Search

One of the main objectives of the time series analysis is forecasting, so both Machine Learning methods and statistical methods have been proposed in the literature. In this study, we compare the forecasting performance of some of these approaches. In addition to traditional forecasting methods, which are the Naive and Seasonal Naive Methods, S/ARIMA, Exponential Smoothing, TBATS, Bayesian Exponential Smoothing Models with Trend Modifications and STL Decomposition, the forecasts are also obtained using seven different machine learning methods, which are Random Forest, Support Vector Regression, XGBoosting, BNN, RNN, LSTM, and FFNN, and the hybridization of both statistical time series and machine learning methods. The data set is selected proportionally from various time domains in M4 Competition data set. Thereby, we aim to create a forecasting guide by considering different preprocessing approaches, methods, and data sets having various time domains. After the experiment, the performance and impact of all methods are discussed. Therefore, most of the best models are mainly selected from machine learning methods for forecasting. Moreover, the forecasting performance of the model is affected by both the time frequency and forecast horizon. Lastly, the study suggests that the hybrid approach is not always the best model for forecasting. Hence, this study provides guidelines to understand which method will perform better at different time series frequencies. [ABSTRACT FROM AUTHOR]

In this paper, we study the energy decay of a coupled wave system with one local Kelvin–Voigt damping. The system consists of two wave equations locally coupled by zero‐order terms. When the damping coefficient function and the coupling coefficient function satisfy suitable assumptions, by Carleman estimate and the frequency‐domain method, we show that the energy of the system decays logarithmically for sufficiently smooth initial data. [ABSTRACT FROM AUTHOR]

Competing procedures, involving data smoothing, weighting, imputation, outlier removal, etc., may be available to prepare data for parametric model estimation. Often, however, little is known about the best choice of preparatory procedure for the planned estimation and the observed data. A machine learning‐based decision rule, an "oracle," can be constructed in such cases to decide the best procedure from a set C$$ \mathcal{C} $$ of available preparatory procedures. The oracle learns the decision regions associated with C$$ \mathcal{C} $$ based on training data synthesized solely from the given data using model parameters with high posterior probability. An estimator in combination with an oracle to guide data preparation is called an oracle estimator. Oracle estimator performance is studied in two estimation problems: slope estimation in simple linear regression (SLR) and changepoint estimation in continuous two‐linear‐segments regression (CTLSR). In both examples, the regression response is given to be increasing, and the oracle must decide whether to isotonically smooth the response data preparatory to fitting the regression model. A measure of performance called headroom is proposed to assess the oracle's potential for reducing estimation error. Experiments with SLR and CTLSR find for important ranges of problem configurations that the headroom is high, the oracle's empirical performance is near the headroom, and the oracle estimator offers clear benefit. [ABSTRACT FROM AUTHOR]

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

Radiation fog nowcasting remains a complex yet critical task due to its substantial impact on traffic safety and economic activity. Current numerical weather prediction models are hindered by computational intensity and knowledge gaps regarding fog‐influencing processes. Machine‐Learning (ML) models, particularly those employing the eXtreme Gradient Boosting (XGB) algorithm, may offer a robust alternative, given their ability to learn directly from data, swiftly generate nowcasts, and manage non‐linear interrelationships among fog variables. However, unlike recurrent neural networks XGB does not inherently process temporal data, which is crucial in fog formation and dissipation. This study proposes incorporating preprocessed temporal data into the model training and applying a weighted moving‐average filter to regulate the substantial fluctuations typical in fog development. Using an ML training and evaluation scheme for time series data, we conducted an extensive bootstrapped comparison of the influence of different smoothing intensities and trend information timespans on the model performance on three levels: overall performance, fog formation and fog dissipation. The performance is checked against one benchmark and two baseline models. A significant performance improvement was noted for the station in Linden‐Leihgestern (Germany), where the initial F1 score of 0.75 (prior to smoothing and trend information incorporation) was improved to 0.82 after applying the smoothing technique and further increased to 0.88 when trend information was incorporated. The forecasting periods ranged from 60 to 240 min into the future. This study offers novel insights into the interplay of data smoothing, temporal preprocessing, and ML in advancing radiation fog nowcasting. [ABSTRACT FROM AUTHOR]

This article presents an approach to forecasting count time series with a form of exponential smoothing built from observation‐driven models. The proposed method is easy to implement and simple to interpret. A variant of the approach is also proposed to handle the impact of outliers on the forecast. The performance of the methodology is studied with simulations and illustrated with an analysis of the number of monthly cases of dengue fever observed in Italy for the years 2008–2021. An R package is made available to enable the reader to reproduce the results discussed in the article. [ABSTRACT FROM AUTHOR]

Wind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments. [ABSTRACT FROM AUTHOR]

Electricity generation in Taiwan is mainly from thermal, nuclear, and renewable sources. With nearly four decades of monthly data (from January 1982 to January 2022) used in analytics, the forecasting results reveal the patterns of electricity generation in Taiwan. Exponential smoothing and Holt–Winters seasonal models are used for time‐series forecasting, and scenario analysis is applied to simulate possible paths for nuclear phase‐out in Taiwan by 2025. Taking the fact of Taiwan's lack of energy independence into account, transition for nuclear phase‐out may require a substantial leap to meet the demand. The feasibility of electricity generation using current thermal power capacity and renewable energy remain challenging. [ABSTRACT FROM AUTHOR]

All signals obtained as instrumental response of analytical apparatus are affected by noise, as in Raman spectroscopy. Whereas Raman scattering is an inherently weak process, the noise background may lead to misinterpretations. Although surface amplification of the Raman signal using metallic nanoparticles has been a strategy employed to partially solve the signal‐to‐noise problem, the preprocessing of Raman spectral data through the use of mathematical filters has become an integral part of Raman spectroscopy analysis. In this paper, a Tikhonov modified method to remove random noise in experimental data is presented. In order to refine and improve the Tikhonov method as a filter, the proposed method includes Euclidean norm of the fractional‐order derivative of the solution as an additional criterion in Tikhonov function. In the strategy used here, the solution depends on the regularization parameter, λ, and on the fractional derivative order, α. As will be demonstrated, with the algorithm presented here, it is possible to obtain a noise‐free spectrum without affecting the fidelity of the molecular signal. In this alternative, the fractional derivative works as a fine control parameter for the usual Tikhonov method. The proposed method was applied to simulated data and to surface‐enhanced Raman scattering (SERS) spectra of crystal violet dye in Ag nanoparticles colloidal dispersion. [ABSTRACT FROM AUTHOR]

