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Summary: Two significant drawbacks of current self‐healing materials are that they are: (1) Passive and as such do not guarantee a match between the healing and damage rate; (2) Not monitored during and after healing, so that the performance of the healed material is not known without retrospective offline testing. As a consequence their application is currently limited in some sectors, such as the aerospace sector where high performance needs to be guaranteed within strict guidelines. This article proposes the first active self‐healing material that integrates with control and fault diagnosis to provide a system with a desired healing response. A fault diagnosis algorithm using supervised regression is used to estimate the measure of damage. Then based on this estimate, adaptive feedback control is used to ensure a match between the healing response and the damage rate, while taking into account the nonlinear system dynamics and uncertainty. The system is demonstrated in simulation using a self‐healing material based on piezoelectricity and electrolysis. This shows the ability of the integrated subsystems to tackle these two significant drawbacks of most current self‐healing systems and will benefit applications with strict performance requirements, or systems operating under harsh conditions or that are remotely accessed. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes new methods of fault detection for fuel systems in order to improve system availability. Novel fault systems are required for environmentally friendly lean burn combustion, but can carry high risk failure modes particularly through their control valves. The purpose of the developed technology is the rapid detection of these failure modes, such as valve sticking or impending sticking, and therefore to reduce this risk. However, sensing valve state is challenging due to hot environmental temperatures, which results in a low reliability for conventional position sensing. Design/methodology/approach: Starting with the business needs elicited from stakeholders, a quality functional deployment process is performed to derive sensing system requirements. The process acknowledges the difference between test-bed and in-service aerospace needs through weightings on requirements and maps these customer requirements to systems performance metrics. The design of the system must therefore optimise the sensor suite, on- and off-board signal processing and acquisition strategy. Findings: Against this systems engineering process, two sensing strategies are outlined which illustrate the span of solutions, from conventional gas path sensing with advanced signal processing to novel non-invasive sensing concepts. While conventional sensing may be feasible within a test cell, the constraints of aerospace in-service operation may necessitate more novel alternatives. Acoustic emission (detecting very high frequency surface vibration waves) sensing technology is evaluated to provide a non-invasive, remote and high temperature tolerant solution. Through this comparison, the considerations for the end-to-end system design are highlighted to be critical to sensor deployment success in-service. Practical implications: The paper provides insight into different means of addressing the important problem of monitoring faults in combustor systems in gas turbines. By casting of the complex design problem within a systems engineering framework, the outline of a toolset for solution evaluation is provided. Originality/value: The paper provides three areas of significant contributions: a diversity of methods to diagnosing fuel system malfunctions by measuring changes fuel flow distributions, through novel means, and the combustor exit temperature profiles (cause and effect); the use of analytical methods to support the selection (types and quantities) and placement of sensors to ensure adequate state awareness while minimising their impact on the engine system cost and weight; and an end-to-end data processing approach to provide optimised information for the engine maintainers allowing informed decision-making. [ABSTRACT FROM AUTHOR]

Several natural phenomena are known to exhibit a spatio-temporal evolution process. The study of such processes, which is pivotal to our understanding of how best to predict and control spatio-temporal systems, has motivated researchers to develop appropriate tools that infer models and their parameters from observed data. This paper reviews this active area of research by providing an insight into the fundamental ideas spanning the development of spatio-temporal models, dimensionality reduction methods and techniques for state and parameter estimation. Recent advances are discussed in the context of novel spatio-temporal approaches proposed for applications in three specific domains – engineering, healthcare and social science. They illustrate the wide applicability of estimation and identification of spatio-temporal processes as novel advances in sensor systems and data collection are used to observe them. [ABSTRACT FROM AUTHOR]

A bootstrap-based methodology is developed for parameter estimation and polyspectral density estimation in the case of the approximating model of the underlying stochastic process being non-minimum phase autoregressive-moving-average (ARMA) type, given a finite realisation of a single time series data. The method is based on a minimum phase/maximum phase decomposition of the system function together with a time reversal step for the parameter and polyspectral confidence interval estimation. Simulation examples are provided to illustrate the proposed method. [ABSTRACT FROM PUBLISHER]

As we begin to understand the signals that drive chemotaxis in vivo, it is becoming clear that there is a complex interplay of chemotactic factors, which changes over time as the inflammatory response evolves. New animal models such as transgenic lines of zebrafish, which are near transparent and where the neutrophils express a green fluorescent protein, have the potential to greatly increase our understanding of the chemotactic process under conditions of wounding and infection from video microscopy data. Measurement of the chemoattractants over space (and their evolution over time) is a key objective for understanding the signals driving neutrophil chemotaxis. However, it is not possible to measure and visualise the most important contributors to in vivo chemotaxis, and in fact the understanding of the main contributors at any particular time is incomplete. The key insight that we make in this investigation is that the neutrophils themselves are sensing the underlying field that is driving their action and we can use the observations of neutrophil movement to infer the hidden net chemoattractant field by use of a novel computational framework. We apply the methodology to multiple in vivo neutrophil recruitment data sets to demonstrate this new technique and find that the method provides consistent estimates of the chemoattractant field across the majority of experiments. The framework that we derive represents an important new methodology for cell biologists investigating the signalling processes driving cell chemotaxis, which we label the neutrophils eye-view of the chemoattractant field. [ABSTRACT FROM AUTHOR]

