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Non-volatile memory devices have been getting significant attention from researchers worldwide in recent years due to their application in resistive random access memory and neuromorphic computing. Here, we have fabricated polyvinyl alcohol-graphene oxide (PVA-GO) composite as an active material for the resistive switching with different concentrations of GO (0.0, 0.1, 0.2, 0.3, 0.4, and 0.5 wt. % GO solution) dispersed in 5 wt. % PVA matrix in a 2:1 volume ratio. We demonstrate the non-volatile forming free resistive switching properties of Ag/PVA-GO/Ag devices. Structural properties of PVA-GO composites are established from the x-ray diffraction pattern, which indicates the complete dispersion of GO inside the PVA matrix. The Ag/PVA-GO-0.1 wt. %/Ag device shows better bipolar resistive switching at VSET ∼ 0.4 V and VRESET at ∼−0.8 V. This device indicates well-resolved two distinct states at a read voltage of 0.1 V in endurance and retention measurements. The fabricated device switches successfully tested for 2.5 × 103 cycles and retains its state for 3.36 × 103 s without any observable degradation. Furthermore, the non-volatile retention property was modeled using time series analysis. For this, Holt–Winter's exponential smoothing technique was utilized. Additionally, the charge–flux linkage characteristic shows the double-valued function, and time domain–charge and time domain–flux show asymmetric behaviors. The electrical conduction mechanism exhibits ohmic behavior in the entire region of the low resistance state and the lower voltage region of the high resistance state. In the high-voltage region of the high resistance state, the space charge-limited conduction mechanism is observed. The resistive switching behavior is explained with the help of an appropriate model. [ABSTRACT FROM AUTHOR]

In this paper, we consider the initial-boundary value problems of the compressible isentropic Navier-Stokes equations with density-dependent viscosity on two dimensional solid balls which was first introduced by Kazhikhov where shear viscosity μ is assumed to be constant and the bulk viscosity λ is a polynomial of density up to power β. Under the condition of β > 1 , we prove the global existence of the radially symmetric strong solutions to the Kazhikhov models under Dirichlet boundary conditions for arbitrary large initial smooth data. Moreover, the density is shown to be uniformly bounded with respect to time when β ∈ (max ⁡ { 1 , γ + 2 4 } , γ ]. This improves the previous result of [14,15] for general 2D domains where they require β > 4 / 3 to ensure global existence and is the first result concerning the global existence of classical solutions to the radially symmetric compressible Navier-Stokes equations in 2D solid balls under Dirichlet boundary condition. [ABSTRACT FROM AUTHOR]

Accepted by: Zied Babai Exponential smoothing in state space form (ETS) is a popular forecasting technique, widely used in research and practice. While the additive error ETS models have been well studied, the multiplicative error ones have received much less attention in forecasting literature. Still, these models can be useful in cases, when one deals with positive data, because they are supposed to work in such situations. Unfortunately, the classical assumption of normality for the error term might break this property and lead to non-positive forecasts on positive data. In order to address this issue, we propose using Log-Normal, Gamma and Inverse Gaussian distributions, which are defined for positive values only. We demonstrate what happens with ETS(M,*,*) models in this case, discuss conditional moments of ETS with these distribution and show that they are more natural for the models than the Normal one. We conduct the simulation experiments in order to study the bias introduced by point forecasts in these models and then compare the models with different distributions. We finish the paper with an example of application, showing how pure multiplicative ETS with a positive distribution works. [ABSTRACT FROM AUTHOR]

From an economic perspective, rice is not only a principal staple food for nearly half of the world's population but also a significant commodity in many countries. Consequently, the accuracy of demand uncertainty concerning rice imports, which can be useful information to support critical decision-making on trading and food security management, is very challenging. The proposed model of simple exponential smoothing, support vector regression, and generalized simulated annealing is proposed and developed to predict annual rice imports based on twenty datasets across importer countries. The proposed model takes advantage of both suitable parameter selection and noise reduction in systematic error reduction with smoothing series to achieve more accuracy and precision. The empirical results revealed that the proposed model can improve accuracy based on five accuracy measures and is significantly different from other models at 0.05 significance levels. Moreover, the proposed model can provide consistency and reliability for forecasting rice imports in advance. Consequently, the proposed model can be a promising tool to support decision-making for policymakers. [ABSTRACT FROM AUTHOR]

COVID-19 prediction has been essential in the aid of prevention and control of the disease. The motivation of this case study is to develop predictive models for COVID-19 cases and deaths based on a cross-sectional data set with a total of 28,955 observations and 18 variables, which is compiled from 5 data sources from Kaggle. A two-part modeling framework, in which the first part is a logistic classifier and the second part includes machine learning or statistical smoothing methods, is introduced to model the highly skewed distribution of COVID-19 cases and deaths. We also aim to understand what factors are most relevant to COVID-19's occurrence and fatality. Evaluation criteria such as root mean squared error (RMSE) and mean absolute error (MAE) are used. We find that the two-part XGBoost model perform best with predicting the entire distribution of COVID-19 cases and deaths. The most important factors relevant to either COVID-19 cases or deaths include population and the rate of primary care physicians. [ABSTRACT FROM AUTHOR]

