AbstractÐDiscovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. In this paper, we propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRITÐSequential Pattern mIning with Regular expressIon consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like antimonotonicity) are integrated into the mining process. A quantitative exploration of these trade-offs is conducted through an extensive experimental study on synthetic and real-life data sets. The experimental results clearly validate the effectiveness of our approach, showing that speedups of more than an order of magnitude are possible when RE constraints are pushed deep inside the mining process. Our experimentation with real-life data also illustrates the versatility of REs as a user-level tool for focusing on interesting patterns.

Summary To better understand outcomes in postpartum patients who receive peripartum anaesthetic interventions, we aimed to assess quality of recovery metrics following childbirth in a UK‐based multicentre cohort study. This study was performed during a 2‐week period in October 2021 to assess in‐ and outpatient post‐delivery recovery at 1 and 30 days postpartum. The following outcomes were reported: obstetric quality of recovery 10‐item measure (ObsQoR‐10); EuroQoL (EQ‐5D‐5L) survey; global health visual analogue scale; postpartum pain scores at rest and movement; length of hospital stay; readmission rates; and self‐reported complications. In total, 1638 patients were recruited and responses analysed from 1631 (99.6%) and 1282 patients (80%) at one and 30 days postpartum, respectively. Median (IQR [range]) length of stay postpartum was 39.3 (28.5–61.0 [17.7–513.4]), 40.3 (28.5–59.1 [17.8–220.9]), and 35.9 (27.1–54.1 [17.9–188.4]) h following caesarean, instrumental and vaginal deliveries, respectively. Median (IQR [range]) ObsQoR‐10 score was 75 ([62–86] 4–100) on day 1, with the lowest ObsQoR‐10 scores (worst recovery) reported by patients undergoing caesarean delivery. Of the 1282 patients, complications within the first 30 days postpartum were reported by 252 (19.7%) of all patients. Readmission to hospital within 30 days of discharge occurred in 69 patients (5.4%), with 49 (3%) for maternal reasons. These data can be used to inform patients regarding expected recovery trajectories; facilitate optimal discharge planning; and identify populations that may benefit most from targeted interventions to improve postpartum recovery experience.

In this paper, we present and demonstrate a methodology to improve probabilistic fatigue crack growth (FCG) predictions by using the concept of Bayesian updating using Markov chain Monte Carlo simulations. The methodology is demonstrated on a cracked pipe undergoing fatigue loading. Initial estimates of the FCG rate are made using the Paris law. The prior probability distributions of the Paris law parameters are taken from the tests on specimen made of the same material as that of pipe. Measured data on crack depth over number of loading cycles are used to update the prior distribution using the Markov chain Monte Carlo. The confidence interval on the predicted FCG rate is also estimated. In actual piping placed in a plant, the measured data can be considered equivalent to the data received from in‐service inspection. It is shown that the proposed methodology improves the fatigue life prediction. The number of observations used for updating is found to leave a significant effect on the accuracy of the updated prediction.

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