Background: Reaction-diffusion systems are frequently used in systems biology to model developmental and signalling processes. In many applications, count numbers of the diffusing molecular species are very low, leading to the need to explicitly model the inherent variability using stochastic methods. Despite their importance and frequent use, parameter estimation for both deterministic and stochastic reaction-diffusion systems is still a challenging problem. Results: We present a Bayesian inference approach to solve both the parameter and state estimation problem for stochastic reaction-diffusion systems. This allows a determination of the full posterior distribution of the parameters (expected values and uncertainty). We benchmark the method by illustrating it on a simple synthetic experiment. We then test the method on real data about the diffusion of the morphogen Bicoid in Drosophila melanogaster. The results show how the precision with which parameters can be inferred varies dramatically, indicating that the ability to infer full posterior distributions on the parameters can have important experimental design consequences. Conclusions: The results obtained demonstrate the feasibility and potential advantages of applying a Bayesian approach to parameter estimation in stochastic reaction-diffusion systems. In particular, the ability to estimate credibility intervals associated with parameter estimates can be precious for experimental design. Further work, however, will be needed to ensure the method can scale up to larger problems. [ABSTRACT FROM AUTHOR]

Fast sampling is desirable to describe signal transmission through wide-bandwidth systems. The delta-operator provides an ideal discrete-time modeling description for such fast-sampled systems. However, the estimation of delta-domain model parameters is usually biased by directly applying the delta-transformations to a sampled signal corrupted by additive measurement noise. This problem is solved here by expectation-maximization, where the delta-transformations of the true signal are estimated and then used to obtain the model parameters. The method is demonstrated on a numerical example to improve on the accuracy of using a shift operator approach when the sample rate is fast. [ABSTRACT FROM AUTHOR]

The maximum likelihood estimation of a dynamic spatiotemporal model is introduced, centred around the inclusion of a prior arbitrary spatiotemporal neighborhood description. The neighborhood description defines a specific parameterization of the state transition matrix, chosen on the basis of prior knowledge about the system. The model used is inspired by the spatiotemporal ARMA (STARMA) model, but the representation used is based on the standard state-space model. The inclusion of the neighborhood into an expectation-maximization based joint state and parameter estimation algorithm allows for accurate characterization of the spatiotemporal model. The process of including the neighborhood, and the effect it has on the maximum likelihood parameter estimate is described and demonstrated in this paper. [ABSTRACT FROM AUTHOR]

Abstract: A method for obtaining the statistical distribution of parametric polyspectra when applied to a single set of short data record is presented. The method applies model-based bootstrap to a time-series data to obtain the statistical distribution of the coefficients of the approximating process of autoregressive moving average (ARMA) type with known order . As the polyspectral estimate depend on these coefficients, relevant statistical information of the estimated polyspectrum is obtained from the distribution function of the coefficients. Examples of a simulated and real data are provided to illustrate the method. [Copyright &y& Elsevier]

Abstract: In this paper, a modelling and simulation approach for the analysis of a supply chain system taking into account the relations of the system to its contextual components is presented. The purpose is to address several issues particularly on whether decision policies would provide stability in the presence of disturbances, as well as evaluating the effect of disturbances on the system. The task of developing a contextual load model requires the identification of the possible sources of disturbances that exist in the system, the modelling of these disturbances and the particular parameters in the model that are to be changed to simulate such disturbances. Results revealed the effectiveness of the various decision policies to provide stability under the different types of disturbances. The findings demonstrate the ability of the approach to provide a wealth of potential solutions, and confirm the qualitative behaviour of a supply chain in response to the different policies. [Copyright &y& Elsevier]

An adaptive receiver based on hybrid system theory is developed for a multiuser multiple-input multiple-output (MIMO) fading code-division multiple-access (CDMA) system. The basic idea is to treat the transmitted symbols and channel gains as unknown states (discrete and continuous) within a hybrid systems framework. The Bayesian-inference-based state estimation is derived using multiple model theory resulting in an optimal joint sequence estimator, which is shown to be intractable in its computational complexity. A suboptimal receiver (IMM-SIC) is then derived based on the well-known Interacting Multiple Model (IMM) algorithm and successive interference cancellation (SIC) scheme. This paper shows the specific approximations made to the probability densities of the optimal receiver in deriving the IMM-SIC receiver with complexity linear in number of users. This receiver design is well suited for online recursive processing of space-time coded CDMA system, where the decoding stage is incorporated within the multiple model framework. [ABSTRACT FROM AUTHOR]

This paper presents the development of a new method for solving fault detection and isolation (FDI) problem in general non-linear stochastic systems. In this paper, the faults are modelled as unknown changes in system parameters and adaptive Monte Carlo filtering approach is used in deriving an FDI scheme. Essentially, a set of adaptive Monte Carlo filters are designed based on the augmented system models along with a nominal Monte Carlo filter designed based on the nominal system model. The likelihood functions of the observations are then evaluated using the particles from these (adaptive) Monte Carlo filters and FDI is eventually achieved via the likelihood ratio test. The simulation results on a highly non-linear system are provided which demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Focuses on the development of a particle filtering (PF) based method for fault detection and isolation (FDI) in stochastic nonlinear dynamic systems. Formulation of the FDI problem in a multiple model environment; Creation of an FDI scheme by combining likelihood ratio test with the PF; Provision of a highly nonlinear systems for the efficacy of the methods.

