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Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across weather forecasting, disaster preparedness, and urban planning, but researchers lack the tools to run these models effectively and efficiently, limiting progress. One of the current challenges is to assimilate observations in highly non-linear dynamical systems, as the practical need is often to detect abrupt changes. We have developed a software platform to improve the use of real-time data in non-linear system representations where non-Gaussianity limits the applicability of data assimilation algorithms such as the ensemble Kalman filter and variational methods. Particle-filter-based data assimilation algorithms have been implemented within a user-friendly open-source software platform in Julia – ParticleDA.jl. To ensure the applicability of the developed platform in realistic scenarios, emphasis has been placed on numerical efficiency and scalability on high-performance computing systems. Furthermore, the platform has been developed to be forward-model agnostic, ensuring that it is applicable to a wide range of modelling settings, for instance unstructured and non-uniform meshes in the spatial domain or even state spaces that are not spatially organized. Applications to tsunami and numerical weather prediction demonstrate the computational benefits and ease of using the high-level Julia interface with the package to perform filtering in a variety of complex models. [ABSTRACT FROM AUTHOR]

Digital twins of physical and human systems informed by real-time data, are becoming ubiquitous across weather forecasting, disaster preparedness, and urban planning, but researchers lack the tools to run these models effectively and efficiently, limiting progress. One of the current challenges is to assimilate observations in highly nonlinear dynamical systems, as the practical need is often to detect abrupt changes. We developed a software platform to improve the use of real-time data in highly nonlinear system representations where non-Gaussianity prevents the use of more standard Data Assimilation. Opti- mal Particle filtering data assimilation (DA) techniques have been implemented within an user-friendly open source software platform in Julia - ParticleDA.jl. To ensure the applicability of the developed platform in realistic scenarios, emphasis has been placed on numerical efficiency, scalability and optimisation for high performance computing frameworks. Furthermore, the platform has been developed to be forward model agnostic, ensuring that it is applicable to a wide range of modelling settings, for instance unstructured and non-uniform meshes in the spatial domain or even state spaces that are not spatially organised. Applications to tsunami and numerical weather prediction demonstrate the computational benefits in terms of lower errors, lower computational costs (due to ensemble size and the algorithm's overheads being minimised) and versatility thanks to flexible I/O in a high level language Julia. [ABSTRACT FROM AUTHOR]

To review the evidence around self-management interventions used to improve mobility post-stroke. An integrative review was carried out. Eight databases were searched from 1992 to July 2021 using keywords based on the PICOS strategy. Two reviewers independently screened and extracted the relevant data. Quality of studies was assessed and a quantitatively led narrative synthesis of data, supported by qualitative evidence, was then conducted. Twenty-four studies with 823 participants were reviewed. Self-management strategies such as patient education, providing information, goal setting, problem-solving, action planning, self-monitoring, and social support were integrated with rehabilitation therapy to improve mobility post-stroke. The reviewed studies showed improvements in functional mobility and walking ability, self-efficacy, participation in physical activity, and quality of life to various extents. Participants in qualitative studies considered the self-management interventions as a valuable addition to their therapy and perceived the improvement in their mobility following them. There is some evidence that self-management interventions help to improve mobility outcomes post-stroke. Heterogeneity of data in the studies made meta-analysis impossible. Most of the identified studies examined the feasibility and fidelity of the interventions and further research is warranted to examine the efficacy of these interventions to improve functional mobility post-stroke. Self-management interventions can improve mobility-related outcomes, which are considered a priority goal for many stroke survivors. Survivors valued their participation in self-management integrated care programmes and linked that to the perceived improvement in their rehabilitation outcomes. Self-management interventions such as patient education, goal-setting, self-monitoring, and professionals/carers support have been found to improve mobility outcomes for stroke survivors. The outcomes that benefited to a slight extent using self-management were; functional mobility, walking ability (speed, distance, and endurance), and a number of steps per day. [ABSTRACT FROM AUTHOR]

In the present era of large-scale surveys, big data present new challenges to the discovery process for anomalous data. Such data can be indicative of systematic errors, extreme (or rare) forms of known phenomena, or most interestingly, truly novel phenomena that exhibit as-of-yet unobserved behaviours. In this work, we present an outlier scoring methodology to identify and characterize the most promising unusual sources to facilitate discoveries of such anomalous data. We have developed a data mining method based on k -nearest neighbour distance in feature space to efficiently identify the most anomalous light curves. We test variations of this method including using principal components of the feature space, removing select features, the effect of the choice of k , and scoring to subset samples. We evaluate the performance of our scoring on known object classes and find that our scoring consistently scores rare (<1000) object classes higher than common classes. We have applied scoring to all long cadence light curves of Quarters 1–17 of Kepler's prime mission and present outlier scores for all 2.8 million light curves for the roughly 200k objects. [ABSTRACT FROM AUTHOR]