In this paper, we study the stability of a Schrödinger system with one boundary damping, which consists of two constant coefficients Schrödinger equations coupled through zero‐order terms. First, we show that when ϱ=1,$$ \varrho =1, $$ the one‐dimensional Schrödinger system is not exponentially stable by the asymptotic expansions of eigenvalues. Then, by the frequency domain approach and the multiplier method, we show that the energy decay rate of the multidimensional Schrödinger system is t−1$$ {t}^{-1} $$ for sufficiently smooth initial data when ϱ=1,$$ \varrho =1, $$|α|$$ \mid \alpha \mid $$ is sufficiently small, and the boundary of domain satisfies suitable geometric assumption. Next, by solving the characteristic equation of unbounded operator, we show that the strong stability of the one‐dimensional Schrödinger system is completely determined by ϱ$$ \varrho $$ and α$$ \alpha $$ and give the necessary and sufficient condition that ϱ$$ \varrho $$ and α$$ \alpha $$ satisfy. Finally, by solving the resolvent equation of unbounded operator and using the frequency domain approach, we show that when ϱ≠1$$ \varrho \ne 1 $$ and |α|$$ \mid \alpha \mid $$ is small enough, the energy of the one‐dimensional Schrödinger system decays polynomially and the decay rate depends on the arithmetic property of ϱ.$$ \varrho. $$ [ABSTRACT FROM AUTHOR]

The Moho is the interface between crust and mantle, and accurate location of the Moho is important for both resource exploration and deep earth condition and structural change investigations. The theory of the traditional Parker‐Oldenburg (P‐O) method is quite simple and it is widely applied in the frequency domain of Moho depth inversion. However, Moho fluctuation simulations using the P‐O method are not reliable because of the lack of field geographic data constraints during the inversion process and the excessively smoothing of data details caused by using filters to correct the source data signals. To solve those problems, we propose an improved iteration P‐O method with a variable density contrast model, the iterative process is constrained by geological data in the inversion parameters, and the variable depth of the gravity interface is iterated using an equivalent form of upward continuation in the Fourier domain, which is more stable and convergent than downward continuation term in original P‐O method. Synthetic experiments indicate that improved method has the better consistency among the simulations than original method, and our improved method has the smallest root mean square (RMS) of 0.59 km. In a real case, we employed the improved method to invert for the Moho depth of the South China Sea (SCS), and the RMS between our Moho depth model and the seismological data is the smallest value of 2.27 km. The synthetic experiments and application of the model to the SCS further prove that our method is practical and efficient. Key Points: We developed an improved Parker‐Oldenburg method using an equivalent form of upward continuation in the Fourier domain instead of applying a low‐pass filter, which achieves a better stable performanceGravity inversion combines the actual density variations between layersMoho inversion in the South China Sea fits well with the seismic data, and our Moho depth model have the smallest root mean square (2.27 km) compared to other gravimetric Moho models [ABSTRACT FROM AUTHOR]

We analyze the shock formation process for the 3D nonisentropic Euler equations with the ideal gas law, in which sound waves interact with entropy waves to produce vorticity. Building on our theory for isentropic flows in [3, 4], we give a constructive proof of shock formation from smooth initial data. Specifically, we prove that there exist smooth solutions to the nonisentropic Euler equations which form a generic stable shock with explicitly computable blowup time, location, and direction. This is achieved by establishing the asymptotic stability of a generic shock profile in modulated self‐similar variables, controlling the interaction of wave families via: (i) pointwise bounds along Lagrangian trajectories, (ii) geometric vorticity structure, and (iii) high‐order energy estimates in Sobolev spaces. © 2022 Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this study, exponential smoothing (ES)‐based quadrature amplitude modulation (QAM) is applied to detect and classify the power quality (PQ) events with high accuracy and low complexity. Considering the IEEE‐1159 standards, in the simulation environment, 14 different power quality distortion signals are produced. Each of these signals is modulated at the fundamental frequency by QAM. The high‐frequency constituents in the in‐phase and quadratic components resulting from the QAM are filtered by the ES. PQ events are detected and classified by extracting features from the filtered in phase and quadrature component. The performance of the proposed method is examined by taking 1000$$ 1000 $$ randomly selected data samples for each PQ event, and its performance is 100%$$ 100\% $$ and 98.28%$$ 98.28\% $$, respectively, at noiseless and 30$$ 30 $$ dB SNR environments. The obtained results are compared with the others in the literature for the detection and classification of PQ events, and it is shown that the proposed method has better performance in noisy and noiseless conditions. In addition, to verify that the proposed method can be applied to real systems, oscillating transitions produced by capacitor banks in Simulink environment and voltage sag and swell events are generated in experimental environment. The proposed method successfully detected and classified PQ events. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper aims to present an overview of Semiparametric additive models. An estimation of the finite‐parameters of semiparametric regression models that involve additive nonparametric components is explained, including their historical background. In addition, three different smoothing techniques are considered in order to show the working procedures of the estimators and to explore their statistical properties: smoothing splines, kernel smoothing and local linear regression. These methods are compared with respect to both their theoretical and practical behaviors. A simulation study and a real data example are carried out to reveal the performances of the three methods. Accordingly, the advantages and disadvantages of each method regarding semiparametric additive models are presented based on their comparative scores using determined evaluation metrics for loss of information. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Multivariate AnalysisStatistical Models > Semiparametric Models [ABSTRACT FROM AUTHOR]

This article presents a curated database of the sea‐level measurements acquired by the network of the five geodetic tide gauges managed over French Polynesia by the Geodesy Observatory of Tahiti from 13 June 2009 to 28 January 2021. A unique feature of this database, with respect to previous databases that host the same raw data, like the Intergovernmental Oceanographic Commission database (IOC, www.ioc‐sealevelmonitoring.org) and the database of 'Réseaux de référence des observations marégraphiques' (REFMAR, http://refmar.shom.fr) is that all the time‐tags of the raw measurements (1‐ or 2‐min sampling) have been validated and, if necessary, corrected with a precision of 2 min (time shifts of up to 1 hr can be present in the raw data). Possible outliers have also been flagged, but not removed. In addition, smoothed hourly data are also provided, along with tidal analysis results and estimations of the sea‐level trends for the five tide gauges, with respect to their local geodetic datum. The database, entitled 'SEA LEVEL collected from TIDE STATIONS in South Pacific Ocean from 2009‐06‐13 to 2021‐01‐28', can be accessed on the NOAA data servers as 'NCEI Accession 0244182' and contains two subsets: The first one is relative to the original sampling rate and the second one is relative to an hourly re‐sampling with harmonic tide models for each tide gauge station. [ABSTRACT FROM AUTHOR]