Presents an adaptive control technique that grow in time according to the location of the system's state in space. Use of dynamic structure Gaussian radial basis function neural networks; Control of affine nonlinear systems; Dynamic network adaptive controller.

A wetland is an ecosystem, which is inundated by water. Several biogeochemical processes related to wetlands place it into an important environmental system class. This study employs for the first time, a data-driven methodology, specifically one based on system identification, to develop a nonlinear-autoregressive-exogenous-input model structure corresponding to the tropical wetland dynamics, as an alternative to the classical topographic modelling approaches. Altogether 94 wetland sites from Amazon, Africa, and Asia constitute the model structure development. The topographical variations among the wetlands entail a unique set of model parameters for each wetland site, which is achieved by using the approximate Bayesian computation sequential Monte Carlo method. The performance of the single model structure is subsequently evaluated by simulating all the sample wetland sites. The obtained wetland model structure in this study can be used to parameterise any tropical wetland site as well as simulate their dynamical characteristics. [ABSTRACT FROM AUTHOR]

Manufacturing digitalisation is a critical part of the transition towards Industry 4.0. Digital twin plays a significant role as the instrument that enables digital access to precise real-time information about physical objects and supports the optimisation of the related processes through conversion of the big data associated with them into actionable information. A number of frameworks and conceptual models has been proposed in the research literature that addresses the requirements and benefits of digital twins, yet their applications are explored to a lesser extent. A time-domain machining vibration model based on a generative adversarial network (GAN) is proposed as a digital twin component in this paper. The developed conditional StyleGAN architecture enables (1) the extraction of knowledge from existing models and (2) a data-driven simulation applicable for production process optimisation. A novel solution to the challenges in GAN analysis is then developed, where the comparison of maps of generative accuracy and sensitivity reveals patterns of similarity between these metrics. The sensitivity analysis is also extended to the mid-layer network level, identifying the sources of abnormal generative behaviour. This provides a sensitivity-based simulation uncertainty estimate, which is important for validation of the optimal process conditions derived from the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, a two-degrees-of-freedom control algorithm based on uncertainty and disturbance estimator (UDE), aimed to minimize the total harmonic distortion of inverter output voltage is proposed, possessing enhanced robustness to fundamental frequency variations. A multiple-time-delay action is combined with a commonly utilized low-pass UDE filter to increase the range of output impedance magnitude minimization around odd multiples of fundamental frequency for enhanced rejection of typical single-phase nonlinear loads harmonics. Marginal robustness improvement achieved by increasing the number of time delays is quantified analytically and revealed to be independent of delay order. The performance of the proposed control approach and its superiority over two recently proposed methods is validated successfully by experimental results. [ABSTRACT FROM AUTHOR]

Instability problem is an important issue for dc/dc conversion cascaded systems (Cascaded system in short). Though most of the existing stabilization methods can stabilize the whole system very well, they may ignore their impacts on the dynamic performance of the original cascaded system. Unfortunately, these impacts are negative to some extent. Recently, an adaptive-series-virtual-impedance (ASVI) control strategy has been reported to address the above problem. It not only can stabilize the cascaded system via shaping the load input impedance, but also can reduce its impact on the original load converter. However, though the ASVI control strategy has already greatly reduced its impact on the load converter, its remaining impact is negative. To solve this problem, this paper moves the ASVI from the load side to the source side via a proposed source-side series-virtual-impedance (SSVI) control strategy for the source converter. This SSVI control strategy not only has the same stabilization function and adaptive characteristics as the ASVI control strategy, but also improves the performance of the source converter. In addition, since the SSVI control strategy is realized by changing the control block of the source converter, the performance of the load converter is not affected. Therefore, the SSVI control strategy can be treated as a supplement and expansion of the ASVI control strategy. Moreover, depending on the method of realization, the SSVI control strategy can be divided into the source stabilization methods of the cascaded system. Finally, a 100 W 48 V–32 V–24 V cascaded system has been fabricated to validate the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a two-degrees-of-freedom control structure is proposed to minimize both total harmonic distortion and tracking error of inverter output voltage, adopting a resonant tracking controller and a modified uncertainty and disturbance estimator (UDE). Owing to the two-degrees-of-freedom feature of the proposed control strategy, tracking and disturbance rejection tasks are decoupled and treated almost independently. A time-delay action is introduced into a commonly adopted low-pass UDE filter to minimize the output impedance magnitude around the odd harmonics, which is typical to nonlinear loads. Once the disturbance is properly rejected, a tracking resonant controller is designed to force the output of the nominal system to follow a sinusoidal reference with near-zero amplitude and phase error. The performance of the proposed control structure is fully verified by the experimental results. [ABSTRACT FROM AUTHOR]

In this paper, a method to directly shape the output impedance of an inverter is proposed to reduce the total harmonic distortion of the output voltage, based on the uncertainty and disturbance estimator (UDE)-based robust control framework. It is shown that, because of the two-degree-of-freedom feature of the UDE-based control strategy, the UDE filter directly affects the inverter output impedance. A multi-band-stop filter instead of a commonly adopted low-pass filter is then proposed to directly minimize the output impedance around the harmonics to reduce the effect of nonlinear loads and assure robustness to frequency variations. Two tradeoffs are revealed: one between filter bandwidth and stability and the other between robustness and the number of harmonics suppressed. The effectiveness of the proposed control strategy is fully supported by experimental results. [ABSTRACT FROM PUBLISHER]

