Transport Algorithm Research Articles

Flow of a concentrated emulsion with surfactant through a periodic porous medium

High-resolution, long-time three-dimensional simulations are presented for slow, pressure-driven flow of a periodic emulsion of deformable drops through a dense, simple cubic array of solid spheres (one drop and one particle per periodic cell). The drops, covered with insoluble, non-diffusive surfactant, are large compared with pores, and they squeeze with high resistance, very closely coating the solids to overcome surface tension and lubrication effects. The solid volume fraction is 50 %, the emulsion concentration $c_{em}$ in the pore space is 36 % or 50 %, the drop-to-medium viscosity ratio $\lambda$ is 0.25 to 4. The contamination measure $\beta \leq 0.1$ keeps the linear surfactant model (assumed in most of the work) physically relevant. The boundary-integral solution requires extreme resolutions (tens of thousands of boundary elements per surface) achieved by multipole acceleration with special desingularizations, combined with flow-biased surfactant transport algorithms for numerical stability. The time-periodic regime is typically attained after a few squeezing cycles; the motion period is used in the extrapolation scheme to evaluate critical capillary numbers $Ca_{crit}$ demarcating squeezing from trapping. Due to Marangoni stresses, even light ( $\beta =0.05$ ) to moderate ( $\beta =0.1$ ) contaminations significantly reduce the average drop-phase migration velocity (up to 2.8 times, compared with clean drops), especially at small $\lambda =0.25$ . In contrast, $Ca_{crit}$ is weakly sensitive to contamination and levels off completely at $\beta =0.05$ . At $\lambda =0.25$ and $c_{em}=0.36$ , the average drop-phase velocities are much different for lightly and moderately contaminated emulsions, except for near-critical squeezing when they become the same. Nonlinear surfactant models (Langmuir, Frumkin) are used to validate the linear model.

Evaluating approximate flavor instability metrics in neutron star mergers

Neutrinos can rapidly change flavor in the inner dense regions of core-collapse supernovae and neutron star mergers due to the neutrino fast flavor instability. If the amount of flavor transformation is significant, the FFI could significantly affect how supernovae explode and how supernovae and mergers enrich the universe with heavy elements. Since many state of the art supernova and merger simulations rely on neutrino transport algorithms based on angular moments of the radiation field, there is incomplete information with which to determine if the distributions are unstable to the FFI. In this work we test the performance of several proposed moment-based instability tests in the literature. We perform time-independent general relativistic neutrino transport on a snapshot of a 3D neutron star merger simulation to generate reasonable neutrino distributions and check where each of these criteria correctly predict instability. In addition, we offer a new ``maximum entropy'' instability test that is somewhat more complex, but offers more detailed (though still approximate) estimates of ELN crossing width and depth. We find that this maximum entropy test and the resonant trajectory test are particularly accurate at predicting instability in this snapshot, though all tests predict instability where significant flavor transformation is most likely.

Retraction Note: Multimodal transport path optimization model and algorithm considering carbon emission multitask

Retraction Note: Multimodal transport path optimization model and algorithm considering carbon emission multitask

Impact of Advection Schemes on Tracer Interrelationships in Large-Eddy Simulations of Deep Convection

