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ABSTRACT We are surrounded by overwhelming big data, which brings substantial advances but meanwhile poses many challenges. A very large portion of big data contains geospatial information and hence geospatial big data, which is crucial for decision making if being utilized strategically. Among others, volumes in size and high dimensions are two major challenges that prevent strategic decision making from geospatial big data. Interactive map‐based and geovisualization enabled web applications are intuitive and useful to construct knowledge. More importantly, such interactive web map applications are powerful to intuitively reveal insights from high‐dimensional geospatial big data for actionable decision making. We propose an interactive and data‐driven web mapping framework, named dciWebMapper, for visualizing and sensing high‐dimensional geospatial (big) data in an interactive and scalable manner. To demonstrate the wide applicability and usefulness of our framework, we have applied our dciWebMapper framework to three real‐world case studies and implemented three corresponding web map applications: iLit4GEE‐AI, iWURanking, and iTRELISmap. We expect and hope the three web maps demonstrated in different domains, from literature big data analysis to world university ranking to scholar mapping, will provide a good start and inspire researchers and practitioners in various domains to apply our dciWebMapper to solve and/or aid in solving impactful problems. [ABSTRACT FROM AUTHOR]

A new avenue by transforming the conventional electrochemical domain into an electro‐chemo‐mechanical domain is proposed to achieve less hysteretic fast‐charging based on the three pictures: i) electro‐chemo‐mechanics, ii) phase transition kinetics, and iii) ionic kinetics. Each concept is demonstrated leading to the electrochemical improvement using data‐driven computation and experimental analyses, and its novel framework is generalized based on alkali‐ion chalcogenide models. The mechanical strain lowers and delays the electrochemical yield point, which gives rise to extending the elastic region and enhancing the phase and ionic kinetics. This is experimentally verified with less hysteresis upon (dis)charging for all models. Implementing the electro‐chemo‐mechanical domain is central in alkali‐ion chalcogenide batteries and broadens its application in other rechargeable batteries, ultimately playing a critical role in providing practical fast‐charging solutions. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) has become a hotspot due to its high efficiency, compact structure, and good dynamic operation efficiency. However, problems such as poor reliability and short lifespan create bottlenecks in its large‐scale applications. There has been a large amount of research on fault diagnosis and health management techniques dedicated to addressing the lifespan issues of PEMFC systems. This article provides an in‐depth analysis on the fault mechanism of PEMFC and systematically sorts out the types, causes, and impacts of faults. On this basis, the research progress of PEMFC fault diagnosis technology is summarized, and the measurement characterization methods for fuel cell status monitoring and fault detection are summarized. The literatures of model‐based and data‐driven fault identification methods are summarized and compared. The relevant mitigation measures for PEMFC faults are discussed. Finally, based on the challenges in the current research of fault diagnosis, people mainly conduct research on fault model, online diagnostic technology, and improving diagnostic mechanisms. Overall, this article can provide useful summary and guidance for future research. [ABSTRACT FROM AUTHOR]

The converter is a complex, high temperature, high pressure reactor with limited internal moitoring. At present, data‐driven models mainly focus on the prediction differences between algorithms, and there is relatively little analysis of the impact of different hyperparameters on prediction accuracy. Taking a 120 t converter in a Chinese steel plant as an example, this paper explores the application of particle swarm optimization‐back propagation neural network (PSO‐BP) in converter temperature prediction. First, the Pauta criterion or Box plot method was used to preprocess the data by prescreening. Subsequently, the influence of the activation function, learning rate, and number of hidden layer nodes of BP on the prediction accuracy of the endpoint temperature were explored. Then we investigated the influence of PSO parameters on the optimal result of BP initial value. Comparing the temperature prediction hit rate before and after optimization, the BP model has hit rates of 63.64%, 79.22%, and 87.45% within ±10, ±15, and ±20 °C, respectively, and the PSO‐BP model has hit rates of 68.40%, 84.85%, and 94.81%, respectively. In comparison, PSO‐BP extracts data features more accurately, has higher stability, and has better accuracy in predicting the endpoint temperature of the converter. [ABSTRACT FROM AUTHOR]