Raman spectroscopy shows promise as a biomarker for complex nerve and muscle (neuromuscular) diseases. To maximise its potential, several challenges remain. These include the sensitivity to different instrument configurations, translation across preclinical/human tissues and the development of multivariate analytics that can derive interpretable spectral outputs for disease identification. Nonnegative matrix factorisation (NMF) can extract features from high‐dimensional data sets and the nonnegative constraint results in physically realistic outputs. In this study, we have undertaken NMF on Raman spectra of muscle obtained from different clinical and preclinical settings. First, we obtained and combined Raman spectra from human patients with mitochondrial disease and healthy volunteers, using both a commercial microscope and in‐house fibre optic probe. NMF was applied across all data, and spectral patterns common to both equipment configurations were identified. Linear discriminant models utilising these patterns were able to accurately classify disease states (accuracy 70.2–84.5%). Next, we applied NMF to spectra obtained from the mdx mouse model of a Duchenne muscular dystrophy and patients with dystrophic muscle conditions. Spectral fingerprints common to mouse/human were obtained and able to accurately identify disease (accuracy 79.5–98.8%). We conclude that NMF can be used to analyse Raman data across different equipment configurations and the preclinical/clinical divide. Thus, the application of NMF decomposition methods could enhance the potential of Raman spectroscopy for the study of fatal neuromuscular diseases. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease in urgent need of disease biomarkers for the assessment of promising therapeutic candidates in clinical trials. Raman spectroscopy is an attractive technique for identifying disease related molecular changes due to its simplicity. Here, we describe a fibre optic fluid cell for undertaking spontaneous Raman spectroscopy studies of human biofluids that is suitable for use away from a standard laboratory setting. Using this system, we examined serum obtained from patients with ALS at their first presentation to our centre (n = 66) and 4 months later (n = 27). We analysed Raman spectra using bounded simplex-structured matrix factorization (BSSMF), a generalisation of non-negative matrix factorisation which uses the distribution of the original data to limit the factorisation modes (spectral patterns). Biomarkers associated with ALS disease such as measures of symptom severity, respiratory function and inflammatory/immune pathways (C3/C-reactive protein) correlated with baseline Raman modes. Between visit spectral changes were highly significant (p = 0.0002) and were related to protein structure. Comparison of Raman data with established ALS biomarkers as a trial outcome measure demonstrated a reduction in required sample size with BSSMF Raman. Our portable, simple to use fibre optic system allied to BSSMF shows promise in the quantification of disease-related changes in ALS over short timescales. [ABSTRACT FROM AUTHOR]

The article presents the author's view on the "International Journal of Systems Science." He comments on the significance of web-based submission and peer review system used by the journal. He refers to the launch of two prizes International Journal of Systems Science Prize Paper and International Journal of Systems Science Outstanding Reviewer. He remarks that over 600 new paper submissions are received by the journal each year.

Introduces the articles published in the January 2005 issue of the "International Journal of Systems Science."

This paper investigates the stability of meshed DC micro-grids with constant power loads (CPLs), under decentralized primary and distributed secondary control schemes to achieve accurate power sharing and voltage restoration, with the latter being more significant to the case of parallel converters in a micro-grid architecture. The DC micro-grid consists of multiple DC/DC boost converters, which have nonlinear dynamics, feeding local CPLs, which also exhibit a nonlinear behaviour and introduce negative impedance characteristics that are well-known to yield instability. At the primary control layer, the droop control concept is suitably formulated and implemented using the recently proposed state-limiting PI controller to accomplish an inherent current limitation for each converter and simultaneously facilitate the stability analysis. Using limited information of the injected power from neighbouring converters and, depending on the micro-grid configuration, the load voltage, a distributed secondary controller is formulated to enhance the power sharing and accurately regulate the voltage to the rated value. By analysing for the first time both the dynamics of the converters with the CPLs and the two-layer control, singular perturbation theory is applied to analytically prove the stability of the entire DC micro-grid. The scalability of the system is also ensured through relevant passivity analysis. Simulation and experimental testings are performed to confirm the effectiveness and validity of the proposed method. [ABSTRACT FROM AUTHOR]

• Tensor electrical impedance myography reduces complex multi-frequency, multi-electrode configuration impedance data down to a single metric. • This metric captures disease progression more sensitively than other bulbar disease biomarkers, including tongue strength and the ALS-FRS-R. • Tensor electrical impedance myography improves the bulbar biomarker potential of electrical impedance measurements in ALS. Electrical impedance myography (EIM) is a promising biomarker for amyotrophic lateral sclerosis (ALS). A key issue is how best to utilise the complex high dimensional, multi-frequency data output by EIM to fully characterise the progression of disease. Muscle volume conduction properties were obtained from EIM recordings of the tongue across three electrode configurations and 14 input frequencies (76 Hz–625 kHz). Analyses of individual frequencies, averaged EIM spectra and non-negative tensor factorisation were undertaken. Longitudinal data were collected from 28 patients and 17 healthy volunteers at 3-monthly intervals for a maximum of 9 months. EIM was evaluated against the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar sub-score, tongue strength and an overall bulbar disease burden score. Longitudinal changes to individual patient EIM spectra demonstrated complex shifts in the spectral shape. At a group level, a clear pattern emerged over time, characterised by an increase in centre frequency and general shift to the right of the spectral shape. Tensor factorisation reduced the spectral data from a total of 168 data points per participant per recording to a single value which captured the complexity of the longitudinal data and which we call tensor EIM (T-EIM). The absolute change in tensor EIM significantly increased within 3 months and continued to do so over the 9-month study duration. In a hypothetical clinical trial scenario tensor EIM required fewer participants (n = 64 at 50% treatment effect), than single frequency measures (n range 87–802) or ALSFRS-R bulbar subscore (n = 298). Changes to tongue EIM spectra over time in ALS are complex. Tensor EIM captured and quantified disease progression and was more sensitive to changes than single frequency EIM measures and other biomarkers of bulbar disease. Objective biomarkers for the assessment of bulbar disease in ALS are lacking. Tensor EIM enhances the biomarker potential of EIM data and can improve bulbar symptom monitoring in clinical trials. [ABSTRACT FROM AUTHOR]