The mechanisms of meniscus fibrosis and novel ways to enhance fibrosis is unclear. This work reveals human meniscus fibrosis initiated at E24 weeks. Smooth muscle cell cluster is identified in embryonic meniscus, and the combined analysis with previous data suggests smooth muscle cell in embryonic meniscus as precursors of progenitor cells in the mature meniscus. NOTCH3 is constantly expressed in smooth muscle cells throughout embryogenesis to adulthood. Inhibition of NOTCH3 signaling in vivo inhibits meniscus fibrosis and exacerbates degeneration. Continuous histological sections show that HEYL, NOTCH3 downstream target gene, is expressed consistently with NOTCH3. HEYL knockdown in meniscus cells attenuated the COL1A1 upregulation by CTGF and TGF‐β stimulation. Thus, this study discovers the existence of smooth muscle cells and fibers in the meniscus. Inhibition of NOTCH3 signaling in meniscus smooth muscle cells in a HEYL‐dependent manner prevented meniscus fibrosis and exacerbated degeneration. Therefore, NOTCH3/HEYL signaling might be a potential therapeutic target for meniscus fibrosis. [ABSTRACT FROM AUTHOR]

Spatial dependence into environmental data is an influential criterion in clustering processes, as the resulting clustering outputs depend very much upon such spatial structure. As classical methods do not take spatial dependence in consideration, the inclusion of this structure produces unexpected but more realistic results and clusters of curves that may not be similar in shape or behavior. In this paper, clustering is made using the KMSCFD algorithm for spatially correlated functional data. The methodology was developed through weighting the distance matrix between the curves with the trace‐variogram calculated with the coefficients of the basis functions resulting from a data smoothing operation. For the validation of the method, a number of simulated scenarios were tested together with an application to Normalized Difference Vegetation Index data derived from a high elevation ecosystem in the Ecuadorian Andes. Quality indices are implemented to obtain the appropriate number of clusters. The analysis showed five different regions that were latitudinally distributed. [ABSTRACT FROM AUTHOR]

In this paper, we use the fading regularization method to solve a biharmonic inverse problem, represented by the Cauchy problem. Two formulations are studied and implemented numerically using a finite element method (FEM). We present numerical reconstructions of the missing data on the inaccessible part of the boundary from the knowledge of over‐prescribed noisy data for both smooth and piecewise smooth two‐dimensional geometries. Numerical examples validate the convergence, stability and efficiency of the proposed numerical algorithm, as well as its capability to deblur the noisy data. [ABSTRACT FROM AUTHOR]

In this paper, we consider the three dimensional Cauchy problem of the compressible micropolar viscous flows. We prove the existence of unique global classical solution for smooth initial data with small initial energy but possibly large oscillations and the initial density may allowed to contain the interior and far field vacuum states. Furthermore, the large time behavior of the solution is obtained as well. [ABSTRACT FROM AUTHOR]

Purpose: Accurate delineation of the urethra is a prerequisite for urethral dose reduction in prostate radiotherapy. However, even in magnetic resonance‐guided radiation therapy (MRgRT), consistent delineation of the urethra is challenging, particularly in online adaptive radiotherapy. This paper presented a fully automatic MRgRT‐based prostatic urethra segmentation framework. Methods: Twenty‐eight prostate cancer patients were included in this study. In‐house 3D half fourier single‐shot turbo spin‐echo (HASTE) and turbo spin echo (TSE) sequences were used to image the Foley‐free urethra on a 0.35 T MRgRT system. The segmentation pipeline uses 3D nnU‐Net as the base and innovatively combines ground truth and its corresponding radial distance (RD) map during training supervision. Additionally, we evaluate the benefit of incorporating a convolutional long short term memory (LSTM‐Conv) layer and spatial recurrent convolution layer (RCL) into nnU‐Net. A novel slice‐by‐slice simple exponential smoothing (SEPS) method specifically for tubular structures was used to post‐process the segmentation results. Results: The experimental results show that nnU‐Net trained using a combination of Dice, cross‐entropy and RD achieved a Dice score of 77.1 ± 2.3% in the testing dataset. With SEPS, Hausdorff distance (HD) and 95% HD were reduced to 2.95 ± 0.17 mm and 1.84 ± 0.11 mm, respectively. LSTM‐Conv and RCL layers only minimally improved the segmentation precision. Conclusion: We present the first Foley‐free MRgRT‐based automated urethra segmentation study. Our method is built on a data‐driven neural network with novel cost functions and a post‐processing step designed for tubular structures. The performance is consistent with the need for online and offline urethra dose reduction in prostate radiotherapy. [ABSTRACT FROM AUTHOR]

In this work, we consider a system of multidimensional wave equations coupled by velocities with one localized fractional boundary damping. First, using a general criteria of Arendt–Batty, by assuming that the boundary control region satisfy some geometric conditions, under the equality speed propagation and the coupling parameter of the two equations is small enough, we show the strong stability of our system in the absence of the compactness of the resolvent. Our system is not uniformly stable, in general, since it is the case of the interval. Hence, we look for a polynomial decay rate for smooth initial data for our system by applying a frequency domain approach combining with a multiplier method. Indeed, by assuming that the boundary control region satisfy some geometric conditions, the waves propagate with equal speed, and the coupling parameter term is small enough, we establish a polynomial energy decay rate for smooth solutions, which depends on the order of the fractional derivative. [ABSTRACT FROM AUTHOR]

