This study addresses the limitations of the Kalman Filter (KF) by extending the application of the Unscented Kalman Filter (UKF) and the variational Bayes method (VBM) for estimating long-memory (LM) volatility models. Our methodology formulated the Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) and Hyperbolic Generalized Autoregressive Conditional Heteroskedasticity (HYGARCH) processes within a state-space framework and employed the UKF alongside the VBM to achieve robust estimation. The findings demonstrated that the UKF excelled based on key performance metrics and forecasts, showing superior training data and validation data volatility predictions. The UKF-FIGARCH (1, 0.4029, 1) was a better model for gold, followed by the VBM_FIGARCH model (1, 0.3525, 1). For tobacco, the VBM-FIGARCH model (1, 0.3025, 1) was superior to the UKF-FIGARCH (1, 0.1320, 1) model. Both methods yielded estimates consistent with the parameters used for simulation, falling within the established 95% confidence interval defined by the critical values.

Quantum probes offer a powerful platform for exploring environmental dynamics, particularly through their sensitivity to decoherence processes. In this work, we investigate the emergence of critical behavior in the estimation of the environmental memory time τc, modeled as an Ornstein–Uhlenbeck process characterized by a Lorentzian spectral density. Using dynamically controlled qubit-based sensors—realized experimentally via solid-state Nuclear Magnetic Resonance (NMR) and supported by numerical simulations—we implement tailored filter functions to interrogate the environmental noise spectrum and extract τc from its spectral width. Our results reveal a sharp transition in estimation performance between short-memory (SM) and long-memory (LM) regimes, reflected in a non-monotonic estimation error that resembles a phase transition. This behavior is accompanied by an avoided-crossing-like structure in the estimated parameter space, indicative of two competing solutions near the critical point. These features underscore the interplay between control, decoherence, and inference in open quantum systems. Beyond their fundamental significance, these critical phenomena offer a practical diagnostic tool for identifying dynamical regimes and optimizing quantum sensing protocols. By exploiting this criticality, our findings pave the way for adaptive control strategies aimed at enhancing precision in quantum parameter estimation—particularly in complex or structured environments such as spin networks, diffusive media, and quantum materials.

We study a mathematical model for a quasistatic behavior of electro-viscoelastic materials. The problem is related to highly nonlinear and non-smooth phenomena like contact, friction and normal compliance with wear. Then, a fully discrete scheme is introduced based on the finite element method to approximate the spatial variable and the backward Euler scheme to discretize the time derivatives. For a numerical scheme, we prove the existence and uniqueness of the solutions, and derive optimal order error estimates under certain regularity assumption on the solution of the continuous problem. AMS subject classification. 35J85 · 49J40 · 47J20 · 74M15. REFERENCES [1] Aoun, M. S. M., Dehda, B., & Douib, B. (2024). Numerical study of a thermo-elasto-viscoplastic contact problem with adhesion using a hybrid method. Studies in Engineering and Exact Sciences, 5(2), e8308-e8308. Search in Google Scholar. https://doi.org/10.54021/seesv5n2-255 [2] Aoun, M. S. M., Selmani, M., & Ahmed, A. A. 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Analysis and numerical solution of a piezoelectric frictional contact problem. Applied mathematical modelling, 36(9), 4483-4501. Search in Google Scholar. https://doi.org/10.1016/j.apm.2011.11.077 Communicated Editor: T.J. RABEHERIMANAN Manuscript received Sep. 19, 2024; revised Feb 02, 2025; accepted Feb 11, 2025; published Feb 28, 2025.

Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells

In this study, we prove the strong consistency of the least squares estimator in a random sampled linear regression model with long-memory noise and an independent set of random times given by renewal process sampling. Additionally, we illustrate how to work with a random number of observations up to time T = 1. A simulation study is provided to illustrate the behavior of the different terms, as well as the performance of the estimator under various values of the Hurst parameter H.

Orthogonal/vector approximate message-passing (AMP) is a powerful message-passing (MP) algorithm for signal reconstruction in compressed sensing. This paper proves the convergence of Bayes-optimal orthogonal/vector AMP in the large system limit. The proof strategy is based on a novel long-memory (LM) MP approach: A first step is a construction of LM-MP that is guaranteed to converge systematically. A second step is a large-system analysis of LM-MP via an existing framework of state evolution. A third step is to prove the convergence of state evolution recursions for Bayes-optimal LM-MP via a new statistical interpretation of existing LM damping. The last is an exact reduction of the state evolution recursions for Bayes-optimal LM-MP to those for Bayes-optimal orthogonal/vector AMP. The convergence of the state evolution recursions for Bayes-optimal LM-MP implies that for Bayes-optimal orthogonal/vector AMP. Numerical simulations are presented to show the verification of state evolution results for damped orthogonal/vector AMP and a negative aspect of LM-MP in finite-sized systems.

