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1. As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. 2. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. 3. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. 4. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. 5. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement.

Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing parameter selection in semiparametric regression. However, existing REML or marginal likelihood (ML) based methods for semiparametric generalized linear models (GLMs) use iterative REML or ML estimation of the smoothing parameters of working linear approximations to the GLM. Such indirect schemes need not converge and fail to do so in a non-negligible proportion of practical analyses. By contrast, very reliable prediction error criteria smoothing parameter selection methods are available, based on direct optimization of GCV, or related criteria, for the GLM itself. Since such methods directly optimize properly defined functions of the smoothing parameters, they have much more reliable convergence properties. The paper develops the first such method for REML or ML estimation of smoothing parameters. A Laplace approximation is used to obtain an approximate REML or ML for any GLM, which is suitable for efficient direct optimization. This REML or ML criterion requires that Newton-Raphson iteration, rather than Fisher scoring, be used for GLM fitting, and a computationally stable approach to this is proposed. The REML or ML criterion itself is optimized by a Newton method, with the derivatives required obtained by a mixture of implicit differentiation and direct methods. The method will cope with numerical rank deficiency in the fitted model and in fact provides a slight improvement in numerical robustness on the earlier method of Wood for prediction error criteria based smoothness selection. Simulation results suggest that the new REML and ML methods offer some improvement in mean-square error performance relative to GCV or Akaike's information criterion in most cases, without the small number of severe undersmoothing failures to which Akaike's information criterion and GCV are prone. This is achieved at the same computational cost as GCV or Akaike's information criterion. The new approach also eliminates the convergence failures of previous REML- or ML-based approaches for penalized GLMs and usually has lower computational cost than these alternatives. Example applications are presented in adaptive smoothing, scalar on function regression and generalized additive model selection.

The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a 3D rib reconstruction method based on point cloud adaptive smoothing and denoising. We converted voxel data from CT images to multi-attribute point cloud data. Then, we applied point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques were employed to achieve high-accuracy and comprehensive 3D rib reconstruction results. Experimental calculations demonstrated that compared to voxel-based 3D rib reconstruction methods, the 3D rib models generated by the proposed method achieved a 40% improvement in reconstruction accuracy and were twice as efficient as the former.

Traditional kernel methods for estimating the spatially-varying density of points in a spatial point pattern may exhibit unrealistic artefacts, in addition to the familiar problems of bias and over- or under-smoothing. Performance can be improved by using diffusion smoothing, in which the smoothing kernel is the heat kernel on the spatial domain. This paper develops diffusion smoothing into a practical statistical methodology for two-dimensional spatial point pattern data. We clarify the advantages and disadvantages of diffusion smoothing over Gaussian kernel smoothing. Adaptive smoothing, where the smoothing bandwidth is spatially-varying, can be performed by adopting a spatially-varying diffusion rate: this avoids technical problems with adaptive Gaussian smoothing and has substantially better performance. We introduce a new form of adaptive smoothing using lagged arrival times, which has good performance and improved robustness. Applications in archaeology and epidemiology are demonstrated. The methods are implemented in open-source R code.

Statistical models used to estimate the spatiotemporal pattern in disease risk from areal unit data represent the risk surface for each time period with known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterized by a spatially smooth evolution between some pairs of adjacent areal units whereas other pairs exhibit large step changes. This spatial heterogeneity is not consistent with existing global smoothing models, in which partial correlation exists between all pairs of adjacent spatial random effects. Therefore we propose a novel space-time disease model with an adaptive spatial smoothing specification that can identify step changes. The model is motivated by a new study of respiratory and circulatory disease risk across the set of local authorities in England and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk and exhibit some common step changes in the unmeasured component of risk between neighbouring local authorities.

In this paper, we propose an adaptive smoothing spline (AdaSS) estimator for the function-on-function linear regression model where each value of the response, at any domain point, depends on the full trajectory of the predictor. The AdaSS estimator is obtained by the optimization of an objective function with two spatially adaptive penalties, based on initial estimates of the partial derivatives of the regression coefficient function. This allows the proposed estimator to adapt more easily to the true coefficient function over regions of large curvature and not to be undersmoothed over the remaining part of the domain. A novel evolutionary algorithm is developed ad hoc to obtain the optimization tuning parameters. Extensive Monte Carlo simulations have been carried out to compare the AdaSS estimator with competitors that have already appeared in the literature before. The results show that our proposal mostly outperforms the competitor in terms of estimation and prediction accuracy. Lastly, those advantages are illustrated also in two real-data benchmark examples. The AdaSS estimator is implemented in the R package adass, openly available online on CRAN.

