Search

PM2.5 is one of the major contributors to air pollution in India, resulting in poor air quality and human health issues. The current study explores the applicability of TTAinterfaceTrendAnalysis (an 'R' language-based Graphical User Interface package) in estimating the annual and month-wise trends in PM2.5 over five Indian megacities (New Delhi, Kolkata, Mumbai, Hyderabad, and Chennai). The package offers a one-stop solution for various statistical analyses such as data imputation, outlier detection, trend analysis, data smoothing, diagnostic tests, etc. The trends are estimated using seven (2014–2020) years of PM2.5 data. All estimated month-wise trends of PM2.5 over the study cities were found to be statistically non-significant. Annual trends (both with and without applying data smoothing techniques) were found to be statistically significant with magnitudes ranging between − 0.27 and − 5.2 µg m−3 y−1 (negative sign indicates a declining trend). Autocorrelation analysis and normality tests are also conducted using the inbuilt options of TTAinterfaceTrendAnalysis. Results obtained are thoroughly discussed and compared with those of similar contemporary studies. To our knowledge, the current study is the first to apply TTAinterfaceTrendAnalysis for quantifying long-term trends in PM2.5. The sensitivity of the trend estimates due to the inclusion of COVID-19 lockdown PM2.5 data was also examined. The net impact of lockdown on the PM2.5 long-term trend ranged between − 0.13 and 2.29 µg m−3 y−1 across the study cities. [ABSTRACT FROM AUTHOR]

A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication. The expression of the geometric stiffness matrix is derived, the finite element linear buckling analysis is conducted, and the sensitivity solution of the linear buckling factor is achieved. For a specific problem in linear buckling topology optimization, a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells. The aggregation function method is used to consider the high-order eigenvalues, so as to obtain continuous sensitivity information and refined structural design. With cyclic matrix programming, a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted. To maximize the buckling load, under the constraint of the given buckling load, two types of topological optimization columns are constructed. The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm. The vertex method and the matching point method are used to carry out an uncertainty propagation analysis, and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance. Finally, the differences in the structural topology optimization under different reliability degrees are illustrated by examples. [ABSTRACT FROM AUTHOR]

As the performance of Radio Frequency Identification (RFID) devices is susceptible to the influence of the surrounding environment, making the original data collected by RFID devices with uncertain, of which missed data and redundant data are the main source of uncertainty data, these uncertainty data will seriously affect the quality of RFID upper layer applications. Therefore, to solve the uncertainty in RFID data, the original data must be cleaned. For the shortcomings of tag dynamics detection in the traditional data cleaning algorithm named Statistical Smoothing for Unreliable RFID data (SMURF), an adaptive sliding window-based data cleaning algorithm for RFID single-tag is proposed. The method takes into account the influence of tag speed on tag integrity judgment, and divides sliding sub-windows to accurately detect tag changes and then reasonably adjusts the sliding window size. In addition, considering that the average reading rate of tags is affected by the collision between multiple tags, an RFID multi-tag cleaning method based on twice-tag number estimation is proposed. The method accurately estimates the number of tags by the twice-tag estimation method, controls the read period by the Markov chain, and reduces the occurrence of multiple tag collisions by using an unequal time slot optimization method. Experimental results show that the proposed method in this paper can form a complete set of RFID data stream cleaning algorithms, which effectively reduces the uncertain data and improves the data accuracy. [ABSTRACT FROM AUTHOR]

Background and objectives: The acute respiratory infection caused by severe acute respiratory syndrome coronavirus disease (COVID-19) has resulted in increased mortality among pregnant, puerperal, and neonates. Brazil has the highest number of maternal deaths and a distressing fatality rate of 7.2%, more than double the country's current mortality rate of 2.8%. This study investigates the impact of the COVID-19 pandemic on the Brazilian Maternal Mortality Ratio (BMMR) and forecasts the BMMR up to 2025. Methods: To assess the impact of the COVID-19 pandemic on the BMMR, we employed Holt-Winters, Autoregressive Integrated Moving Average (ARIMA), and Neural Networks Autoregression (NNA). We utilized a retrospective time series spanning twenty-five years (1996–2021) to forecast the BMMR under both a COVID-19 pandemic scenario and a controlled COVID-19 scenario. Results: Brazil consistently exhibited high maternal mortality values (mean BMMR [1996–2019] = 57.99 ±6.34/100,000 live births) according to World Health Organization criteria. The country experienced its highest mortality peak in the historical BMMR series in the second quarter of 2021 (197.75/100,000 live births), representing a more than 200% increase compared to the previous period. Holt-Winter and ARIMA models demonstrated better agreement with prediction results beyond the sample data, although NNA provided a better fit to previous data. Conclusions: Our study revealed an increase in BMMR and its temporal correlation with COVID-19 incidence. Additionally, it showed that Holt-Winter and ARIMA models can be employed for BMMR forecasting with lower errors. This information can assist governments and public health agencies in making timely and informed decisions. [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]