Abstract This study investigates the preservation of tracer interrelationships during advection in large-eddy simulations of an idealized deep convective cloud, which is particularly relevant to chemistry, aerosol, and cloud microphysics models. Employing the Cloud Model 1, advection is represented using third-, fifth-, and seventh-order weighted essentially non-oscillatory schemes. As a simplified analogy for cloud hydrometeors and aerosols, several inert passive tracers following linear and nonlinear relationships are initialized after the cloud reaches ∼6-km depth. Numerical mixing in the simulated turbulent convective clouds leads to significant deviations from the initial nonlinear relationships between tracers. In these simulations, a considerable fraction of the grid points where the tracers’ nonlinear relationships are altered from advection are classified as unrealistic (e.g., ∼13% for the environmental tracers on average), including errors from range-preserving unmixing and overshooting. Errors in the sum of three tracers are also relatively large, ranging between ∼1% and 16% for 5% of the grid points in and near the cloud. The magnitude of unrealistic mixing and errors in the sum of three tracers generally increase with the order of accuracy of the advection scheme. These results are consistent across model grid spacings ranging from 50 to 200 m, and across three different flow realizations for each combination of grid spacing and advection scheme tested. Tests employing a previously proposed scalar normalization procedure show substantially reduced errors in the sum of three tracers with a relatively small negative impact on other tracer relationships. This analysis, therefore, suggests efficacy of the normalization procedure when applied to turbulent three-dimensional cloud simulations. Significance Statement In nature, transporting several quantities through bulk motions of a fluid does not affect preexisting relationships between them. However, this is not always accomplished in numerical models of the atmosphere, because of intrinsic limitations in the transport algorithms employed. We aim to investigate how these errors behave in 3D realistic simulations of a cumulus cloud, where the turbulent flow constitutes a particular challenge. We show that relationships between quantities are significantly and frequently perturbed during bulk transport in the model. Moreover, our results suggest that increasing complexity of the bulk-transport algorithms (in a way that is conventionally employed for improving the representation of individual quantities) tends to worsen the representation of relationships between two or three quantities.

Error-correcting neural networks for semi-Lagrangian advection in the level-set method

We present a machine learning framework that blends image super-resolution technologies with passive, scalar transport in the level-set method. Here, we investigate whether we can compute on-the-fly, data-driven corrections to minimize numerical viscosity in the coarse-mesh evolution of an interface. The proposed system's starting point is the semi-Lagrangian formulation. And, to reduce numerical dissipation, we introduce an error-quantifying multilayer perceptron. The role of this neural network is to improve the numerically estimated surface trajectory. To do so, it processes localized level-set, velocity, and positional data in a single time frame for select vertices near the moving front. Our main contribution is thus a novel machine-learning-augmented transport algorithm that operates alongside selective redistancing and alternates with conventional advection to keep the adjusted interface trajectory smooth. Consequently, our procedure is more efficient than full-scan convolutional-based applications because it concentrates computational effort only around the free boundary. Also, we show through various tests that our strategy is effective at counteracting both numerical diffusion and mass loss. In simple advection problems, for example, our method can achieve the same precision as the baseline scheme at twice the resolution but at a fraction of the cost. Similarly, our hybrid technique can produce feasible solidification fronts for crystallization processes. On the other hand, tangential shear flows and highly deforming simulations can precipitate bias artifacts and inference deterioration. Likewise, stringent design velocity constraints can limit our solver's application to problems involving rapid interface changes. In the latter cases, we have identified several opportunities to enhance robustness without forgoing our approach's basic concept. Despite these circumstances, we believe all the above assets make our framework attractive to parallel level-set algorithms. Its appeal resides in the possibility of avoiding further mesh refinement and decreasing expensive communications between computing nodes.

Allpix2 — silicon detector Monte Carlo simulations for particle physics and beyond

Allpix2 is a versatile, open-source simulation framework for silicon pixel detectors. Its goal is to ease the implementation of detailed simulations for both single sensors and more complex setups with multiple detectors. While originally created for silicon detectors in high-energy physics, it is capable of simulating a wide range of detector types for various application scenarios, through its interface to Geant4 to describe the interaction of particles with matter, and the different algorithms for charge transport and digitization. The simulation chain is arranged with the help of intuitive configuration files and an extensible system of modules, which implement the individual simulation steps. Detailed electric field maps imported from TCAD simulations can be used to precisely model the drift behavior of the charge carriers, bringing a new level of realism to the Monte Carlo simulation of particle detectors.Recently, Allpix2 has seen major improvements to its core framework to take full advantage of multi- and many-core processor architectures for simulating events fully parallel. Furthermore, new physics models such as charge carrier recombination in silicon have been introduced, further extending the application range. This contribution provides an overview of the framework and its components, highlighting the versatility and recent developments.