Reference prices (RPs) are consumers' subjective perceptions of prices that have important influences on purchase decisions. The standard RP formulation, which defines RP as an exponentially weighted average of past prices, ignores a certain asymmetry in weights between the regime of a price decrease and that of a price increase, which can be observed by the demand trend during the few days after a price decrease or increase. Such oversight usually leads to overestimation in demand as we illustrate by empirical evidence. We introduce the novel concept of RP with exposure effect (RPEE) that captures such asymmetry in RP formulation by imposing a weight proportional to how much the price is exposed to consumers. The exposure effect can be measured by clickstream data that are available for most e‐retailing platforms. We develop a customer behavioral model that can explain the formation of standard RP, and extend it in a natural way to provide foundation to the use of RPEE, especially for products with few repeat purchases. We then establish empirically the extensive benefit of forecasting from RPEE for e‐retailers that sell thousands of products. We demonstrate that RPEE exhibits significant and consistent improvement over standard RP for products, with around 20%$$ 20\% $$ reduced weighted mean absolute percentage error. [ABSTRACT FROM AUTHOR]

A large UK forestry company has been used as the case study to assess the organisational aspects that hinder forestry organisations from reaching data maturity and becoming data driven. This study focuses on the organisational aspects of dealing with data, as they are often disregarded in favour of dealing only with the technical aspects. The barriers to reaching data maturity have been assessed by means of an online survey distributed across the company. Additionally, the study explores how willing the company is to make changes towards becoming more data driven, assess the data capabilities of the company, identify which benefits of harnessing data are viewed more positively and to estimate the data maturity level of the organisation. The results showed that the company case study was in the early‐mid stages of data maturity with significant opportunity for improvement. The main barriers identified were cultural, along with some other organisational barriers related to data roles and data strategy. Improving the data maturity of the organisation case study consisted of increasing data‐skills, prioritising data‐driven decision making at management level and finally elaborating an organisation‐wide data strategy. [ABSTRACT FROM AUTHOR]

The access of a high proportion of renewable energies has deepened the randomness and complexity of cascading failures (CFs) in power systems. In this regard, a real‐time risk assessment method for CFs in power systems with high proportion of new energy is proposed. First, combined with historical statistical data and relevant national standards, a CF simulation model that considers the off‐grid protection action of renewable energy units in the event of a power grid fault is proposed. The model is based on the continuous steady‐state power flow model, which simulates the spread of CFs via continuous power flow calculations. Second, via introducing the concept of a fault graph chain, the electrical and topological characteristics of the continuous dynamic of the power system in the process of CFs can be described. Then, through continuous CF simulation and replay buffer, a data‐driven method is used to calculate the CF risk index corresponding to the fault graph. Finally, a cascaded graph neural network is employed to fit the nonlinear mapping relationship between fault graphs and CF risk indicators. The simulation results in the IEEE 39‐bus system show that the proposed method can accurately and real‐time evaluate the risk of CFs. [ABSTRACT FROM AUTHOR]

Online health monitoring of thyristor converter valve is very important for the stable operation of high voltage direct current (HVDC) system. However, in practical projects, only offline annual maintenance can be realized. This paper proposes a method for parameter identification of thyristor valve based on wavelet packet key feature extraction and Elman neural network. By preprocessing the measurable characteristic parameters of thyristor valve in practical engineering, such as wavelet packet time‐frequency domain feature extraction, variation coefficient optimization and so on, the characteristic vector that can characterize the health state of thyristor valve is obtained and sent to Elman neural network for training, which realizes the rapid online identification of multiple electrical parameters of thyristor modules in converter valve. Simulation and comparative experiments show that the key feature extraction method proposed in this paper has significant advantages in identification accuracy, and can better identify the damping capacitance and leakage resistance of all thyristor modules. Among them, the deterioration of the damping capacitance is more easily identified, the identification accuracy is the highest and the relative identification error is less than 5%. The parameter identification method proposed in this paper has the advantages of fast identification speed, strong model generalization ability and wide application. This provides a new idea for the online condition monitoring of thyristor valves. [ABSTRACT FROM AUTHOR]

