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There have been a number of digital twin (DT) frameworks proposed for multiple disciplines in recent years. However, there is a need to develop systematic methodologies to improve our ability to produce DT solutions for the nuclear fuel industry considering specific requirements and conditions exclusive to the nuclear fuel manufacturing cycle. A methodology tailored for nuclear fuel production is presented in this paper. Due to the nature of the chemical processes involved in fuel manufacturing, we highlight the importance of using a combination of physics-based and data-driven modelling. We introduce key technologies for DT construction and the technical challenges for DT are discussed. Furthermore, we depict typical application scenarios, such as key stages of the nuclear manufacturing cycle. Finally, a number of technology issues and research questions related to DT and nuclear fuel manufacturing are identified.

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing ETD methods cannot efficiently handle the sheer volume of data now available, being limited by issues such as missing values, high variance and non-linearity. An integrated infrastructure is also required for synchronizing diverse procedures in electricity theft classification. To help address such problems, a novel ETD framework is proposed that combines three distinct modules. The first module handles missing values, outliers, and unstandardized electricity consumption data. The second module employs a newly proposed hybrid class balancing approach to deal with highly imbalanced datasets. The third module utilises an improved artificial neural network (iANN) based classification engine, to predict electricity theft cases accurately and efficiently. We propose three distinctive mechanisms, including hyper-parameters tuning, regularization and skip connections, to improve the performance of standard ANN to handle more complex classification tasks using smart meter (SM) data. Furthermore, various structures of iANN are investigated to improve the generalization and function fitting capabilities of the final classification. Numerical results from real-world energy usage datasets confirm that the proposed ETD model has superior performance compared to existing machine learning and deep learning methods, and can effectively be applied to industrial applications.

Introduction: Urine drug screens (UDS) have unproven clinical utility in emergency department (ED) chest pain presentations. A test with such limited clinical utility may exponentiate biases in care, but little is known about the epidemiology of UDS use for this indication. We hypothesized that UDS utilization varies nationally across race and gender.Methods: This was a retrospective observational analysis of adult ED visits for chest pain in the 2011–2019 National Hospital Ambulatory Medical Care Survey. We calculated the utilization of UDS across race/ethnicity and gender and then characterized predictors of use via adjusted logistic regression models.Results: We analyzed 13,567 adult chest pain visits, representative of 85.8 million visits nationally. Use of UDS occurred for 4.6% of visits (95% CI 3.9%-5.4%). White females underwent UDS at 3.3% of visits (95% CI 2.5%-4.2%), and Black females at 4.1% (95% CI 2.9%-5.2%). White males were tested at 5.8% of visits (95% CI 4.4%-7.2%), while Black males were tested at 9.3% of visits (95% CI 6.4%-12.2%). A multivariate logistic regression model including race, gender, and time period shows significantly increased odds of ordering UDS for Black patients (odds ratio [OR] 1.45 (95% CI 1.11-1.90, p = 0.007)) and male patients (OR 2.0 (95% CI 1.55-2.58, p < 0.001) as compared to White patients and female patients.Conclusion: We identified wide disparities in the utilization of UDS for the evaluation of chest pain. If UDS were used at the rate observed for White women, Black men would undergo nearly 50,000 fewer tests annually. Future research should weigh the potential of the UDS to magnify biases in care against the unproven clinical utility of the test.

Wave energy shows potential to provide electricity in a renewable manner. The TALOS WEC (Wave Energy Converter) is a unique design with six PTO (Power Take-Off) elements to provide six Degrees of Freedom (DOFs), which is potentially able to harvest energy more efficiently than traditional single-DOF devices. As a step towards its optimisation and control, a power prediction model is developed, using the wave elevation and motions of the WEC to predict the power output of each PTO. The results show that using LSTM (Long-Short Term Memory) has a higher prediction accuracy than the other approaches considered.

Hyperspectral Imaging (HSI) is a well-established technology able to capture the same spatial scene or image in hundreds of different wavelengths across the electromagnetic spectrum, covering not only the visible but also the shortwave infrared range (1000-2500nm), potentially revealing information otherwise invisible to the human eye. In this work, we explored whether HIS technology can be used for the fast-non-invasive characterization and identification of sintered UO2 fuel pellets. Preliminary experiments included the imaging of the pellets, revealing their spectral responses. These show promising features that could be used for their identification, where two different groups of pellets, pure and doped, seem to be easily recognized based on their spectral response. The experiments included a pixel-wise classification map generated via the Spectral Angle Mapper (SAM) technique in which the pure and doped pellets are successfully identified.