Inference With Aggregate Data in Probabilistic Graphical Models: An Optimal Transport Approach

We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief propagation type algorithm over tree graphs with polynomial computational complexity as well as a global convergence guarantee. This is in contrast to previous methods that either exhibit prohibitive complexity as the population grows or do not guarantee convergence. Our method is based on optimal transport, or more specifically, multimarginal optimal transport theory. In particular, we consider an inference problem with aggregate observations, that can be seen as a structured multimarginal optimal transport problem where the cost function decomposes according to the underlying graph. Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP). We further specialize the SBP algorithm to cases associated with hidden Markov models due to their significance in control and estimation. We demonstrate the performance of our algorithm on applications such as inferring population flow from aggregate observations. We also show that in the special case where the aggregate observations are in Dirac form, our algorithm naturally reduces to the standard belief propagation algorithm.

A Comprehensive Analysis of Radiosensitization Properties of Metallic Nanoparticles in Brachytherapy of Gastric Adenocarcinoma by I-125 Seed: A Simulation Study by MCNPX and MCNP6 Codes

Purpose: In the current study, we aimed to look into the macroscopic and microscopic dose enhancement effect of metallic nanoparticles in interstitial brachytherapy of gastric adenocarcinoma by Iodine-125 source using a nano-lattice model in MCNPX (2.7) and MCNP6.1 codes. Materials and methods: Based on a nano-lattice simulation model containing a radiation source and a tumor tissue with cellular compartments loaded with 7 mg/g spherical nanoparticles, the microscopic and macroscopic levels of energy deposition by the secondary electrons was estimated. Results: The results show that the values of macroscopic DEF are higher than microscopic DEF values and the macroscopic DEF values decrease by increasing the distance from the surface of brachytherapy source. Accordingly, it could be noted that gold nanoparticles have the highest radiosensitization effect among the other nanoparticles and the related DEF value is close to the resultant DEF values for bismuth nanoparticles. Moreover, the results revealed a remarkable discrepancy between the DEF and secondary electron spectra calculated by MCNPX (2.7) and MCNP6.1 codes, which could be justified by the difference in energy cut-off and electron transport algorithms of two codes. Conclusions: According to the both MCNPX (2.7) and MCNP6.1 outputs, it could be concluded that the presence of metallic nanoparticles in the tumor tissue of gastric adenocarcinoma increases the physical effectiveness of brachytherapy by I-125 source. This study aims to provide recommendations for future preclinical studies. Actually, the results presented herein give a physical view of radiosensitization potential of different metallic nanoparticles and could be considered in design of analytical and experimental radiosensitization studies in tumor regions using various radiotherapy modalities in the presence of heavy nanomaterials.

Investigation of Monte Carlo simulations of the electron transport in external magnetic fields using Fano cavity test

PurposeMonte Carlo simulations are crucial for calculating magnetic field correction factors kB for the dosimetry in external magnetic fields. As in Monte Carlo codes the charged particle transport is performed in straight condensed history (CH) steps, the curved trajectories of these particles in the presence of external magnetic fields can only be approximated. In this study, the charged particle transport in presence of a strong magnetic field B→ was investigated using the Fano cavity test. The test was performed in an ionization chamber and a diode detector, showing how the step size restrictions must be adjusted to perform a consistent charged particle transport within all geometrical regions. MethodsMonte Carlo simulations of the charged particle transport in a magnetic field of 1.5 T were performed using the EGSnrc code system including an additional EMF-macro for the transport of charged particle in electro-magnetic fields. Detailed models of an ionization chamber and a diode detector were placed in a water phantom and irradiated with a so called Fano source, which is a monoenergetic, isotropic electron source, where the number of emitted particles is proportional to the local density. ResultsThe results of the Fano cavity test strongly depend on the energy of charged particles and the density within the given geometry. By adjusting the maximal length of the charged particle steps, it was possible to calculate the deposited dose in the investigated regions with high accuracy (<0.1%). The Fano cavity test was performed in all regions of the detailed detector models. Using the default value for the step size in the external magnetic field, the maximal deviation between Monte Carlo based and analytical dose value in the sensitive volume of the ion chamber and diode detector was 8% and 0.1%, respectively. ConclusionsThe Fano cavity test is a crucial validation method for the modeled detectors and the transport algorithms when performing Monte Carlo simulations in a strong external magnetic field. Special care should be given, when calculating dose in volumes of low density. This study has shown that the Fano cavity test is a useful method to adapt particle transport parameters for a given simulation geometry.