We consider the 2D isentropic compressible Euler equations, with pressure law p(ρ) = (1γ)ργ, with γ > 1. We provide an elementary constructive proof of shock formation from smooth initial data of finite energy, with no vacuum regions, and with nontrivial vorticity. We prove that for initial data which has minimum slope −1ε, for ε > 0 taken sufficiently small relative to the O1 amplitude, there exist smooth solutions to the Euler equations which form a shock in time Oε. The blowup time and location can be explicitly computed and solutions at the blowup time are of cusp‐type, with Hölder C13 regularity. Our objective is the construction of solutions with inherent O1 vorticity at the shock. As such, rather than perturbing from an irrotational regime, we instead construct solutions with dynamics dominated by purely azimuthal wave motion. We consider homogenous solutions to the Euler equations and use Riemann‐type variables to obtain a system of forced transport equations. Using a transformation to modulated self‐similar variables and pointwise estimates for the ensuing system of transport equations, we show the global stability, in self‐similar time, of a smooth blowup profile. © 2020 Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In sandwich laminates made of metal–polymer–metal (MPM) layers under forming conditions, the thickness changes along the thin‐layered specimens are of particular interest. To measure this distribution, it is common to take microscopic images and determine manually the thicknesses at particular discrete points. We compare and provide several methods determining the thickness distribution from microscopic images. First, an interpolation scheme is chosen to obtain a continuous function of the interfaces between the layers instead of the discrete pixel data. Afterwards, the methods—ruled surface approach, orthogonal projection, embedded circle scheme, and a dull but simple method—are applied and compared with each other. For very smooth data, most of the schemes show equivalent results. However, the "brute force"—called the dull approach—turns out to be the most robust scheme, particularly, if noisy data is studied. The Gaussian error propagation concept is applied to study the uncertainties resulting from noisy pixel data. The schemes are adapted to finite element simulation results as well so that a direct comparison of experimental and numerical data would be possible. [ABSTRACT FROM AUTHOR]

We consider a Bayesian multi‐model fault slip estimation (BMMFSE), which incorporates many candidates of the underground structure (Earth structure and plate boundary geometry) model characterized by a prior probability density function (PDF). The technique is used to study long‐term slow slip events (L‐SSEs) that occurred beneath the Bungo Channel, southwest Japan, in around 2010 and 2018. We here focus on the two advantages of BMMFSE: First, it allows for estimating slip distribution without introducing relatively strong prior information such as smoothing constraints, by combining a fully Bayesian inference and better consideration of model uncertainty to avoid overfitting. Second, the posterior PDF for the underground structure is also obtained during the fault slip estimation, which can be used as priors for the estimation of slip distribution for recurring events. The estimated slip distribution obtained using BMMFSE agreed better with the distribution of deep tectonic tremors at the down‐dip side of the main rupture area than those based on stronger prior constraints when the corresponding Coulomb failure stress changes are compared. This finding suggests a mechanical relationship between the L‐SSE and the synchronized tremors. The use of the posterior PDF of the underground structure estimated for the 2010 L‐SSE as prior PDF for the 2018 event resulted in more consistent estimation with the data, indicated by a smaller value of an information criterion. Plain Language Summary: This study attempts to accurately estimate the slow motion between two plates, which does not produce seismic waves and is known as slow slip events (SSEs), targeting those occurred in southwest Japan. This was accomplished by analyzing satellite data of ground movement during the SSEs using a novel approach called the Bayesian multi‐model fault slip estimation (BMMFSE) framework, which considers multiple candidates of assumptions for Earth structures. BMMFSE stabilizes the analysis and removes artifacts from the estimation results which are otherwise introduced because of the choice of a wrong Earth model. These advantages were validated by comparing the estimation results obtained based on previous approaches that do not consider multiple Earth models. The result of BMMFSE exhibited spatial distributions of motion between plates that are more consistent with other slow earthquakes which occurred synchronously in the nearby fault. In addition, BMMFSE improved the consistency of the estimates with the data by taking over the model uncertainty in the sequential analysis of repeated SSEs. Key Points: Fault slip distributions for slow slip events (SSE) at southwest Japan were estimated considering the uncertainty of underground structureThe mechanical relationship between the SSEs and synchronized tectonic tremors are better justified than in previous studiesInformation criterion indicates that estimates for recurring SSEs taking over model uncertainty result in better consistency with the data [ABSTRACT FROM AUTHOR]

Mechanical ventilation is a highly utilized life‐saving tool, particularly in the current era. The use of EEG in a brain–ventilator interface (BVI) to detect respiratory discomfort (due to sub‐optimal ventilator settings) would improve treatment in mechanically ventilated patients. This concept has been realized via development of an EEG covariance‐based classifier that detects respiratory‐related cortical activity associated with respiratory discomfort. The aim of this study was to determine if head movement, detected by an accelerometer, can detect and/or improve the detection of respiratory‐related cortical activity compared to EEG alone. In 25 healthy participants, EEG and acceleration of the head were recorded during loaded and quiet breathing in the seated and lying postures. Detection of respiratory‐related cortical activity using an EEG covariance‐based classifier was improved by inclusion of data from an Accelerometer‐based classifier, i.e. classifier 'Fusion'. In addition, 'smoothed' data over 50s, rather than one 5 s window of EEG/Accelerometer signals, improved detection. Waveform averages of EEG and head acceleration showed the incidence of pre‐inspiratory potentials did not differ between loaded and quiet breathing, but head movement was greater in loaded breathing. This study confirms that compared to event‐related analysis with >5 min of signal acquisition, an EEG‐based classifier is a clinically valuable tool with rapid processing, detection times, and accuracy. Data smoothing would introduce a small delay (<1 min) but improves detection results. As head acceleration improved detection compared to EEG alone, the number of EEG signals required to detect respiratory discomfort with future BVIs could be reduced if head acceleration is included. [ABSTRACT FROM AUTHOR]

The mechanical advantage of the knee extensor mechanism depends heavily on the patellar tendon moment arm (PTMA). Understanding which factors contribute to its variation may help improve functional outcomes following arthroplasty. This study optimized PTMA measurement, allowing us to quantify the contribution of different variables. The PTMA was calculated about the instantaneous helical axis of tibiofemoral rotation from optical tracked kinematics. A fabricated knee model facilitated calculation optimization, comparing four data smoothing techniques (raw, Butterworth filtering, generalized cross‐validated cubic spline‐interpolation and combined filtering/interpolation). The PTMA was then measured for 24 fresh‐frozen cadaveric knees, under physiologically based loading and extension rates. Combined filtering/interpolation enabled sub‐mm PTMA calculation accuracy throughout the range of motion (root‐mean‐squared error 0.2 mm, max error 0.4 mm), whereas large errors were measured for raw, filtered‐only and interpolated‐only techniques at terminal flexion/extension. Before scaling, the mean PTMA was 46 mm; PTMA magnitude was consistently larger in males (mean differences: 5 to 10 mm, p <.05) and was strongly related to knee size: larger knees have a larger PTMA. However, while scaling eliminated sex differences in PTMA magnitude, the peak PTMA occurred closer to terminal extension in females (female 15°, male 29°, p =.01). Knee size accounted for two‐thirds of the variation in PTMA magnitude, but not the flexion angle where peak PTMA occurred. This substantial variation in angle of peak PTMA has implications for the design of musculoskeletal models and morphotype‐specific arthroplasty. The developed calculation framework is applicable both in vivo and vitro for accurate PTMA measurement. [ABSTRACT FROM AUTHOR]