Weld toe and weld root failures dominate the fatigue failures of the load‐carrying cruciform fillet welded joints. Compared to weld toe failure, the weld root failure is strongly affected by its inherent weld quality and throat size, which is more unpredictable and should be eliminated. This work proposes a weld sizing criterion in terms of two weld leg sizes and weld penetration to avoid root failure. The criterion is obtained by a data‐driven approach with the help of the mechanical‐directed data augmentation technique. The traction‐stress‐based method is applied for the data augmentation to identify the fatigue failure mode, which is statistically tested against 372 experimentally obtained fatigue data. [ABSTRACT FROM AUTHOR]

To explain the variance in core hardness of 18CrNi8 nozzle bodies after industrial heat treatment, several data sources, including steel melt composition, sensor process data, and measurement errors, of five years are aggregated. In order to predict hardness variations caused by alloy composition, traditional physical models by Maynier are compared with data‐driven machine learning models, which show no advantage due to low data variability. Neither method can fully explain the visible drifts, which are better tracked by an alternative (i. e., filter model) that uses past measurements. Machine learning on features from heat treatment is not successful in predicting hardness change, presumably because the process is too stable. Finally, a large part of the variance is caused by the HV 1 measurement error. [ABSTRACT FROM AUTHOR]

Establishing a unified framework for describing the structures of molecular and periodic systems is a long‐standing challenge in physics, chemistry, and material science. With the rise of machine learning methods in these fields, there is a growing need for such a method. This perspective aims to discuss the development and use of three promising approaches—topological, atom‐density, and symmetry‐based—for the prediction and rationalization of physical, chemical, and mechanical properties of atomistic systems across different scales and compositions. [ABSTRACT FROM AUTHOR]

In the current context of an aging population in many developed countries, the issue of healthy aging is at the forefront of the political, scientific, and technological concerns. The frailty accompanying the late years of elderly people (>70 years old) deserves special consideration due to its great economical and personal costs and the workload imposed on the health care system. Hospital readmissions under a short time after hospital discharge are one of the sources of concern, and much effort is being devoted to their prediction for better care of the elder and optimized resource management. In this paper, we consider the prediction of readmissions for patients that are evaluated positively in the frailty scales. The computational experiments are carried out over a gender‐balanced cohort of 645 patients recruited at the University Hospital of Alava. We report machine‐learning prediction results of the readmission before the standard readmission limit of 30 days. We apply an upsampling technique to correct for class imbalance. Results are positive, encouraging further research and the creation of larger cohorts in international efforts. [ABSTRACT FROM AUTHOR]

Summary: An extension of the probabilistic learning on manifolds (PLoM), recently introduced by the authors, has been presented: In addition to the initial data set given for performing the probabilistic learning, constraints are given, which correspond to statistics of experiments or of physical models. We consider a non‐Gaussian random vector whose unknown probability distribution has to satisfy constraints. The method consists in constructing a generator using the PLoM and the classical Kullback‐Leibler minimum cross‐entropy principle. The resulting optimization problem is reformulated using Lagrange multipliers associated with the constraints. The optimal solution of the Lagrange multipliers is computed using an efficient iterative algorithm. At each iteration, the Markov chain Monte Carlo algorithm developed for the PLoM is used, consisting in solving an Itô stochastic differential equation that is projected on a diffusion‐maps basis. The method and the algorithm are efficient and allow the construction of probabilistic models for high‐dimensional problems from small initial data sets and for which an arbitrary number of constraints are specified. The first application is sufficiently simple in order to be easily reproduced. The second one is relative to a stochastic elliptic boundary value problem in high dimension. [ABSTRACT FROM AUTHOR]