Practical Q-Learning-Based Route-Guidance and Vehicle Assignment for OHT Systems in Semiconductor Fabs

We introduce and analyze vehicle-allocation algorithms for overhead hoist transport (OHT) systems in semiconductor wafer-fabrication facilities (fabs). OHT is the most widely used type of automated material-handling system in fabs, comprising hundreds of vehicles delivering lots between processing machines. Timely transport unit delivery by OHT systems are critical to the efficient overall operation of a modern fab. We first describe the limitations of current OHT vehicle-allocation algorithms, and then detail an improved system that can be implemented in practical environments to manage the operation of hundreds of OHT vehicles. Our proposed system is based on reinforcement learning. In particular, it uses Q-learning-based dynamic vehicle-allocation algorithms to examine traffic conditions and then allocate an OHT vehicle to a delivery job. We propose multiple algorithms based on the Q-learning approach and compare their performance with that of conventional allocation approaches, which reveals the appropriate algorithms for use in industry. We demonstrate that our algorithms are efficient and sufficiently fast to be used in a practical large-scale setting.

Trusted Execution Environments: Applications and Organizational Challenges

A lack of trust in the providers is still a major barrier to cloud computing adoption – especially when sensitive data is involved. While current privacy-enhancing technologies, such as homomorphic encryption, can increase security, they come with a considerable performance overhead. As an alternative Trusted Executing Environment (TEE) provides trust guarantees for code execution in the cloud similar to transport layer security for data transport or advanced encryption standard algorithms for data storage. Cloud infrastructure providers like Amazon, Google, and Microsoft introduced TEEs as part of their infrastructure offerings. This review will shed light on the different technological options of TEEs, as well as give insight into organizational issues regarding their usage.

Adoption of ICRU report 90 recommendations in the Canadian Co-60 air-kerma primary standard

The Canadian air-kerma primary standard for 60Co beams is updated according to ICRU report 90 (ICRU-90) recommendations. The effect of these recommendations and a more detailed chamber model on the primary standard is investigated. Dosimetric quantities and corrections required for the realization of air kerma are calculated using the EGSnrc Monte Carlo (MC) simulation system. The validity of Spencer–Attix (SA) cavity theory as function of Δ, the secondary particle production threshold energy, is investigated to assess the accuracy of its current selection criterion. Although individual changes in some correction factors are statistically non-negligible, the overall effect amounts to only a 0.04% increase of the standard. The mean restricted mass electronic stopping power ratio graphite to air, decreases from the previous value of 1.0010 to a value of 0.9949. If this value is used with the recommended W air value of 33.97 eV, the effect on the standard is a reduction of 0.613%. When the recommended value of 33.72 eV is used instead, the change results in a 0.835% reduction in K air. Using SA cavity theory to obtain K air with a Δ value of 20 keV, corresponding to the cavity’s mean chord length, reproduces a direct MC calculation at the 0.03% level. Type B uncertainties due to uncertainty in the photon and electron cross sections for the largest corrections K wall and K comp are estimated to be 0.01% and 0.10%, respectively. No statistically significant effect on the standard is observed due to changes in the correction factors. Fano tests and comparison to single scattering calculations demonstrate EGSnrc’s electron transport algorithm accuracy to be within 0.03% relative to its own cross sections. The latter also validates the criterion for selecting Δ based on the average chord length of the chamber’s cavity. A 0.2% difference in the restricted stopping power ratio obtained in this work and the value resulting from ICRU-90 recommendations has the potential for affecting standards relying on the absolute value of W air, thus further investigation is granted. The current update results in a 0.8% decrease of the Canadian 60Co air-kerma primary standard with a reduced uncertainty of 0.2%. This change is in excellent agreement with the reported change to the BIPM 60Co air-kerma standard.