Retail demand forecasting is a highly intricate and multilevel problem as it may include numerous products, under multiple categories, and potentially sold in many different stores. Forecasts may also process products in isolation or collectively so as to detect mutual correlations. In technical terms, retail demand forecasting may be broken down to a multivariate time series forecasting problem where the time axis is prevalent and introduces added complexity which necessitates proper usage of advanced methodologies with machine/deep learning backgrounds. Popular boosting regressors, such as XGBoost and LightGBM, are excellent machine learning candidates with extensive bibliographic backgrounds covering multiple scientific fields and applications including generic multivariate time series forecasts. The Temporal Convolutional Network and the Temporal Fusion Transformer are recently proposed deep neural network architectures that found success in specific multivariate scenarios but are yet to be tested in a variety of related fields such as retail demand forecasting. Therefore, within this paper, these novel in-the-field models are compared to the aforementioned boosting approaches alongside popular statistical univariate methods, namely Exponential Smoothing and SARIMA. To properly compare the selected models and attempt to generalize their usability, two separate datasets are analyzed and forecasted; an item sales dataset with 500 time series and a category sales dataset with 540 time series. The findings suggest that the deep learning solutions are the better predictors, with the Transformer model surpassing the Boosting solutions by up to 16.8% sMAPE decrease and the statistical approaches by up to 28.6% decrease, further substantiating the notion that deep learning techniques are exceptionally promising in handling large-scale, non-linear and outlier data whilst suggesting that retail demand forecasting does benefit from multivariate approaches and the application of advanced machine learning methods. [ABSTRACT FROM AUTHOR]

In this paper, a reliable Jacobi Galerkin method is developed and analyzed to solve a particular class of cordial Volterra integral equations. Existence and uniqueness theorems approve that these kinds of equations possess smooth solutions versus smooth data, so representing their Galerkin solutions based on suitable polynomial basis produces spectrally accurate approximations. Moreover, in order to control condition number growth, we designed a reliable approach that calculates the approximate solutions recursively without solving any high-conditioning systems. Indeed, this approach deals with solving equations in a long integration domain with even high oscillatory solutions. The convergence analysis of the presented approach is established, and the familiar spectral accuracy is justified in L ∞ -norm. Numerical results are presented to demonstrate the effectiveness of the proposed method. Finally, we provide an application of this method to approximate solution of a two-dimensional case. [ABSTRACT FROM AUTHOR]

Monitoring ecosystems at regional or continental scales is paramount for biodiversity conservation, climate change mitigation, and sustainable land management. Effective monitoring requires satellite imagery with both high spatial resolution and high temporal resolution. However, there is currently no single, freely available data source that fulfills these needs. A seamless fusion of data from the Sentinel-3 and Sentinel-2 optical sensors could meet these monitoring requirements as Sentinel-2 observes at the required spatial resolution (10 m) while Sentinel-3 observes at the required temporal resolution (daily). We introduce the Efficient Fusion Algorithm across Spatio-Temporal scales (EFAST), which interpolates Sentinel-2 data into smooth time series (both spatially and temporally). This interpolation is informed by Sentinel-3's temporal profile such that the phenological changes occurring between two Sentinel-2 acquisitions at a 10 m resolution are assumed to mirror those observed at Sentinel-3's resolution. The EFAST consists of a weighted sum of Sentinel-2 images (weighted by a distance-to-clouds score) coupled with a phenological correction derived from Sentinel-3. We validate the capacity of our method to reconstruct the phenological profile at a 10 m resolution over one rangeland area and one irrigated cropland area. The EFAST outperforms classical interpolation techniques over both rangeland (−72% in the mean absolute error, MAE) and agricultural areas (−43% MAE); it presents a performance comparable to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (+5% MAE in both test areas) while being 140 times faster. The computational efficiency of our approach and its temporal smoothing enable the creation of seamless and high-resolution phenology products on a regional to continental scale. [ABSTRACT FROM AUTHOR]

In recent years, deep learning models have been successfully applied to large-scale data analysis, including image classification, video caption, natural language processing, etc. Large-scale data analyses take advantage of parallel computing to accelerate the speed of model training, in which data parallelism has become the dominant method for deep learning model training due to its high throughput rate. Synchronous stochastic gradient descent optimization becomes a well-recognized optimization method to ensure model convergence, but the overhead of gradients synchronization increases linearly as the number of workers increases, causing a huge waste of time. Although some efficiency-first asynchronous methods have been proposed, these methods cannot guarantee their convergence in large-scale distributed training. To solve this problem, we propose an efficient pseudo-synchronous approach that updates the network with the previous gradient, performing the synchronization of a new gradient to overlap computation and synchronization. This idea will obviously affect the normal convergence of the model, so we propose a novel adaptive exponential smoothing predicted gradient algorithm for model optimization, which can adaptively adjust the confidence coefficient of the history gradient to ensure the normal convergence of the training process. Experiments prove that our method can speed up the training process and achieve a comparable accuracy rate with standard synchronous SGD. Besides, our method has more efficient weak scalability compared to the traditional synchronous SGD and those in previous related work. We apply our methods to image recognition and video caption applications at most 12288 cores with strong scalability on Tianhe II. Evaluations show that, when configured appropriately, our method attains near-linear scalability using 128 nodes. We get 93.4% weak scaling efficiency on 64 nodes, 90.5% on 128 nodes. [ABSTRACT FROM AUTHOR]