We study the asymptotic behaviour of the solution to a singularly perturbed model differential equation. The analytical solution is asymptotically represented by a formal power series in the perturbation parameter. This paper provides an explanation of the relation between the pre‐factor functions P(z) and Q(z) and the singulants of the expansion of the general second‐order singularly perturbed differential equation in higher levels of the asymptotic analysis. Exponential asymptotics typically relies upon the derivation of the singulant function as it is crucial to determine the location of the Stokes lines. By doing the careful analysis, we connect the subsequent order singulants with the previous order singulants and determine the asymptotics of the late coefficients. By this relation, one can predict and investigate the Stokes phenomenon and exponential smoothing of the equations in the form of the model differential equation. [ABSTRACT FROM AUTHOR]

The counterpoise (CP) correction by Boys and Bernardi has been well accepted as a reliable strategy to account for basis set superposition error (BSSE) in intermolecular complexes. The behavior of the CP correction was thoroughly studied in individual molecules of molecular complexes. This work studies the performance of the CP correction in many‐body clusters including three‐body clusters of organic compounds in the 3B‐69 dataset. Additionally, we used crystal structures of polymorphs of benzene, aspirin, and oxalyl dihydrazide (ODH) to construct a many‐body cluster dataset, abbreviated as the MBC‐36 dataset, consisting of two, four and eight molecules, and 16 molecules in the case of benzene. A series of Dunning's basis sets—cc‐pXZ and aug‐cc‐pXZ (X = D and T)—were used to predict CP‐corrected Hartree–Fock (HF) interaction energies of the 3B‐69 and MBC‐36 datasets. The CP‐corrected interaction energies were found to be basis‐set independent, whereas the non‐CP corrected interaction energies were found not to a follow a smooth exponential fitting as previously found for electronic energies of individual molecules. This observation was attributed to the presence of non‐additive induction forces in some clusters. Two 2 × 2 × 2 supercells of benzene polymorphs were constructed to explore the local nature of BSSE effects. A cut‐off radius of 10 Å was demonstrated to be sufficient to fully recover these effects. Although the behavior of CP correction was found to be non‐conventional in many‐body clusters of organic compounds, the use of a small basis set such as cc‐pVDZ showed excellent performance in the prediction of HF interaction energies. [ABSTRACT FROM AUTHOR]

Wind power generation has recently emerged in many countries. Therefore, the availability of long‐term historical wind speed data at various potential wind farm sites is limited. In these situations, such forecasting models are needed that comprehensively address the uncertainty of raw data based on small sample size. In this study, a hybrid first‐order accumulated generating operation‐based double exponential smoothing (AGO‐HDES) model is proposed for very short‐term wind speed forecasts. Firstly, the problems of traditional Holt's double exponential smoothing model are highlighted considering the wind speed data of Palmerston North, New Zealand. Next, three improvements are suggested for the traditional model with a rolling window of six data points. A mixed initialization method is introduced to improve the model performance. Finally, the superiority of the novel model is discussed by comparing the accuracy of the AGO‐HDES model with other forecasting models. Results show that the AGO‐HDES model increased the performance of the traditional model by 10%. Also, the modified model performed 7% better than other considered models with three times faster computational time. [ABSTRACT FROM AUTHOR]

Tenderness is an index for evaluating meat quality. A prediction model of tenderness was established based on the chicken deformation, which was determined by a viscoelasticity system combined with airflow and optical technique. Different preprocessing methods were used to preprocess the deformation. The interval variables that represent the viscoelasticity of the chicken in deformation, were screen by synergy interval partial least squares algorithm (Si‐PLS) and moving window partial least squares algorithm (Mw‐PLS). The prediction model was established by principal component regression (PCR) and partial least squares regression (PLSR). The optimum PLSR prediction model was established when Mw‐PLS was used to screen the interval variables of Savitzy‐Golay (S‐G) smoothing data. The correlation coefficient and the root mean square error of the calibration set were 0.965 and 0.874 kg, respectively. The corresponding value of the prediction set was 0.943 and 1.005 kg. This research provides a new method to assess the quality of poultry meat that conducts on airflow and optical techniques. [ABSTRACT FROM AUTHOR]

In many clinical studies, evaluating the association between longitudinal and survival outcomes is of primary concern. For analyzing data from such studies, joint modeling of longitudinal and survival data becomes an appealing approach. In some applications, there are multiple longitudinal outcomes whose longitudinal pattern is difficult to describe by a parametric form. For such applications, existing research on joint modeling is limited. In this article, we develop a novel joint modeling method to fill the gap. In the new method, a local polynomial mixed‐effects model is used for describing the nonparametric longitudinal pattern of the multiple longitudinal outcomes. Two model estimation procedures, that is, the local EM algorithm and the local penalized quasi‐likelihood estimation, are explored. Practical guidelines for choosing tuning parameters and for variable selection are provided. The new method is justified by some theoretical arguments and numerical studies. [ABSTRACT FROM AUTHOR]

In this paper, using the compatible theory of differential invariants, a class of exact solutions is obtained for nonhomogeneous quasilinear hyperbolic system of partial differential equations (PDEs) describing rate type materials; these solutions exhibit genuine nonlinearity that leads to the formation of discontinuities such as shocks and rarefaction waves. For certain nonconstant and smooth initial data, the solution to the Riemann problem is presented providing a complete characterization of the solutions. [ABSTRACT FROM AUTHOR]