We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence. This rules out Poisson process based models where the rate function itself is not long range dependent. The proposed class of FRActional Probit (FRAP) models is based on thresholding, a latent process. This latent process is modeled by a smooth Gaussian process and a fractional Brownian motion by assuming an additive structure. We develop a Bayesian approach to inference using Markov chain Monte Carlo and show good performance in simulation studies. Applying the methods to the Amazon bird vocalization data, we find substantial evidence for self-similarity and non-Markovian/Poisson dynamics. To accommodate the bird vocalization data in which there are many different species of birds exhibiting their own vocalization dynamics, a hierarchical expansion of FRAP is provided in the Supplementary Material.

In this paper, we introduce a new hybrid model namely Autoregressive Fractional Unit Root Integrated Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFURIMA-APARCH) model. The Nigeria daily COVID19 records and Bitcoin to EURO exchange rate that exhibit a type of Long Memory (LM) called Interminable LM (ILM), volatility and asymmetric (leverage) effect were used to show the applications of the proposed ARFURIMA-APARCH model. The existing Autoregressive Fractional Integrated Moving Average-Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARFIMA-APARCH) model were estimated and compared with the ARFURIMA-APARCH model. Results showed that the new hybrid model is better based on goodness-of-fit, serial correlation tests and forecast measures of accuracy. As a conclusion, our study showed that the ARFURIMA-APARCH model performed better compared to the ARFIMA-APARCH hybrid model. Therefore, the ARFURIMA-APARCH model is a better option for modeling ILM, volatility and leverage effect of health and financial data. Future study should focus on the application of the developed hybrid ARFURIMA-APARCH model using some major economic indicators, for example, Gross Domestic Product (GDP), currency exchange rate, stock price index, interest rate and other financial data.

The aim of the paper is to describe a bootstrap, contrary to the sieve bootstrap, valid under either long memory (LM) or short memory (SM) dependence. One of the reasons of the failure of the sieve bootstrap in our context is that under LM dependence, the sieve bootstrap may not be able to capture the true covariance structure of the original data. We also describe and examine the validity of the bootstrap scheme for the least squares estimator of the parameter in a regression model and for model specification. The motivation for the latter example comes from the observation that the asymptotic distribution of the test is intractable.

The Vehicular Ad-hoc Network (VANET) is massively used in challenging traffic regulatory systems in the recent times. The bulky data being articulated by VANET is the critical part leading to the spectrum limitation issues. Cognitive Radio (CR) technology is a prominent stream to manage uncertainty in the spectrum distribution. The CR chooses the idle path in the entire spectrum and allocate as per the requirement for handling smooth traffic flow. Furthermore, parameters such as a multipath fading, primary user static problem and dynamic topology of vehicular communications remains to be challenge for implementing the CR in VANET for an effective and intelligent spectrum distribution. Moreover conventional methods for CR-VANET spectrum allocation is limited yet to handle Considering the higher mobility and uncertainty constraints, a novel deep learning adopted CR-VANET model is proposed. This paper proposes the new deep learning model which works on the principle of Lion Optimized Long Short Term Memory (LOL-SM) models which overcomes the drawbacks of the traditional LSTM and these learning models are implemented in the road side units (RSU) which predicts the vacant models and sends it to the vehicles. Comparing with the existing spectrum sensing strategies, the proposed LOL-SM based -CR VANET model attains a reduced overall transmission delay with minimum loss probability. Also, the false alarm rate is almost nullified in the proposed approach thus enhancing an effective spectrum usage in VANETs

Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification. Signal-processing methods can extract the potent time-frequency-domain characteristics of signals; however, the performance of conventional characteristics-based classification needs to be improved. Widely used deep learning algorithms (e.g., convolutional neural networks (CNNs)) can conduct classification by extracting high-dimensional data features, with outstanding performance. Hence, combining the advantages of signal processing and deep-learning algorithms can significantly enhance vibration recognition performance. A novel vibration recognition method based on signal processing and deep neural networks is proposed herein. First, environmental vibration signals are collected; then, signal processing is conducted to obtain the coefficient matrices of the time-frequency-domain characteristics using three typical algorithms: the wavelet transform, Hilbert–Huang transform, and Mel frequency cepstral coefficient extraction method. Subsequently, CNNs, long short-term memory (LSTM) networks, and combined deep CNN-LSTM networks are trained for vibration recognition, according to the time-frequency-domain characteristics. Finally, the performance of the trained deep neural networks is evaluated and validated. The results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning.