China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity. The country has entered a new development stage designed to achieve its second centenary goal. However, ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty. In this paper, the Liupan Mountain Region of China was used as a case study, and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area. The indicator data for returning to ecological poverty from 2011–2020 were collected and summarized in three dimensions: ecological, economic and social. The autoregressive integrated moving average model (ARIMA) time series and exponential smoothing method (ES) were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties (districts) in the Liupan Mountain Region for 2021–2030. The back propagation neural network (BPNN) and geographic information system (GIS) were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty (RREP index). The results show that 1) ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region. 2) The RREP index for the 61 counties (districts) exhibited a downward trend from 2021–2030. The RREP index declined more in medium- and high-risk areas than in low-risk areas. From 2021 to 2025, the RREP index exhibited a slight downward trend. From 2026 to 2030, the RREP index was expected to decline faster, especially from 2029–2030. 3) Based on the RREP index, it can be roughly divided into three types, namely, the high-risk areas, the medium-risk areas, and the low-risk areas. The natural resource conditions in low-risk areas of returning to ecological poverty, were better than those in medium- and high-risk areas. [ABSTRACT FROM AUTHOR]

We consider local polynomial estimation for varying coefficient models and derive corresponding equivalent kernels that provide insights into the role of smoothing on the data and fill a gap in the literature. We show that the asymptotic equivalent kernels have an explicit decomposition with three parts: the inverse of the conditional moment matrix of covariates given the smoothing variable, the covariate vector, and the equivalent kernels of univariable local polynomials. We discuss finite-sample reproducing property which leads to zero bias in linear models with interactions between covariates and polynomials of the smoothing variable. By expressing the model in a centered form, equivalent kernels of estimating the intercept function are asymptotically identical to those of univariable local polynomials and estimators of slope functions are local analogues of slope estimators in linear models with weights assigned by equivalent kernels. Two examples are given to illustrate the weighting schemes and reproducing property. [ABSTRACT FROM AUTHOR]

We establish a minimal positive existence time of the parametric Willmore flow for any smooth initial data (smooth immersion of a closed oriented surface). The minimal existence time is a function exclusively of geometric data which in particular are all well defined for general weak Lipschitz W 2 , 2 immersions. This fact opens in particular the possibility for defining the Willmore flow for weak Lipschitz W 2 , 2 initial data. [ABSTRACT FROM AUTHOR]

Pavement maintenance causes an instant change in the pavement's material or structural properties and affects the subsequent development of pavement distresses and performance. The occurrence of maintenance action significantly limits the applicability of predictive models that rely heavily on the continuity of time and pavement exposure conditions. Using rutting of asphalt pavement as an example, this study treated the partitioned rutting development as short-term time series. The proposed average cosine similarity of pavement rutting development effectively integrated the data collected within a characteristic length in the longitudinal direction of the pavement. Integration of the raw data effectively mitigated the data inconsistency caused by measuring errors and simplified the model construction. We employed convolutional neural network (CNN) and long short-term memory (LSTM) as two typical deep-learning (DL) models to capture the characteristics of rutting development from limited data and make corresponding predictions. The effects of model hyperparameters and input type on the model performance (e.g., accuracy and stability) were investigated to identify the optimal setting for various modeling data. The comparisons with two statistical models—exponential smoothing (ES) model and autoregressive integrated moving average (ARIMA) model—indicated the potential of applied DL models in accurately predicting rutting development in field pavement. Finally, three strategies of improving model performance were explored and discussed for future applications, i.e., increasing the input length, input dimensionality, and model complexity. This study stands in the position of transportation engineers to solve a practical problem with computer-aided tools, which is the prediction of asphalt pavement performance with deep-learning (DL) models. The data utilized in this study were collected from the field, resulting in inherently challenging characteristics such as being short-term and noisy. This study investigated corresponding strategies to allow DL models to provide accurate and stable predictions. Besides, this study makes DL models more accessible to practicing engineers. A significant amount of model information obtained in this study can be extended to various types of performance indices. Finally, DL models constructed in this study were compared with classic and currently applied models using the same data. The results reveal that DL models can serve as reliable and promising tools for predicting asphalt pavement performance in the natural deterioration stage of the pavement. [ABSTRACT FROM AUTHOR]

We describe the intersection of the Torelli locus j(\mathcal {M}_4^{ct}) = \mathcal {J}_4 with Newton and Ekedahl-Oort strata related to the supersingular locus in characteristic 2. We show that the locus of supersingular Jacobians \mathcal {S}_4\cap \mathcal {J}_4 in characteristic 2 is pure of dimension three. One way to obtain that result uses an analysis of the data of smooth genus four curves and principally polarized abelian fourfolds defined over \mathbb {F}_2, and another involves a more careful study of some relevant Ekedahl-Oort loci. [ABSTRACT FROM AUTHOR]

