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Photometric stereo uses images of objects illuminated from various directions to calculate surface normals which can be used to generate 3D meshes of the object. Such meshes can be used by engineers to estimate damage of a concrete surface, or track damage progression over time to inform maintenance decisions. This dataset [1] was collected to quantify the uncertainty in a photometric stereo test rig through both the comparison with a well characterised method (coordinate measurement machine) and experiment virtualisation. Data was collected for 9 real objects using both the test rig and the coordinate measurement machine. These objects range from clay statues to damaged concrete slabs. Furthermore, synthetic data for 12 objects was created via virtual renders generated using Blender (3D software) [2]. The two methods of data generation allowed the decoupling of the physical rig (used to light and photograph objects) and the photometric stereo algorithm (used to convert images and lighting information into 3D meshes). This data can allow users to: test their own photometric stereo algorithms, with specialised data created for structural health monitoring applications; provide an industrially relevant case study to develop and test uncertainty quantification methods on test rigs for structural health monitoring of concrete; or develop data processing methodologies for the alignment of scaled, translated, and rotated data.

Abstract Spot pricing is often suggested as a method of increasing demand‐side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to financial fraud via false data injection (FDI) style attacks. The authors consider attacks which aim to alter the consumer load profile to exploit intraday price dips. The authors examine an anomaly detection protocol for cyber‐attacks that seek to leverage spot prices for financial gain. In this way the authors outline a methodology for detecting attacks on industrial load smart meters. The authors first create a feature clustering model of the underlying business, segregated by business type. The authors then use these clusters to create an incentive‐weighted anomaly detection protocol for false data attacks against load profiles. This clustering‐based methodology incorporates both the load profile and spot pricing considerations for the detection of injected load profiles. To reduce false positives, the authors model incentive‐based detection, which includes knowledge of spot prices, into the anomaly tracking, enabling the methodology to account for changes in the load profile which are unlikely to be attacks.

Traditionally, electricity distribution networks were designed for unidirectional power flow without the need to accommodate generation installed at the point of use. However, with the increase in Distributed Energy Resources and other Low Carbon Technologies, the role of distribution networks is changing. This shift brings challenges, including the need for intensive metering and more frequent reconfiguration to identify threats from voltage and thermal violations. Mitigating action through reconfiguration is informed by State Estimation, which is especially challenging for low voltage distribution networks where the constraints of low observability, non-linear load relationships, and highly unbalanced systems all contribute to the difficulty of producing accurate state estimates. To counter low observability, this paper proposes the application of a novel transfer learning methodology, based upon the concept of conditional online Bayesian transfer, to make forward predictions of bus pseudo-measurements. Day ahead load forecasts at a fully observed point on the network are adjusted using the intraday residuals at other points in the network to provide them with load forecasts without the need for a complete set of forecast models at all substations. These form pseudo-measurements that then inform the state estimates at future time points. This methodology is demonstrated on both a representative IEEE Test network and on an actual GB 11 kV feeder network.

To avoid the operational consequence of thermal rating exceedance and the financial consequence of excessive reinforcement, the impact of domestic charging of electric vehicles (EVs) on power distribution networks must be accurately assessed prior to accepting vehicle electrification at the community-scale. Although driven by routine, charging behaviour patterns are also influenced by geography, meteorological conditions and season, hence will have a localised element to them that could reduce the diversity of charging load profiles. To model this uncertainty, this article develops a probabilistic methodology to quantify EV home charging demands based on vehicle mobility data and underlying trip characteristics. Models articulate the departure time distribution using a mixture of von Mises distributions, and incorporate non-negative conditional distributions of trip durations, distances and parking durations, which in turn generalise localised charging behaviours. The resulting load profiles are used to drive a community electric network model based on a distribution feeder in Qatar, a country with high per km energy consumption, to quantify impact scenarios of high temperature and driving habit in terms of voltage and thermal stability. Results indicate that overnight domestic charging is sufficient to support daily trips and local networks are capable of hosting high EV penetration despite peaks.

