Springer Science and Business Media LLC

We study the synchronization behavior of a class of identical FitzHugh-Nagumo-type oscillators under adaptive coupling. We describe the oscillators by a circuit model and we provide a sufficient synchronization condition that relies on the shape of the nonlinear conductance’s (i, u)-curve and the connectivity of the adaptive coupling network. The coupling network is allowed to be time-variant, state-dependent and locally adaptive, where we treat memristive coupling elements as a special case. We provide a physical interpretation of synchronization in terms of power dissipation and investigate the sharpness of our condition.

In this paper we discuss the use of a metaheuristic (MH) gradient-free optimization method, specifically the slime mold algorithm (SMA), combined with the topology optimization (TO) method to design metasurfaces using a spectral modal method. The motivation behind using a MH approach comes from the drawbacks associated with traditional gradient-based methods. Normally, gradient-based methods require the calculation of the electromagnetic (EM) field at certain nodes within the computation domain. However, in spectral modal methods, this is unnecessary since these methods can compute the EM response without the need for field component values. Second, optimizing metagratings often involves a multimodal objective function with multiple local minimums and gradient-based methods might struggle with finding the global optimum. So to overcome these drawbacks, we propose using a MH approach, specifically the slime mold algorithm (SMA). We apply SMA to a metasurface design, especially in the context of TO and spectral methods, which is relatively unexplored. By coupling both TO with SMA, we successfully design metagratings capable of deflecting incident waves into a desired transmission angle.

The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose Computed Tomography (NDCT) and the Low-Dose Computed Tomography (LDCT) images from the dataset are subjected to the FNSST decomposition procedure during the training phase, producing high-frequency sub-images that act as input for the CNN. The CNN creates a meaningful connection between the high-frequency sub-images from LDCT and their corresponding residual sub-images during the training operation. The CNN is given the capacity to distinguish between LDCT high-frequency sub-images and expected high-frequency sub-images, which frequently have varying levels of noise or artifacts, especially in a fuzzy setting. The FNSST-CNN then successfully distinguishes LDCT high-frequency sub-images from the expected high-frequency sub-images during the testing phase, thereby reducing noise and artifacts. When compared to other approaches like KSVD, BM3D, and conventional image domain CNNs, the performance of FNSST-CNN is impressive as shown by better peak signal-to-noise ratios, stronger structural similarity, and a closer likeness to NDCT pictures.

The present study aimed to analyze and spatially model maximum rainfall in the southern and southwestern regions of Minas Gerais using spatial statistical methods. Daily data on maximum rainfall were collected from 29 cities in the region. To obtain predictions of maximum rainfall for return periods of 2, 5, 10, 50, and 100 years, Bayesian Inference was employed, utilizing the most appropriate prior for each locality. The spatial analysis of the phenomenon based on results obtained through Bayesian Inference was conducted using interpolation methods, including Inverse Distance Weighting (IDW) and Kriging (Ordinary Kriging (OK) and Log-Normal Kriging (LK)). Different semivariogram models were used, and the most suitable one was selected based on cross-validation results for each method, which were also compared to those of IDW. Additionally, a spatial analysis was carried out using max-stable processes and spatial Generalized Extreme Value (GEV) distribution, with the models evaluated based on Takeuchi’s Information Criteria. All models were also assessed by calculating the mean prediction error for six locations that were not used in model fitting. The results indicated that the most suitable models among Kriging and IDW for return periods of 2, 5, and 10 years were Gaussian (LK), Spherical (OK), and Wave (OK), respectively. Among the max-stable models and spatial GEV, the most suitable for modeling was the Smith max-stable model. Consequently, for spatial prediction over 50- and 100-year return periods, OK (Wave) and the Smith max-stable model were employed.