Turbulent kinetic energy in the energy balance of a solar flare

The energy released in solar flares derives from a reconfiguration of magnetic fields to a lower energy state, and is manifested in several forms, including bulk kinetic energy of the coronal mass ejection, acceleration of electrons and ions, and enhanced thermal energy that is ultimately radiated away across the electromagnetic spectrum from optical to X-rays. Using an unprecedented set of coordinated observations, from a suite of instruments, we here report on a hitherto largely overlooked energy component -- the kinetic energy associated with small-scale turbulent mass motions. We show that the spatial location of, and timing of the peak in, turbulent kinetic energy together provide persuasive evidence that turbulent energy may play a key role in the transfer of energy in solar flares. Although the kinetic energy of turbulent motions accounts, at any given time, for only \sim (0.5-1)\% of the energy released, its relatively rapid (\sim1-10~s) energization and dissipation causes the associated throughput of energy (i.e., power) to rival that of major components of the released energy in solar flares, and thus presumably in other astrophysical acceleration sites

Prediction of blast loading in an internal environment using artificial neural networks

Explosive loading in a confined internal environment is highly complex and is driven by nonlinear physical processes associated with reflection and coalescence of multiple shock fronts. Prediction of this loading is not currently feasible using simple tools, and instead specialist computational software or practical testing is required, which are impractical for situations with a wide range of input variables. There is a need to develop a tool which balances the accuracy of experiments or physics-based numerical schemes with the simplicity and low computational cost of an engineering-level predictive approach. Artificial neural networks (ANNs) are formed of a collection of neurons that process information via a series of connections. When fully trained, ANNs are capable of replicating and generalising multi-parameter, high-complexity problems and are able to generate new predictions for unseen problems (within the bounds of the training variables). This article presents the development and rigorous testing of an ANN to predict blast loading in a confined internal environment. The ANN was trained using validated numerical modelling data, and key parameters relating to formulation of the training data and network structure were critically analysed in order to maximise the predictive capability of the network. The developed network was generally able to predict specific impulses to within 10% of the numerical data: 90% of specific impulses in the unseen testing data, and between 81% and 87% of specific impulses for data from four additional unseen test models, were predicted to this accuracy. The network was highly capable of generalising in areas adjacent to reflecting surfaces and as those close to ambient outflow boundaries. It is shown that ANNs are highly suited to modelling blast loading in a confined internal environment, with significant improvements in accuracy achievable if a robust, well distributed training dataset is used with a network structure that is tailored to the problem being solved

The Direction-encoded Neural Network: A machine learning approach to rapidly predict blast loading in obstructed environments

Machine learning (ML) methods are becoming more prominent in blast engineering applications, with their adaptability to new scenarios and rapid computation times providing key benefits when compared to empirical methods and physics-based approaches, respectively. However, ML approaches commonly used for blast analyses are regularly provided with inputs relating to domain-specific parameters, restricting their use beyond the initial problem set and reducing their generality. This article presents the ‘Direction-encoded Neural Network’ (DeNN); a novel way to structure an Artificial Neural Network (ANN) to predict blast loading in obstructed environments. Each point of interest (POI) is represented by the proximity to its surroundings and the shortest travel path of the blast wave in order to prime the network to learn the underlying physics of the problem. Furthermore, a bespoke wave reflection equation creates a zone of influence around each point so that obstacles are only captured in the network’s inputs if they would alter the path of the wave. It is shown that the DeNN can predict peak overpressures with mean absolute errors ∼5 kPa for unseen, complex domains of any shape or size, when compared to the results from physics-based numerical models with ∼30 times the solution time of the DeNN. The network is used to develop maps of likely human injury following detonation of a high explosive in an internal environment, with eardrum rupture levels being correctly predicted for over 93% of unseen test points. It is therefore highly suited for use in probabilistic, risk-based analyses which are currently impractical due to excessive computational cost

Clinical-diagnostic and therapeutic features of acute appendicitis in children

Acute appendicitis is the most frequent disease in childhood. It requires an emergency surgical intervention and has a number of features in comparison with adults. It is more severe, and diagnostics is more complex. This is due primarily to the large number of diseases occurring with pseudo abdominal syndrome, difficulties of inspection and revealing of local symptoms particularly in young children. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3658

Inflationary potentials in DBI models

We study DBI inflation based upon a general model characterized by a power-law flow parameter $\epsilon(\phi)\propto\phi^{\alpha}$ and speed of sound $c_s(\phi)\propto\phi^{\beta}$, where $\alpha$ and $\beta$ are constants. We show that in the slow-roll limit this general model gives rise to distinct inflationary classes according to the relation between $\alpha$ and $\beta$ and to the time evolution of the inflaton field, each one corresponding to a specific potential; in particular, we find that the well-known canonical polynomial (large- and small-field), hybrid and exponential potentials also arise in this non-canonical model. We find that these non-canonical classes have the same physical features as their canonical analogs, except for the fact that the inflaton field evolves with varying speed of sound; also, we show that a broad class of canonical and D-brane inflation models are particular cases of this general non-canonical model. Next, we compare the predictions of large-field polynomial models with the current observational data, showing that models with low speed of sound have red-tilted scalar spectrum with low tensor-to-scalar ratio, in good agreement with the observed values. These models also show a correlation between large non-gaussianity with low tensor amplitudes, which is a distinct signature of DBI inflation with large-field polynomial potentials.Comment: Minor changes, reference added. Version submitted to JCA