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics. Author summary: The US and European Covid-19 Forecast Hubs focus on metrics such as deaths, new cases, and hospital admissions, but do not offer measurements of hospital strain like critical care bed occupancy, which was essential for the provisioning of healthcare resources during the COVID-19 pandemic. Furthermore, forecasting support was only guaranteed on the national level leaving many countries to look elsewhere for valuable sub-national forecasts. In France statistical modelling approaches were proposed to anticipate hospital stain at the sub-national level but these were limited by a two-week forecast horizon. We present a sub-national French modelling framework and online application for anticipating hospital strain at the four-week horizon that can account for abrupt changes in key epidemiological parameters. It was the only publicly available real-time non-Markovian mechanistic model for the French epidemic when implemented in January 2021 and, to our knowledge, it still was at the time it stopped in early 2022. Further adaptations of this surveillance system can serve as an anticipation tool for hospital strain across sub-national localities to aid in the prevention of short-noticed ward closures and patient transfers. [ABSTRACT FROM AUTHOR]

The author studies the 2D isentropic Euler equations with the ideal gas law. He exhibits a set of smooth initial data that give rise to shock formation at a single point near the planar symmetry. These solutions to the 2D isentropic Euler equations are associated with non-zero vorticity at the shock and have uniform-in-time 1 3 -Hölder bound. Moreover, these point shocks are of self-similar type and share the same profile, which is a solution to the 2D self-similar Burgers equation. The proof of the solutions, following the 3D construction of Buckmaster, Shkoller and Vicol (in 2023), is based on the stable 2D self-similar Burgers profile and the modulation method. [ABSTRACT FROM AUTHOR]

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]

Photovoltaic (PV) power generation prediction is a significant research topic in photovoltaics due to the clean and pollution-free characteristics of solar energy, which have contributed to its popularity worldwide. Photovoltaic data, as a type of time series data, exhibit strong periodicity and volatility. Researchers typically employ time–frequency signal processing methods, like empirical mode decomposition (EMD), to smooth the data during the feature engineering stage. However, improper operations at this stage could result in information leakage. Unfortunately, many existing studies on photovoltaic prediction fail to provide sufficient details on how signal processing methods are used during model training. To address this issue, this paper proposes the similarity-day extension EMD that avoids information leakage. The proposed method is validated through experiments conducted on the PV dataset of the Desert Knowledge Australia Solar Center, using mainstream models such as GRU, LSTM, CNN-LSTM, LSTN-CNN, and Bi-LSTM. The experimental results demonstrate an average improvement of 3.67% in MAE and 5.71% in RMSE when using this method, thus verifying its feasibility and effectiveness. Moreover, the proposed method can be applied to other data processing methods that may suffer from information leakage. [ABSTRACT FROM AUTHOR]

Pixel value ordering (PVO) and prediction error expansion (PEE) techniques are now being combined for improved performance in the domain of reversible data hiding (RDH). Conventional PVO can only embed two data bits in a smooth picture block in a row or column, which is insufficient to meet the contemporary demands of much payload in RDH. There is a lot of room to enhance the bit stuffing by inserting more than two data bits in a smooth block while maintaining acceptable visual image quality and data security. In this study, we suggest a context pixel-based data concealing strategy based on PVO combined with PEE, in which each block can insert two or more data bits. This proposed approach has three distinct features: smooth block selection, minimum and maximum position selection, and pixel difference with median value for data embedding. Furthermore, more than two pixels can be used to embed the secret data bit. The block is recommended by the selection of the neighbouring and present block's pixel. The use of context pixels in context pixel-based PVO (CPPVO) improves both embedding efficiency and picture quality. When the block size is Z = (3 × 3) , the average embedding capacity (EC) of our novel approach is over 70,000 bits, and the average PSNR is over 40 dB. The results of the experiments reveal that our technique is more dependable than other state-of-the-art systems in terms of security, picture quality, and secret volume. [ABSTRACT FROM AUTHOR]

In this study, we propose a cointegration test in which structural changes are modeled gradually through the Fourier approach and the nonlinear structure is modeled with an exponential smooth transition autoregressive (ESTAR) model. In the traditional tests, structural changes are mostly modeled by dummy variables. However, the extended literature supposes that the flexible Fourier approach is an alternative approach to dummy variables since there is no need to know the form, date, and number of breaks with the Fourier approach. The lack of a cointegration test to combine the ESTAR model and Fourier approach in the literature constitutes the motivation of this study. Monte Carlo simulation results demonstrate that the size properties are close to the nominal size and the power of the test shows good performance even in the small sample simulation experiment. Results also indicate that the proposed test shows better power performance compared to the ESTAR cointegration test ignoring breaks. [ABSTRACT FROM AUTHOR]

[Display omitted] • The Ag/FWO/FTO memristive devices exhibit analogue-type bipolar resistive switching behavior. • The optimized device shows reliable non-volatile memory characteristics. • Statistical analysis reveals the better operational uniformity of the memristive devices. • The stable negative differential resistance (NDR) effect was observed in the devices. • Switching currents were modeled using Holt's Winter Exponential Smoothing technique. The memristors offer significant advantages as a key element in non-volatile and brain-inspired neuromorphic systems because of their salient features such as remarkable endurance, ability to store multiple bits, fast operation speed, and extremely low energy usage. This work reports the resistive switching (RS) characteristics of the hydrothermally synthesized iron tungstate (FeWO 4) based thin film memristive device. The detailed physicochemical analysis was investigated using Rietveld's refinement, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) techniques. The fabricated Ag/FWO/FTO memristive device exhibits bipolar resistive switching (BRS) behavior. In addition, the devices exhibit negative differential resistance (NDR) at both positive and negative bias. The charge-flux relation portrayed the non-ideal or memristive nature of the devices. The reliability in the RS process was analyzed in detail using Weibull distribution and time series analysis techniques. The device exhibits stable and multilevel endurance and retention characteristics which demonstrates the suitability of the device for the high-density non-volatile memory application. The current conduction of the device was dominated by Ohmic and trap controlled-space charge limited current (TC-SCLC) mechanisms and filamentary RS process responsible for the BRS in the device. In a nutshell, the present investigations reveal the potential use of the iron tungstate for the fabrication of memristive devices for the non-volatile memory application. [ABSTRACT FROM AUTHOR]