In this paper, we prove the existence of smooth initial data for the two-dimensional free boundary incompressible viscous magnetohydrodynamics (MHD) equations, for which the interface remains regular but collapses into a splash singularity (self-intersects in at least one point) in finite time. The existence of the splash singularities is guaranteed by a local existence theorem, in which we need suitable spaces for the modified magnetic field together with the modification of the velocity and pressure such that the modified initial velocity is zero, and a stability result which allows us to construct a class of initial velocities and domains for an arbitrary initial magnetic field. It turns out that the presence of the magnetic field does not prevent the viscous fluid from forming splash singularities for certain smooth initial 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]

A novel algorithm is proposed for quantitative comparisons between compact surfaces embedded in the three-dimensional Euclidian space. The key idea is to identify those objects with the associated surface measures and compute a weak distance between them using the Fourier transform on the ambient space. In particular, the inhomogeneous Sobolev norm of negative order for a difference between two surface measures is evaluated via the Plancherel theorem, which amounts to approximating a weighted integral norm of smooth data on the frequency space. This approach allows several advantages, including high accuracy due to fast-converging numerical quadrature rules, acceleration by the nonuniform fast Fourier transform, and parallelization on many-core processors. In numerical experiments, the 2-sphere, which is an example whose Fourier transform is explicitly known, is compared with its icosahedral discretization, and it is observed that the piecewise linear approximations converge to the smooth object at the quadratic rate up to small truncation. [ABSTRACT FROM AUTHOR]

A prior distribution for the underlying graph is introduced in the framework of Gaussian graphical models. Such a prior distribution induces a block structure in the graph's adjacency matrix, allowing learning relationships between fixed groups of variables. A novel sampling strategy named Double Reversible Jumps Markov chain Monte Carlo is developed for learning block structured graphs under the conjugate G-Wishart prior. The algorithm proposes moves that add or remove not just a single edge of the graph but an entire group of edges. The method is then applied to smooth functional data. The classical smoothing procedure is improved by placing a graphical model on the basis expansion coefficients, providing an estimate of their conditional dependence structure. Since the elements of a B-Spline basis have compact support, the conditional dependence structure is reflected on well-defined portions of the domain. A known partition of the functional domain is exploited to investigate relationships among portions of the domain and improve the interpretability of the results. for this article are available online. [ABSTRACT FROM AUTHOR]

Exponential smoothing is one of the most widely used forecasting methods for univariate time series data. Based on the difference between protected and confidential time series data, we derive theoretical bounds for the absolute change to forecasts generated from additive exponential smoothing models. Given time series data up to time t, we discover a functional form of robust bounds for the absolute change to forecasts for any T ≥ t + 1 , which can be represented as a compact form of geometric series. We also find robust bounds for the Change in Mean Absolute Error ( Δ MAE ) and Measured Mean Absolute Error (MMAE). [ABSTRACT FROM AUTHOR]

Surface quasi-geostrophy (SQG) describes the two-dimensional active transport of a temperature field in a strongly stratified and rotating environment. Besides its relevance to geophysics, SQG bears formal resemblance with various flows of interest for turbulence studies, from passive scalar and Burgers to incompressible fluids in two and three dimensions. This analogy is here substantiated by considering the turbulent SQG regime emerging from deterministic and smooth initial data prescribed by the superposition of a few Fourier modes. While still unsettled in the inviscid case, the initial value problem is known to be mathematically well-posed when regularised by a small viscosity. In practice, numerics reveal that in the presence of viscosity, a turbulent regime appears in finite time, which features three of the distinctive anomalies usually observed in three-dimensional developed turbulence: (i) dissipative anomaly, (ii) multifractal scaling, and (iii) super-diffusive separation of fluid particles, both backward and forward in time. These three anomalies point towards three spontaneously broken symmetries in the vanishing viscosity limit: scale invariance, time reversal, and uniqueness of the Lagrangian flow, a fascinating phenomenon that Krzysztof Gawȩdzki dubbed spontaneous stochasticity. In the light of Gawȩdzki's work on the passive scalar problem, we argue that spontaneous stochasticity and irreversibility are intertwined in SQG and provide numerical evidence for this connection. Our numerics, though, reveal that the deterministic SQG setting only features a tempered version of spontaneous stochasticity, characterised in particular by non-universal statistics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models. [ABSTRACT FROM AUTHOR]