With the trend towards large‐scale structure and complexity in modern chemical processes, it is difficult to describe the system operating status and fault conditions via traditional approaches. In addition, there are obstacles to fault detection research in complex chemical processes due to the characteristics of the equipment hardware used in chemical systems, data collection and handling, strong internal correlations in chemical systems, factors affecting transmission, and randomness and cascade in process failures. Therefore, this paper presents a fault detection method based on horizontal visibility graph (HVG) analysis‐integrated complex networks. The data for each variable in the system are regarded as a time series, each time series is modelled into a network by the horizontal visibility algorithm, and each single‐layer network corresponding to a time series is abstracted as a node. Meanwhile, the correlation between two single‐layer networks is used to characterize the correlation between the corresponding nodes. Moreover, a complex network structure representing the chemical system can be constructed from the correlations. In addition, according to the correlation ratio matrix obtained from the fault state and the normal state, the variance of each node determines the faulty node. Finally, we verify the validity and the effectiveness of the proposed method by applying it to the Tennessee Eastman (TE) process. [ABSTRACT FROM AUTHOR]

In water resources applications (e.g., streamflow, rainfall‐runoff, urban water demand [UWD], etc.), ensemble member selection and ensemble member weighting are two difficult yet important tasks in the development of ensemble forecasting systems. We propose and test a stochastic data‐driven ensemble forecasting framework that uses archived deterministic forecasts as input and results in probabilistic water resources forecasts. In addition to input data and (ensemble) model output uncertainty, the proposed approach integrates both ensemble member selection and weighting uncertainties, using input variable selection and data‐driven methods, respectively. Therefore, it does not require one to perform ensemble member selection and weighting separately. We applied the proposed forecasting framework to a previous real‐world case study in Montreal, Canada, to forecast daily UWD at multiple lead times. Using wavelet‐based forecasts as input data, we develop the Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, the first multiwavelet ensemble stochastic forecasting framework that produces probabilistic forecasts. For the considered case study, several variants of Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, produced using different input variable selection methods (partial correlation input selection and Edgeworth Approximations‐based conditional mutual information) and data‐driven models (multiple linear regression, extreme learning machines, and second‐order Volterra series models), are shown to outperform wavelet‐ and nonwavelet‐based benchmarks, especially during a heat wave (first time studied in the UWD forecasting literature). Key Points: A stochastic data‐driven ensemble framework is introduced for probabilistic water resources forecastingEnsemble member selection and weighting uncertainties are explicitly considered alongside input data and model output uncertaintiesWavelet‐based model outputs are used as input to the framework for an urban water demand forecasting study outperforming benchmark methods [ABSTRACT FROM AUTHOR]

Coastal river deltas are complex and dynamic ecosystems where vegetation plays an essential role in influencing, as well as being influenced by, physical processes, creating ecogeomorphic feedbacks between vegetation canopy characteristics and topography. However, this feedback is poorly understood. This knowledge gap is due to difficulties in detecting and quantifying the interactions that define the feedback. Emerging technology and data analysis techniques like transfer entropy have made it possible to overcome former difficulties associated with sampling constraints and delineate bidirectional feedback within many vegetation classes at the delta scale. Here the transfer entropy analysis was consistent with widespread understanding of marsh zonation, yet produced additional insight into which vegetation classes specifically had a dominant impact on topographic change. Ecogeomorphic feedback was resolvable only within native vegetation classes (Nelumbo and Polygonum) that occur over low to moderate elevations within the Wax Lake Delta Louisiana. In contrast, nonnative vegetation classes (Colocasia and Eichhornia) were not as effective at accreting sediment as native classes. The transfer entropy analysis suggests that different vegetation communities play functionally different roles in landscape evolution that should be differentiated in ecogeomorphic models. Within such models, it would be most imperative to resolve detailed flow characteristics at lower to low‐middle island elevations. Furthermore, within elevation zones, it is likely important to differentiate between the roles of multiple vegetation communities rather than treating the entire elevation zone as a single ecogeomorphic entity. Key Points: We use a data‐driven approach to detect the information flow between topography and surrogate biovolume for seven dominant vegetation types in Wax Lake DeltaAlong deltaic elevation gradients from low to high, vegetation shifts from forcing future changes in elevation, to exhibiting feedback with elevation, to responding to changes in elevationNonnative vegetation (Colocasia and Eichhornia) is not as effective at accreting sediment as native vegetation in the Wax Lake Delta [ABSTRACT FROM AUTHOR]