This paper is concerned with the control of systems composed of multiple coupled subsystems. In such architectures, communication between different local controllers is desired in order to achieve a better overall control performance. Any resultant improvement in control performance needs, however, to be significant enough to warrant the additional design complexity and higher energy consumption and costs associated with introducing communication channels between controllers. A practical distributed control design aims, therefore, to achieve an acceptable balance between minimising the use of communication between controllers and maximising the system-wide performance. In this article, a new approach to the problem of synthesising stabilising distributed control laws for discrete-time linear systems that balances performance and communication is presented. The approach employs a supervisory agent that, periodically albeit not necessarily at every sampling instant, solves an optimisation problem in order to synthesise a stabilising state feedback control law for the system. The online optimisation problem, which maximises sparsity of the control law while minimising an infinite-horizon performance cost, is formulated as a bilinear matrix inequality (BMI) problem; subsequently, it is then relaxed to a linear matrix inequality (LMI) problem, and (i) convergence to a solution as well as (ii) that early termination guarantees a feasible (but suboptimal) control law are proved. Stability of the closed-loop system under what is a switched control law is guaranteed by the inclusion of dwell-time constraints in the LMI problem. Finally, the efficacy of the approach is demonstrated through numerical simulation examples. [ABSTRACT FROM AUTHOR]

Abstract: In this contribution we derive a computational Bayesian approach to NARMAX model identification. The identification algorithm exploits continuing advances in computational processing power to numerically obtain posterior distributions for both model structure and parameters via sampling methods. The main advantage of this approach over other NARMAX identification algorithms is that for the first time model uncertainty is characterised as a byproduct of the identification procedure. The algorithm is based on the reversible jump Markov chain Monte Carlo (RJMCMC) procedure. Key features of the approach are (i) sampling of unselected model terms for testing for inclusion in the model (the birth move), which encourages global searching of the model term space, (ii) sampling of previously selected model terms for testing for exclusion from the model—a naturally incorporated pruning step (the death move), which leads to model parsimony, and (iii) estimation of model and parameter distributions, which are naturally generated in the Bayesian framework. We present a numerical example to demonstrate the algorithm and a comparison with a forward regression method: the results show that the RJMCMC approach is competitive and gives useful additional information regarding uncertainty in both model parameters and structure. [Copyright &y& Elsevier]

Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. Using ideas from statistics, signal processing, and ecology, we provide a predictive framework able to assimilate data and give confidence estimates on the predictions. We demonstrate our methods on the WikiLeaks Afghan War Diary. Our results show that the approach allows deeper insights into conflict dynamics and allows a strikingly statistically accurate forward prediction of armed opposition group activity in 2010, based solely on data from previous years. [ABSTRACT FROM AUTHOR]

Spatiotemporal models are ubiquitous in science and engineering, yet estimation in these models from discrete observations remains computationally challenging. We propose a practical novel approach to inference in spatiotemporal processes, both from continuous and from discrete (point-process) observations. The method is based on a finite-dimensional reduction of the spatiotemporal model, followed by a mean field variational approximate inference approach. To cater for the point-process case, a variational-Laplace approach is proposed which yields tractable computations of approximate variational posteriors. Results show that variational Bayes is a viable and practical alternative to statistical methods such as expectation maximization or Markov chain Monte Carlo. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we consider structure detection and parameter estimation of the nonlinear auto-regressive with exogenous inputs (NARX) model, using the EM (expectation–maximisation) algorithm. The parameter estimation step uses particle smoothing to obtain the necessary expectations in the E-step and the parameters are then estimated in closed form in the M-step. The model structure detection is performed using an -test, which makes use of the parameter information matrix (inverse of the covariance matrix), obtained from an augmentation of the EM algorithm. The steps for obtaining the information matrix are robust, guaranteeing a positive semi-definite information matrix to use in the structure detection step. For the case of unknown model orders, a method is proposed using the stochastic complexity (SC) information criterion for selecting between candidate models. The SC is composed of the information matrix (representing model complexity) and a likelihood estimate (representing model accuracy), which are both generated as byproducts of the augmented EM algorithm. Numerical results demonstrate that the EM approach performs well in comparison to a standard alternative based on orthogonal least squares, and also avoids the need to estimate a noise model for the case of measurement noise corrupted output signals. [Copyright &y& Elsevier]