We consider the Cauchy problem for the Keller-Segel system of consumption type coupled with the incompressible Euler equations in R 2. This coupled system describes a biological phenomenon in which aerobic bacteria living in slightly viscous fluids (such as water) move towards a higher oxygen concentration to survive. We firstly prove the local existence of smooth solutions for arbitrary smooth initial data. Then we show that these smooth solutions can be extended globally if the initial density of oxygen is sufficiently small. The main ingredient in the proof is the W 1 , q -energy estimate (q > 2) motivated by the partially inviscid two-dimensional Boussinesq system. Our result improves the well-known global well-posedness of the two-dimensional Keller-Segel system of consumption type coupled with the incompressible Navier-Stokes equations. [ABSTRACT FROM AUTHOR]

In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA. [ABSTRACT FROM AUTHOR]

Pupillometry has been widely implemented to investigate cognitive functioning since infancy. Like most psychophysiological and behavioral measures, it implies hierarchical levels of arbitrariness in preprocessing before statistical data analysis. By means of an illustrative example, we checked the robustness of the results of a familiarization procedure that compared the impact of audiovisual and visual stimuli in 12-month-olds. We adopted a multiverse approach to pupillometry data analysis to explore the role of (1) the preprocessing phase, that is, handling of extreme values, selection of the areas of interest, management of blinks, baseline correction, participant inclusion/exclusion and (2) the modeling structure, that is, the incorporation of smoothers, fixed and random effects structure, in guiding the parameter estimation. The multiverse of analyses shows how the preprocessing steps influenced the regression results, and when visual stimuli plausibly predicted an increase of resource allocation compared with audiovisual stimuli. Importantly, smoothing time in statistical models increased the plausibility of the results compared to those nested models that do not weigh the impact of time. Finally, we share theoretical and methodological tools to move the first steps into (rather than being afraid of) the inherent uncertainty of infant pupillometry. [ABSTRACT FROM AUTHOR]

In areas ranging from neuroimaging to climate science, advances in data storage and sensor technology have led to a proliferation in multidimensional functional datasets. A common approach to analyzing functional data is to first map the discretely observed functional samples into continuous representations, and then perform downstream statistical analysis on these smooth representations. It is well known that many of the traditional approaches used for 1D functional data representation are plagued by the curse of dimensionality and quickly become intractable as the dimension of the domain increases. In this article, we propose a computational framework for learning continuous representations from a sample of multidimensional functional data that is immune to several manifestations of the curse. The representations are constructed using a set of separable basis functions that are defined to be optimally adapted to the data. We show that the resulting estimation problem can be solved efficiently by the tensor decomposition of a carefully defined reduction transformation of the observed data. Roughness-based regularization is incorporated using a class of differential operator-based penalties. Relevant theoretical properties are also discussed. The advantages of our method over competing methods are thoroughly demonstrated in simulations. We conclude with a real data application of our method to a clinical diffusion MRI dataset. for this article are available online. [ABSTRACT FROM AUTHOR]

Machine learning models perform satisfactorily in landslide displacement prediction, but they are generally black-box models that are difficult to gain the trust of decision-makers. Therefore, a three-stage prediction framework based on the Hodrick–Prescott (HP) filter, double exponential smoothing (DES), natural gradient boosting (NGBoost), and Shapley additive explanations (SHAP) was proposed. The framework quantifies the uncertainty in the predictions and provides fully transparent outputs. In the first stage, the HP filter decomposes cumulative displacements into trend and period displacements. The second stage uses DES and NGBoost to predict them separately. In the third stage, we compute the SHAP values of the features to analyze the impact of the features on the model output. It is applied to the Bazimen and Baishuihe landslides in the Three Gorges Reservoir area and compared with other literature. The results show that the framework can achieve high accuracy in both point and interval prediction, and its performance is similar to or even better than other models. And it is easier to operate and applicable to a wider range of people. Most importantly, the framework can interpret the model, allows users to verify the consistency of the model prediction basis with the landslide evolution mechanism, and reduces random errors by optimizing the data. Overall, the prediction framework can capture the complex nonlinear relationships between features, solving the current challenge of machine learning models that cannot be applied in the final optimization scenario due to the lack of convincing, and providing a reliable tool for intelligent disaster prevention. [ABSTRACT FROM AUTHOR]