Towards practical physical-optics rendering

Physical light transport (PLT) algorithms can represent the wave nature of light globally in a scene, and are consistent with Maxwell's theory of electromagnetism. As such, they are able to reproduce the wave-interference and diffraction effects of real physical optics. However, the recent works that have proposed PLT are too expensive to apply to real-world scenes with complex geometry and materials. To address this problem, we propose a novel framework for physical light transport based on several key ideas that actually makes PLT practical for complex scenes. First, we restrict the spatial coherence shape of light to an anisotropic Gaussian and justify this restriction with general arguments based on entropy. This restriction serves to simplify the rest of the derivations, without practical loss of generality. To describe partially-coherent light, we present new rendering primitives that generalize the radiometric radiance and irradiance, and are based on the well-known Stokes parameters. We are able to represent light of arbitrary spectral content and states of polarization, and with any coherence volume and anisotropy. We also present the wave BSDF to accurately render diffractions and wave-interference effects. Furthermore, we present an approach to importance sample this wave BSDF to facilitate bi-directional path tracing, which has been previously impossible. We show good agreement with state-of-the-art methods, but unlike them we are able to render complex scenes where all the materials are new, coherence-aware physical optics materials, and with performance approaching that of "classical" rendering methods.

Learning the Physics of Particle Transport via Transformers

Particle physics simulations are the cornerstone of nuclear engineering applications. Among them radiotherapy (RT) is crucial for society, with 50% of cancer patients receiving radiation treatments. For the most precise targeting of tumors, next generation RT treatments aim for real-time correction during radiation delivery, necessitating particle transport algorithms that yield precise dose distributions in sub-second times even in highly heterogeneous patient geometries. This is infeasible with currently available, purely physics based simulations. In this study, we present a data-driven dose calculation algorithm predicting the dose deposited by mono-energetic proton beams for arbitrary energies and patient geometries. Our approach frames particle transport as sequence modeling, where convolutional layers extract important spatial features into tokens and the transformer self-attention mechanism routes information between such tokens in the sequence and a beam energy token. We train our network and evaluate prediction accuracy using computationally expensive but accurate Monte Carlo (MC) simulations, considered the gold standard in particle physics. Our proposed model is 33 times faster than current clinical analytic pencil beam algorithms, improving upon their accuracy in the most heterogeneous and challenging geometries. With a relative error of 0.34±0.2% and very high gamma pass rate of 99.59±0.7% (1%, 3 mm), it also greatly outperforms the only published similar data-driven proton dose algorithm, even at a finer grid resolution. Offering MC precision 4000 times faster, our model could overcome a major obstacle that has so far prohibited real-time adaptive proton treatments and significantly increase cancer treatment efficacy. Its potential to model physics interactions of other particles could also boost heavy ion treatment planning procedures limited by the speed of traditional methods.

Efficient Optimal Transport Algorithm by Accelerated Gradient Descent

Optimal transport (OT) plays an essential role in various areas like machine learning and deep learning. However, computing discrete optimal transport plan for large scale problems with adequate accuracy and efficiency is still highly challenging. Recently, methods based on the Sinkhorn algorithm add an entropy regularizer to the prime problem and get a trade off between efficiency and accuracy. In this paper, we propose a novel algorithm to further improve the efficiency and accuracy based on Nesterov's smoothing technique. Basically, the non-smooth c-transform of the Kantorovich potential is approximated by the smooth Log-Sum-Exp function, which finally smooths the original non-smooth Kantorovich dual functional. The smooth Kantorovich functional can be optimized by the fast proximal gradient algorithm (FISTA) efficiently. Theoretically, the computational complexity of the proposed method is lower than current estimation of the Sinkhorn algorithm in terms of the precision. Empirically, compared with the Sinkhorn algorithm, our experimental results demonstrate that the proposed method achieves faster convergence and better accuracy with the same parameter.