The study of wave‐structure interactions involving nonlinear forces would often make use of the popular hybrid frequency–time domain method. In the hybrid method, the frequency‐domain analysis could firstly provide the reliable and accurate dynamic parameters and responses; then these parameters and responses are transformed to the parameters to establishing the basic time domain equation. Additionally, with the addition of the required linear and nonlinear forces, the time‐domain analysis can be used to solve for the practical problems. However, the transformation from the frequency domain to the time domain is not straightforward, and the implementation of the time‐domain equation could become increasingly complicated when different modes of motion are coupled. This research presents a systematic introduction on how to implement the time‐domain analysis for floating structures, including the parameter transformations from the frequency domain to the time domain, and the methods for calculating and approximating the impulse functions and the fluid‐memory effects, with special attention being paid to the coupling terms among the different motion modes, and the correctness of the time‐domain‐equation implementation. The main purpose of this article is to provide relevant information for those who wish to build their own time‐domain analyses with the open‐source hydrodynamic analysis packages, although commercial packages are available for time‐domain analyses.

An algorithm to investigate nonlinear systems with time delay variability is proposed. It automatically cycles through segments of an open loop experiment, capturing the directional change in the output via a pair of regression equations, and estimates the time delay accordingly. The method is a black-box, statistical approach that does not require a dynamic model nor any knowledge of the systematic causes of the time delay variability. It is designed for systems with approximately integrating behaviour, hence it is used in this article for the analysis of hydraulically actuated robotic manipulators with this characteristic and time-varying delays. In the context of control, the new algorithm provides insight into the variable time delay behaviour and hence can guide control design decisions e.g. by uncovering state dependencies.

Current panel methods for wave-structure interactions employ the potential flow theory, which provide fast, reliable and relatively accurate predictions for the marine structures, and now some open source packages, NEMOH and HAMS, are available. In this research, the relative utility and performance of NEMOH and HAMS is compared with the well-known, state-of-art software, WAMIT. To bring focus to these comparisons, this research is based on three different floating structures: the truncated cylinder; the truncated cylinder with heave plate; and a novel multi-axis TALOS wave energy converter. To make the comparison more useful, this research investigates the incomplete and overlapped panels for the simple cylinder, to examine whether the respective code can handle these and still provide a meaningful solution. The comparisons may help us to understand whether the incomplete and/or overlapped panels can be used for simplifying the numerical modelling of those very complicated marine structures. From the comparisons, it can be seen the open source software, NEMOH and HAMS, both could produce very good results for the simple single marine structure, but also exhibit different capacities in dealing with more complicated marine structures. Specifically, HAMS could handle the thin structures and the overlapped panels effectively as WAMIT.

Systems with time-varying time delays present a particularly challenging control problem. They have been observed across a wide array of domains, from hydraulic actuators to insulin delivery control systems. Control systems that address system time-delays, nonlinearities and uncertainty are the subject of much research but, whilst the specific concept of varying time delays is sometimes acknowledged (for example in the control of hydraulic manipulators), this appears to be less widely investigated than some other types of nonlinearity. In part motivated by recent research into internal multi-model control, as similarly applied to systems with unknown time-varying delays, the present work utilises a Gaussian radial basis function to switch between two or more partial controllers. Each partial controller is based on a linear model with a (time-invariant) time delay. The new algorithm is developed and evaluated via simulation using a non-minimal state space (NMSS) framework, with pole assignment as the design criterion. Simulation results suggest that it yields improved performance in comparison to a simpler switching approach and the equivalent linear control system. However, laboratory examples and further research into robustness and stability is required in the next step.

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing methods for theft detection can struggle to handle large electricity consumption datasets because of missing values, data variance and nonlinear data relationship problems, and there is a lack of integrated infrastructure for coordinating electricity load data analysis procedures. To help address these problems, a simple yet effective ETD model is developed. Three modules are combined into the proposed model. The first module deploys a combination of data imputation, outlier handling, normalization and class balancing algorithms, to enhance the time series characteristics and generate better quality data for improved training and learning by the classifiers. Three different machine learning (ML) methods, which are uncorrelated and skillful on the problem in different ways, are employed as the base learning model. Finally, a recently developed deep learning approach, namely a temporal convolutional network (TCN), is used to ensemble the outputs of the ML algorithms for improved classification accuracy. Experimental results confirm that the proposed framework yields a highly-accurate, robust classification performance, in comparison to other well-established machine and deep learning models and thus can be a practical tool for electricity theft detection in industrial applications.

