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Eddy current dampers (ECDs), which employ magnetic induction to produce eddy current damping for energy dissipation, demonstrate superior sensitivity and durability in comparison to traditional viscous dampers. However, the inherent, velocity-dependent nonlinearity of ECDs poses significant challenges in research and engineering implementations. The present study addresses this challenge by exploring the stochastic linearization of ECDs, innovatively incorporating the non-Gaussian distribution of velocity responses. Central to our analysis, we proposed four closed-form expressions for the equivalent damping ratios of ECDs, derived from both Gaussian and non-Gaussian distributions. This achievement is facilitated by the utilization of the statistical linearization technique (SLT), which integrates both force-based and energy-based equivalent criteria. To validate the accuracy of these expressions, Monte Carlo simulation (MCS) was employed. Our results demonstrate that, regardless of soil conditions and velocity distributions, the force-based criteria, in most cases, outperform the energy-based criteria in assessing the stochastic linearization of ECDs. Notably, in scenarios where ECD nonlinearity is prominent, the probability density of the exact velocity response of ECDs in firm soil exhibits a significantly steeper profile near zero, resembling an exponential-like distribution more closely than the Gaussian distribution. Conversely, in soft soil conditions, the actual velocity distribution lies intermediate between Gaussian and exponential-like distributions. Additionally, a series of parameter studies were conducted, further revealing that the force-based Gaussian SLT method offers the highest accuracy in soft soil and firm soil with critical velocities exceeding 0.05m/s. In contrast, the force-based non-Gaussian SLT method proves to be the more precise approach in firm soil with critical velocities below 0.05m/s. [ABSTRACT FROM AUTHOR]

The dependence of the extremes and zeros of the excess kurtosis γ4(p, ã ) on the weight coefficient P is researched. Formulas for finding the extrema points, the values of the minimums and maximums of the excess kurtosis are obtained. Conditions on the shift parameter ã under which the extrema points belong to the interval [0;1] are determined. Formulas for finding the zeros of the excess kurtosis are obtained and conditions on shift parameter under which the roots of the equation γ4(p, ã ) are real and belong to the interval [0;1] are determined. Examples of calculating extremes and zeros of the excess kurtosis of two-component mixtures of shifted non-Gaussian distributions are considered. The results of the research justify the possibility of practical application of two-component mixtures of shifted distributions for mathematical and computer modeling of an infinite number of non-Gaussian random variables with negative, positive and zero excess kurtosis. [ABSTRACT FROM AUTHOR]

Many current image denoising works focus on Gaussian noise removal. However, in many real-life applications, the noise in an image can follow non-Gaussian distributions, such as compressed speckle noise, salt-and-pepper impulse noise (SPIN), and random-valued impulse noise (RVIN). In such scenarios, image denoising models with mean squared error loss will fail to obtain the best noise estimation results. Thus, we propose a non-Gaussian denoising convolutional neural network (DTNet) with truncation loss, which can address compressed speckle noise, SPIN, and RVIN with the same denoising framework. The model is composed of Res2Net blocks with the ability to extract multiscale information. Experiments show that our proposed DTNet method can effectively remove compressed speckle noise and impulse noise, including SPIN and RVIN. In particular, DTNet can recover the original images from images with 90% SPIN or images with 70% RVIN. [ABSTRACT FROM AUTHOR]