Learning from Label Proportions with Prototypical Contrastive Clustering

The use of priors to avoid manual labeling for training machine learning methods has received much attention in the last few years. One of the critical subthemes in this regard is Learning from Label Proportions (LLP), where only the information about class proportions is available for training the models. While various LLP training settings verse in the literature, most approaches focus on bag-level label proportions errors, often leading to suboptimal solutions. This paper proposes a new model that jointly uses prototypical contrastive learning and bag-level cluster proportions to implement efficient LLP classification. Our proposal explicitly relaxes the equipartition constraint commonly used in prototypical contrastive learning methods and incorporates the exact cluster proportions into the optimal transport algorithm used for cluster assignments. At inference time, we compute the clusters' assignment, delivering instance-level classification. We experimented with our method on two widely used image classification benchmarks and report a new state-of-art LLP performance, achieving results close to fully supervised methods.

Research and Development of Source Term Activity Reconstruction System Based on Deep Learning

The radiation damage weakens the efficiency of decommissioning nuclear facilities, and the source term is hard to be located and measured because it often distributes in or inside objectives. In practice, the hot spot and source intensity are measured by the gamma camera then the radiation field is estimated by particle transport algorithm as a reference in scheme planning. This technology is expensive in measuring and simulating. Hence, this paper presents a source term activity reconstruction (hereinafter referred to as STAR) method based on deep learning to solve this problem. Firstly, a UNET liked framework is constructed to establish the correlation between the radiation field and source activity. Secondly, a source activity reconstruction problem is used to validate the framework. Then we deploy it to the Raspberry Pi with a γ-ray detector to verify it can be applied in practical works. The verification results show that Raspberry Pi can complete source term activity reconstruction in a few seconds without consuming too much computing resources. In framework validating, 5000 samples consist of randomly generated activity distribution and its grid dose value which is calculated by the Monte-Carlo program. The results show that the average reconstruction error is less than 15% and the trained framework is performing well in Raspberry Pi. This method reduces the requirements for instruments and the dose detection is parallelable. Therefore, it can be widely used in nuclear facility decommissioning to improve the efficiency of source reconstruction.

On numerical broadening of particle-size spectra: a condensational growth study using PyMPDATA 1.0

Abstract. This work discusses the numerical aspects of representing the condensational growth of particles in models of aerosol systems such as atmospheric clouds. It focuses on the Eulerian modelling approach, in which fixed-bin discretisation is used for the probability density function describing the particle-size spectrum. Numerical diffusion is inherent to the employment of the fixed-bin discretisation for solving the arising transport problem (advection equation describing size spectrum evolution). The focus of this work is on a technique for reducing the numerical diffusion in solutions based on the upwind scheme: the multidimensional positive definite advection transport algorithm (MPDATA). Several MPDATA variants are explored including infinite-gauge, non-oscillatory, third-order terms and recursive antidiffusive correction (double-pass donor cell, DPDC) options. Methodologies for handling coordinate transformations associated with both particle-size spectrum coordinate choice and with numerical grid layout choice are expounded. Analysis of the performance of the scheme for different discretisation parameters and different settings of the algorithm is performed using (i) an analytically solvable box-model test case and (ii) the single-column kinematic driver (“KiD”) test case in which the size-spectral advection due to condensation is solved simultaneously with the advection in the vertical spatial coordinate, and in which the supersaturation evolution is coupled with the droplet growth through water mass budget. The box-model problem covers size-spectral dynamics only; no spatial dimension is considered. The single-column test case involves a numerical solution of a two-dimensional advection problem (spectral and spatial dimensions). The discussion presented in the paper covers size-spectral, spatial and temporal convergence as well as computational cost, conservativeness and quantification of the numerical broadening of the particle-size spectrum. The box-model simulations demonstrate that, compared with upwind solutions, even a 10-fold decrease in the spurious numerical spectral broadening can be obtained by an apt choice of the MPDATA variant (maintaining the same spatial and temporal resolution), yet at an increased computational cost. Analyses using the single-column test case reveal that the width of the droplet size spectrum is affected by numerical diffusion pertinent to both spatial and spectral advection. Application of even a single corrective iteration of MPDATA robustly decreases the relative dispersion of the droplet spectrum, roughly by a factor of 2 at the levels of maximal liquid water content. Presented simulations are carried out using PyMPDATA – a new open-source Python implementation of MPDATA based on the Numba just-in-time compilation infrastructure.