As societal dependence on transionospheric radio signals grows, space weather impact on these signals becomes increasingly important yet our understanding of the effects remains inadequate. This challenge is particularly acute at high latitudes where the effects of space weather are most direct and no reliable predictive capability exists. We take advantage of a large volume of data from Global Navigation Satellite Systems (GNSS) signals, increasingly sophisticated tools for data‐driven discovery, and a machine learning algorithm known as the support vector machine (SVM) to develop a novel predictive model for high‐latitude ionospheric phase scintillation. This work, to our knowledge, represents the first time an SVM model has been created to predict high‐latitude phase scintillation. We use the true skill score to evaluate the SVM model and to establish a benchmark for high‐latitude ionospheric phase scintillation prediction. The SVM model significantly outperforms persistence (i.e., current and future scintillation are identical), doubling the predictive skill according to the true skill score for a 1‐hr lead time. For a 3‐hr lead time, persistence is comparable to a random chance prediction, suggesting that the memory of the ionosphere in terms of high‐latitude plasma irregularities is on the order of, or shorter than, a few hours. The SVM model predictive skill only slightly decreases between the 1‐ and 3‐hr predictive tasks, pointing to the potential of this method. Our findings can serve as a foundation on which to evaluate future predictive models, a critical development toward the resolution of space weather impact on transionospheric radio signals. Plain Language Summary: Society is increasingly dependent on radio signals, particularly those from the Global Navigation Satellite Systems (GNSS), and the technologies (e.g., navigation and financial transactions) that they enable. The integrity and reliability of these signals is threatened by their travel from the GNSS satellites to the ground, which includes passage through a charged region between 100 and 1,000 km known as the ionosphere. Disturbances to the ionosphere from solar energy, or space weather, cause variations in GNSS signals that adversely affect the dependent systems and technologies. Currently, the effect of the ionosphere on these signals cannot be reliably predicted, and the challenge is particularly important at latitudes above 45° where space weather impacts are most direct. We have compiled a large volume of data from the regions important to space weather (i.e., from the Sun to the Earth) to develop a novel machine learning model capable of skillfully predicting disruptions to GNSS signals at high latitudes. To our knowledge, this model is the first of its kind. We find that the new model is capable of more accurate predictions than current methods and position this model as a benchmark on which future predictive models can be measured. Key Points: Support vector machine yields predictive performance roughly double that of persistence; the benefit increases with longer prediction timesElectron precipitation information is critical to the prediction of high‐latitude phase scintillationA benchmark is established for high‐latitude ionospheric phase scintillation using the robust true skill score (TSS) metric [ABSTRACT FROM AUTHOR]

Summary: The possibility of mismatch between prior uncertainty modeling assumptions and reality is a problem both for robust control and for model‐based adaptive control algorithms that aim to use real‐time data to adaptively identify and correct such problems. Mismatches between prior model assumptions may fool adaptive algorithms intended to improve robustness into persistently preferring destabilizing controllers over stabilizing ones even when the instability is patently obvious to the eyes of the most casual observer. To eliminate all possibility of being thusly fooled, the assumption‐free unfalsified control concept was introduced in the early 1990s and has since been developed to the point where it now provides a unifying overarching theoretical framework for understanding the relationships, benefits, and weakness of various adaptive control methods. We review how the theory allows one to parsimoniously sift the individual elements of accumulating evidence and experimental data to determine precisely which of the elements falsify a given performance level and very briefly discuss recent research on cost‐detectable fading‐memory cost functions for time‐varying plants. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Purpose: Respiratory gating has been used in PET imaging to reduce the amount of image blurring caused by patient motion. Optimal binning is an approach for using the motion‐characterized data by binning it into a single, easy to understand/use, optimal bin. To date, optimal binning protocols have utilized externally driven motion characterization strategies that have been tuned with population‐derived assumptions and parameters. In this work, we are proposing a new strategy with which to characterize motion directly from a patient's gated scan, and use that signal to create a patient/instance‐specific optimal bin image. Methods: Two hundred and nineteen phase‐gated FDG PET scans, acquired using data‐driven gating as described previously, were used as the input for this study. For each scan, a phase‐amplitude motion characterization was generated and normalized using principle component analysis. A patient‐specific “optimal bin” window was derived using this characterization, via methods that mirror traditional optimal window binning strategies. The resulting optimal bin images were validated by correlating quantitative and qualitative measurements in the population of PET scans. Results: In 53% (n = 115) of the image population, the optimal bin was determined to include 100% of the image statistics. In the remaining images, the optimal binning windows averaged 60% of the statistics and ranged between 20% and 90%. Tuning the algorithm, through a single acceptance window parameter, allowed for adjustments of the algorithm's performance in the population toward conservation of motion or reduced noise—enabling users to incorporate their definition of optimal. In the population of images that were deemed appropriate for segregation, average lesion SUV max were 7.9, 8.5, and 9.0 for nongated images, optimal bin, and gated images, respectively. The Pearson correlation of FWHM measurements between optimal bin images and gated images were better than with nongated images, 0.89 and 0.85, respectively. Generally, optimal bin images had better resolution than the nongated images and better noise characteristics than the gated images. Discussion: We extended the concept of optimal binning to a data‐driven form, updating a traditionally one‐size‐fits‐all approach to a conformal one that supports adaptive imaging. This automated strategy was implemented easily within a large population and encapsulated motion information in an easy to use 3D image. Its simplicity and practicality may make this, or similar approaches ideal for use in clinical settings. [ABSTRACT FROM AUTHOR]