Based on data from the Central Bureau of Statistics, the divorce rate in Indonesia shows a tendency to increase from year to year. Similar conditions were experienced by Cibinong district. The forecasting method that will be used in this study is the Single Moving Average and Winter's Exponential Smoothing. The results of forecasting the number of lawsuits in 2023 from July to December with movements with two obtained forecasts in July were 602, August 283, MAD value = 67.29, MSE value = 8.722, MAPE = 11.61, RMSE = 1.46, Accuracy value of 88.39%. Movements with four forecasting periods in July were 620, August 448, September 301, October 141. MAD value = 99.33, MSE value = 14,722, MAPE = 16.59, RMSE = 1.90, Accuracy value of 83.41%. Forecasting with the Winters Exponential Smoothing Method with Alpha: 0.1, Beta: 0.3 and Gamma: 0.5, the forecast results obtained in July were 504.10026, August 491.61306, September 663.18788, October 745.41004, November 732.42766 and December 732.10904. MAE value is 171.65116, MSE value is 686.63361, MAD value is 38.74, MSE value is 2.797 MAPE value is 6.0 and RMSE value is 0.83 and accuracy value is 94.00%. Based on the calculation results above, it is concluded that forecasting the number of divorce filings at Cibinong Religious Court from January 2018 to December 2023 with the Winter Exponential Smoothing method has MAD, MSE, MAPE, RMSE values smaller than values in the Single Moving Average method. Winter Exponential Smoothing method is more appropriate with an accuracy value of 94.00%. [ABSTRACT FROM AUTHOR]

This study aimed to identify the best forecasting model and evaluate if time (in months and years) is a predictor for Myocardial Infarction cases in the Philippines. The research design was quantitative research, specifically descriptive study design. Secondary data on monthly reported cases of myocardial infarction for the past five years (November 2018-November 2023) from Google Trends was utilized. Microsoft Excel and SPSS were the software used to obtain results. Simple Moving Average (SMA) and Exponential Smoothing Method (ESM) were the forecasting techniques used in this study. At the same time, Mean, Standard Deviation, Analysis of Variance, and Independent sample t - test were the statistical tools utilized for both descriptive and inferential analyses. Results revealed that the mean level of cases reported for the past five years was 54. Also, according to SMA and ESM, the forecasted cases for the next succeeding month (December 2023) were 66 and 61, respectively. Results also showed that the Mean Squared Error (MSE) value of SMA is lower than ESM, making SMA a better forecasting method. Moreover, there was a significant difference between the reported cases when grouped according to year, with Year 5 having the highest number of cases and Year 1 having the lowest. Further, there was no significant difference between the SMA and ESM forecasted cases. Furthermore, time (year and month) significantly predicted the number of myocardial infarction cases reported in the Philippines. [ABSTRACT FROM AUTHOR]

In this paper, exact solutions of semilinear equations having exponential growth in the space variable x are found. Semilinear Schrödinger equation with logarithmic nonlinearity and third-order evolution equations arising in optics with logarithmic and power-logarithmic nonlinearities are investigated. In the parabolic case, the solution u is written as u = b e − a x 2 , a < 0 , a , b being real-valued functions. We are looking for the solutions u of Schrödinger-type equation of the form u = b e − a x 2 2 , respectively, for the third-order PDE, u = A e i Φ , where the amplitude b and the phase function a are complex-valued functions, A > 0 , and Φ is real-valued. In our proofs, the method of the first integral is used, not Hirota's approach or the method of simplest equation. [ABSTRACT FROM AUTHOR]

When combined with conducive atmospheric conditions, air pollution caused by fossil fuel consumption associated with transportation, industry and electricity production for households, create and sustain continuous pollution over the megalopolis of Tehran, Iran. In conjunction with daily meteorological forecasts, remote sensing can be used to identify and predict days with hazardous levels of air pollution, providing an opportunity for air quality alert systems to be triggered and warnings circulated to reduce health risks to citizens of Tehran province. Combining remotely sensed ozone column density (OCD) data from the Sentinel-5 TROPOMI sensor with NASA Giovanni data concerning meteorological parameters (temperature (T), wind speed (WS) and specific humidity (SH)), geographical parameters and population data, this study considers the drivers and effects of ozone pollution on the urban climate and vegetation condition (normalized difference vegetation index (NDVI)) of 16 counties in Tehran province, Iran during 12 months (i.e., January 2021 to December 2021). Future monthly forecasts of the OCD, climatic and terrestrial factors in 2022 are also presented. Google Earth Engine and the NASA Giovanni platforms were employed for the processing and analysis of data using an interpolation technique. Additionally, a Box–Jenkins ARIMA and Exponential Smoothing (ETS) models were compared and tailored to generate monthly forecasts of OCD, T, WS, SH and NDVI. The highest and lowest OCD was obtained in June and December 2021, with a concentration of 0.14277 mol/m2 and 0.12383 mol/m2, respectively. However, the annual average OCD was higher in the cities of Shahriar and Pakdasht in March, with values of 0.13237 mol/m2 and 0.13244 mol/m2, respectively. The lowest OCD recorded was 0.13105 mol/m2, in Shemiranat city, in the north of Tehran. The results indicate a positive correlation between OCD and NDVI, and a negative correlation between OCD, SH, WS and T. A strong seasonal trend in OCD was identified for all cities, but across the entire province, altitude and population size were the most significant explanatory variables for spatial variations in OCD. This research demonstrates that an effective OCD monitoring and forecasting model may be generated from remote sensing and meteorological variables. The implementation and utilization of these models are of paramount importance as they offer vital information to authorities for continuous air quality monitoring and strategic planning, particularly for days with hazardous air pollution. By effectively implementing the OCD model, it has the potential to directly contribute to improved health outcomes in major cities across Iran. [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]