A highly complex dynamic non-linear reactor is the blast furnace iron manufacturing system. It has possible dangers, including carbon monoxide, wide variety of chemical reactions, fire, high pressure and explosion, noise, split and fall, hot metal sparks, hit etc. To ensure a secure working, organizations must take the required measures to manage the risks and their effects. The approach for risk assessment discussed in this research attempts to increase blast furnace safety performance and reduce workers injuries. This approach uses probability distribution and an improved machine learning techniques such as radial basis function artificial neural networks (RBANN). The novelty here is to calculate a multivariate risk using a proposed method, namely exponential smoothing combined with radial basis function artificial neural networks (ES-RBANN). To identify their limits, the results of a research comparing conventional and novel techniques are confirmed using real data collected from the steel production operations ArcelorMittal-Annaba, Algeria. [ABSTRACT FROM AUTHOR]

Motivated by [2,3] , we prove the global existence of solutions to the two-dimensional isentropic compressible Navier-Stokes equations with smooth initial data which are slowly varying in one direction and with initial density being away from vacuum. In particular, we present examples of initial data which generate unique global smooth solutions to 2 D compressible Navier-Stokes equations with constant viscosity and with initial data which are neither small perturbation of some constant equilibrium state nor of small energy. [ABSTRACT FROM AUTHOR]

This paper deals with the coupled chemotaxis-haptotaxis model with gradient-dependent flux-limitation ... where Ω ⊂ ℝn(n ≥ 1) is a bounded domain with smooth boundary, χ, ξ and μ are positive parameters. * Under the condition that 1 < p < n/ n - 1, then for any χ, ξ,μ > 0 and all reasonably smooth initial data (u0,w0), the above system possesses a globally classical solution, which is uniformly bounded in time. * Under the conditions that 1 < p < n/n - 1 and μ > χ²/8, it asserts exponential decay of w in the large time limit, whereas both u and v persist in some certain sense. [ABSTRACT FROM AUTHOR]

This article examines the disparities between tuberculosis burden estimates provided by the World Health Organization (WHO) and the Institute for Health Metrics and Evaluation (IHME). The authors emphasize the importance of clearer explanations and reproducibility of IHME's methods. They suggest enhancing comparisons and dialogue between IHME and WHO to better understand the differences in data and methodologies. The article also proposes considering method changes to incorporate country-specific data and addressing inconsistencies in epidemiological patterns. Additionally, the authors recommend exploring statistical smoothing in the estimates and utilizing covariates to capture changes in TB programs. The article further discusses the use of projection estimates by IHME for predicting future disease burden, particularly for TB. The authors recommend that IHME take into account programmatic changes for TB in their methodology and emphasize the need for stronger explanations of methods, comparisons, and dialogue with WHO, as well as improved communication with stakeholders. While acknowledging the value of both IHME and WHO estimates, the authors identify areas for improvement. [Extracted from the article]

The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022-- 2031. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the annual report of Ministry of Road Transport and Highways, India (2020) and Accidental Deaths & Suicides in India (ADSI) Report of National Crime Record Bureau (2021). After examining all the probable models, it is observed that ARIMA (2, 2, 2) model and exponential smoothing (M, A, N) model are suitable for the given data. Amongst the two, ARIMA (2, 2, 2) model has a lower AIC and BIC value. Thus, this comes out to be the best model as per our model selection criterion. Further, the study also reveals an upward trend of number of road accidental deaths for the upcoming 10 years in India. [ABSTRACT FROM AUTHOR]

Sampling variance smoothing is an important topic in small area estimation. In this article, we propose sampling variance smoothing methods for small area proportion estimation. In particular, we consider the generalized variance function and design effect methods for sampling variance smoothing. We evaluate and compare the smoothed sampling variances and small area estimates based on the smoothed variance estimates through analysis of survey data from Statistics Canada. The results from real data analysis and simulation study indicate that the proposed sampling variance smoothing methods perform very well for small area estimation. [ABSTRACT FROM AUTHOR]