Background: The 2015 Lancet Commission on global surgery identified surgery and anaesthesia as indispensable parts of holistic health-care systems. However, COVID-19 exposed the fragility of planned surgical services around the world, which have also been neglected in pandemic recovery planning. This study aimed to develop and validate a novel index to support local elective surgical system strengthening and address growing backlogs. Methods: First, we performed an international consultation through a four-stage consensus process to develop a multidomain index for hospital-level assessment (surgical preparedness index; SPI). Second, we measured surgical preparedness across a global network of hospitals in high-income countries (HICs), middle-income countries (MICs), and low-income countries (LICs) to explore the distribution of the SPI at national, subnational, and hospital levels. Finally, using COVID-19 as an example of an external system shock, we compared hospitals’ SPI to their planned surgical volume ratio (SVR; ie, operations for which the decision for surgery was made before hospital admission), calculated as the ratio of the observed surgical volume over a 1-month assessment period between June 6 and Aug 5,2021, against the expected surgical volume based on hospital administrative data from the same period in 2019 (ie, a pre-pandemic baseline). A linear mixed-effects regression model was used to determine the effect of increasing SPI score. Findings: In the first phase, from a longlist of 103 candidate indicators, 23 were prioritised as core indicators of elective surgical system preparedness by 69 dinicians (23133%) women; 46 [67%] men; 41 from HICs, 22 from MICs, and six from LICs) from 32 countries. The multidomain SPI included 11 indicators on facilities and consumables, two on staffing, two on prioritisation, and eight on systems. Hospitals were scored from 23 (least prepared) to 115 points (most prepared). In the second phase, surgical prepar

BackgroundKawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort.MethodsA single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan.FindingsOur diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks.InterpretationThis work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.

This work presents a new methodology for the automated inspection of nuclear fuel pellets based on Single Image Super Resolution (SISR) and Hyperspectral Imaging (HSI). HSI technology provides optical images in which the pixels contain comprehensive spectral information, normally hundreds of channels (wavelengths) covering the Visible Near InfraRed (VNIR) region in the electromagnetic spectrum. Therefore, the spectral information provided by HSI can be used for inspecting images of pellets pixel-wise. However, the spatial resolution in HSI is lower in comparison to conventional imaging, and SISR is proposed for enhancing the HSI images. Results showed how techniques such as Principal Component Analysis (PCA) can be applied to SR-HSI images to effectively exploit the HSI spatialspectral content and generate maps for the automated detection of potential abnormalities on the surface of nuclear fuel pellets. While experiments used color chalk as analogues of PWR pellets, results with sintered UO2 will be presented at the conference.

A technique for the in-situ localization of radioactivity is described in which the influence of gamma-radiation impinging on a high-Z collimator, by which the angular response of a scintillation detector is constrained in order to identify the position of the radiation source, is expressed mathematically by way of a normalized sinc transform. We test this approach by examining the utility of the sinc transform to express the angular responses derived from a slot-collimated cerium bromide detector, across a variety of energy regions. Individual spectra have been acquired as a function of angle to explore how the shape of the response of the collimator-detector arrangement changes for X-rays and gamma rays. A 90% improvement in localization is observed when defined in terms of the area described by the variance between the known location and that indicated by the response of the collimated system. This approach has the potential to improve source location accuracy and to further optimize autonomous robot exploration routines used to characterize contaminated environments associated with nuclear legacies and radiological emergencies.

Direct oral anticoagulants are now commonplace, and reversal agents are recently becoming available. Andexanet alfa (AnXa), approved by the United States Food and Drug Administration in 2018, is a novel decoy molecule that reverses factor Xa inhibitors in patients with major hemorrhage. We present a case of a 70-year-old man taking rivaroxaban with hemodynamic instability from a ruptured abdominal aortic aneurysm. He received AnXa prior to endovascular surgery, and intraoperatively he could not be heparinized for graft placement. Consideration should be given to the risks and benefits of AnXa administration in patients who require anticoagulation after hemorrhage has been controlled.

A discussion of college policy concerning the recording of student name changes reviews existing laws and professional guidelines about changing names and offers a suggested administrative protocol for adjusting student records. Policy and procedures concerning the records of former students, cross-referencing, and reprinting diplomas are also examined. (MSE)

A control theory perspective on determination of optimal dynamic treatment regimes is considered. The aim is to adapt statistical methodology that has been developed for medical or other biostatistical applications so as to incorporate powerful control techniques that have been designed for engineering or other technological problems. Data tend to be sparse and noisy in the biostatistical area and interest has tended to be in statistical inference for treatment effects. In engineering fields, experimental data can be more easily obtained and reproduced and interest is more often in performance and stability of proposed controllers rather than modelling and inference per se. We propose that modelling and estimation be based on standard statistical techniques but subsequent treatment policy be obtained from robust control. To bring focus, we concentrate on A-learning methodology as developed in the biostatistical literature and h-infinity synthesis from control theory. Simulations and two applications demonstrate robustness of the h-infinity strategy compared to standard A-learning in the presence of model misspecification or measurement error.