Cables are the largest assets by volume on power distribution networks and the assets with the least health information routinely gathered. Projections over the next 8 years suggest increased penetration of low-carbon technology (LCT) at the distribution level with higher loads resulting from electric vehicle (EV) and heat pump uptake. Over this period, increased cable loading will directly influence their lifetimes and may mean that existing asset management practices need to be revised to understand the specific impact on end-of-life assessment. Accordingly, this paper uses a physics-based thermal lifetime model based on projected uptake trends for LCTs to evaluate the impact on distribution cable lifetime. Two case studies are presented considering portions of network and the ultimate impact on asset life over the next decade. Two commonly used cables are considered to quantify the lifetime reduction caused by LCT for asset fleets. The paper shows that the projected uptake of EVs and heat pumps will have a detrimental effect on cable life with a 30% reduction in cable lifetime possible.

With the increasing application of battery energy storage in buildings, networks and transportation, an emerging challenge to overall system resilience is in understanding the constituent asset health. Current battery energy storage considerations focus on adhering to the technical specification of the service in the short term, rather than the long-term consequences to battery health. However, accurately determining battery health generally requires invasive measurements or computationally expensive physics-based models which do not scale up to a fleet of assets cost-effectively. This paper alternatively proposes capturing cumulative maloperation through a physics model-free proxy for cell health, articulated via the strong influence misuse has on the internal chemical state. A Hidden Markov Chain approach is used to automatically recognize violations of chemistry specific usage preferences from sequences of observed charging actions. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios.

Current liquid chromatography-tandem mass spectrometry (LC-MS/MS) applications for circulating androgen measurements are technically diverse. Previously, variable results have been reported for testosterone. Data are scarce for androstenedione and absent for dehydroepiandrosterone sulfate (DHEAS). We assessed the agreement of androstenedione, DHEAS and testosterone LC-MS/MS measurements among nine European centers and explored benefits of calibration system unification. Androgens were measured twice by laboratory-specific procedures in 78 patient samples and in EQA materials. Results were obtained by in-house and external calibration. Intra- and inter-laboratory performances were valued. Intra-laboratory CVs ranged between 4.2–13.2 % for androstenedione, 1.6–10.8 % for DHEAS, and 4.3–8.7 % and 2.6–7.1 % for female and male testosterone, respectively. Bias and trueness in EQA materials were within ±20 %. Median inter-laboratory CV with in-house vs. external calibration were 12.0 vs. 9.6 % for androstenedione (p<0.001), 7.2 vs. 4.9 % for DHEAS (p<0.001), 6.4 vs. 7.6 % for female testosterone (p<0.001) and 6.8 and 7.4 % for male testosterone (p=0.111). Median bias vs. all laboratory median with in-house and external calibration were −13.3 to 20.5 % and −4.9 to 18.7 % for androstenedione, −10.9 to 4.8 % and −3.4 to 3.5 % for DHEAS, −2.7 to 6.5 % and −11.3 to 6.6 % for testosterone in females, and −7.0 to 8.5 % and −7.5 to 11.8 % for testosterone in males, respectively. Methods showed high intra-laboratory precision but variable bias and trueness. Inter-laboratory agreement was remarkably good. Calibration system unification improved agreement in androstenedione and DHEAS, but not in testosterone measurements. Multiple components, such as commutability of calibrators and EQA materials and internal standard choices, likely contribute to inter-laboratory variability. [ABSTRACT FROM AUTHOR]

The evolving role for electricity network operators means that load forecasting at the distribution level has become increasingly important, presenting the need for anticipation of the behavior of highly dynamic and diversely distributed loads. The commonly held assumption of Gaussian residuals in forecasting does not always hold for distribution network loads, increasing the uncertainty in balancing a system at this network level. To reduce the operational impact of forecast errors, this paper utilizes different multivariate joint probability distributions to capture the intra-day dependency structure of forecast residuals. Transforming these to the conditional form enables forecast corrections to be made at variable horizons even in the absence of the forecast model. Improvements in accuracy are demonstrated on benchmark load forecast models at distribution level low voltage substations. A practical distribution system application on scheduling embedded energy storage shows substantial reductions in grid imports and hence costs to distribution level customers from utilizing the proposed intraday correction approach.