This paper concerns a two-fluid Euler-Maxwell system for plasmas with two small parameters. We study the non-relativistic quasi-neutral limit for smooth solutions of the system in whole space R 3. When the initial data are smooth and sufficiently close to constant equilibrium states, we give a rigorous justification of the limit for all time. The limit system is governed by a compressible Euler system. The proof is based on uniform energy estimates and various dissipative estimates of smooth solutions with respect to the parameters and time. A key step in these estimates is to control the quasi-neutrality of the velocities by using a projection operator. [ABSTRACT FROM AUTHOR]

For the 3D quasilinear wave equation − (1 + (∂ t ϕ) p) ∂ t 2 ϕ + Δ ϕ = 0 with the short pulse initial data (ϕ , ∂ t ϕ) (1 , x) = (δ 2 − ε 0 ϕ 0 (r − 1 δ , ω) , δ 1 − ε 0 ϕ 1 (r − 1 δ , ω)) , where p ∈ N , p ≥ 2 , 0 < ε 0 < 1 , r = | x | , ω = x r ∈ S 2 , and δ > 0 is sufficiently small, under the outgoing constraint condition (∂ t + ∂ r) k ϕ (1 , x) = O (δ 2 − ε 0 − k max ⁡ { 0 , 1 − (1 − ε 0) p }) for k = 1 , 2 , we will establish the global existence of smooth large data solution ϕ when p > p c with p c = 1 1 − ε 0 being the critical exponent. In the forthcoming paper, when 1 ≤ p ≤ p c , we show the formation of the outgoing shock before the time t = 2 under the same outgoing constraint condition and the other suitable assumption of (ϕ 0 , ϕ 1). [ABSTRACT FROM AUTHOR]

Smooth Cauchy data on S3 for the Einstein- λ -vacuum field equations with cosmological constant λ>0 that are sufficiently close to de Sitter data develop into a solution that admits a smooth conformal boundary J+ in its future. The conformal Einstein equations determine a smooth conformal extension across J+ that defines on 'the other side' again a λ -vacuum solution. In this article, we discuss to what extent these properties generalize to the future asymptotic behaviour of solutions to the Einstein- λ equations with matter. We study Friedmann–Lemaitre–Robertson–Walker (FLRW) solutions and the Einstein- λ equations coupled to conformally covariant matter transport equations, to conformally privileged matter equations, and to conformally non-covariant matter equations. We present recent results on the Einstein- λ -perfect-fluid equations with a nonlinear asymptotic dust or asymptotic radiation equation of state. This article is part of a discussion meeting issue 'At the interface of asymptotics, conformal methods and analysis in general relativity'. [ABSTRACT FROM AUTHOR]

In space–time dimensions n+1≥4 , we show the existence of solutions of the Einstein vacuum equations which describe asymptotically de Sitter space–times with prescribed smooth data at the conformal boundary. This provides a short alternative proof of a special case of a result by Shlapentokh-Rothman and Rodnianski, and generalizes earlier results of Friedrich and Anderson to all dimensions. This article is part of a discussion meeting issue 'At the interface of asymptotics, conformal methods and analysis in general relativity'. [ABSTRACT FROM AUTHOR]

We study the singularity formation of a quasi-exact 1D model proposed by Hou and Li (2008 Commun. Pure Appl. Math. 61 661–97). This model is based on an approximation of the axisymmetric Navier–Stokes equations in the r direction. The solution of the 1D model can be used to construct an exact solution of the original 3D Euler and Navier–Stokes equations if the initial angular velocity, angular vorticity, and angular stream function are linear in r. This model shares many intrinsic properties similar to those of the 3D Euler and Navier–Stokes equations. It captures the competition between advection and vortex stretching as in the 1D De Gregorio (De Gregorio 1990 J. Stat. Phys. 59 1251–63; De Gregorio 1996 Math. Methods Appl. Sci. 19 1233–55) model. We show that the inviscid model with weakened advection and smooth initial data or the original 1D model with Hölder continuous data develops a self-similar blowup. We also show that the viscous model with weakened advection and smooth initial data develops a finite time blowup. To obtain sharp estimates for the nonlocal terms, we perform an exact computation for the low-frequency Fourier modes and extract damping in leading order estimates for the high-frequency modes using singularly weighted norms in the energy estimates. The analysis for the viscous case is more subtle since the viscous terms produce some instability if we just use singular weights. We establish the blowup analysis for the viscous model by carefully designing an energy norm that combines a singularly weighted energy norm and a sum of high-order Sobolev norms. [ABSTRACT FROM AUTHOR]

