On robust statistical outlier analysis for damage identification

This thesis aims to contribute towards the development of reliable and accurate damage detection monitoring frameworks, applicable for a range of structural health and condition monitoring problems. Central to this purpose, is to be able to detect damage patterns embedded in a system's vibration signal responses sufficiently early. This will enable a condition-based maintenance and inspection to be carried out so as to prevent potentially catastrophic events, as related to each application domain. Firstly, to obviate reliance on data labels, an inclusive outlier analysis study is conducted by means of robust multivariate statistical analysis and a range of other (more common) outlier detection techniques, in both multivariate and time-series settings. Given the parametric nature of robust multivariate statistical techniques, it has also been possible to characterise outliers according to their influence on a method's estimates. Secondly, novelty detection is explored, in which a set of samples representing the nominal state of the system, is assumed to be available. This set includes observations from a system with its dynamics being significantly influenced by environmental and operational variability. Finally, this thesis explored the potential of utilising certain robust techniques as a pre-processing step on damage sensitive features (contaminated with outliers) for novelty detection tasks. Given the large volume of observations, both experimental and computational, different damage sensitive features were extracted, some of which were specific to the range of problems / types of damage being investigated. The performance, in terms of both sensitivity in damage detection and immunity to environmental and operational variability, was assessed for each damage sensitive feature, in combination to the outlier and novelty detection technique used. This thesis has introduced to the condition and structural health monitoring fields a range of methods from robust statistics with attractive properties, such as the effective unmasking of outliers

Effect of pomegranate juice consumption on biochemical parameters and complete blood count

Pomegranate has been used therapeutically for centuries. The aim of the present study was to examine the effects of pomegranate juice (PJ) supplementation on complete blood count (CBC), glucose, blood lipids and C‑reactive protein (CRP) in healthy subjects. A total of 5 males and 5 females (aged 31.8±6.6 years, weighing 66.2±12.9 kg) were randomly assigned into one of two groups and either consumed 500 ml PJ/day or no PJ for 14 days. Blood samples were obtained from participants prior to and following the experimental period. PJ consumption resulted in a significant increase in red blood cell count (P<0.05), hemoglobin levels (P<0.001) and hematocrit levels (P<0.05). Other CBC parameters, glucose, cholesterol, triglycerides, high‑density lipoprotein, low‑density lipoprotein and CRP levels did not significantly change following PJ consumption. These results indicate that PJ intake for a short period of time may result in increased erythropoiesis or decreased degradation without any significant alterations in factors associated with metabolic health and inflammation in healthy individuals

On robust statistical outlier analysis for damage identification

This thesis aims to contribute towards the development of reliable and accurate damage detection monitoring frameworks, applicable for a range of structural health and condition monitoring problems. Central to this purpose, is to be able to detect damage patterns embedded in a system's vibration signal responses sufficiently early. This will enable a condition-based maintenance and inspection to be carried out so as to prevent potentially catastrophic events, as related to each application domain. Firstly, to obviate reliance on data labels, an inclusive outlier analysis study is conducted by means of robust multivariate statistical analysis and a range of other (more common) outlier detection techniques, in both multivariate and time-series settings. Given the parametric nature of robust multivariate statistical techniques, it has also been possible to characterise outliers according to their influence on a method's estimates. Secondly, novelty detection is explored, in which a set of samples representing the nominal state of the system, is assumed to be available. This set includes observations from a system with its dynamics being significantly influenced by environmental and operational variability. Finally, this thesis explored the potential of utilising certain robust techniques as a pre-processing step on damage sensitive features (contaminated with outliers) for novelty detection tasks. Given the large volume of observations, both experimental and computational, different damage sensitive features were extracted, some of which were specific to the range of problems / types of damage being investigated. The performance, in terms of both sensitivity in damage detection and immunity to environmental and operational variability, was assessed for each damage sensitive feature, in combination to the outlier and novelty detection technique used. This thesis has introduced to the condition and structural health monitoring fields a range of methods from robust statistics with attractive properties, such as the effective unmasking of outliers

Processing strain data generated from distributed acoustic sensing for monitoring tasks

Signal and image analysis methods for data from distributed acoustic sensing measurements are presented. These techniques improved monitoring capability, by enhancing signal quality and providing a robust and accurate detection of significant events

Classifying space-time images obtained from distributed acoustic sensing

In this paper we present a classifier that was trained on space-time images obtained from distributed acoustic sensing, for the purpose of monitoring earthquakes. The model is capable of discriminating between actual and non-earthquake events

Distributed acoustic sensing spatiotemporal maps from Cape Muroto

This dataset contains the raw experimental data that were produced as part of the NERC-funded project with colleagues from JAMSTEC. The data contains around 1500 spatiotemporal maps / waterfall plots. These maps represent strain values obtained from two different Distributed Acoustic Sensing (DAS) devices attached to a subsea fibre optic cable at Cape Muroto in Japan. On the y-axis it is the spatial, while on the x-axis the temporal dimensions. A portion of this Dataset was processed and labelled in order to train a Convolutional Neural Network classifier. This is available in a different dataset: DOI https://doi.org/10.5258/SOTON/D2831</span

Association of Broad-Spectrum Antibiotic Therapy and Vitamin E Supplementation with Vitamin K Deficiency-Induced Coagulopathy: A Case Report and Narrative Review of the Literature

Vitamin K is a lipid-soluble vitamin that is normally maintained within appropriate levels by means of dietary intake and bacterial production in the intestinal microflora. It holds a central role in coagulation homeostasis, and thus its depletion leads to hypocoagulation and haemorrhagic diathesis. The association of antibiotic therapy and vitamin E supplementation with vitamin K deficiency was previously described in animal experiments, clinical studies, and case reports. Broad-spectrum antibiotic therapy potentially leads to intestinal microflora dysbiosis and restriction of vitamin K-producing bacterial populations, resulting in decreased vitamin K levels, whereas antibiotics of the cephalosporin class with 1-N-methyl-5-thiotetrazole (NMTT) or 2-methyl-1,3,4-thiadiazole (MTD) side groups inhibit vitamin K function. Vitamin E supplementation interferes with both the bioavailability and function of vitamin K, yet its mechanisms are not fully understood. We present the case of a 45-year-old male patient, with a history of epilepsy and schizophrenia, catatonically incapacitated and immobilised, who was hospitalised in our centre for the investigation and management of aspiration pneumonia. He demonstrated a progressively worsening prolongation of international normalised ratio (INR), which was attributed to both broad-spectrum antibiotic therapy and vitamin E supplementation and was reversed upon administration of vitamin K. We highlight the need for close monitoring of coagulation parameters in patients receiving broad-spectrum antibiotic therapy, especially those with underlying malnutritive or malabsorptive conditions, and we further recommend the avoidance of NMTT- or MTD-containing antibiotics or vitamin E supplementation, unless absolutely necessary, in those patients

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)