We present a simple smoother designed for smooth data adjustments in sequentially generated reanalysis products by utilizing knowledge of future assimilation increments. A decay time parameter is applied to the smoother increments to account for memory decay timescales in the ocean. The result is different from simply time smoothing the reanalysis itself as only the increments are smoothed so the reanalysis product can retain high frequency variability that is being internally generated by the model and the atmospheric forcing. The smoother is applied first to the Lorenz 1963 model and then to the daily Met Office GloSea5 Global ¼° ocean reanalysis during 2016. Results show significant improvement over the original reanalysis in the 3D temperature and salinity state and variability, as well as in the sea surface height (SSH) and ocean currents. Comparisons are made directly against temperature, salinity and SSH observations, as well as independent 15 m drifter velocities. The impact on the time variability of conservative quantities, particularly ocean heat and salt content, as well as kinetic energy and the Atlantic Meridional Overturning Circulation (AMOC), is also demonstrated. Plain Language Summary: The ocean observing system is sparse and ocean circulation is slow relative to the atmosphere, giving the system a longer memory of state properties. Therefore an ocean reanalysis could seek to use more "future data" to help produce the best historical ocean state reconstruction for a given day. Conventional sequential assimilation approaches used operationally make no use of "future" data outside the window of current data analyzed for forecasting. Here we introduce a new approach for application to large global ocean reanalysis systems that have only been run in "forward mode" (using past data), using the history of stored data increments to produce a more physically plausible time‐evolving ocean state with smoother temporal adjustments toward the available observations. Key Points: A novel temporal smoother algorithm is designed for improving ocean reanalysis products by utilizing the stored history of assimilation incrementsHigh frequency variability internally generated by the model and the atmospheric forcing is retained in the outputThe smoother significantly improves the reanalysis temperature, salinity, velocity, and sea surface height fields [ABSTRACT FROM AUTHOR]

In contrast to the analysis of abrupt changes, methods for detecting gradual change points are less developed. In this paper, we are interested in the scenario that the variance of data may vary gradually while the mean of data changes in a smooth fashion. We propose a penalized weighted least squares approach with an iterative estimation procedure to detect the gradual variance change point with a smoothly changing mean function. The null distribution of the test statistic is derived in association with the consistency of estimates. The performance of our method is illustrated by means of a simulation study and a real data example. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) declared the Coronavirus Disease (COVID‐19) a pandemic due to the huge upsurge in the number of reported cases worldwide. The COVID‐19 pandemic in India has become a public health threat, and if we go by the number of confirmed cases then the situation seems to be a matter of grave concern. According to real‐time data, the numbers of confirmed cases are growing exponentially. No doubt, substantial public health interventions both at the national and state levels are implemented immediately by the Government of India; there is a need for improved preparedness plans and mitigation strategies along with accurate forecasting. The present study aims to forecast the COVID‐19 outbreak infected cases in India. The data have been obtained from https://www.covid19india.org, https://www.worldometers.info/coronavirus, and ICMR reported publicly available information about COVID‐19 confirmation cases. We have used the double exponential smoothing method for forecasting the trends in terms of confirmed, active, recovered and death cases from COVID‐19 for emergency preparedness and future predictions. Findings reveal that the estimated value of point forecast is just 8.22% of the total number of confirmed cases reported on a daily basis across the country. It was observed that the deaths were lower for the states and union territories with a higher detection rate. It is suggested that by keeping in view the limited healthcare resources in the country, accurate forecasting, early detection, and avoidance of acute care for the majority of infected cases is indispensable. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the Cauchy problem of the subcritical semilinear generalized Tricomi equations with dimension n ≥ 3. Under the assumption that the initial data are smooth and compactly supported, the Cauchy problem is shown to be locally well‐posed and the lower bound estimate of the lifespan is established. We also obtain the upper bound of the lifespan with the same scale; hence, the sharp lifespan is determined. The proof is based on the improved versions of the weighted Strichartz estimates. [ABSTRACT FROM AUTHOR]

Purpose: This study develops a viscous damping model (VDM) based on Rayleigh Damping (RD) with potential use in low cost, non‐invasive breast cancer diagnostics using Digital Image Elasto Tomography (DIET). Methods: A clinical trial involving 13 subjects, each with a tumor in one breast, resulted in 13 cancerous and 13 healthy breasts. Displacement data following actuator induced steady state vibration in the breast tissue were captured using the DIET system. Over 14 000 reference points on the breast surface were split into four segments and viscous damping constant calculated for each reference point. The VDM was fit to median‐filtered data for each breast segment and VDM coefficients compared within each breast. One model coefficient, relating to stiffness, was hypothesized to differ in breast segments containing a tumor. Comparison of "b" coefficients in different breast segments using percentage tolerances provided an unbiased, generalizable diagnostic method. Bootstrapping with replacement was used to upsample the data and create smooth receiver operator characteristic (ROC) curves. A total of 12 breast segmentation configurations were used to demonstrate the robustness of the method. Results: Fitting the VDM to median‐filtered data gave consistent results for one VDM coefficient ("a") across all breasts. The second VDM coefficient ("b") showed diagnostic potential with breast segments having consistent coefficients in healthy breasts. In cancerous breasts "b" coefficients were found to be statistically different in segments containing and adjacent to the tumor compared with the segment furthest from the tumor with p < 0.02 using the Student t‐Test. Large discrepancies in "b" coefficients were found to be indicative of a tumor with a 14.5% tolerance resulting in sensitivity and specificity of 76.9%. The optimal breast configuration resulted in an area under the ROC curve (AUC) of 0.81 with sensitivity and specificity at 77% and 72%, respectively. Conclusion: This VDM method enables a computationally simple diagnostic technique using DIET for comfortable breast screening for women of all ages. Regular screening potential allows for tolerance alteration based on age, prior subject‐specific results, and other risk factors to manage false positives, reducing psychological harm while optimizing early detection for successful treatment and decreased mortality. [ABSTRACT FROM AUTHOR]

