnitude-based inference’ approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

Observation of sea-salt fraction in sub-100 nm diameter particles at Cape Grim

Volatility-hygroscopicity tandem differential mobility analyzer measurements were used to infer the composition of sub-100 nm diameter Southern Ocean marine aerosols at Cape Grim in November and December 2007. This study focuses on a short-lived high sea spray aerosol (SSA) event on 7–8 December with two externally mixed modes in the Hygroscopic Growth Factor (HGF) distributions (90% relative humidity (RH)), one at HGF > 2 and another at HGF~1.5. The particles with HGF > 2 displayed a deliquescent transition at 73–75% RH and were nonvolatile up to 280°C, which identified them as SSA particles with a large inorganic sea-salt fraction. SSA HGFs were 3–13% below those for pure sea-salt particles, indicating an organic volume fraction (OVF) of up to 11–46%. Observed high inorganic fractions in sub-100 nm SSA is contrary to similar, earlier studies. HGFs increased with decreasing particle diameter over the range 16–97 nm, suggesting a decreased OVF, again contrary to earlier studies. SSA comprised up to 69% of the sub-100 nm particle number, corresponding to concentrations of 110–290 cm−3. Air mass back trajectories indicate that SSA particles were produced 1500 km, 20–40 h upwind of Cape Grim. Transmission electron microscopy (TEM) and X-ray spectrometry measurements of sub-100 nm aerosols collected from the same location, and at the same time, displayed a distinct lack of sea salt. Results herein highlight the potential for biases in TEM analysis of the chemical composition of marine aerosols.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.

Detecting drift from event streams of unpredictable business processes

Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.