In this work, a general class of interpolation and smoothing natural exponential splines with respect to fourth order differential operators with two real parameters is considered. Some sufficient conditions for the associated matrix R to be a diagonally dominant matrix are given. Based on these, fast algorithms for computing the coefficients of this general class of exponential splines are developed. The obtained splines have C 2 continuity and are the minimum solution of the combination of interpolation and smoothing energy integral. The performances of the resulting splines in financial data from the S &P500 index are given. Numerical experiments show that the resulting splines have more freedom to adjust the shape and control the energy of the curves. Cross-validation and generalized cross-validation for determining an appropriate smoothing parameter are also given. [ABSTRACT FROM AUTHOR]

In this work, we apply the extended constant-roll condition to non-canonical scalar fields. We use in particular two types of non-canonical fields, namely, the tachyon field and the Dirac–Born–Infeld (DBI) field. We assume, in particular, that the tachyon scalar field satisfies the condition ϕ ̈ = α (ϕ) V ′ (ϕ) / V (ϕ) , while the DBI scalar field satisfies the condition ϕ ̈ = α (ϕ) V ′ (ϕ). We use the extended constant-roll condition to calculate the inflation indices for each of these non-canonical fields. In order to test the extended constant-roll condition, we use three types of potentials, namely chaotic potential, linear power-law potential and exponential potential, and choose power-law and exponential forms for the smooth function α (ϕ). It is then shown that the models under consideration are compatible with the latest Planck 2018 constraints. [ABSTRACT FROM AUTHOR]

Whether the 3D incompressible Euler equations can develop a finite time singularity from smooth initial data is one of the most challenging problems in nonlinear PDEs. In this paper, we present some new numerical evidence that the 3D axisymmetric incompressible Euler equations with smooth initial data of finite energy develop a potential finite time singularity at the origin. This potential singularity is different from the blow-up scenario revealed by Luo and Hou (111:12968–12973, 2014) and (12:1722–1776, 2014), which occurs on the boundary. Our initial condition has a simple form and shares several attractive features of a more sophisticated initial condition constructed by Hou and Huang in (arXiv:2102.06663, 2021) and (435:133257, 2022). One important difference between these two blow-up scenarios is that the solution for our initial data has a one-scale structure instead of a two-scale structure reported in Hou and Huang (arXiv:2102.06663, 2021) and (435:133257, 2022). More importantly, the solution seems to develop nearly self-similar scaling properties that are compatible with those of the 3D Navier–Stokes equations. We will present numerical evidence that the 3D Euler equations seem to develop a potential finite time singularity. Moreover, the nearly self-similar profile seems to be very stable to the small perturbation of the initial data. [ABSTRACT FROM AUTHOR]

Whether the 3D incompressible Navier–Stokes equations can develop a finite time singularity from smooth initial data is one of the most challenging problems in nonlinear PDEs. In this paper, we present some new numerical evidence that the incompressible axisymmetric Navier–Stokes equations with smooth initial data of finite energy seem to develop potentially singular behavior at the origin. This potentially singular behavior is induced by a potential finite time singularity of the 3D Euler equations that we reported in a companion paper published in the same issue, see also Hou (Potential singularity of the 3D Euler equations in the interior domain. arXiv:2107.05870 [math.AP], 2021). We present numerical evidence that the 3D Navier–Stokes equations develop nearly self-similar singular scaling properties with maximum vorticity increased by a factor of 10 7 . We have applied several blow-up criteria to study the potentially singular behavior of the Navier–Stokes equations. The Beale–Kato–Majda blow-up criterion and the blow-up criteria based on the growth of enstrophy and negative pressure seem to imply that the Navier–Stokes equations using our initial data develop a potential finite time singularity. We have also examined the Ladyzhenskaya–Prodi–Serrin regularity criteria (Kiselev and Ladyzhenskaya in Izv Akad Nauk SSSR Ser Mat 21(5):655–690, 1957; Prodi in Ann Math Pura Appl 4(48):173–182, 1959; Serrin in Arch Ration Mech Anal 9:187–191, 1962) that are based on the growth rate of L t q L x p norm of the velocity with 3 / p + 2 / q ≤ 1 . Our numerical results for the cases of (p , q) = (4 , 8) , (6 , 4) , (9 , 3) and (p , q) = (∞ , 2) provide strong evidence for the potentially singular behavior of the Navier–Stokes equations. The critical case of (p , q) = (3 , ∞) is more difficult to verify numerically due to the extremely slow growth rate in the L 3 norm of the velocity field and the significant contribution from the far field where we have a relatively coarse grid. Our numerical study shows that while the global L 3 norm of the velocity grows very slowly, the localized version of the L 3 norm of the velocity experiences rapid dynamic growth relative to the localized L 3 norm of the initial velocity. This provides further evidence for the potentially singular behavior of the Navier–Stokes equations. [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]