The Arctic is warming at a faster rate compared to the globe on average, a phenomenon commonly referred to as Arctic amplification. Sea ice has been linked to Arctic amplification and has gathered attention recently due to the decline in summer sea ice extent. Data assimilation (DA) is the act of combining observations with prior forecasts to obtain a more accurate model state. Sea ice poses a unique challenge for DA because sea ice variables have bounded distributions, leading to non-Gaussian distributions. The non-Gaussian nature violates the Gaussian assumptions built into DA algorithms. This study presents different observing system simulation experiments (OSSEs), which will provide a data assimilating testing framework through experimental observation networks and synthetic observations. The OSSE framework will help determine the best data assimilation configuration for assimilating sea ice and snow observations. Findings indicate that assimilating both sea ice thickness and snow depth observations while omitting sea ice concentration observations produced the best sea ice and snow forecasts in our idealized experimental setup. A simplified DA experiment helped demonstrate that the DA solution is biased when assimilating sea ice concentration observations. The biased DA solution is related to the observation error distribution being a truncated normal distribution, and the assumed observation likelihood is normal for the DA method. Additional OSSEs show that using a non-Gaussian DA method does not alleviate the non-Gaussian effects of sea ice concentration observations, and assimilating sea ice surface temperatures has a positive impact on snow updates. Finally, it is shown that the perturbed sea ice model parameters used to create additional ensemble spread in the free forecasts lead to a year-long negative snow volume bias. [ABSTRACT FROM AUTHOR]

All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non‐Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a locally robust semiparametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non‐Gaussianity when it is present, but yields correct size/coverage for local‐to‐Gaussian densities. Empirically, we revisit two macroeconomic SVAR studies where we document mixed results. For the oil price model of Kilian and Murphy (2012), we find that non‐Gaussianity can robustly identify reasonable confidence sets, whereas for the labor supply–demand model of Baumeister and Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty when using non‐Gaussianity for identification. [ABSTRACT FROM AUTHOR]

It is shown that the chaotic Zeeman effect of a quantum system can be formally viewed as a result of fractional calculus. The fractional calculation brings into the equations the angle θ formed between the internal and the external magnetic field applied to the atom. The further the fractional coefficient α is from the ordinary case corresponding to α = 1 , the more important the chaotic effect is. The case corresponding to α = 1 does not depend on the angle θ , obtaining the nonchaotic situation known in the literature. Non-Gaussian distributions correspond to non-stationary variables. Considering a Lorenzian type distribution, we can make a connection between the fractional formalism and random matrix theory. The connection validates the link between fractional calculus and chaos, and at the same time due to the θ angle, it gives the phenomenon a physical interpretation. [ABSTRACT FROM AUTHOR]

To realize multitarget trajectory tracking under non-Gaussian heavy-tailed noise, we propose a Gaussian–Student t-mixture distribution-based trajectory cardinality probability hypothesis density filter (GSTM-TCPHD). We introduce the multi-sensor GSTM-TCPHD (MS-GSTM-TCPHD) filter to enhance tracking performance. Conventional cardinality probability hypothesis density (CPHD) filters typically assume Gaussian noise and struggle to accurately establish target trajectories when faced with heavy-tailed non-Gaussian distributions. Heavy-tailed noise leads to significant estimation errors and filter dispersion. Moreover, the exact trajectory of the target is crucial for tracking and prediction. Our proposed GSTM-TCPHD filter utilizes the GSTM distribution to model heavy-tailed noise, reducing modeling errors and generating a set of potential target trajectories. Since single sensors have a limited field of view and limited measurement information, we extend the filter to a multi-sensor scenario. To tackle the issue of data explosion from multiple sensors, we employed a greedy approximation method to assess measurements and introduced the MS-GSTM-TCPHD filter. The simulation results demonstrate that our proposed filter outperforms the CPHD/TCPHD filter and Student's t-based TCPHD filter in terms of accurately estimating the trajectories of multiple targets during tracking while also achieving improved accuracy and shorter processing time. [ABSTRACT FROM AUTHOR]