Experimental measurements to explore the effect of detector voltage, pulse-shape discrimination (PSD) threshold and detector shielding on time-of-flight measurements of the 252Cf neutron spectrum made with organic scintillation detectors are described. It is found that detector voltage has a major effect, whilst changing the PSD threshold and shielding the detectors to optimize sensitivity to the desired gamma-neutron correlation results in a small effect.

Radiation detection is important in many fields, and it poses significant challenges for instrument designers. Radiation detection instruments, particularly for nuclear decommissioning and security applications, are required to operate in unknown environments and should detect and characterise radiation fields in real time. This book covers both theory and practice, and it solicits recent advances in radiation detection, with a particular focus on radiation detection instrument design, real-time data processing, radiation simulation and experimental work, robot design, control systems, task planning and radiation shielding.

In Smart Grids (SG), Electricity Theft Detection (ETD) is of great importance because it makes the SG cost-efficient. Existing methods for ETD cannot efficiently handle data imbalance, missing values, variance and non-linear data problems in the smart meter data. Therefore, an effective integrated strategy is required to address underlying issues and accurately detect electricity theft using big data. In this work, a simple yet effective approach is proposed by integrating two different modules, such as data pre-processing and classification, in a single framework. The first module involves data imputation, outliers handling, standardization and class balancing steps to generate quality data for classifier training. The second module classifies honest and dishonest users with a Support Vector Machine (SVM) classifier. To improve the classifier’s learning trend and accuracy, a Bayesian optimization algorithm is used to tune SVM’s hyperparameters. Simulation results confirm that the proposed framework for ETD significantly outperforms previous machine learning approaches such as random forest, logistic regression and SVM in terms of accuracy.

A novel, semi-autonomous radiological scanning system for inspecting irregularly shaped and radiologically uncharacterised objects in various orientations is presented. The system utilises relatively low cost, commercial-off-the-shelf (COTS) electronic components, and is intended for use within relatively low to medium radioactive dose environments. To illustrate the generic concepts, the combination of a low-cost COTS vision system, a six DoF manipulator and a gam-ma radiation spectrometer are investigated. Three modes of vision have been developed, al-lowing a remote operator to choose the most appropriate algorithm for the task. The robot arm subsequently scans autonomously across the selected object, determines the scan positions and enables the generation of radiological spectra using the gamma spectrometer. These data inform the operator of any likely radioisotopes present, where in the object they are located and thus whether the object should be treated as LLW, ILW or HLW.

The impact of gamma radiation on a commercial off the shelf microcontroller board has been investigated. Three different tests have been performed to ascertain the radiation tolerance of the device from a nuclear decommissioning deployment perspective. The first test analyses the effect of radiation on the output voltage of the on-board voltage regulator during irradiation. The second test evaluated the effect of gamma radiation on the voltage characteristics of analogue and digital inputs and outputs. The final test analyses the functionality of the microcontroller when using an external, shielded voltage regulator instead of the on-board voltage regulator. The results suggest that a series of latch-ups occurs in the microcontroller during irradiation, causing increased current drain which can damage the voltage regulator if it does not have short-circuit protection. The analogue to digital conversion functionality appears to be more sensitive to gamma radiation than digital and analogue output functionality. Using an external, shielded voltage regulator can prove beneficial when used for certain applications. The collected data suggests that detaching the voltage regulator can extend the lifespan of the platform up to approximately 350 Gy.

We describe the implementation of a bespoke two arm hydraulically actuated robotic platform which is used to semi-autonomously cut approximately 50 mm diameter pipes of three different materials: cardboard, ABS plastic and aluminium. The system is designed to be utilised within radiologically active environments where human access is limited due to dose limits and thus remote operation is greatly beneficial. The remotely located operator selects the object from an image via a bespoke algorithm featuring a COTS 3 D vision system, along with the desired positions for gripping with one manipulator, and cutting with the other. A pseudo-Jacobian inverse kinematic technique and a programmable automation controller are used to achieve the appropriate joint positions within the dual arm robotic platform. In this article, we present the latest developments to the system and the lessons learnt from the new cutting experiments with a reciprocating saw. A comparison to tele-operated control and manual cutting is also made, with this technique shown to be slower than manual cutting, but faster than pure tele-operational control, where the requirements for highly trained users and operator fatigue are further deleterious factors.

Uncertain time-delays can reduce the performance of hydraulic manipulator control systems. Variations in the time delay between, for example, an applied voltage and the associated manipulator movement, may be caused by the internal dynamics of the system and other nonlinear characteristics, such as fluid compressibility, dead-band of the pump, valve flow properties and friction characteristics. Robot control that addresses system time-delays, nonlinearities and uncertainty is the subject of much research but, whilst the specific concept of varying time delays in hydraulic systems is sometimes acknowledged, it appears to be less widely investigated than other types of uncertainty. The present article discuses some of the issues involved, exemplified by a dual manipulator device used in the laboratory for research into nuclear decommissioning, and presents a review of the relevant control literature in this area.

