Multivariate data mining for estimating the rate of discolouration material accumulation in drinking water distribution systems

Particulate material accumulates over time as cohesive layers on internal pipeline surfaces in water distribution systems (WDS). When mobilised, this material can cause discolouration. This paper explores factors expected to be involved in this accumulation process. Two complementary machine learning methodologies are applied to significant amounts of real world field data from both a qualitative and a quantitative perspective. First, Kohonen self-organising maps were used for integrative and interpretative multivariate data mining of potential factors affecting accumulation. Second, evolutionary polynomial regression (EPR), a hybrid data-driven technique, was applied that combines genetic algorithms with numerical regression for developing easily interpretable mathematical model expressions. EPR was used to explore producing novel simple expressions to highlight important accumulation factors. Three case studies are presented: UK national and two Dutch local studies. The results highlight bulk water iron concentration, pipe material and looped network areas as key descriptive parameters for the UK study. At the local level, a significantly increased third data set allowed K-fold cross validation. The mean cross validation coefficient of determination was 0.945 for training data and 0.930 for testing data for an equation utilising amount of material mobilised and soil temperature for estimating daily regeneration rate. The approach shows promise for developing transferable expressions usable for pro-active WDS management

Modelling both the continual erosion and regeneration of discolouration material in drinking water distribution systems

The erosion of the cohesive layers of particulate matter that causes discolouration in water distribution system mains has previously been modelled using the Prediction of Discolouration in Distribution Systems (PODDS) model. When first proposed, PODDS featured an unvalidated means by which material regeneration on pipe walls could be simulated. Field and laboratory studies of material regeneration have yielded data that suggest that the PODDS formulations incorrectly model these processes. A new model is proposed to overcome this shortcoming. It tracks the relative amount of discolouration material that is bound to the pipe wall over time at each of a number of shear strengths. The model formulations and a mass transport model have been encoded as software, which has been used to verify the model’s constructs and undertake sensitivity analyses. The new formulations for regeneration are conceptually consistent with field and laboratory observed data and has potential value in the proactive management of water distribution systems, such as evaluating change in discolouration risk and planning timely interventions

Delivering computationally-intensive digital patient applications to the clinic: An exemplar solution to predict femoral bone strength from CT data

Background and objective:Whilst fragility hip fractures commonly affect elderly people, often causing permanent disability or death, they are rarely addressed in advance through preventive techniques. Quantification of bone strength can help to identify subjects at risk, thus reducing the incidence of fractures in the population. In recent years, researchers have shown that finite element models (FEMs) of the hip joint, derived from computed tomography (CT) images, can predict bone strength more accurately than other techniques currently used in the clinic. The specialised hardware and trained personnel required to perform such analyses, however, limits the widespread adoption of FEMs in clinical contexts. In this manuscript we present CT2S (Computed Tomography To Strength), a system developed in collaboration between The University of Sheffield and Sheffield Teaching Hospitals, designed to streamline access to this complex workflow for clinical end-users. Methods:The system relies on XNAT and makes use of custom apps based on open source software. Available through a website, it allows doctors in the healthcare environment to benefit from FE based bone strength estimation without being exposed to the technical aspects, which are concealed behind a user-friendly interface. Clinicians request the analysis of CT scans of a patient through the website. Using XNAT functionality, the anonymised images are automatically transferred to the University research facility, where an operator processes them and estimates the bone strength through FEM using a combination of open source and commercial software. Following the analysis, the doctor is provided with the results in a structured report. Results:The platform, currently available for research purposes, has been deployed and fully tested in Sheffield, UK. The entire analysis requires processing times ranging from 3.5 to 8 h, depending on the available computational power. Conclusions:The short processing time makes the system compatible with current clinical workflows. The use of open source software and the accurate description of the workflow given here facilitates the deployment in other centres

X-ray nano-tomography of complete scales from the ultra-white beetles Lepidiota stigma and Cyphochilus

High resolution X-ray nano-tomography experiments are often limited to a few tens of micrometer size volumes due to detector size. It is possible, through the use of multiple overlapping tomography scans, to produce a large area scan which can encompass a sample in its entirety. Mounting and positioning regions to be scanned is highly challenging and normally requires focused ion beam approaches. In this work we have imaged intact beetle scale cells mounted on the tip of a needle using a micromanipulator stage. Here we show X-ray holotomography data for single ultra-white scales from the beetles Lepidiota stigma (L. stigma) and Cyphochilus which exhibit the most effective scattering of white light in the literature. The final thresholded matrices represent a scan area of 25 × 70 × 362.5 µm and 25 × 67.5 × 235µm while maintaining a pixel resolution of 25 nm. This tomographic approach allowed the internal structure of the scales to be captured completely intact and undistorted by the sectioning required for traditional microscopy techniques

Liquid–liquid phase separation morphologies in ultra-white beetle scales and a synthetic equivalent

Cyphochilus beetle scales are amongst the brightest structural whites in nature, being highly opacifying whilst extremely thin. However, the formation mechanism for the voided intra-scale structure is unknown. Here we report 3D x-ray nanotomography data for the voided chitin networks of intact white scales of Cyphochilus and Lepidiota stigma. Chitin-filling fractions are found to be 31 ± 2% for Cyphochilus and 34 ± 1% for Lepidiota stigma, indicating previous measurements overestimated their density. Optical simulations using finite-difference time domain for the chitin morphologies and simulated Cahn-Hilliard spinodal structures show excellent agreement. Reflectance curves spanning filling fraction of 5-95% for simulated spinodal structures, pinpoint optimal whiteness for 25% chitin filling. We make a simulacrum from a polymer undergoing a strong solvent quench, resulting in highly reflective (~94%) white films. In-situ X-ray scattering confirms the nanostructure is formed through spinodal decomposition phase separation. We conclude that the ultra-white beetle scale nanostructure is made via liquid–liquid phase separation