A new type of modeling method is put forward based on pattern recognition (PR) technology for some industrial production processes. The proposed method is a pure data-driven modeling method since the model is independent of the controlled plant, and it is based on the measured input and output (I/O) data of the controlled plant in a closed loop. Different from the traditional modeling method, the system dynamics is described by I/O classes, which are obtained from raw I/O data through partitioning of the data space respectively and I/O orders of the model resort to the conditional entropy. The covering algorithm based pattern classification (PC) is used to establish the mapping between input and output of the proposed model in metric spaces. The experimental results illustrate the feasibility of the modeling method. [ABSTRACT FROM AUTHOR]

Failures caused by acid erosion corrosion occur frequently in air cooler systems because of the use of increasingly low-quality crude oil with high sulfur, acid and chlorine content. The pH value is one of the key parameters used to evaluate the corrosion risk of an air cooler. However, this value is difficult to measure online because of the severe measurement environment, which includes high temperatures, high pressures and corrosion risks. In this paper, a pH soft-sensor modelling method for an air cooler system in a refinery production processing is developed. Using modelling and simulation in Aspen together with site data, the correlated influence factors are determined first. Then, a soft-sensor model based on a fast search pruned-extreme learning machine is proposed in which the pruning problem of hidden-layer nodes with random weights is solved by adopting the fast search density peak clustering algorithm. The proposed fast search pruned-extreme learning machine can improve the model's prediction performance by pruning redundant hidden layer nodes with a simpler structure. The feasibility and efficiency of the developed method are demonstrated by the results in the form of benchmark data and real air cooler system data. [ABSTRACT FROM AUTHOR]

Legacy effects of land cover/use on carbon fluxes require considering both present and past land cover/use change dynamics. To assess past land use dynamics, model-based reconstructions of historic land cover/use are needed. Most historic reconstructions consider only the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). Studies about the impact of gross and net land change accounting methods on the carbon balance are still lacking. In this study, we assessed historic changes in carbon in soils for five land cover/use types and of carbon in above-ground biomass of forests. The assessment focused on Europe for the period 1950 to 2010 with decadal time steps at 1-km spatial resolution using a bookkeeping approach. To assess the implications of gross land change data, we also used net land changes for comparison. Main contributors to carbon sequestration between 1950 and 2010 were afforestation and cropland abandonment leading to 14.6 PgC sequestered carbon (of which 7.6 PgC was in forest biomass). Sequestration was highest for old-growth forest areas. A sequestration dip was reached during the 1970s due to changes in forest management practices. Main contributors to carbon emissions were deforestation (1.7 PgC) and stable cropland areas on peaty soils (0.8 PgC). In total, net fluxes summed up to 203 TgC yr−1 (98 TgC yr−1 in forest biomass and 105 TgC yr−1 in soils). For areas that were subject to land changes in both reconstructions (35% of total area), the differences in carbon fluxes were about 68%. Overall for Europe the difference between accounting for either gross or net land changes led to 7% difference (up to 11% per decade) in carbon fluxes with systematically higher fluxes for gross land change data. [ABSTRACT FROM AUTHOR]

