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The global emergence of Batrachochytrium dendrobatidis (Bd) has caused the extinction of hundreds of amphibian species worldwide. It has become increasingly important to be able to precisely predict time to Bd arrival in a population. The data analyzed herein present a unique challenge in terms of modeling because there is a strong spatial component to Bd arrival time and the traditional proportional hazards assumption is grossly violated. To address these concerns, we develop a novel marginal Bayesian nonparametric survival model for spatially correlated right‐censored data. This class of models assumes that the logarithm of survival times marginally follow a mixture of normal densities with a linear‐dependent Dirichlet process prior as the random mixing measure, and their joint distribution is induced by a Gaussian copula model with a spatial correlation structure. To invert high‐dimensional spatial correlation matrices, we adopt a full‐scale approximation that can capture both large‐ and small‐scale spatial dependence. An efficient Markov chain Monte Carlo algorithm with delayed rejection is proposed for posterior computation, and an R package spBayesSurv is provided to fit the model. This approach is first evaluated through simulations, then applied to threatened frog populations in Sequoia‐Kings Canyon National Park.

Automated sensing instruments on satellites and aircraft have enabled the collection of massive amounts of high-resolution observations of spatial fields over large spatial regions. If these datasets can be efficiently exploited, they can provide new insights on a wide variety of issues. However, traditional spatial-statistical techniques such as kriging are not computationally feasible for big datasets. We propose a multi-resolution approximation (M-RA) of Gaussian processes observed at irregular locations in space. The M-RA process is specified as a linear combination of basis functions at multiple levels of spatial resolution, which can capture spatial structure from very fine to very large scales. The basis functions are automatically chosen to approximate a given covariance function, which can be nonstationary. All computations involving the M-RA, including parameter inference and prediction, are highly scalable for massive datasets. Crucially, the inference algorithms can also be parallelized to take full advantage of large distributed-memory computing environments. In comparisons using simulated data and a large satellite dataset, the M-RA outperforms a related state-of-the-art method. Supplementary materials for this article are available online.

Gaussian process (GP) models encounter computational difficulties with large spatial data sets, because the models’ computational complexity grows cubically with the sample size n. Although a full-scale approximation (FSA) using a block modulating function provides an e ective way to approximate GP models, it has several shortcomings. These include a less smooth prediction surface on block boundaries and sensitivity to the knot set under small-scale data dependence. To address these issues, we propose a smoothed full-scale approximation (SFSA) method for analyzing large spatial data sets. The SFSA leads to a class of scalable GP models, with covariance functions that consist of two parts: a reduced-rank covariance function that captures large-scale spatial dependence, and a covariance that adjusts the local covariance approximation errors of the reduced-rank part, both within blocks and between neighboring blocks. This method reduces the prediction errors on block boundaries, and leads to inference and prediction results that are more robust under different dependence scales owing to the better approximation of the residual covariance. The proposed method provides a unied view of approximation methods for GP models, grouping several existing computational methods for large spatial data sets into one common framework. These methods include the predictive process, FSA, and nearest neighboring block GP methods, allowing efficient algorithms that provide robust and accurate model inferences and predictions for large spatial data sets within a united framework. We illustrate the e ectiveness of the SFSA approach using simulation studies and a total column ozone data set.

Gaussian process models have been widely used in spatial statistics but face tremendous modeling and computational challenges for very large nonstationary spatial datasets. To address these challenges, we develop a Bayesian modeling approach using a nonstationary covariance function constructed based on adaptively selected partitions. The partitioned nonstationary class allows one to knit together local covariance parameters into a valid global nonstationary covariance for prediction, where the local covariance parameters are allowed to be estimated within each partition to reduce computational cost. To further facilitate the computations in local covariance estimation and global prediction, we use the full-scale covariance approximation (FSA) approach for the Bayesian inference of our model. One of our contributions is to model the partitions stochastically by embedding a modified treed partitioning process into the hierarchical models that leads to automated partitioning and substantial computational benefits. We illustrate the utility of our method with simulation studies and the global Total Ozone Matrix Spectrometer (TOMS) data. Supplementary materials for this article are available online.

Cellular thin-shell structures are widely applied in ultralightweight designs due to their high bearing capacity and strength-to-weight ratio. In this paper, a full-scale isogeometric topology optimization (ITO) method based on Kirchhoff–Love shells for designing cellular tshin-shell structures with excellent damage tolerance ability is proposed. This method utilizes high-order continuous nonuniform rational B-splines (NURBS) as basis functions for Kirchhoff–Love shell elements. The geometric and analysis models of thin shells are unified by isogeometric analysis (IGA) to avoid geometric approximation error and improve computational accuracy. The topological configurations of thin-shell structures are described by constructing the effective density field on the control mesh. Local volume constraints are imposed in the proximity of each control point to obtain bone-like cellular structures. To facilitate numerical implementation, the p-norm function is used to aggregate local volume constraints into an equivalent global constraint. Several numerical examples are provided to demonstrate the effectiveness of the proposed method. After simulation and comparative analysis, the results indicate that the cellular thin-shell structures optimized by the proposed method exhibit great load-carrying behavior and high damage robustness.