Optimizing Prehospital Stroke Systems of Care-Reacting to Changing Paradigms (OPUS-REACH): a pragmatic registry of large vessel occlusion stroke patients to create evidence-based stroke systems of care and eliminate disparities in access to stroke care

BackgroundLarge vessel occlusion (LVO) strokes are best treated with rapid endovascular therapy (EVT). There are two routes that LVO stroke patients can take to EVT therapy when transported by EMS: primary transport (ambulance transports directly to an endovascular stroke center (ESC) or secondary transport (EMS transports to a non-ESC then transfers for EVT). There is no clear evidence which path to care results in better functional outcomes for LVO stroke patients. To find this answer, an analysis of a large, real-world population of LVO stroke patients must be performed.MethodsA pragmatic registry of LVO stroke patients from nine health systems across the United States. The nine health systems span urban and rural populations as well as the spectrum of socioeconomic statuses. We will use univariate and multivariate analysis to explore the relationships between type of EMS transport, socioeconomic factors, and LVO stroke outcomes. We will use geographic information systems and spatial analysis to examine the complex movements of patients in time and space. To detect an 8% difference between groups, with a 3:1 patient ratio of primary to secondary transports, 95% confidence and 80% power, we will need approximately 1600 patients.The primary outcome is the patients with modified Rankin Scale (mRS) ≤ 2 at 90 days. Subgroup analyses include patients who receive intravenous thrombolysis and duration of stroke systems. Secondary analyses include socioeconomic factors associated with poor outcomes after LVO stroke.DiscussionUsing the data obtained from the OPUS-REACH registry, we will develop evidence based algorithms for prehospital transport of LVO stroke patients. Unlike prior research, the OPUS-REACH registry contains patient-level data spanning from EMS dispatch to ninety day functional outcomes. We expect that we will find modifiable factors and socioeconomic disparities associated with poor outcomes in LVO stroke. OPUS-REACH with its breadth of locations, detailed patient records, and multidisciplinary researchers will design the optimal prehospital stroke system of care for LVO stroke patients.

Numerical Simulations of Coastal Overwash Using A Phase-Averaged Wave—Current—Sediment Transport Model

AbstractCoastal overwash is a natural phenomenon that commonly occurs during storm events and can cause considerable changes in nearshore morphology within a short time. In this study, a complete set of empirical overwash transport algorithms is developed and introduced into a phase-averaged wave-current-sediment transport coupling model that integrates the Finite-Volume Community Ocean Model (FVCOM) and the Simulating Waves Nearshore (SWAN) model. The resulting morphological evolution model can simulate coastal overwash. Validation against the data obtained from multiple sets of laboratory overwash experiments demonstrates that the model performs relatively well in simulating morphological changes caused by runup overwash and inundation overwash under different hydrodynamic and beach profile conditions. The sensitivity of each empirical coefficient in the overwash transport algorithms is comprehensively analyzed. The effects of each coefficient on the output of the model are discussed, and a recommended value range is provided for each coefficient.