Thailand's economy, like many other ASEAN countries, heavily relies on remittances from Thai overseas workers. Predicting the number of such workers in the coming years is crucial for stakeholders engaged in economic planning and growth. This study aimed to find a reliable statistical model for this purpose, utilizing data from pre-pandemic years to compare two predictive methods. The dataset spans 120 months, from January 2010 to December 2019, and was obtained from the Library System, Department of Employment of the Royal Thai Government. The chosen forecasting techniques are the Box-Jenkins method and Holt-Winter's Exponential Smoothing method. Primary parameters were first estimated based on the given data, and then evaluated through the Bayesian Information Criterion (BIC). To assess model performance, the dataset was split into two subsets. The primary parameters were applied to the first 60 months' data (January 2010 to December 2014) to estimate secondary parameters. The remaining 60 months' data were then used to evaluate how well each model predicts the response variable, measured by the prediction mean absolute error (PMAE) and prediction root mean squared error (PRMSE). Based on the findings, the Box-Jenkins method outperformed Holt-Winter's method in forecasting the monthly number of fresh Thai overseas workers. This study's insights can serve as a valuable template for predicting overseas workers' numbers in other ASEAN countries with socio-economic-cultural contexts similar to Thailand. Accurate predictions can aid decision-making and planning for sustainable economic development in the region. [ABSTRACT FROM AUTHOR]

As a heavy equipment distributor company with a widespread population of units across Indonesia, PT. Kobexindo Tractors Tbk holds a significant spare parts inventory to meet their customers' needs. However, efficiently and effectively managing spare parts inventory is a challenge the company faces. Over the period from 2016 to 2023, the company experienced an average annual loss of Rp. 1,176,438,113, due to the inadequate analysis of spare parts demand, which serves as a reference in the procurement process. To address this issue, this research focuses on developing a model that can generate accurate forecasts for spare parts inventory, particularly Jungheinrich parts, to support appropriate management decisions in the procurement process at the company. The Exponential Smoothing method is chosen for its ability to handle data with fluctuating patterns and trends. Based on a review of previous research studies, the Exponential Smoothing method has proven to be the most effective in forecasting spare parts inventory with a high level of accuracy. This study will compare the Simple Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing methods. The attributes included in this research are spare parts inventory data collected from the company during the period of 2016-2023, which will be processed using Exponential Smoothing techniques. The results of these calculations will be evaluated using the Root Mean Square Error (RSME) and Mean Absolute Percentage Error (MAPE) techniques to test the accuracy and performance of the forecasting models. The data ratio used in this research is 70% for training data and 30% for testing data. The prototype development is conducted using the Python programming language. The research results indicate that the Holts Winter Exponential Smoothing Model with Multiplicative Seasonality and Multiplicative Trend (Triple Exponential) is the best method among others, as seen from the modeling evaluation results as follows: 1) Train RSME (7.082307), a low RSME value on training data indicates that this model has a small used for training. 2) Test MAPE (6.343268), a low MAPE value on test data indicates that this model provides fairly accurate predictions in percentage terms of the actual values on the test data. 3) Test RSME Values (23.160521), a sufficiently low RSME value on test data indicates that this model also successfully generalizes well on unseen data.prediction error rate on the data [ABSTRACT FROM AUTHOR]

Indonesia is widely known as a country with rich biodiversity. Medicinal plants that thrive in Indonesia are utilized as traditional medicine locally known as "jamu". One of the islands famous for jamu production is Madura Island. As a well-known jamu producer, Madura Island are facing problems related to jamu production. Procurement of medicinal plants is not well controlled. There are no reports of spices procurement and production. When there is an increase in demand or sale of certain jamu, the stock of jamu is commonly inadequate/insufficient This may result in order cancellation. The solution to this problem is to create a production forecasting information system by using single exponential smoothing. The data used is a weekly report on the number of sales of 3 types of jamu from August to October 2024. Mean Absolute Percentage Error (MAPE) testing using an alpha value of 0.1 to 0.9 resulted in "high" accuracy and the forecasted values were close to the actual data values. [ABSTRACT FROM AUTHOR]

Renewable energy forecasting services comprise various modules for intra-day and day-ahead forecasts. This work specifically addresses day-ahead forecasts, utilizing specifications based on endogenous, historical measurements. These specifications are designed to be computationally efficient, requiring fewer input variables and less training data. Such weather-independent specifications serve as benchmarks against the more computationally demanding forecasts based on numerical weather predictions. A series of experiments, designed to simulate the real-world application of an online system, were conducted on sliding windows of back-contact photovoltaic (installed at KAUST, Saudi Arabia) output series, solar irradiance recorded in Hawaii, and simulated data. Our analysis evaluated 24 specifications, which are variants of (i) functional time series models (including two novel shrinkage procedures); (ii) time series nearest neighbor schemes; (iii) exponential smoothing procedures; (iv) autoregressive integrated moving average processes; (v) automatic techniques based on time series decomposition; and (vi) the persistence model. In addition to employing outlier-robust accuracy metrics, such as mean absolute error, our evaluation also prioritized prediction-interval accuracy, quantified by the mean scaled interval score. Our findings suggest that practitioners can achieve significant improvements over the persistence model by forecasting daily profiles using adaptive nonparametric or functional data analysis-based procedures. Moreover, applying shrinkage to nearest neighbor (NN) forecasts toward smooth, average daily profiles significantly enhances NN performance. Conversely, some popular, computationally intensive models fail to perform adequately to justify their additional cost. [ABSTRACT FROM AUTHOR]

Background: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country. Methodology: Stochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model. Results: The findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023. Conclusion: The selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics. [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]

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]