The characteristic feature of stratified flows in large bodies of water is generation of intense short-period internal waves at the front of long-wave disturbances. The nonlinear processes are most pronounced during the propagation of bottom and near-surface disturbances. The effective tool for studying wave processes in the ocean is the theory of multilayer shallow water with regard for the effects of nonlinearity and dispersion. It is shown that the developed mathematical models are suitable for describing the transformation of nonlinear internal waves both in the shelf zone of the sea and in the deep freshwater reservoirs. In particular, the structure of near-bottom internal waves in the shelf zone of the Sea of Japan and recently discovered near-surface internal waves in Lake Teletskoye are compared. The mechanism of generation of intense internal waves during excitation of seiche oscillations in narrow water reservoirs is discussed. Traveling waves in a multilayer fluid are constructed and numerical solutions to the nonstationary problem of generating internal waves are found. A comparison with the laboratory experiments on generation of a packet of short-period internal waves during seiche oscillations of a two-layer fluid in a long channel, as well as with the recorded near-surface internal solitary wave in Lake Teletskoye, is carried out.

This paper provides an overview of a series of papers aimed at creating a regional model based on the WAVEWATCH III spectral wave model adapted to conditions of an inland water body using the WRF atmospheric model. The models are adapted and verified based on results of a series of full-scale experiments on studying the wind–wave regime of the Gorky Reservoir in 2012–2019 using an autonomous buoy station based on the oceanographic Froude buoy. Within the framework of the WAVEWATCH III model, the influence of the WAM 3 wind forcing parameterization on the simulation result is analyzed and its parameters are adjusted along with the Discrete Interaction Approximation (DIA) scheme for approximate calculation of the Boltzmann integral. Within the framework of the WRF model, calculations are carried out using different parameterizations of the planetary boundary layer and the near-surface layer of the atmosphere, and the advantage of using the Large Eddy Simulation eddy-resolving method is shown. In addition to the review, the paper presents preliminary results of coupling the wave and atmospheric models, which makes it possible to adjust the interchange of parameters between the models at each time step.

This article is about solving the problem of maintaining the resonant vibration mode in a vibration transport and production machine with self-synchronizing vibration exciters at an undetermined mass of work material. An algorithm for maintaining automatic resonant mode maintenance is proposed. This algorithm is based on using preliminary numerical modeling (dynamic portrait) results and allows calculating the natural frequency (work material mass) in the real-time mode and implementing the necessary control over the vibration exciter rotation frequency according to the phase difference between the perturbation force and the oscillations of the working body. The results of numerically modeling the tuning of the vibration machine to the resonant mode while using the developed algorithm with variations in the mass of the work material are provided. A laboratory oscillation wobbler screen prototype is designed and equipped with an automatic resonant mode maintenance system. The operational ability and efficiency of the proposed solutions is confirmed by numerical and full-scale test results.

An assessment of the performance of FireFOAM in simulating a large-scale compartment fire scenario is presented in this study, using the Edinburgh Tall Building Fire Test I (2017) as the basis for evaluation. Different mesh geometries and sizes are tested, and both theory-based and experiment-based validation approaches are employed. The theory-based validation revealed that the implemented finite volumes method is generally conservative, but unaccounted deviations of up to 20% for the heat release rate were observed due to errors in numerically modelling subgrid phenomena, particularly with tetrahedral meshes. In the experiment-based validation, the simulated data showed good agreement with experimental measurements for flow patterns inside the compartment, neutral plane height, and temperatures outside the ceiling jet. However, there were relatively large errors in incident radiation in the hot gas zone, thermal boundary layer transient temperatures, and compartment inflow/outflow velocities. Systematic errors were attributed to deficient heat transfer boundary conditions and under-estimated air entrainment. The study also identified ways to improve run-time efficiency by implementing parallel processing or reducing solid angles in FVDOM, although using large meshes (30 cm and 40 cm cell size) resulted in faster run-times at the cost of accuracy.

The placing engineering-technical objects lead to the necessity of the development and of the applying of the protective and of the preventive of measures in areas with increased geodynamics (karst, landslides, mudflows, etc.). One of the measures is the application of systems of the geodynamic control to prevent of the negative development of geodynamics. However, the development and the introduction of systems of this class are associated with the low methodological study of problems of the control of probabilistic geological processes, and of the calibration and of the assessment of metrological characteristics of existing systems of the geodynamic control and monitoring. In the article the questions are considered, which are devoted to the development, the research and the approbation of the new method of the mathematical modeling and fullscale tests of systems of the geoelectric control of geodynamic objects. The developed method assumes the creation of the effect of the presence of near-surface and deep heterogeneities by of additional points or extended sources. The problem of known methods of physical (full-scale) modeling of geoelectric sections is the rigid specification of the geometric and electrical parameters of the created volumetric and planar models of environments. A feature of the proposed method is the possibility of flexible variation of the field of the heterogeneity by the simply moving of additional sources, which is unattainable in the known methods of full-scale modeling. The executed mathematical modeling confirmed the high accuracy of the approximation of real geoelectric fields by model sources. The executed approbation of the method confirmed its effectiveness for the calibration of geoelectric systems of geodynamic control in full-scale tests of multi-pole electrolocation units in conditions of complex construction and the impact of industrial and climatic noises.