We investigate a two-dimensional transmission model consisting of a wave equation and a Kirchhoff plate equation with dynamical boundary controls under geometric conditions. The two equations are coupled through transmission conditions along a steady interface between the domains in which the wave and plate equations evolve, respectively. Our primary concern is the stability analysis of the system, which has not appeared in the literature. For this aim, using a unique continuation theorem, the strong stability of the system is proved without any geometric condition and in the absence of compactness of the resolvent. Then, we show that our system lacks exponential (uniform) stability. However, we establish a polynomial energy decay estimate of type 1 / t for smooth initial data using the frequency domain approach from semigroup theory, which combines a contradiction argument with the multiplier technique. This method leads to certain geometrical conditions concerning the wave's and the plate's domains. [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]

Objective: To establish the exponential smoothing prediction model and SARIMA model to predict the number of inpatients in a third-class hospital in Zhejiang Province, and evaluate the prediction effect of the two models, and select the best number prediction model. Methods: The data of hospital admissions from January 2019 to September 2022 were selected to establish the exponential smoothing prediction model and the SARIMA model respectively. Then compare the fitting parameters of different models: R2_adjusted, R2, Root Mean Square Error (RMSE)、Mean Absolute Percentage Error (MAPE)、Mean Absolute Error(MAE) and standardized BIC to select the best model. Finally, the established model was used to predict the number of hospital admissions from October to December 2022, and the prediction effect of the average relative error judgment model was compared. Results: The best fitting exponential smoothing prediction model was Winters Addition model, whose R2_adjusted was 0.533, R2 was 0.817, MAPE was 6.133, MAE was 447.341. The best SARIMA model is SARIMA(2,2,2)(0,1,1)12 model, whose R2_adjusted is 0.449, R2 is 0.199, MAPE is 8.240, MAE is 718.965. The Winters addition model and SARIMA(2,2,2)(0,1,1)12 model were used to predict the number of hospital admissions in October-December 2022, respectively. The results showed that the average relative error was 0.038 and 0.015, respectively. The SARIMA(2,2,2)(0,1,1)12 model had a good prediction effect. Conclusion: Both models can better fit the number of admissions, and SARIMA model has better prediction effect. [ABSTRACT FROM AUTHOR]

We investigate a two-dimensional transmission model consisting of a wave equation and a Kirchhoff plate equation with dynamical boundary controls under geometric conditions. The two equations are coupled through transmission conditions along a steady interface between the domains in which the wave and plate equations evolve, respectively. Our primary concern is the stability analysis of the system, which has not appeared in the literature. For this aim, using a unique continuation theorem, the strong stability of the system is proved without any geometric condition and in the absence of compactness of the resolvent. Then, we show that our system lacks exponential (uniform) stability. However, we establish a polynomial energy decay estimate of type 1 / t for smooth initial data using the frequency domain approach from semigroup theory, which combines a contradiction argument with the multiplier technique. This method leads to certain geometrical conditions concerning the wave's and the plate's domains. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is one of the main causes of blindness in people around the world. Early diagnosis and treatment of DR can be accomplished by organizing large regular screening programs. Still, it is difficult to spot diabetic retinopathy timely because the situation might not indicate signs in the primary stages of the disease. Due to a drastic increase in diabetic patients, there is an urgent need for efficient diabetic retinopathy detecting systems. Auto-encoders, sparse coding, and limited Boltzmann machines were used as a few past deep learning (DL) techniques and features for the classification of DR. Convolutional Neural Networks (CNN) have been identified as a promising solution for detecting and classifying DR. We employ the deep learning capabilities of efficient net batch normalization (BNs) pre-trained models to automatically acquire discriminative features from fundus images. However, we successfully achieved F1 scores above 80% on all efficient net BNs in the EYE-PACS dataset (calculated F1 score for DeepDRiD another dataset) and the results are better than previous studies. In this paper, we improved the accuracy and F1 score of the efficient net BNs pre-trained models on the EYE-PACS dataset by applying a Gaussian Smooth filter and data augmentation transforms. Using our proposed technique, we have achieved F1 scores of 84% and 87% for EYE-PACS and DeepDRiD. [ABSTRACT FROM AUTHOR]