We reanalyse the recent compilation of distance measurements to M87 by collecting the data from published literature. Different from the traditional statistical methods, based on the principle of minimum information loss, we use a robust most frequent value (MFV) procedure to estimate the distance to M87, irrespective of the Gaussian or non-Gaussian distributions. The MFV-based robust estimate for the M87 distance modulus is given by |$31.09^{+0.04}_{-0.03}$| (statistical) |$^{+0.05}_{-0.07}$| (systematic) mag corresponding to a 68.27 per cent confidence interval, whereas the result of combining the two uncertainties in quadrature is |$31.09^{+0.06}_{-0.08}$|  mag. We also construct the error distributions of M87 distance moduli values according to the weighted mean, median, and MFV, which is non-Gaussian. This demonstrates that the MFV method offers a more accurate and robust estimate of the distance to M87 compared to methods that depend on the unfulfilled assumption of Gaussianity. [ABSTRACT FROM AUTHOR]

Process capability indices (PCIs) are major tools in geometric dimensioning and tolerancing (GD&T) for quantifying the production quality, monitoring production, or prioritizing projects. Initially, PCIs were constructed for studying each characteristic of the process independently. Then, they have been extended to analyze several dependent characteristics simultaneously. Nowadays, with the increasing complexity of the production parts, for example, in aircraft engines, the conformity of one part may rely on the conformity of hundreds of characteristics. Also, because those characteristics are dependent, it may be misleading to make decisions based only on univariate PCIs. However, classical multivariate PCIs in the literature do not allow treating such amount of data efficiently, unless assuming Gaussian distribution, which may be false. Regarding those issues, we advocate for PCI based on some transformation of the conformity rates. This presents the advantage of being free from distributional assumptions, such as the Gaussian distribution. In addition, it has direct interpretation, allowing it to compare different processes. To estimate the PCIs of parts with hundreds of characteristics, we propose to use Vine Copulas. This is a very flexible class of models, which gives precise estimation even in high dimension. From an industrial perspective, the computation of the estimator can be costly. To answer this point, one explains how to compute a lower bound of the proposed PCI, which is faster to calculate. We illustrate our method adaptability with simulations under Gaussian and non-Gaussian distributions. We apply it to compare the production of fan blades of two different factories. [ABSTRACT FROM AUTHOR]

Random vibration excitation is a standard method for obtaining the dynamic properties of aerospace structures and simulating loading conditions. Shaker-based random excitation and traditional finite element analysis techniques use random vibrations, assuming a Gaussian distribution of excitation impulse magnitudes. While Gaussian signals may sufficiently describe some vibrational environments, many real-world excitations have non-Gaussian distributions. These signals may contain vibrational impulses larger than those observed in a Gaussian signal and could cause damage if not considered. This paper examines the influence of non-Gaussian signals on flexible vs. rigid robotic architectures using NASA JPL's Pop-Up Flat Folding Explorer Robot (PUFFER) and Cooperative Autonomous Distributed Robotic Explorer (CADRE). The results indicate that flexible systems can be significantly impacted by a non-Gaussian excitation profile and that neither a typical enveloping of a Gaussian excitation nor a superposition of the responses to a worst-case impulse load with the response to a Gaussian signal was sufficient to bound the response due to non-Gaussian excitation. The driving mechanism for increased sensitivity of flexible robotic systems appears to be inertial, and designers may consider shifting the relative flexibility of the system or its constraints to force particular deformation modes or designing hinge mechanisms with increased damping to minimize the influence of non-Gaussian signals on the response of a flexible robotic structure. [ABSTRACT FROM AUTHOR]

Process monitoring and fault diagnosis systems are essential for ensuring the quality and safety of industrial processes. Real industrial process data always has dynamic features, autocorrelation features, and non‐Gaussian distributions, making many multivariate statistical process monitoring (MSPM) methods, such as principal component analysis (PCA), not work well. To solve the problem, a new process monitoring method (named DPCA‐ICA‐moving window Kolmogorov–Smirnov test [MWKS]) is proposed in the paper, which is a combination of DPCA and independent component analysis (ICA) with adaptive weight parameters and introduces a moving window technique and two‐sample K‐S test to obtain the K‐S distance of the commonly used Hotelling's T2 and square predictive error as new monitoring parameters. Experimental results on the Tennessee Eastman chemical process demonstrate the effectiveness and accuracy of the proposed method, which shows it has better performance than existing commonly used methods. The average fault detection rate (FDR) is 93.131%, while the average false alarm rate (FAR) of the proposed method is only 0.1%. [ABSTRACT FROM AUTHOR]