Optimal planning of hybrid energy systems has always been a considerable task. While several studies have conducted techno‐economic analysis on hybrid energy systems, most of them have neglected to consider appropriate electric and thermal load growth rates. This paper proposes an efficient strategy for optimizing a grid‐connected hybrid energy system considering electric and thermal load growth rates. HOMER software is used for optimizing hybrid energy systems. It uses a nonderivative optimization to recognize the system with the minimum net present cost (NPC) among hundreds of configurations. The exponential smoothing method is also used for predicting upcoming load peak values based on historical data. The proposed hybrid system includes solar photovoltaic (SPV) system, wind turbine (WT) system, converter, battery storage system (BSS), gas generator (GasGen), fuel cell (FC), fuel‐fired boiler, and resistive boiler. Unlike previous studies, electricity price growth and SPV degradation rates are considered in this study. The grid‐connected system has also been compared to the stand‐alone system to understand grid connection impacts on the optimization results. Environmental analysis has been performed to analyze the greenhouse gas emissions of each system. The effects of growth rates in thermal and electric loads and electricity price are comprehensively investigated. Neglecting load growth rates affected the financial results severely. In this condition, the NPC and COE are achieved −11 383.4 US$ and −0.02376 US$/kWh. Results indicate that the combination of WT system (15 kW), converter (10 kW), resistive, and fuel‐fired (10 kW) boilers is the most economical configuration in grid‐connected mode. The WT system plays an essential role in the electric power provision and revenues of the hybrid system. The net present cost (NPC) and the cost of energy (COE) for grid‐connected operating mode are achieved −0.0162272 US$/kWh and −5243.956 US$, respectively. For off‐grid operating mode, the NPC and COE are obtained 47 024.19 US$ and 0.1983879 US$/kWh, respectively. Thanks to the feed‐in‐tariff (FiT) policy, the proposed grid‐connected system is more economical and environmentally friendly than the stand‐alone system. In off‐grid operating mode, 63 575.45285 kg CO2 is produced, which is higher than CO2 production in the grid‐connected mode (more than 8262 kg). [ABSTRACT FROM AUTHOR]

We present a novel method of analysis and prove finite time asymptotically self‐similar blowup of the De Gregorio model [13, 14] for some smooth initial data on the real line with compact support. We also prove self‐similar blowup results for the generalized De Gregorio model [41] for the entire range of parameter on ℝ or S1 for Hölder‐continuous initial data with compact support. Our strategy is to reformulate the problem of proving finite time asymptotically self‐similar singularity into the problem of establishing the nonlinear stability of an approximate self‐similar profile with a small residual error using the dynamic rescaling equation. We use the energy method with appropriate singular weight functions to extract the damping effect from the linearized operator around the approximate self‐similar profile and take into account cancellation among various nonlocal terms to establish stability analysis. We remark that our analysis does not rule out the possibility that the original De Gregorio model is well‐posed for smooth initial data on a circle. The method of analysis presented in this paper provides a promising new framework to analyze finite time singularity of nonlinear nonlocal systems of partial differential equations. © 2021 Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Many physically‐based models for climate change impact studies require sub‐daily temporal resolution of the forcing data to provide meaningful predictions. However, climate scenarios are typically available at daily time step, severely limiting the application of such physically‐based models. In this study, we propose an enhanced delta‐change method for downscaling climate change scenarios from daily to hourly resolution. The approach presented provides objective criteria for assessing the quality of the determined delta and downscaled time series, while also fixing issues of common quantile mapping methods used for spatial downscaling related to the decrease of correlation between different variables. However, this new approach has limitations in correctly representing statistically extreme events and changes in the frequency of discontinuous events such as precipitation. Smoothing of historical and future data is required prior to applying the delta‐change method, and the related parameters are found to have a subtle impact on the correctness of the representation of the seasonal means as well as the resulting (artificial) variability in the scenario data product. This new method is universal and can be applied with smoothing approaches apart from the harmonic fitting used in this work and in the past. In this study, the assessment suggested the use of seven harmonics for the smoothing of the input data as a best choice of this parameter for the data used. The method is applied to a Swiss climate change scenario data set, CH2018, and to a complement of this set to a Swiss alpine measurement network obtained by spatial transfer of CH2018, resulting in a set of 68 climate change scenarios at hourly resolution for 188 stations over Switzerland significantly expanding upon the spatial and temporal resolution of the CH2018 data set. All source code to perform such an analysis and the complete data product are provided open access. [ABSTRACT FROM AUTHOR]

DNA profiles generated by different STR kits may show different alleles for identical amplified loci. This well‐known phenomenon affects the smooth transition of data generated by new STR kits to a database or casework laboratory or cross‐laboratory comparison of STR profiles. As in other DNA databases throughout the world, it has become clear that the number of the analyzed loci should be expanded for a variety of reasons, such as partial profiles resulting from low copy‐number DNA template or degraded samples, working with mixtures or when prevalence of familial inbreeding. In the course of introducing a new STR kit, VeriFiler™ Express (Applied Biosystems, Foster City, CA, USA), we compared genotyping data of 1568 samples amplified by the VeriFiler™ Express with the data generated on the same samples by the Powerplex™ ESI FAST (Promega, Madison WI, USA) kit. Discordance was noted in 20 samples (1.27%), 14 (0.89%) of them showing allele dropout mismatch and six (0.38%) showing an additional fixed‐size third allele. These rates are well above the reported rates of 0.4% for this kit. Since correct genotyping and accurate consistent allele assignment is of paramount importance, it seems timely to recommend for DNA laboratories and genetic‐match search systems to take these possible inconsistencies into account. [ABSTRACT FROM AUTHOR]

Maximum approximate Bernstein likelihood estimates of the baseline density function and the regression coefficients in the proportional hazard regression models based on interval‐censored event time data result in smooth estimates of the survival functions which enjoys an almost n1/2‐rate of convergence faster than the n1/3‐rate for the existing estimates. The proposed method was shown by a simulation to have better finite sample performance than its main competitors. Some examples including real data are used to illustrate the usage of the proposed method. [ABSTRACT FROM AUTHOR]

The performance of ensemble‐based data assimilation techniques that estimate the state of a dynamical system from partial observations depends crucially on the prescribed uncertainty of the model dynamics and of the observations. These are not usually known and have to be inferred. Many approaches have been proposed to tackle this problem, including fully Bayesian, likelihood maximization and innovation‐based techniques. This work focuses on maximization of the likelihood function via the expectation–maximization (EM) algorithm to infer the model error covariance combined with ensemble Kalman filters and particle filters to estimate the state. The classical application of the EM algorithm in a data assimilation context involves filtering and smoothing a fixed batch of observations in order to complete a single iteration. This is an inconvenience when using sequential filtering in high‐dimensional applications. Motivated by this, an adaptation of the algorithm that can process observations and update the parameters on the fly, with some underlying simplifications, is presented. The proposed technique was evaluated and achieved good performance in experiments with the Lorenz‐63 and Lorenz‐96 dynamical systems designed to represent some common scenarios in data assimilation such as nonlinearity, chaoticity and model mis‐specification. [ABSTRACT FROM AUTHOR]

In contrast to the analysis of abrupt changes, methods for detecting gradual change points are less developed. In this paper, we are interested in the scenario that the variance of data may vary gradually while the mean of data changes in a smooth fashion. We propose a penalized weighted least squares approach with an iterative estimation procedure to detect the gradual variance change point with a smoothly changing mean function. The null distribution of the test statistic is derived in association with the consistency of estimates. The performance of our method is illustrated by means of a simulation study and a real data example. [ABSTRACT FROM AUTHOR]