We propose a data-driven method to realize high-quality detailed hair animations in interactive applications like games. By devising an error metric method to evaluate hair animation similarities, we take hair features into consideration as much as possible. We also propose a novel database construction algorithm based on Secondary Motion Graph. Our algorithm can improve the efficiency of such graphs to reduce redundant data and also achieves visually smooth connection of two animation clips while taking into consideration their future motions. The costs for the run-time process using our Secondary Motion Graph are relatively low, allowing real-time interactive operations. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a new data-driven fault-detection method is proposed. This method is based on a new nonparametric system identification approach, which constitutes the principal contribution to this work. The fault-detection method is a parametric model-free approach that can be applied to nonlinear systems that work at various operating points. Not only can the fault-detection process be applied to the steady state of each operating point, but it can also be applied to the transient state resulting from a change in the operating point. In order to detect faults, the proposed method uses an interval predictor based on bounded-error techniques. The utilization of techniques based on bounded error enables system uncertainties to be included in an explicit way. This in turn leads to the possibility of obtaining interval predictions of the behaviour of the system, which include information on the reliability of the prediction itself. In order to show the effectiveness of the fault-detection method, two examples are presented: in the form of a simulated process (counter-flow shell-and-tube heat-exchanger system) and an example of a real application (two-tanks system). A comparison with two fault-detection methods has also been included. [ABSTRACT FROM AUTHOR]

Background The chronic nature of MDD has been acknowledged as one of the key determinants of the burden associated with depression. Unfortunately, so far described prognostic factors have been inconsistent, possibly due to used course outcomes that are often based on arbitrary criteria/cut-offs. Therefore, the aim of the current study was to use data-driven trajectory groups based on closely spaced weekly severity ratings, as outcomes in prognostic research. Methods The sample consisted of primary care patients with MDD ( n = 153), who were followed up for a year with 52 consecutive weekly ratings of the nine DSM-IV MDD criterion symptoms. Growth Mixture Modeling (GMM) was used to reduce the interpersonal growth variation to an optimal set of clinically interpretable trajectory groups. Next, baseline course predictors were investigated and the prognostic (added) value of course-group membership was investigated for clinical outcomes after 1, 2, and 3 years. Results GMM resulted in four trajectory groups: 'early remission' (40.2%), 'late remission' (9.8%), 'remission and recurrence' (17.0%), and 'chronic' (33.0%). Multivariate predictors of 'chronic' group membership were a prior suicide attempt, comorbid dysthymia, and lower levels of somatic depressive symptoms. Group membership predicted differences in depression severity and/or quality of life after 1, 2, and 3 years. Conclusions The used data-driven approach provided a parsimonious and clinically informative way to describe course variation across MDD patients. Using the trajectory groups to investigate prognostic factors of MDD provided insight in potentially useful prognostic factors. Importantly, trajectory-group membership was itself a strong predictor of future mental well-being. [ABSTRACT FROM AUTHOR]

Subject motion during scan is a major source of artifacts in MR examinations. Prospective motion correction is a promising technique that tracks subject motion and adjusts the imaging volume in real time; however, additional retrospective correction may be necessary to achieve robust image quality and compatibility with other imaging options. Real-time realignment of the imaging volume by prospective motion correction changes the coil sensitivity weighting and the field inhomogeneity relative to the imaging volume. This can pose image reconstruction problems with parallel imaging and partial Fourier imaging, which rely on coil sensitivity and image phase information, respectively. This work presents a practical method for reconstructing images acquired using prospective motion correction with parallel imaging and/or partial Fourier imaging. Our proposed approach is data driven and noniterative; data are binned into several position bins based on motion measurements made during the prospective motion correction acquisition and the data in each bin are processed through intrabin operations such as parallel imaging reconstruction (in case of undersampling), phase correction, and coil combination before combination of the position bins. We demonstrate the effectiveness of our technique through simulation studies and in vivo experiments using a prospectively motion-corrected three-dimensional fast spin echo sequence. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]