Advances in astronomy are often driven by serendipitous discoveries. As survey astronomy continues to grow, the size and complexity of astronomical data bases will increase, and the ability of astronomers to manually scour data and make such discoveries decreases. In this work, we introduce a machine learning-based method to identify anomalies in large data sets to facilitate such discoveries, and apply this method to long cadence light curves from NASA's Kepler Mission. Our method clusters data based on density, identifying anomalies as data that lie outside of dense regions. This work serves as a proof-of-concept case study and we test our method on four quarters of the Kepler long cadence light curves. We use Kepler's most notorious anomaly, Boyajian's star (KIC 8462852), as a rare 'ground truth' for testing outlier identification to verify that objects of genuine scientific interest are included among the identified anomalies. We evaluate the method's ability to identify known anomalies by identifying unusual behaviour in Boyajian's star; we report the full list of identified anomalies for these quarters, and present a sample subset of identified outliers that includes unusual phenomena, objects that are rare in the Kepler field, and data artefacts. By identifying <4 per cent of each quarter as outlying data, we demonstrate that this anomaly detection method can create a more targeted approach in searching for rare and novel phenomena. [ABSTRACT FROM AUTHOR]

In this paper, we present the VOLNA-OP2 tsunami model and implementation; a finite-volume non-linear shallow-water equation (NSWE) solver built on the OP2 domain-specific language (DSL) for unstructured mesh computations. VOLNA-OP2 is unique among tsunami solvers in its support for several high-performance computing platforms: central processing units (CPUs), the Intel Xeon Phi, and graphics processing units (GPUs). This is achieved in a way that the scientific code is kept separate from various parallel implementations, enabling easy maintainability. It has already been used in production for several years; here we discuss how it can be integrated into various workflows, such as a statistical emulator. The scalability of the code is demonstrated on three supercomputers, built with classical Xeon CPUs, the Intel Xeon Phi, and NVIDIA P100 GPUs. VOLNA-OP2 shows an ability to deliver productivity as well as performance and portability to its users across a number of platforms. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease worldwide. While it is well-accepted that inflammation is central to NAFLD pathogenesis, the immune pathway(s) orchestrating disease progression are poorly defined. Notably, IL-17RA signaling, via IL-17A, plays an important role in obesity-driven NAFLD pathogenesis. However, the role of the IL-17F, another IL-17RA ligand, in NAFLD pathogenesis has not been examined. Further, the cell types expressing IL-17RA and producing IL-17RA ligands in the pathogenesis of NAFLD have not been defined. Here, IL-17RA-/-, IL-17A-/-, IL-17F-/- and wild-type (WT) mice were fed either standard chow diet or methionine and choline deficient diet (MCDD)—a diet known to induce steatosis and hepatic inflammation through beta-oxidation dysfunction—and hepatic inflammation and NAFLD progression were subsequently quantified. MCDD feeding augmented hepatic IL-17RA expression and significantly increased hepatic infiltration of macrophages and IL-17A and IL-17F producing CD4+ and CD8+ T cells in WT mice. In contrast, IL-17RA-/-, IL-17A-/-, and IL-17F-/- mice, despite increased steatosis, exhibited significant protection from hepatocellular damage compared to WT controls. Protection from hepatocellular damage correlated with decreased levels of hepatic T-cell and macrophage infiltration and decreased expression of inflammatory mediators associated with NAFLD. In sum, our results indicate that the IL-17 axis also plays a role in a MCDD-induced model of NAFLD pathogenesis. Further, we show for the first time that IL-17F, and not only IL-17A, plays an important role in NAFLD driven inflammation. [ABSTRACT FROM AUTHOR]

The Sylvester or smallest enclosing circle problem involves finding the smallest circle enclosing a finite number of points in the plane. We consider generalized versions of the Sylvester problem in which the points are replaced by sets. Based on the log-exponential smoothing technique and Nesterov's accelerated gradient method, we present an effective numerical algorithm for solving these problems. [ABSTRACT FROM AUTHOR]

Abstract: Syngas is produced through a gasification process using variety of fossil fuels, including coal, biomass, organic waste, and refinery residual. Although, its composition may vary significantly, it generally contains CO and H2 as the dominant fuel components with varying amount of methane and diluents. Due to its wide flexibility in fuel sources and superior pollutants characteristics, the syngas is being recognized as a viable energy source worldwide, particularly for stationary power generation. There are, however, gaps in the fundamental understanding of syngas combustion and emissions, as most previous research has focused on flames burning individual fuel components such as H2 and CH4, rather than syngas mixtures. This paper reports a numerical investigation on the effects of syngas composition and diluents on the structure and emission characteristics of syngas nonpremixed flames. The counterflow syngas flames are simulated using two representative syngas mixtures, 50%H2/50%CO and 45%H2/45%CO/10%CH4 by volume, and three diluents, N2, H2O, and CO2. The effectiveness of these diluents is characterized in terms of their ability to reduce NO x in syngas flames. Results indicate that syngas nonpremixed flames are characterized by relatively high temperatures and high NO x concentrations and emission indices. The presence of methane in syngas decreases the peak flame temperature, but increases the formation of prompt NO significantly. Consequently, while the total NO formed is predominantly due to the thermal mechanism for the 50%H2/50%CO mixture, it is due to the prompt mechanism for the 45%H2/45%CO/10%CH4 mixture. For both mixtures, CO2 and H2O are more effective than N2 in reducing NO x in syngas flames. H2O is the most effective diluent on a mass basis, while CO2 is more effective than N2. The effectiveness of H2O is due to its high specific heat that decreases the thermal NO, and its ability to significantly reduce the concentration of CH radicals, which decreases the prompt NO. The presence of methane in syngas reduces the effectiveness of all three diluents. [Copyright &y& Elsevier]