As a microwave radiometer seeing through clouds, Advanced Technology Microwave Sounder (ATMS) observations play a critical role in visually monitoring hurricane warm core structures. However, the presence of orbit gaps within the ATMS observations at low latitudes regions, where hurricanes frequently develop, raises concerns in monitoring hurricanes' spatial variability. To resolve this issue, this study generates gap‐filled ATMS brightness temperature data by using the smoothing algorithm of Penalized Least Square Discrete Cosine Transform. The accuracies of the missing‐filled brightness temperatures for temperature sounding channels are approximately within 1 K. Furthermore, the gap‐filled brightness temperature data from channels 5–12 are utilized to establish a three‐dimensional Hurricane Warm Core Animation System (HWCAS) in near real time (NRT), which helps to visually observe realistic warm core structures of a hurricane system. The information of hurricane warm core over open oceans and coastal areas is derived using a combination of three new regression‐based atmospheric temperature retrieval algorithms, with the averaged error typically within ±1 K at the vertical levels a warm core could occur. Each animation consists of 97 two‐dimensional atmospheric temperature anomaly images at different cross‐sections through hurricane core regions. The retrieved maximum temperature anomalies show well the formation, intensification, weakening, reintensification, and dissipation stages of Hurricane Florence that are similar to those from the European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis. They also show similar weakening and reintensifying stages to those of the maximum sustained winds by the best track data, albeit with some temporal lead/lag. With its strength in NRT hurricane monitoring, the HWCAS demonstrate its great potential in providing meteorologists with timely information of temperature anomaly fields in the inner‐core regions of a hurricane. Key Points: This study fills orbital gaps within ATMS Sensor Data Record (SDR) data to visually observe whole hurricane warm core structuresThis study establishes a 3‐D Hurricane Warm Core Animation System (HWCAS) from near real‐time ATMS observationsEach 3‐D animation contains 97 2‐D atmospheric temperature anomaly images at different cross‐sections through hurricane core regions [ABSTRACT FROM AUTHOR]

We propose a flexible semiparametric stochastic mixed effects model for bivariate cyclic longitudinal data. The model can handle either single cycle or, more generally, multiple consecutive cycle data. The approach models the mean of responses by parametric fixed effects and a smooth nonparametric function for the underlying time effects, and the relationship across the bivariate responses by a bivariate Gaussian random field and a joint distribution of random effects. The proposed model not only can model complicated individual profiles, but also allows for more flexible within‐subject and between‐response correlations. The fixed effects regression coefficients and the nonparametric time functions are estimated using maximum penalized likelihood, where the resulting estimator for the nonparametric time function is a cubic smoothing spline. The smoothing parameters and variance components are estimated simultaneously using restricted maximum likelihood. Simulation results show that the parameter estimates are close to the true values. The fit of the proposed model on a real bivariate longitudinal dataset of pre‐menopausal women also performs well, both for a single cycle analysis and for a multiple consecutive cycle analysis. The Canadian Journal of Statistics 48: 471–498; 2020 © 2020 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Clone node attack in IoT sensor devices remains a grave security concern as it paves the way for sinkhole, wormhole, and selective forwarding attacks. In this paper, a two‐level authentication scheme named Fingerprint‐based Zero‐Knowledge Authentication (FZKA) algorithm is proposed to improve the detection rate of clone node among the sensor devices. In the fingerprint generation phase, the base station calculates a distinct fingerprint value for each and every node in the network by gathering neighborhood information, represented in the form of superimposed s‐disjunct code matrix. The calculated fingerprint is considered as a secret value and distributed to each cluster nodes for the process of authentication. The FZKA algorithm improves the cloned node detection accuracy with minimal detection time. The simulation results highlight the cloned node detection rate of the proposed scheme by a margin of 92.5% against the existing Exponential Smoothing Algorithm (ETS), Position Verification Method, and Message Verification and Passing algorithms. [ABSTRACT FROM AUTHOR]

We discuss a simple methodology to enable a statistical comparison of human population with the vegetation of North America over the past 13,000 years. Nonparametric kernel methods are applied for temporal and spatial smoothing of point data obtained from the Neotoma Paleoecology Database and the Canadian Archaeological Radiocarbon Database, which results in sequences of maps showing the development of population and different plant taxa during the Holocene. The estimation of smooth spatial and spatio‐temporal cross‐correlation functions is proposed in order to detect relationships between population and vegetation in fixed time intervals. Furthermore, the effects of varying environment on demographic changes as well as potential impacts of populations on plant taxa over time are analyzed. Pointwise confidence bands for cross‐correlation functions are computed and a robustness analysis is performed to assess the significance of obtained results. Considering the example of oak, an interpretation of our results for eastern North America shows the value of this methodology. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a mode transformation algorithm based on traffic prediction in virtual multiple optical line terminal (OLT) passive optical network (PON) is proposed. By proposing exponential smoothing algorithm based on weight update (WU‐ESA), user traffic is predicted well. WU‐ESA is a combination of two algorithms: exponential smoothing algorithm (ESA) and genetic algorithm (GA). The weight in ESA is optimized by GA based on real‐number encoding. By setting two periods, GA part and ESA part can be separated effectively. By presenting elastic packing algorithm (EPA), the load balance problem in virtual multi‐OLT PON is solved. EPA is implemented based on WU‐ESA. By the simulation and analysis, the effectiveness of the proposed algorithms is demonstrated. Compared with traditional mode transformation algorithm, EPA shows good performances in delay, throughput, and packet loss. Compared with traditional mode transformation algorithm, the EPA makes the packet loss decrease by at least 5% when the system load is greater than 0.9. Meanwhile, the delay of the two subsystems can be kept at a relatively balanced level by the implement of EPA. For throughput, the use of EPA improves the throughput by 20% when the system load is high. [ABSTRACT FROM AUTHOR]

The paper is devoted to an identification of the slip bound function g in the Stokes system with threshold slip boundary conditions assuming that g depends on the tangential velocity uτ. To this end the optimal control approach is used. To remove its nonsmoothness we use a regularized form of the slip conditions in the state problem. The mutual relation between solutions to the original optimization problem and the problems with regularized states is analyzed. The paper is completed by numerical experiments. [ABSTRACT FROM AUTHOR]