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake. [ABSTRACT FROM AUTHOR]

Background: According to the World Health Organization, foodborne disease is a significant public health issue. We will choose the best model to predict foodborne disease by comparison, to provide evidence for government policies to prevent foodborne illness. Methods: The foodborne disease monthly incidence data from June 2017 to April 2022 were obtained from the Chongqing Nan'an District Center for Disease Prevention and Control. Data from June 2017 to June 2021 were used to train the model, and the last 10 months of incidence were used for prediction and validation The incidence was fitted using the seasonal autoregressive integrated moving average (SARIMA) model, Holt-Winters model and Exponential Smoothing (ETS) model. Besides, we used MSE, MAE, RMSE to determine which model fits better. Results: During June 2017 to April 2022, the incidence of foodborne disease showed seasonal changes, the months with the highest incidence are June to November. The optimal model of SARIMA is SARIMA (1,0,0) (1,1,0)12. The MSE, MAE, RMSE of the Holt-Winters model are 8.78, 2.33 and 2.96 respectively, which less than those of the SARIMA and ETS model, and its prediction curve is closer to the true value. The optimal model has good predictive performance. Conclusion: Based on the results, Holt-Winters model produces better prediction accuracy of the model. [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]

The importance of forecasting in the energy sector as part of electrical power equipment maintenance encourages researchers to obtain accurate electrical forecasting models. This study investigates simple to complex automatic methods and proposes two weighted ensemble approaches. The automated methods are the autoregressive integrated moving average; the exponential smoothing error–trend–seasonal method; the double seasonal Holt–Winter method; the trigonometric Box–Cox transformation, autoregressive, error, trend, and seasonal model; Prophet and neural networks. All accommodate trend and seasonal patterns commonly found in monthly, daily, hourly, or half-hourly electricity data. In comparison, the proposed ensemble approaches combine linearly (EnL) or nonlinearly (EnNL) the forecasting values obtained from all the single automatic methods by considering each model component's weight. In this work, four electrical time series with different characteristics are examined, to demonstrate the effectiveness and applicability of the proposed ensemble approach—the model performances are compared based on root mean square error (RMSE) and absolute percentage errors (MAPEs). The experimental results show that compared to the existing average weighted ensemble approach, the proposed nonlinear weighted ensemble approach successfully reduces the RMSE and MAPE of the testing data by between 28% and 82%. [ABSTRACT FROM AUTHOR]

Network congestion is one of the major issues in wireless sensor networks (WSNs) that result in packet loss, reduced network lifetime, low throughput and energy waste. Determining a better path to mitigate the congestion is a better approach to improve the performance of WSNs. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) based path determination approach is proposed to mitigate the congestion with black widow optimization (BWO) algorithm. The proposed approach first develops a framework to mitigate the congestion in WSNs. Then it forecast the buffer occupancy with the exponential smoothing technique. Finally, ANFIS is applied in the proposed approach for determining the path with appropriate weights by considering the remaining energy, hop count and buffer occupancy. Here, the hop count, buffer occupancy and remaining energy are considered as the input factors for the ANFIS. The simulation results of the proposed method show better quality of service, high energy, low delay, high packet delivery ratio with number of increasing alive nodes when compared to existing methods. [ABSTRACT FROM AUTHOR]

Particle Filtering (PF) has been widely used for predicting Remaining Useful Life (RUL) of industrial products, especially for those with nonlinear degradation behavior and non-Gaussian noise. Traditional PF is a recursive Bayesian filtering framework that updates the posterior probability density function of RULs when new observation data become available. In engineering practice, due to the limited accuracy of monitoring/inspection techniques, the observation data available for PF are inevitably imprecise and often need to be treated as interval data. In this article, a novel Interval Particle Filtering (IPF) approach is proposed to effectively leverage such interval-valued observations for RUL prediction. The IPF is built on three pillars: (i) an interval contractor that mitigates the error explosion problem when the epistemic uncertainty in the interval-valued observation data is propagated; (ii) an interval intersection method for constructing the likelihood function based on the interval observation data; and (iii) an interval kernel smoothing algorithm for estimating the unknown parameters in the IPF. The developed methods are applied on the interval-valued capacity data of batteries and fatigue crack growth data of railroad tracks. The results demonstrate that the developed methods could improve the performance of RUL predictions based on interval observation data. [ABSTRACT FROM AUTHOR]