A state space model of the stochastic spatio-temporal Integro-Difference Equation (IDE) is derived. Based on multidimensional sampling theory, the dimensions of the state space and parameter space of the model are identified from the spatial band- width of the system and the support of the redistribution kernel of the IDE. When both the bandwidth and the kernel support are unknown, a method to propose a number of state space and parameter space dimensions is presented. These chosen dimensions result in a number of candidate model structures. Bayesian model selection, making use of Bayes factor, the data augmentation algorithm and importance sampling, is then used to identify the model best suited to represent the data in a maximum a posteriori sense. [ABSTRACT FROM AUTHOR]

A continuous-in-space, discrete-in-time dynamic spatio-temporal model known as the Integro-Difference Equation (IDE) model is presented in the context of data-driven modeling. A novel decomposition of the IDE is derived, leading to state-space representation that does not couple the number of states with the number of observation locations or the number of parameters. Based on this state-space model, an expectation-maximization (EM) algorithm is developed in order to jointly estimate the IDE model's spatial field and spatial mixing kernel. The resulting modeling framework is demonstrated on a set of examples. [ABSTRACT FROM AUTHOR]

System identification problems often arise where the only modeling records available consist of multiple short-time-duration signals. This motivates the development of a modeling approach that is tailored for this situation. An identification algorithm is presented here for parameter estimation based on minimizing the simulated prediction error, across multiple signals. The additional complexity of estimating the initial states corresponding to each signal is removed from the estimation algorithm. A numerical simulation demonstrates that the proposed algorithm performs well in comparison to the often-used least squares method (which leads to biased estimates when identifying systems from measurement noise corrupted signals). The approach is applied to the identification of the passive oculomotor plant; parameters are estimated that describe the dynamics of the plant, which represent the time constants of the visco-elastic elements that characterize the plant connective tissue. [ABSTRACT FROM AUTHOR]

Neutrophils are rapidly recruited to inflammatory sites where their coordinated migration forms clusters, a process termed neutrophil swarming. The factors that modulate early stages of neutrophil swarming are not fully understood, requiring the development of new in vivo models. Using transgenic zebrafish larvae to study endogenous neutrophil migration in a tissue damage model, we demonstrate that neutrophil swarming is a conserved process in zebrafish immunity, sharing essential features with mammalian systems. We show that neutrophil swarms initially develop around an individual pioneer neutrophil. We observed the violent release of extracellular cytoplasmic and nuclear fragments by the pioneer and early swarming neutrophils. By combining in vitro and in vivo approaches to study essential components of neutrophil extracellular traps (NETs), we provide in-depth characterisation and high-resolution imaging of the composition and morphology of these release events. Using a photoconversion approach to track neutrophils within developing swarms, we identify that the fate of swarm-initiating pioneer neutrophils involves extracellular chromatin release and that the key NET components gasdermin, neutrophil elastase, and myeloperoxidase are required for the swarming process. Together our findings demonstrate that release of cellular components by pioneer neutrophils is an initial step in neutrophil swarming at sites of tissue injury. [ABSTRACT FROM AUTHOR]

Throughout engineering there are problems where it is required to predict a quantity based on the measurement of another, but where the two quantities possess characteristic variations over vastly different ranges of time and space. Among the many challenges posed by such 'multiscale' problems, that of defining a 'scale' remains poorly addressed. This fundamental problem has led to much confusion in the field of biomedical engineering in particular. The present study proposes a definition of scale based on measurement limitations of existing instruments, available computational power, and on the ranges of time and space over which quantities of interest vary characteristically. The definition is used to construct a multiscale modelling methodology from start to finish, beginning with a description of the system (portion of reality of interest) and ending with an algorithmic orchestration of mathematical models at different scales within the system. The methodology is illustrated for a specific but well-researched problem. The concept of scale and the multiscale modelling approach introduced are shown to be easily adaptable to other closely related problems. Although out of the scope of this paper, we believe that the proposed methodology can be applied widely throughout engineering. [ABSTRACT FROM AUTHOR]

International Journal of Systems Science (IJSS) is an international journal in the fields of Automation & Control Systems, Computer Science, and Operations Research & Management Science. This paper is a celebration of the 50th anniversary of IJSS. A Web of Science count of 7528 documents were derived from 1970 to 2019 as data sources for the bibliometric analysis. First, the fundamental characteristics of the documents were identified from the bibliometric indicators, and features of keywords were revealed over the half century. A timeline and occurrence analyses were conducted via the software tools, through which popular keywords were visualised in different time periods. These were then grouped into four categories by a hierarchical clustering method, and emerging keywords were selected using burst detection analysis. This was followed by the Autoregressive Integrated Moving Average modelling to predict the future research trends in the four clusters as evidenced in the historical data. Documents were discussed in the recent 20 years towards guiding scholars in three aspects, i.e. problems addressed, popular methodologies, and impact in the relevant fields. Finally, a summary of the main findings was given in the conclusions. [ABSTRACT FROM AUTHOR]