Tunneling and propagation of vacuum bubbles on dynamical backgrounds

In the context of bubble universes produced by a first-order phase transition with large nucleation rates compared to the inverse dynamical time scale of the parent bubble, we extend the usual analysis to non-vacuum backgrounds. In particular, we provide semi-analytic and numerical results for the modified nucleation rate in FLRW backgrounds, as well as a parameter study of bubble walls propagating into inhomogeneous (LTB) or FLRW spacetimes, both in the thin-wall approximation. We show that in our model, matter in the background often prevents bubbles from successful expansion and forces them to collapse. For cases where they do expand, we give arguments why the effects on the interior spacetime are small for a wide range of reasonable parameters and discuss the limitations of the employed approximations.Comment: 29 pages, 8 figures, typos corrected, matches published versio

Non-parametric characterization of blast loads

Mathematical analysis of blast pressures has typically involved the empirical fitting of parametric models, which assumes a specific function shape. In reality, the true shape of the blast pressure is unknown and may lack a parametric form, particularly in the negative phase following arrival of the secondary shock. In this work, we develop a non-parametric (NP) representation that makes few assumptions and relies on the observed experimental data to fit a unique and previously unknown model. This differs from traditional approaches by not arbitrarily selecting a single, restrictive class of functions and estimating a minimal set of parameters, but rather estimating the underlying function class for which the blast pressure is generated; learning the model directly from the observed data. The method was applied to experimental blast measurements and the NP estimates matched the experimental data with a high degree of accuracy, both qualitatively and quantitatively. The NP approach was shown to significantly outperform other commonly used approaches, near-perfectly track the entire pressure and specific impulse history and predicting experimental peak specific impulse to within ±0.5% in all cases (compared to ±5.0% for a trained artificial neural network (ANN) and ±7.5% for the UFC semi-empirical approach). The NP approach predicts experimental net specific impulses (positive and negative phases combined) with a maximum variation of 2.7%, compared to maximum variations of −116% and 55% for the UFC and ANN approaches, respectively. Since the framework is probabilistic in nature, it can naturally account for random noise in sensor measurements, which are typically more pronounced in blast experiments than many other machine learning applications

A branching algorithm to reduce computational time of batch models: application for blast analyses

Numerical analysis is increasingly used for batch modelling runs, with each individual model possessing a unique combination of input parameters sampled from a range of potential values. Whilst such an approach can help to develop a comprehensive understanding of the inherent unpredictability and variability of explosive events, or populate training/validation data sets for machine learning approaches, the associated computational expense is relatively high. Furthermore, any given model may share a number of common solution steps with other models in the batch, and simulating all models from birth to termination may result in large amounts of repetition. This paper presents a new branching algorithm that ensures calculation steps are only computed once by identifying when the parameter fields of each model in the batch becomes unique. This enables informed data mapping to take place, leading to a reduction in the required computation time. The branching algorithm is explained using a conceptual walk-through for a batch of 9 models, featuring a blast load acting on a structural panel in 2D. By eliminating repeat steps, approximately 50% of the run time can be saved. This is followed by the development and use of the algorithm in 3D for a practical application involving 20 complex containment structure models. In this instance, a ∼20% reduction in computational costs is achieved

Clinical-diagnostic and therapeutic features of acute appendicitis in children

Acute appendicitis is the most frequent disease in childhood. It requires an emergency surgical intervention and has a number of features in comparison with adults. It is more severe, and diagnostics is more complex. This is due primarily to the large number of diseases occurring with pseudo abdominal syndrome, difficulties of inspection and revealing of local symptoms particularly in young children. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3658

Incidence of end-stage renal disease after heart transplantation and effect of its treatment on survival

Aims: Many heart transplant recipients will develop end-stage renal disease in the post-operative course. The aim of this study was to identify the long-term incidence of end-stage renal disease, determine its risk factors, and investigate what subsequent therapy was associated with the best survival. Methods and results: A retrospective, single-centre study was performed in all adult heart transplant patients from 1984 to 2016. Risk factors for end-stage renal disease were analysed by means of multivariable regression analysis and survival by means of Kaplan–Meier. Of 685 heart transplant recipients, 71 were excluded: 64 were under 18 years of age and seven were re-transplantations. During a median follow-up of 8.6 years, 121 (19.7%) patients developed end-stage renal disease: 22 received conservative therapy, 80 were treated with dialysis (46 haemodialysis and 34 peritoneal dialysis), and 19 received a kidney transplant. Development of end-stage renal disease (examined as a time-dependent variable) inferred a hazard ratio of 6.45 (95% confidence interval 4.87–8.54, P < 0.001) for mortality. Tacrolimus-based therapy decreased, and acute kidney injury requiring renal replacement therapy increased the risk for end-stage renal disease development (hazard ratio 0.40, 95% confidence interval 0.26–0.62, P < 0.001, and hazard ratio 4.18, 95% confidence interval 2.30–7.59, P < 0.001, respectively). Kidney transplantation was associated with the best median survival compared with dialysis or conservative therapy: 6.4 vs. 2.2 vs. 0.3 years (P < 0.0001), respectively, after end-stage renal disease development. Conclusions: End-stage renal disease is a frequent complication after heart transplant and is associated with poor survival. Kidney transplantation resulted in the longest survival of patients with end-stage renal disease