This document provides a summary of a hackathon event organized by the Ministry of Justice (MoJ) and Aon, a reinsurance company, to explore the social impacts of climate change. The hackathon focused on developing consistent scenarios for future climate risk, understanding uncertainties in European windstorm modeling, and providing real-time context for extreme weather events. The teams developed tools and methodologies to assess the severity and potential impact of extreme events on insured losses. The collaboration between the MoJ, Aon, and participants from various institutions aims to further research and develop papers and grant proposals in these areas. [Extracted from the article]

Tsunamis are infrequent events that have the potential to be extremely destructive. The last major tsunami to effect the Irish coastline was the Lisbon 1755 event. That event acts as a candidate worst case scenario for hazard assessment and the impacts on the Irish Coastline are presented here. As there is no general consensus on the 1755 earthquake source, multiple sources highlighted in the literature are investigated. These sources are used to generate the initial conditions and the resultant tsunami waves are simulated with the massively parallelised Volna-OP2 finite volume tsunami code. The hazard associated with the event is captured on three gradated levels. A reduced faster than real time tsunami ensemble is produced for the North-East Atlantic on a regional level in 93 s using two Nvidia V100 GPUs. By identifying the most vulnerable sections of the Irish coastline from this regional forecast, some locally refined simulations are further carried out in a faster than real time setting. As arrival times on the coastline can be on the O (mins), these faster than real time reduced ensembles are of great benefit for tsunami warning. Volna-OP2's capabilities in this respect are clearly demonstrated here. Finally, high resolution inundation simulations, which build upon the ensemble results, are carried out. To date this study provides the best estimate of assessing the hazard associated with a Lisbon-type tsunami event for the Irish coastline. The results of the inundation mapping highlight that along the vulnerable sections of coastline, inundation is constrained to low-lying areas with maximum run-up heights of 3.4 m being found. [ABSTRACT FROM AUTHOR]

In this paper, we study characterizations of differentiability for real-valued functions based on generalized differentiation. These characterizations provide the mathematical foundation for Nesterov's smoothing techniques in infinite dimensions. As an application, we provide a simple approach to image reconstructions based on Nesterov's smoothing and algorithms for minimizing differences of convex (DC) functions that involve the ℓ 1 − ℓ 2 regularization. [ABSTRACT FROM AUTHOR]

In this paper, we develop algorithms to solve generalized Fermat-Torricelli problems with both positive and negative weights and multifacility location problems involving distances generated by Minkowski gauges. We also introduce a new model of clustering based on squared distances to convex sets. Using the Nesterov smoothing technique and an algorithm for minimizing differences of convex functions introduced by Tao and An, we develop effective algorithms for solving these problems. We demonstrate the algorithms with a variety of numerical examples. [ABSTRACT FROM AUTHOR]

This study sought to validate the Brunel Ethnic Behavior Inventory (BEBI) as a measure of two aspects of “doing” (i.e., ethnic speech and ethnic action) among the “ways of ethnicity” identified by Verkuyten. It also evaluates the construct validity of the BEBI alongside the Multigroup Ethnic Identity Measure-Revised (MEIM-R) and evaluates the BEBI and the MEIM-R (the latter of which measures exploration as “thinking” and commitment as “feeling” among ways of ethnicity) regarding criterion-related validity. Across two samples (n = 120 for Sample 1 andn = 148 for Sample 2), the BEBI and the MEIM-R performed suitably with regard to construct validity. However, the BEBI arguably performed better with regard to criterion-related validity (i.e., in predicting scores on life satisfaction, flourishing, positive and negative moods, and positive and negative emotions). Implications for validation studies on ways of ethnicity are discussed. [ABSTRACT FROM AUTHOR]

This article reports the construction of a new survey—specifically, the Brunel Ethnic Behavior Inventory (BEBI)—designed to measure ethnic speech and ethnic action as separate, yet related, aspects of individuals’ ethnic behavior. Using Tajfel’s social identity theory as a conceptual frame of reference, this study sought an answer to the research question of how many factors actually are measured by the BEBI, and tested the hypothesis that a two-factor model (i.e., Ethnic Speech and Ethnic Action as two correlated factors) would provide significantly better goodness of fit to the correlational data than would a one-factor model (i.e., Ethnic Behavior as one undifferentiated factor). Across one pilot sample (n = 101) and two main samples (n = 120 for Sample 1,n = 148 for Sample 2), the study found that not only did the BEBI measure two factors at most (i.e., Ethnic Speech and Ethnic Action) but, consistent with the hypothesis, the two-factor model yielded better goodness of fit than did the one-factor model. Implications for the conceptualization and measurement of Verkuyten’s “ways of ethnicity” are discussed. [ABSTRACT FROM AUTHOR]