Objective: Electrical impedance myography (EIM) performed on the centre of the tongue shows promise in detecting amyotrophic lateral sclerosis (ALS). Lateral recordings may improve diagnostic performance and provide pathophysiological insights through the assessment of asymmetry. However, it is not known if electrode proximity to the muscle edge, or electrode rotation, distort spectra. We evaluated this using finite element-based modelling. Approach: Nine thousand EIM from patients and healthy volunteers were used to develop a finite element model for phase and magnitude. Simulations varied electrode proximity to the muscle edge and electrode rotation. LT-Spice simulations assessed disease effects. Patient data were assessed for reliability, agreement and classification performance. Main results: No effect on phase spectra was seen if all electrodes remained in contact with the tissue. Small effects on magnitude were observed. Cole-Cole circuit simulations indicated capacitance reduced with disease severity. Lateral tongue muscle recordings in both patients and healthy volunteers were reproducible and symmetrical. Combined lateral/central tongue EIM improved disease classification compared to either placement alone. Significance: Lateral EIM tongue measurements using phase angle are feasible. Such measurements are reliable, find no evidence of tongue muscle asymmetry in ALS and improve disease classification. Lateral measurements enhance tongue EIM in ALS. [ABSTRACT FROM AUTHOR]

• Novel electrical impedance myography system uses electrodes on both surfaces of the tongue. • The device produces reliable data, even in inexperienced hands. • The results differentiate between healthy and ALS muscle and reveal disease change over time. In amyotrophic lateral sclerosis (ALS) bulbar disease biomarkers are lacking. We evaluated a novel tongue electrical impedance myography (EIM) system, utilising both 2D and 3D electrode configurations for detection of tongue pathology. Longitudinal multi-frequency phase angle spectra were recorded from 41 patients with ALS (baseline, 3 and 6 months) and 30 healthy volunteers (baseline and 6 months). ALS functional rating scale-revised (ALSFRS-R) data and quantitative tongue strength measurements were collected. EIM data were analysed for reliability (intra-class correlation coefficient; ICC) and differences between patients and volunteers ascertained using both univariate (Mann-Whitney U test) and multivariate techniques (feature selection and L2 norm). The device produced highly reliable data (pooled ICC: 0.836). Significant EIM differences were apparent between ALS patients and healthy volunteers (P < 0.001). EIM data demonstrated a significant relationship to tongue strength and bulbar ALSFRS-R scores (P < 0.015). The EIM recordings revealed a group level longitudinal change over 6 months and consistently identified patients in whom symptoms or tongue strength changed. The novel EIM tongue system produces reliable data and can differentiate between healthy muscle and ALS-related disease. Tongue EIM utilising multiple frequencies and electrode configurations has potential as a bulbar disease biomarker in ALS. [ABSTRACT FROM AUTHOR]

Longitudinal studies of bone adaptation in mice using in vivo micro-computed tomography (μCT) have been commonly used for pre-clinical evaluation of physical and pharmacological interventions. The main advantage of this approach is to use each mouse as its own control, reducing considerably the sample size required by statistical power analysis. To date, multi-scale estimation of bone adaptations become essential since the bone activity that takes place at different scales may be associated with different bone mechanisms. Measures of bone adaptations at different time scales have been attempted in a previous study. This paper extends quantification of bone activity at different spatial scales with a proposition of a novel framework. The method involves applying level-set method (LSM) to track the geometric changes from the longitudinal in vivo μCT scans of mice tibia. Bone low- and high-spatial frequency patterns are then estimated using multi-resolution analysis. The accuracy of the framework is quantified by applying it to two times separated scanned images with synthetically manipulated global and/or local activity. The Root Mean Square Deviation (RMSD) was approximately 1.5 voxels or 0.7 voxels for the global low-spatial frequency or local high-spatial frequency changes, respectively. The framework is further applied to the study of bone changes in longitudinal datasets of wild-type mice tibiae over time and space. The results demonstrate the ability for the spatio-temporal quantification and visualisation of bone activity at different spatial scales in longitudinal studies thus providing further insight into bone adaptation mechanisms. [ABSTRACT FROM AUTHOR]

Tools that provide personalized risk prediction of outcomes after surgical procedures help patients make preference-based decisions among the available treatment options. However, it is unclear which modeling approach provides the most accurate risk estimation. We constructed and compared several parametric and nonparametric models for predicting prosthesis survivorship after knee replacement surgery for osteoarthritis. We used 430,455 patient-procedure episodes between April 2003 and September 2015 from the National Joint Registry for England, Wales, Northern Ireland, and the Isle of Man. The flexible parametric survival and random survival forest models most accurately captured the observed probability of remaining event-free. The concordance index for the flexible parametric model was the highest (0.705, 95% confidence interval (CI): 0.702, 0.707) for total knee replacement and was 0.639 (95% CI: 0.634, 0.643) for unicondylar knee replacement and 0.589 (95% CI: 0.586, 0.592) for patellofemoral replacement. The observed-to-predicted ratios for both the flexible parametric and the random survival forest approaches indicated that models tended to underestimate the risks for most risk groups. Our results show that the flexible parametric model has a better overall performance compared with other tested parametric methods and has better discrimination compared with the random survival forest approach. [ABSTRACT FROM AUTHOR]

Abstract: Turbofan oil systems are used to provide lubrication and cooling in the engine . There is an increasing interest in oil system architectures which utilize electric pumps and/or valves to give optimized control of flows to individual oil chambers, leading to improved thermal management of oil and lubrication efficiency. The challenges here lie in the trade‐off between increasing controllability and minimizing the addition of new components, which adds unwanted production and maintenance costs. This paper formulates the oil system architecture design as a constrained, multiobjective optimization problem. An architecture is described using a graph with nodes representing components and edges representing interconnections between components. A fixed set of nodes called the architecture template is provided as an input and the edges are optimized for a multicriteria objective function. A heuristic method for determining similarities between the different oil chamber flow requirements is presented. This is used in the optimization to evaluate the controllability objective based on the structure of the valve architecture. The methodology provides benefits to system designers by selecting cheaper architectures with fewer valves when the need to control oil chambers separately is small. The effect of manipulating the cost/controllability criteria weightings is investigated to show the impact on the resulting architecture. [ABSTRACT FROM AUTHOR]