In hydrological research, data assimilation (DA) is widely used to fuse the information contained in process‐based models and observational data to reduce simulation uncertainty. However, many popular DA methods are limited by low computational efficiency or their reliance on the Gaussian assumption. To address these limitations, we propose a novel DA method called DA(DL), which leverages the capabilities of DL to model non‐linear relationships and recognize complex patterns. DA(DL) first generates a large volume of training data from the prior ensemble, and then trains a DL model to update the system knowledge (e.g., model parameters in this study) from multiple predictors. For highly non‐linear models, an iterative form of DA(DL) can be implemented. Additionally, strategies of data augmentation and local updating are proposed to enhance DA(DL) for problems involving small ensemble size and the equifinality issue, respectively. In two hydrological DA cases involving Gaussian and non‐Gaussian distributions, DA(DL) shows promising performance compared to two ensemble smoother methods, that is, ES(K) and ES(DL), which respectively apply the Kalman‐ and DL‐based updates. Potential improvements to DA(DL) can be made by designing better DL model architectures, imposing physical constraints to the training of the DL model, and further updating other important variables like model states, forcings and error terms. Key Points: Many popular data assimilation (DA) methods are constrained by the Gaussian assumption or suffer from low computational efficiencyWe propose a novel DA method called DA(DL) based on deep learning (DL)DA(DL) shows promising performance in problems involving non‐linearity, high‐dimensionality and non‐Gaussianity [ABSTRACT FROM AUTHOR]

Atlantic bluefin tuna (ABFT; Thunnus thynnus) is a highly migratory species. To investigate the migrations and vertical behaviours of ABFT migrating to Nordic waters, we deployed pop-up satellite archival transmitting tags on 25 ABFT off Norway (curved fork length: 228–292 cm). We obtained 16 full-year migrations, which differed between individuals, and physically recovered 13 tags, which provided 4699 days of archival depth and temperature data. ABFT occupied waters from the Arctic Circle to as far south as Cabo Verde, Africa, and occupied depths down to 1190 m and temperatures from 0.5 to 27.8°C. During their annual migrations, ABFT spent, on average, 68 days in Norwegian waters, 65 days in the Newfoundland Basin, 35 days around the Canary Islands and 33 days in the West European Basin. Most ABFT entered the Mediterranean Sea with a mean entry date of 13 May and visited known spawning grounds, staying, on average, 44 days. All ABFT with full-year deployments returned to Norwegian waters. ABFT displayed high site-fidelity and dynamic vertical diving behaviours that varied between hotspots and seasons. These spatiotemporal data provide important ecological knowledge for sustainable management and the conservation of the recently recovered eastern ABFT stock. [ABSTRACT FROM AUTHOR]

Understanding the motion of particles with multivalent ligand-receptors is important for biomedical applications and material design. Yet, even among a single design, the prototypical DNA-coated colloids, seemingly similar micrometric particles hop or roll, depending on the study. We shed light on this problem by observing DNA-coated colloids diffusing near surfaces coated with complementary strands for a wide array of coating designs. We find colloids rapidly switch between 2 modes: They hop--with long and fast steps--and crawl--with short and slow steps. Both modes occur at all temperatures around the melting point and over various designs. The particles become increasingly subdiffusive as temperature decreases, in line with subsequent velocity steps becoming increasingly anticorrelated, corresponding to switchbacks in the trajectories. Overall, crawling (or hopping) phases are more predominant at low (or high) temperatures; crawling is also more efficient at low temperatures than hopping to cover large distances. We rationalize this behavior within a simple model: At lower temperatures, the number of bound strands increases, and detachment of all bonds is unlikely, hence, hopping is prevented and crawling favored. We thus reveal the mechanism behind a common design rule relying on increased strand density for longrange self-assembly: Dense strands on surfaces are required to enable crawling, possibly facilitating particle rearrangements. [ABSTRACT FROM AUTHOR]