The article investigates the idea of low-cost, telepresence-based mobile robots for eventual use within villages and rural areas in Nigeria, where diseases such as the Ebola Virus Disease (EVD) and Lassa Haemorrhagic Fever (LHF) are common, yet human intervention is constrained due to the great risk of transmission through bodily fluids. To illustrate the concept and practical issues arising, a systems design approach is taken to identify some of the engineering requirements; and, in the focus of this article, a prototype has been developed at Lancaster University. The robotic device is semi-humanoid in that the upper half features two 7-DOF manipulators, designed in part to resemble human operation, while the lower half consists of a four-wheeled base, prioritising ease of operation and reliability over the flexibility offered by a leg-based system.

A novel, semi-autonomous radiological scanning system for inspecting uncharacterised objects is presented. The research utilises commercially available off-the-shelf (COTS) electronic components, intended for use in relatively low radioactive dose environments. To illustrate the concept, a Microsoft Kinect, a Universal Robots UR3 (6 degree of freedom) manipulator and a Kromek RadAngel gamma radiation spectrometer are utilised. The new control software allows the remote operator to select the required object, before the robot arm autonomously scans it, enabling the generation of various radiological spectra shapes. These data inform the operator of any likely radioisotopes present and where they are located in the object.

Experimental measurements to explore the effect of detector voltage, pulse-shape discrimination (PSD) threshold and detector shielding on time-of-flight measurements of the 252Cf neutron spectrum made with organic scintillation detectors are described. It is found that detector voltage has a major effect, whilst changing the PSD threshold and shielding the detectors to optimize sensitivity to the desired gamma-neutron correlation results in a small effect.

Uncertain time-delays can reduce the performance of hydraulic manipulator control systems. Variations in the time delay between, for example, an applied voltage and the associated manipulator movement, may be caused by the internal dynamics of the system and other nonlinear characteristics, such as fluid compressibility, dead-band of the pump, valve flow properties and friction characteristics. Robot control that addresses system time-delays, nonlinearities and uncertainty is the subject of much research but, whilst the specific concept of varying time delays in hydraulic systems is sometimes acknowledged, it appears to be less widely investigated than other types of uncertainty. The present article discuses some of the issues involved, exemplified by a dual manipulator device used in the laboratory for research into nuclear decommissioning, and presents a review of the relevant control literature in this area.

Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWO-based approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.

We consider a control theory approach to adaptive dose allocation of anticoagulants, based on an analysis of records of 152 patients on long-term warfarin treatment. We consider a selection of statistical models for the relationship between the dose of drug and subsequent blood clotting speed, measured through the international normalized ratio. Our main focus is on subsequent use of the model in guiding the choice of the next dose adaptively as patient-specific information accrues. We compare a naive long-term approach with a proportional-integral-plus method, with parameters estimated by either linear quadratic optimization or by stochastic resource allocation. We demonstrate advantages of the control approaches in comparison with a naive approach in simulations and through calculation of robust stability margins for the observed data.

Spontaneously regressing infantile haemangiomas and aggressive angiosarcomas are vascular tumours with excessive angiogenesis. When analysing haemangiomas and angiosarcomas immunohistochemically with respect to their chaperone profiles we found that angiosarcomas have significantly elevated protein levels of binding immunoglobulin protein (BIP) and PERK with concomitant attenuated IRE1 alpha levels, whereas haemangioma tissue exhibits the same pattern as embryonal skin tissue. We show that BiP is essential for the maintenance of VEGFR2 protein, which is expressed in the endothelium of both tumour types. When studying the effects of BiP, the IRE1 alpha/Xbp1 -, and PERK/ATF4-signalling pathways on the migration and tube-forming potential of endothelial cells, we show that downregulation of BiP, as well as inhibition of the kinase activity of IRE1 alpha, inhibit in vitro angiogenesis. Downregulation of PERK (PKR-like kinase; PKR = protein kinase R) levels promotes Xbp1 splicing in endoplasmic reticulum (ER)-stressed cells, indicating that in angiosarcoma the elevated PERK levels might result in high levels of unspliced Xbp1, which have been reported to promote cell proliferation and increase tumour malignancy. The data presented in this study revealed that in addition to BiP or PERK, the kinase domains of IRE1 alpha and Xbp1 could be potential targets for the development of novel therapeutic approaches for treating angiosarcomas and to control tumour angiogenesis. (c) 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Autonomous analysis of plants, such as for phenotyping and health monitoring etc., often requires the reliable identification and localization of single leaves, a task complicated by their complex and variable shape. Robotic sensor platforms commonly use depth sensors that rely on either infrared light or ultrasound, in addition to imaging. However, infrared methods have the disadvantage of being affected by the presence of ambient light, and ultrasound methods generally have too wide a field of view, making them ineffective for measuring complex and intricate structures. Alternatives may include stereoscopic or structured light scanners, but these can be costly and overly complex to implement. This article presents a fully computer-vision based solution capable of estimating the three-dimensional location of all leaves of a subject plant with the use of a single digital camera autonomously positioned by a three-axis linear robot. A custom trained neural network was used to classify leaves captured in multiple images taken of a subject plant. Parallax calculations were applied to predict leaf depth, and from this, the three-dimensional position. This article demonstrates proof of concept of the method, and initial tests with positioned leaves suggest an expected error of 20 mm. Future modifications are identified to further improve accuracy and utility across different plant canopies.