The introduction of electric heat pumps as a low carbon option for space heating offers a potential pathway for reducing the carbon emissions resulting from domestic heating demand in the UK. However, the additional power demands of heat pumps over conventional domestic loads have the potential to significantly erode network headroom, particularly at the distribution level. The uptake of this technology within the UK is currently limited and the effects of widespread adoption on distribution networks are not well characterized due to the sparse availability of operational heat pump demand data. This paper outlines a methodology for quantifying the demand impact of heat pumps on Low Voltage networks sensitive to local temperature by deriving fundamental thermal relationships from real heat pump electrical demand data. These relations can then be applied to predict demand for new studies independent of the geographic specifics of the original dataset. The strength of this model is in the ability to predict an aggregated hourly heat pump electrical demand profile that reflects local temperature conditions and intra-day usage as well as population size, thereby also accounting for diversity effects that are difficult to capture in physics based models. This work augments the usability of limited existing data by facilitating demand analysis sensitive to local temperature conditions, rather than blanket rescaling of existing customer data as has been performed in previous studies. This creates future opportunities for examining heat pump demand sensitivity for different geographic locations against existing heat pump assessments, as well as performing studies which incorporate multiple low carbon technologies connected to a Low Voltage network.

Used in many industrial applications, centrifugal pumps have optimal operating criteria specified at design. These criteria may not be precisely adhered to during operation which will ultimately reduce the life of the asset. Operators would therefore benefit from anticipating how often the design point is deviated from and hence how much asset degradation results. For centrifugal pumps, a novel set of covariates were proposed in this paper which formally partition observed operating zones with an Empirical Bivariate Quantile Partitioned distribution. This captured the dependency relation between operating parameters across plant configurations to predict the component wear that results from particular settings. The effectiveness of this was demonstrated through an operational case study in civil nuclear generation feedwater pumps where corroboration with bearing movements provides an indicator of plant wear. Such a technique is envisaged to inform operators of optimal plant configuration from multiple possibilities in advance of undertaking them.

While machine learning has made inroads into many industries, power systems have some unique application constraints and barriers that have motivated the creation of this Special Issue on their applications in condition monitoring [...]

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) panels that include glucocorticoid-related steroids are increasingly used to characterize and diagnose adrenal cortical diseases. Limited information is currently available about reproducibility of these measurements among laboratories. The aim of the study was to compare LC-MS/MS measurements of corticosterone, 11-deoxycortisol and cortisone at eight European centers and assess the performance after unification of calibration. Seventy-eight patient samples and commercial calibrators were measured twice by laboratory-specific procedures. Results were obtained according to in-house and external calibration. We evaluated intra-laboratory and inter-laboratory imprecision, regression and agreement against performance specifications derived from 11-deoxycortisol biological variation. Intra-laboratory CVs ranged between 3.3 and 7.7%, 3.3 and 11.8% and 2.7 and 12.8% for corticosterone, 11-deoxycortisol and cortisone, with 1, 4 and 3 laboratories often exceeding the maximum allowable imprecision (MAI), respectively. Median inter-laboratory CVs were 10.0, 10.7 and 6.2%, with 38.5, 50.7 and 2.6% cases exceeding the MAI for corticosterone, 11-deoxycortisol and cortisone, respectively. Median laboratory bias vs. all laboratory-medians ranged from −5.6 to 12.3% for corticosterone, −14.6 to 12.4% for 11-deoxycortisol and −4.0 to 6.5% for cortisone, with few cases exceeding the total allowable error. Modest deviations were found in regression equations among most laboratories. External calibration did not improve 11-deoxycortisol and worsened corticosterone and cortisone inter-laboratory comparability. Method imprecision was variable. Inter-laboratory performance was reasonably good. However, cases with imprecision and total error above the acceptable limits were apparent for corticosterone and 11-deoxycortisol. Variability did not depend on calibration but apparently on imprecision, accuracy and specificity of individual methods. Tools for improving selectivity and accuracy are required to improve harmonization. [ABSTRACT FROM AUTHOR]

The following study investigates the preparation of human blood plasma for metabolomic profiling analysis by ultrahigh performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC/TOFMS) in a novel two-step design study. Four different organic solvents (acetonitrile, acetone, methanol, and ethanol) were used to assess human blood plasma preparation via protein precipitation. The optimal conditions for sample preparation were investigated, with consideration to the number of extracted markers, data quality/reproducibility, and column lifetime prolongation. Isotopically labeled internal standards were used to monitor data quality/reproducibility. Gel electrophoresis was also used to measure the protein content in the supernatant of the 'first design step' allowing assessment of the amount of protein that would be injected/accumulate onto the column after many injections that would be apparent in a global metabolic profiling study. The second design step followed on from the results obtained in step one, with four of the best conditions selected and further investigated, looking at the effects of vortex time and temperature on precipitation/extraction. Two choices of solvent compositions were found to be 'optimal' for preparation of plasma for global metabolic profiling analysis; these were 'methanol/ethanol' (1:1, v/v) and 'methanol/acetonitrile/acetone' (1: 1:1, v/v/v) added to plasma (4:1 ratio, 400 [micro]L total volume).