In this paper, we study the global wellposedness of a radiation hydrodynamics model with viscosity and thermal conductivity. It is now well-understood that, unlike the compressible Euler equations whose smooth solutions must blow up in finite time no matter how small and how smooth the initial data is, the dissipative structure of such a radiation hydrodynamics model can indeed guarantee that its one-dimensional Cauchy problem admits a unique global smooth solution provided that the initial data is sufficiently small, while for large initial data, even if the heat conductivity is taken into account but the viscosity effect is ignored, shock type singularities must appear in finite time for smooth solutions of the Cauchy problem of a one-dimensional radiation hydrodynamics model with thermal conductivity and zero viscosity. Thus a natural question is, If effects of both the viscosity and the thermal conductivity are considered, does the one-dimensional radiation hydrodynamics model with viscosity and thermal conductivity exist as a unique global large solution? We give an affirmative answer to this problem and show in this paper that the initialboundary value problem to the radiation hydrodynamics model in a one-dimensional periodic box T = R/Z with viscosity and thermal conductivity does exist as a unique global smooth solution for any large initial data. The main ingredient in our analysis is to introduce some delicate estimates, especially an improved Lm ([0, T], Lco(T))-estimate on the absolute temperature for some m E N and a pointwise estimate between the absolute temperature, the specific volume, and the first-order spatial derivative of the macro radiation flux, to deduce the desired positive lower and upper bounds on the density and the absolute temperature. [ABSTRACT FROM AUTHOR]

The transport truck is one of the important equipment for open-pit mines, and predicting the truck's fault time is of great significance in improving the economic benefits of open-pit mines. In this paper, we discuss the reason for the large prediction error of the exponential smoothing method. Then, we propose a novel nonlinear exponential smoothing method (ESNN) for predicting the truck's fault time, and demonstrate the equivalence between our approach and the neural network structure. Finally, based on the augmented Lagrange function, the solving method of ESNN is proposed. We conduct experiments on real-world datasets and our results demonstrate the effectiveness of ESNN in comparison to existing state-of-the-art methods. Our approach makes it easier for maintenance personnel to predict fault situations in advance and provides a basis for enterprises to develop preventive maintenance plans. [ABSTRACT FROM AUTHOR]

Car-following models are the cornerstones of microscopic traffic simulation tools and intelligent transportation systems, but the applicability of car-following models to disordered traffic have not been investigated in detail with longer trajectory dataset. To address this gap, two car-following models namely, Intelligent Driver Model (IDM) and Full Velocity Difference Model (FVDM) are calibrated using trajectory data collected on an urban arterial road in Chennai, India using Unmanned Aerial Vehicles. The raw data are smoothed for noise removal and the car-following pairs are identified based on the lateral overlap and following duration. The models are calibrated by minimizing the deviations between the observed and simulated longitudinal gaps between leader and follower using genetic algorithm. The obtained errors are between 2.5% and 19.5%, which are comparable with standard ranges of error reported in literature. The optimal parameter values represent the distinct characteristics of disordered traffic in comparison with the homogeneous traffic. [ABSTRACT FROM AUTHOR]

Under the long-term implementation of the low fare policy for public transport in China, the phenomenon of unsustainable subsidies is increasingly obvious. We take into account three related stakeholders including the public, operators and government, and comprehensively consider various factors affecting public transport subsidies, and establish a linkage mechanism of the subsidy based on passenger ridership, cost, fare and service quality. We propose a combination forecasting model (CFM) composed of quadratic exponential smoothing model (QESM) and Grey theory model (GM) to forecast passenger ridership. The results show that the CFM improves the forecast accuracy of passenger ridership, the factors considered in the linkage mechanism are more comprehensive, and the results are reasonable. By using this linkage mechanism, the government or operators can establish long-term or mid-term development plans to provide a systematic and theoretical reference, which is conducive to the healthy and stable development of public transport. [ABSTRACT FROM AUTHOR]