Sliding Mode Control (SMC) systems are developed for a hydraulic manipulator. The control model is obtained via state-dependent parameter (SDP) system identification. In contrast to previous research using discrete-time SDP models, in which the model coefficients are functions of the sampling interval, the present work develops a new continuous-time approach. It is well known that for conventional SMC there is a trade-off between chattering and robust performance. Hence, a recently developed approach to address this problem is investigated, in which the controller is designed via a fractional exponent of the sliding surface. The approach is developed for both conventional and Nonsingular Terminal SMC (NTSMC). The new continuous version of the NTSMC algorithm successfully reduces chattering and provides the best overall performance of various SMC designs. However, for the preliminary experiments reported in this article, a PID lead-lag controller yields the lowest absolute errors, albeit at the cost of a higher control effort. Hence, given that dead-zone and other uncertainties provide the main motivation for use of SMC in this application, further research into the robustness of the new algorithm is required.

This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hydropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Lognormal and Gumbel probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm's exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE-30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions.

Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inaccurate due to complex hysteresis effects, unmodelled friction and stiction phenomena, and unknown effects during contact situations. A promising approach is to obtain spatio-temporal models in a data-driven way using recurrent neural networks, as they can overcome those issues. However, such models often do not meet accuracy demands sufficiently, degenerate in performance for the required high sampling frequencies and cannot provide uncertainty estimates. We adopt a recent probabilistic recurrent neural network architecture, called Recurrent Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions. RKNs outperform standard recurrent networks such as LSTMs on many state estimation tasks. Inspired by Kalman filters, the RKN provides an elegant way to achieve action conditioning within its recurrent cell by leveraging additive interactions between the current latent state and the action variables. We present two architectures, one for forward model learning and one for inverse model learning. Both architectures significantly outperform existing model learning frameworks as well as analytical models in terms of prediction performance on a variety of real robot dynamics models.

Electricity load forecasting has always been a significant part of the smart grid. It ensures sustainability and helps utilities to take cost-efficient measures for power system planning and operation. Conventional methods for load forecasting cannot handle huge data that has a nonlinear relationship with load power. Hence an integrated approach is needed that adopts a coordinating procedure between different modules of electricity load forecasting. We develop a novel electricity load forecasting architecture that integrates three modules, namely data selection, extraction, and classification into a single model. First, essential features are selected with the help of random forest and recursive feature elimination methods. This helps reduce feature redundancy and hence computational overhead for the next two modules. Second, dimensionality reduction is realized with the help of a t-stochastic neighbourhood embedding algorithm for the best feature extraction. Finally, the electricity load is forecasted with the help of a deep neural network (DNN). To improve the learning trend and computational efficiency, we employ a grid search algorithm for tuning the critical parameters of the DNN. Simulation results confirm that the proposed model achieves higher accuracy when compared to the standard DNN.

Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inaccurate due to complex hysteresis effects, unmodelled friction and stiction phenomena,and unknown effects during contact situations. A promising approach is to obtain spatio-temporal models in a data-driven way using recurrent neural networks, as they can overcome those issues. However, such models often do not meet accuracy demands sufficiently, degenerate in performance for the required high sampling frequencies and cannot provide uncertainty estimates. We adopt a recent probabilistic recurrent neural network architecture, called Re-current Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions. RKNs outperform standard recurrent networks such as LSTMs on many state estimation tasks. Inspired by Kalman filters, the RKN provides an elegant way to achieve action conditioning within its recurrent cell by leveraging additive interactions between the current latent state and the action variables. We present two architectures, one for forward model learning and one for inverse model learning. Both architectures significantly outperform exist-ing model learning frameworks as well as analytical models in terms of prediction performance on a variety of real robot dynamics models., Comment: Accepted to Conference On Robot Learning(CoRL), 2020

The well-known electrical analogy for thermal modelling is based on the observation that Fourier's equation for one dimensional heat transfer takes the same form as Ohm's law. This provides a system for creating and resolving complex heat transfer problems using an established set of physically-based equations. In this article, such a model is developed and evaluated for a four-floor modern university building. The model is represented in state space form for optimisation and simulation purposes. The electrical analogy is chosen so that the model can be extended and used for future research into distributed, demand-side control of multiple buildings on the university network, requiring a fast computation time. The estimation of occupancy, representing a significant internal heat source, is also investigated. Here, wifi usage and return CO2 data are combined in novel manner to improve the model response.