In this article we highlight a novel variation on dynamic time warping (DTW) for aligning chromatogram signals. We are interested in sets of signals that can be aligned well locally, but not globally, by shifting individual signals in time. This kind of alignment is often sufficient for aligning gas chromatography data. Regular DTW often 'over-warps' signals and introduces artificial features into the aligned data. To overcome this we introduce a variable penalty into the DTW process. The penalty is added to the distance metric whenever a nondiagonal step is taken. We select our penalty based on a morphological dilation of the two signals. We showcase our method by aligning GC/MS datafiles from 712 blood plasma samples processed in 23 batches over the course of 6 months. The use of variable penalty DTW significantly reduces the number of nondiagonal moves. In the examples presented here, this reduction is by a factor of 30, with no cost to visual quality of the alignment.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is recommended for measuring circulating steroids. However, assays display technical heterogeneity. So far, reproducibility of corticosteroid LC-MS/MS measurements has received scant attention. The aim of the study was to compare LC-MS/MS measurements of cortisol, 17OH-progesterone and aldosterone from nine European centers and assess performance according to external quality assessment (EQA) materials and calibration. Seventy-eight patient samples, EQA materials and two commercial calibration sets were measured twice by laboratory-specific procedures. Results were obtained by in-house (CAL1) and external calibrations (CAL2 and CAL3). We evaluated intra and inter-laboratory imprecision, correlation and agreement in patient samples, and trueness, bias and commutability in EQA materials. Using CAL1, intra-laboratory CVs ranged between 2.8–7.4%, 4.4–18.0% and 5.2–22.2%, for cortisol, 17OH-progesterone and aldosterone, respectively. Trueness and bias in EQA materials were mostly acceptable, however, inappropriate commutability and target value assignment were highlighted in some cases. CAL2 showed suboptimal accuracy. Median inter-laboratory CVs for cortisol, 17OH-progesterone and aldosterone were 4.9, 11.8 and 13.8% with CAL1 and 3.6, 10.3 and 8.6% with CAL3 (all p<0.001), respectively. Using CAL1, median bias vs. all laboratory-medians ranged from −6.6 to 6.9%, −17.2 to 7.8% and −12.0 to 16.8% for cortisol, 17OH-progesterone and aldosterone, respectively. Regression lines significantly deviated from the best fit for most laboratories. Using CAL3 improved cortisol and 17OH-progesterone between-method bias and correlation. Intra-laboratory imprecision and performance with EQA materials were variable. Inter-laboratory performance was mostly within specifications. Although residual variability persists, adopting common traceable calibrators and RMP-determined EQA materials is beneficial for standardization of LC-MS/MS steroid measurements. [ABSTRACT FROM AUTHOR]

Western populations use prescription and nonprescription drugs extensively, but large-scale population usage is rarely assessed objectively in epidemiological studies. Here we apply statistical methods to characterize structural pathway connectivities of metabolites of commonly used drugs detected routinely in [sup.1]H NMR spectra of urine in a human population study. XH NMR spectra were measured for two groups of urine samples obtained from U.S. participants in a known population study. The novel application of a statistical total correlation spectroscopy (STOCSY) approach enabled rapid identification of the major and certain minor drug metabolites in common use in the population, in particular, from acetaminophen and ibuprofen metabolites. This work shows that statistical connectivities between drug metabolites can be established in routine 'high-throughput' NMR screening of human samples from participants who have randomly self-administered drugs. This approach should be of value in considering interpopulation patterns of drug metabolism in epidemiological and pharmacogenetic studies.

A new analytical strategy for biomarker recovery from directly coupled ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC Tof MS) data on biofluids is presented and exemplified using a study on hydrazine-induced liver toxicity. A key step in the strategy involves a novel procedure for reducing the spectroscopic search space by differential analysis of cohorts of normal and pathological samples using an orthogonal projection to latent structures discriminant analysis (O-PLS-DA). This efficiently sorts principal discriminators of toxicity from the background of thousands of metabolic features commonly observed in data sets generated by UPLC--MS analysis of biological fluids and is thus a powerful tool for biomarker discovery.

Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz [sup.1]H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.