Background/Objectives: Childhood obesity, particularly in girls, is linked to early puberty onset, heightening risks for adult-onset diseases. Addressing childhood obesity and precocious puberty is vital to mitigate societal burdens. Despite existing costly and invasive medical interventions, introducing lifestyle-based alternatives is essential. Our study investigates alternate-day fasting's (ADF) impact on pubertal development in normal-weight and high-fat diet (HFD)-induced obese female mice. Methods: Four groups of female mice were utilized, with dams initially fed control chow during and before pregnancy. Post-parturition, two groups continued on control chow, while two switched to an HFD. Offspring diets mirrored maternal exposure. One control and one HFD group were subjected to ADF. Morphometry and hormone analyses at various time points were performed. Results: Our findings demonstrate that ADF in normal-weight mice led to reduced body length, weight, uterine, and ovarian weights, accompanied by delayed puberty and lower levels of sex hormones and growth hormone (GH). Remarkably, GH treatment effectively prevented ADF-induced growth reduction but did not prevent delayed puberty. Conversely, an HFD increased body length, induced obesity and precocious puberty, and altered sex hormones and leptin levels, which were counteracted by ADF regimen. Our data indicate ADF's potential in managing childhood obesity and precocious puberty. Conclusions: ADF reduced GH and sex hormone levels, contributing to reduced growth and delayed puberty, respectively. Therefore, parents of normal-weight children should be cautious about prolonged overnight fasting. ADF prevented HFD-induced obesity and precocious puberty, offering an alternative to medical approaches; nevertheless, further studies are needed for translation into clinical practice. [ABSTRACT FROM AUTHOR]

The orientation of ice crystals affects their microphysical behaviour, growth, and precipitation. Orientation also affects interaction with electromagnetic radiation, and through this it influences remote sensing signals, in situ observations, and optical effects. Fall behaviours of a variety of 3D-printed plate-like ice crystal analogues in a tank of water–glycerine mixture are observed with multi-view cameras and digitally reconstructed to simulate the falling of ice crystals in the atmosphere. Four main falling regimes were observed: stable, zigzag, transitional, and spiralling. Stable motion is characterised by no resolvable fluctuations in velocity or orientation, with the maximum dimension oriented horizontally. The zigzagging regime is characterised by a back-and-forth swing in a constant vertical plane, corresponding to a time series of inclination angle approximated by a rectified sine wave. In the spiralling regime, analogues consistently incline at an angle between 7 and 28°, depending on particle shape. Transitional behaviour exhibits motion in between spiral and zigzag, similar to that of a falling spherical pendulum. The inclination angles that unstable planar ice crystals make with the horizontal plane are found to have a non-zero mode. This observed behaviour does not fit the commonly used Gaussian model of inclination angle. The typical Reynolds number when oscillations start is strongly dependent on shape: solid hexagonal plates begin to oscillate at Re =237 , whereas several dendritic shapes remain stable throughout all experiments, even at Re > 1000. These results should be considered within remote sensing applications wherein the orientation characteristics of ice crystals are used to retrieve their properties. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method for enhancing time series data preprocessing by integrating a cycling layer into a self-attention mechanism. Traditional approaches often fail to capture the cyclical patterns inherent to time series data, which affects the predictive model accuracy. The proposed method aims to improve models' ability to identify and leverage these cyclical patterns, as demonstrated using the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results show that the proposed method enhances forecast accuracy and speeds up model fitting compared to the conventional techniques. This paper contributes to the field of time series analysis by providing a more effective preprocessing approach. [ABSTRACT FROM AUTHOR]