This article concerns a previously developed, Fractional-order, Generalised Predictive Control (FGPC) approach to control system design. Here, fractional order concepts are utilised to extend the design flexibility of the FGPC algorithm, in comparison to conventional Generalised Predictive Control (GPC). The performance of FGPC is investigated via Monte Carlo simulation. With a focus on plots of the closed-loop eigenvalues, these results are utilised to develop recommendations for how to optimise the extra design coefficients introduced in the fractional order case. One of the simulation examples involves a hydraulically actuated robotic manipulator. Finally, FGPC methods are applied to control airflow in a laboratory 1 m by 2 m by 2 m forced ventilation environmental test chamber.

The well-known electrical analogy for thermal modelling is based on the observation that Fourier's equation for one dimensional heat transfer takes the same form as Ohm's law. This provides a system for creating and resolving complex heat transfer problems using an established set of physically-based laws. The present article illustrates the concept for adjacent rooms in a modern university building, and investigates some of the modelling issues involved. The electrical analogy is chosen so that the models can be extended and used for future research into demand-side control of multiple buildings on the university network, requiring a fast computation time. For illustrative purposes, the present article is limited to a relatively straightforward two-room system, for which the modelling equations are conveniently represented and solved using MATLAB-SIMULINK. The coefficients of this model are estimated from data using standard nonlinear optimisation tools. For comparison, the article also develops an equivalent multiple-input Transfer Function form of the model. Finally, suggestions are made for the inclusion of occupancy estimates in the model.

On the Sellafield site there are a number of legacy storage tanks and silos containing sludge of uncertain properties. While there are efforts to determine the chemical and radiological properties of the sludge, in order to clean out and decommission these vessels the physical properties need to be ascertained as well. Shear behaviour, density and temperature are the key parameters to be understood before decommissioning activities commence. However, limited access, the congested nature of the tanks and presence of radioactive, hazardous substances severely limit sampling and usage of sophisticated characterisation devices within these tanks and therefore these properties remain uncertain. This paper describes the development of a cheap, compact and robust device to analyse the rheological properties of sludge, without the need to extract materials from the site in order to be analysed. Analysis of a sludge test material has been performed in order to create a suitable benchmark material for the rheological measurements with the prototype. Development of the device is being made with commercial off the shelf (COTS) components and modern rapid prototyping techniques. Using these techniques an initial prototype for measuring shear parameters of sludge has been developed, using a micro-controller for remote control and data gathering. The device is also compact enough to fit through a 75 mm opening, maximising deployment capabilities.

The pulse shape discrimination performance of a pixelated organic plastic scintillator has been investigated. The scintillator has been built using 169 plastic scintillator blocks (arranged into a 13 x 13 square array) of 2.8 x 2.8 x 15 mm each. The scintillator was coupled with a single-channel photomultiplier tube. The scintillator was exposed to a mixed-field environment provided by 252Cf and its pulse shape discrimination capabilities are presented in this paper. Initial results revealed that a 150 MS/s digitising system was insufficient to separate neutrons from gamma-ray photons. Therefore, the experiment was repeated with a 500 MS/s system, which provided improved pulse shape discrimination performance. In order to validate the performance of the pixelated plastic scintillator, it was compared to that of a cylindrical plastic sample. Tests were also carried out in moderated neutron and gamma-ray fields of 252Cf. The results indicate that acceptable levels of pulse shape discrimination are obtained for the case of a pixelated scintillator, when the higher sampling rate digitiser was used.

In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) have been integrated with deep learning models; however, such approaches typically rely on approximate inference techniques such as variational inference which makes learning more complex and often less scalable due to approximation errors. We propose a new deep approach to Kalman filtering which can be learned directly in an end-to-end manner using backpropagation without additional approximations. Our approach uses a high-dimensional factorized latent state representation for which the Kalman updates simplify to scalar operations and thus avoids hard to backpropagate, computationally heavy and potentially unstable matrix inversions. Moreover, we use locally linear dynamic models to efficiently propagate the latent state to the next time step. The resulting network architecture, which we call Recurrent Kalman Network (RKN), can be used for any time-series data, similar to a LSTM (Hochreiter & Schmidhuber, 1997) but uses an explicit representation of uncertainty. As shown by our experiments, the RKN obtains much more accurate uncertainty estimates than an LSTM or Gated Recurrent Units (GRUs) (Cho et al., 2014) while also showing a slightly improved prediction performance and outperforms various recent generative models on an image imputation task.