Background: The existing pipeline siltation analysis lacks the analysis of the influence of pipeline siltation intensity on the water level depth of the manhole. Objective: To address these issues, in this study, the high-resolution numerical simulation model of 'silted pipeline-manhole' was established. Methods: First, a spatial and temporal hydraulic response model of the 'silted pipeline-manhole' system is constructed. The vibrational differential equation of the 'silted pipeline-manhole' system is then adopted to quantify the physical boundary constraints on the fluid-solid coupling relationship. The local and global feature search capabilities were enhanced by an embedded self-attention network, which in turn established an intelligent early warning model for manhole water levels in silted pipelines. Results: The results of the test analysis showed that, owing to the blocking effect of siltation, the water level of the manhole at the first end of the silted pipe was high, leading to a decrease in the water level of the manhole at the tail end. The pipe with a transverse siltation intensity of 10% showed an 8.07% reduction in the maximum overflow flow rate, whereas the pipe with a longitudinal siltation intensity of 5.2% exhibited a 15.09% reduction, suggesting that longitudinal siltation intensity has a more significant impact on pipeline failure. Conclusion: The research results provide technical support for the diagnosis of pipeline siltation and flood risk assessment considering the change of water level in the manhole and the inlet and outlet flow velocity of the pipe section.

Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Considering the complex distribution of spectral intensities, the proposed algorithm constructs the HSMM to accurately build the statistical model of the image, making more reasonable use of the spectral information and improving segmentation accuracy. The component weight is defined by the attribute probability of neighborhood pixels to overcome the influence of image noise and make a simple and easy-to-implement structure. To avoid the effects of artificially setting the smoothing coefficient, the gradient optimization method is used to solve the model parameters, and the smoothing coefficient is optimized through iterations. The experimental results suggest that the proposed HSMM can accurately model asymmetric, heavy-tailed, and bimodal distributions. Compared with traditional segmentation algorithms, the proposed algorithm can effectively overcome noise and generate more accurate segmentation results for high-resolution remote sensing images.

P-splines are an attractive approach for modeling nonlinear smooth effects of covariates within the additive and varying coefficient models framework. In this article, we first develop a Bayesian version for P-splines and generalize in a second step the approach in various ways. First, the assumption of constant smoothing parameters can be replaced by allowing the smoothing parameters to be locally adaptive. This is particularly useful in situations with changing curvature of the underlying smooth function or with highly oscillating functions. In a second extension, one-dimensional P-splines are generalized to two-dimensional surface fitting for modeling interactions between metrical covariates. In a last step, the approach is extended to situations with spatially correlated responses allowing the estimation of geoadditive models. Inference is fully Bayesian and uses recent MCMC techniques for drawing random samples from the posterior. In a couple of simulation studies the performance of Bayesian P-splines is studied and compared to other approaches in the literature. We illustrate the approach by two complex application on rents for flats in Munich and on human brain mapping.

Surface wave tomography usually adds a smooth term into the objective function to make the edge smoothing. This paper will make an improvement on the inversion method under Gaussian probability distribution condition during the iterative process of near-surface inversion, which is based on ray tracing, using the sensitivity parameters in G matrix as spatial smoothing constraints. We try to select self-adapted parameters, following the laws of physics. These parameters include damping factor, model deviation, smoothing factor, and sensitivity parameters. Improved inverse algorithm will be used in numerical simulation and real data, from ambient noise of Taipei Basin of Taiwan, to achieve the near-surface shear wave velocity structure. Compared with the earlier research results in this region, we find the same points and differences. By the process above, we verify the reasonability of the selection of the self-adaption parameters, validity of the improved inverse algorithm and the reliability of the code. The improved algorithm provides a fast, self-adapted, less empirical and universal tool and method to study the near-surface shear wave velocity structure.