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation. [ABSTRACT FROM AUTHOR]

Autonomous systems such as Unmanned Aerial Vehicles (UAVs) need to be able to recognise and track crowds of people, e.g. for rescuing and surveillance purposes. Large groups generate multiple measurements with uncertain origin. Additionally, often the sensor noise characteristics are unknown but measurements are bounded within certain intervals. In this work we propose two solutions to the crowds tracking problem— with a box particle filtering approach and with a convolution particle filtering approach. The developed filters can cope with the measurement origin uncertainty in an elegant way, i.e. resolve the data association problem. For the box particle filter (PF) we derive a theoretical expression of the generalised likelihood function in the presence of clutter. An adaptive convolution particle filter (CPF) is also developed and the performance of the two filters is compared with the standard sequential importance resampling (SIR) PF. The pros and cons of the two filters are illustrated over a realistic scenario (representing a crowd motion in a stadium) for a large crowd of pedestrians. Accurate estimation results are achieved. [ABSTRACT FROM AUTHOR]

Iceberg discharge is a major component of the mass balance of the Greenland Ice Sheet (GrIS). While bulk estimates of discharge variation over time exist, inferred remotely from measurements of grounding line ice velocities or surface mass balance calculations, few detailed measurements of discharge itself from individual marine-terminating glaciers existed until recent years. Recently, it has been shown, through a combination of ocean–iceberg modelling and non-linear system identification, that the century-long record of iceberg numbers crossing 48 o N in the West Atlantic is a good first-order proxy for discharge from at least south and west Greenland. Here, we explore the varying relative importance of ice sheet, oceanic and climatic forcing of iceberg discharge from these areas over the twentieth century, by carrying out sensitivity studies of a non-linear auto-regressive mathematical model of the 48 o N time series. We find that the relationships are mainly non-linear, with the contribution of the GrIS surface mass balance to iceberg discharge likely to be dominant in the first half of the century. This period is followed by several decades where oceanic temperature effects are most important in determining the model variation in iceberg discharge. In recent decades, all physical processes play a non-negligible part in explaining the iceberg discharge and the model suggests that the glacial response time to environmental changes may have decreased. [ABSTRACT FROM AUTHOR]

Motivation: Advances in analytical instrumentation towards acquiring high-resolution images of mass spectrometry constantly demand efficient approaches for data analysis. This is particularly true of time-of-flight secondary ion mass spectrometry imaging where recent advances enable acquisition of high-resolution data in multiple dimensions. In many applications, the distribution of different species from a sampled surface is spatially continuous in nature and a model that incorporates the spatial correlation across the surface would be preferable to estimations at discrete spatial locations. A key challenge here is the capability to analyse the high-resolution multidimensional data to extract relevant information reliably and efficiently.Results: We propose a framework based on alternating non–negativity-constrained least squares which accounts for the spatial correlation across the sample surface. The proposed method also decouples the computational complexity of the estimation procedure from the image resolution, which significantly reduces the processing time. We evaluate the performance of the algorithm with biochemical image datasets generated from mixture of metabolites.Contact: p.aram@sheffield.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Neutrophils must be removed from inflammatory sites for inflammation to resolve. Recent work in zebrafish has shown neutrophils can migrate away from inflammatory sites, as well as die in situ. The signals regulating the process of reverse migration are of considerable interest, but remain unknown. We wished to study the behaviour of neutrophils during reverse migration, to see whether they moved away from inflamed sites in a directed fashion in the same way as they are recruited or whether the inherent random component of their migration was enough to account for this behaviour. Using neutrophil-driven photoconvertible Kaede protein in transgenic zebrafish larvae, we were able to specifically label neutrophils at an inflammatory site generated by tailfin transection. The locations of these neutrophils over time were observed and fitted using regression methods with two separate models: pure-diffusion and drift-diffusion equations. While a model hypothesis test (the F-test) suggested that the datapoints could be fitted by the drift-diffusion model, implying a fugetaxis process, dynamic simulation of the models suggested that migration of neutrophils away from a wound is better described by a zero-drift, "diffusion" process. This has implications for understanding the mechanisms of reverse migration and, by extension, neutrophil retention at inflammatory sites. [ABSTRACT FROM AUTHOR]

The mechanism by which an apparently uniform population of cells can generate a heterogeneous population of differentiated derivatives is a fundamental aspect of pluripotent and multipotent stem cell behaviour. One possibility is that the environment and the differentiation cues to which the cells are exposed are not uniform. An alternative, but not mutually exclusive possibility is that the observed heterogeneity arises from the stem cells themselves through the existence of different interconvertible substates that pre-exist before the cells commit to differentiate. We have tested this hypothesis in the case of apparently homogeneous pluripotent human embryonal carcinoma (EC) stem cells, which do not follow a uniform pattern of differentiation when exposed to retinoic acid. Instead, they produce differentiated progeny that include both neuronal and non-neural phenotypes. Our results suggest that pluripotent NTERA2 stem cells oscillate between functionally distinct substates that are primed to select distinct lineages when differentiation is induced. [ABSTRACT FROM AUTHOR]