This article investigates visual servoing for a hydraulically actuated dual-arm robot, in which the user selects the object of interest from an on-screen image, whilst the computer control system implements via feedback control the required position and orientation of the manipulators. To improve on the current joystick direct tele-operation commonly used as standard in the nuclear industry, which is slow and requires extensive operator training, the proposed assisted tele-operation makes use of a single camera mounted on the robot. Focusing on pipe cutting as an example, the new system ensures that one manipulator automatically grasps the user-selected pipe, and appropriately positions the second for a cutting operation. Initial laboratory testing (using a plastic pipe) shows the efficacy of the approach for positioning the manipulators, and suggests that for both experienced and inexperienced users, the task is completed significantly faster than via tele-operation.

Smart grid provides an opportunity for customers as well as for utility companies to reduce electricity costs and regulate generation capacity. The success of scheduling algorithms mainly depends upon accurate information exchange between main grids and smart meters. On the other hand, customers are required to schedule loads, respond to energy demand signals, participate in energy bidding and actively monitor energy prices generated by the utility company. Strengthening communication infrastructure between the utility company and consumers can serve the purpose of consumer satisfaction. We propose a heuristic demand side management model for scheduling smart home appliances in an automated manner, to maximise the satisfaction of the consumers associated with it. Simulation results confirm that the proposed hybrid approach has the ability to reduce the peak-to-average ratio of the total energy demand and reduce the total cost of the energy without compromising user comfort.

The use of ground-based robotic systems for the characterisation of nuclear environments is reviewed. Almost since the dawn of the nuclear energy industry, man has somewhat inadvertently created environments in which access has been constrained primarily due to the risk posed by extreme levels of radiation exposure but also due to space constraints, and because of toxic and combustible atmospheres. Robotic systems pose an ideal solution to some of these difficulties, removing the need for humans to access such places and frequently providing data on the state of such places that would not otherwise be available. However, each of these requirements is often very different in terms of the specification of a given robot, and the detailed characteristics of a given harsh environment can pose significant challenges even for the most robust of platforms. Furthermore, such developments can be expensive in terms of cost and development time. These issues notwithstanding, robotic solutions to nuclear challenges are reaching a level of maturity where their use is destined to add significant value. This paper considers the salient developments in ground-based solutions from the era preceding the Three Mile Island accident, through Chemobyl and on to the present day and, in particular, the needs of Fukushima Daiichi as attentions turn to this complex robotic suite of challenges.

Proportional-integral-plus (PIP) control provides a logical extension to conventional two or three term (proportional-integral-derivative) industrial control, with additional dynamic feedback and input compensators introduced when the process has second order or higher dynamics, or time-delays. Although PIP control has been applied in a range of engineering applications, evaluation of closed-loop robustness has generally relied on empirical methods. In the present article, expressions for the H-infinity norm of two commonly used PIP control implementations, the feedback and forward path forms, are used, for the first time, to quantify closed-loop robustness. It is shown that the forward path form is not robust for unstable plants. Additional expressions for the H-infinity norm that encompass frequency weightings of generalised disturbance inputs are also determined. Novel analytical expressions to minimise the H-infinity norm are derived for the simplest plant, while simulation results based on numerical optimisation are provided for higher order examples. We show that, for certain plants, there are (non-unique) sets of PIP control gains that minimise the H-infinity norm. The H-2 norm is introduced in these cases to determine the controller that balances performance with robustness. Finally, the H-infinity norm is used as a design parameter for a practical example, namely control of airflow in a 2 m by 1 m by 1 m forced ventilation chamber. The performance of the new PIP H-infinity controller is compared to previously developed PIP controllers based on pole placement and linear quadratic design.

In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) have been integrated with deep learning models; however, such approaches typically rely on approximate inference techniques such as variational inference which makes learning more complex and often less scalable due to approximation errors. We propose a new deep approach to Kalman filtering which can be learned directly in an end-to-end manner using backpropagation without additional approximations. Our approach uses a high-dimensional factorized latent state representation for which the Kalman updates simplify to scalar operations and thus avoids hard to backpropagate, computationally heavy and potentially unstable matrix inversions. Moreover, we use locally linear dynamic models to efficiently propagate the latent state to the next time step. The resulting network architecture, which we call Recurrent Kalman Network (RKN), can be used for any time-series data, similar to a LSTM (Hochreiter & Schmidhuber, 1997) but uses an explicit representation of uncertainty. As shown by our experiments, the RKN obtains much more accurate uncertainty estimates than an LSTM or Gated Recurrent Units (GRUs) (Cho et al., 2014) while also showing a slightly improved prediction performance and outperforms various recent generative models on an image imputation task.