In recent trends, discovering classification knowledge from imbalanced data received has grabbed much interest by many researchers. Data set imbalancing might occur if any one class comprises considerably smaller number of examples than remaining classes. Various application greatly necessitates minority class which is regarded as quite interesting aspect. The imbalanced classes’ distribution set up a challenge for standard learning algorithms like k-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Neural Network (NN), subsequently biasing is done towards majority classes. An Improved Coefficient vector based Grey Wolf Optimization (ICGWO) Algorithm with ensemble classifier is deployed in previous approaches for classification. Nonetheless, adequate outcomes in terms of accuracy and execution time cannot be achieved. A Weighted Feature based Imperialist Competitive Algorithm (WFICA) with Ensemble Learning (EL) for imbalanced data classification is chiefly suggested for mitigating this issue. Primarily, normalization scheme is exploited for data transformation from different scales to an identical scale through Z-score normalization technique. Synthetic Minority Oversampling TEchnique (SMOTE) with Locally Linear Embedding (LLE) algorithm is deployed for oversampling process. Weighted Feature based Imperialist Competitive Algorithm (WFICA) is utilized for Optimal features selection which is done for classification accuracy enhancement. Ensemble Learning (EL) incorporated with Improved Bidirectional Long Short Term Memory (IBi-LSTM), Enhanced Weighted Support Vector Machine (EWSVM) and k-Nearest Neighbour (k-NN) classifiers is employed on selected features basis for performing classification. The suggested methodology is validated through experimental result and improved performance is attained when contrasted with prevailing system pertaining to accuracy, precision, recall and f-measure.

Speech Emotion Recognition is an emerging research field and is expected to benefit many application domains by providing effective Human Computer Interface. Researchers are extensively working towards decoding of human emotions through speech signal in order to achieve effective interface and smart response by computers. The perfection of speech emotion recognition greatly depends upon the types of features used and also on the classifier employed for recognition. The contribution of this paper is to evaluate twelve different Long Short Term Memory (LSTM) networks models as classifier based on Mel-Frequency Cepstrum Coefficients (MFCC) feature. The paper presents performance evaluation in terms of important parameters such as: precision, recall, F-measure and accuracy for four emotions like happy, neutral, sad and angry using the emotional speech databases namely Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The measurement accuracy obtained is 89% which is 9.5% more than reported in recent literature. The suitable LSTM model is further successfully implemented on Raspberry PI board creating standalone Speech Emotion Recognition system.

Wind speed prediction based on nested shared weight long short-term memory network

In bridge weigh-in-motion (BWIM), dynamic bridge response is measured during traffic and used to identify overloaded vehicles. Most past studies of BWIM use mechanics-based algorithms to estimate axle weights. This research instead investigates deep learning, specifically the recurrent neural network (RNN), toward BWIM. In order to acquire the large data volume to train a RNN network that uses bridge response to estimate axle weights, a finite element bridge model is built through the commercial software package LS-DYNA. To mimic everyday traffic scenarios, tens of thousands of randomized vehicle formations are simulated, with different combinations of vehicle types, spacings, speeds, axle weights, axle distances, etc. Dynamic response from each of the randomized traffic scenarios is recorded for training the RNN. In this paper we propose a 3-stage Bidirectional RNN toward BWIM. Long short-term memory (LSTM) and attention mechanism are embedded in the BRNN to further improve the network performance. Additional test data indicates that the BRNN network achieves high accuracy in estimating axle weights, in comparison with a conventional moving force identification (MFI) method.

PM10 is a particulate matter with an aerodynamic diameter less than or equal to 10. It is one of the primary pollutants contributing to the ambient air quality level. Air quality monitoring in Brunei Darussalam is using only the PM10 concentrations to measure the nation's daily Pollutant Standard Index (PSI). This study sheds light on a data centric landscape of air pollution prediction in Brunei Darussalam, highlights potential uses of forecasting daily PM10concentrations, and presents comparisons of prediction models built using several methods, namely: moving average, linear regression, recurrent neural network (RNN), long short term memory (LSTM), LSTM with 1D convolutions, and convolutional recurrent neural network (CRNN). This study is using daily PM10 concentrations obtained from the air quality monitoring stations located at every district in Brunei Darussalam for a period of 15 y (2005-2019).

The study aims to investigate the long memory behavior in time-varying beta, a systematic risk indicator, in İstanbul Stock Exchange (BIST) sub-indices. Using the data regarding BIST national indices, sub-indices and two-year benchmark bond interest rate between January 2009 and September 2019, the time-varying beta coefficient is determined with DECO-FIGARCH model, and the long memory behaviors of the beta coefficient are analyzed with GPH, Lo R / S and GSP tests. It is found that the beta coefficient of the three sub-indices (banking, financial and industrial) changes over time and the beta coefficient demonstrates long memory behavior (mean-reverting at a hyperbolic speed). It is indicated that the time-varying beta coefficients are forecastable and our findings contradict the weak-form of market efficiency.

Predicting the future activity location and trajectory of residents can provide essential information for smart urban management such as epidemic control, traffic facilitation, public security, etc. However, the current personal location prediction methods generally focus on the mining of individual's historical travel patterns, and seldom consider the feature of different travel stay points. This paper aims to propose a location prediction model utilizing stay point feature extraction. The model firstly constructs the historical trajectory links based on trajectory data, performs the location discovery rules to transform historical trajectory links into stay point links and clusters the stay points to form clustering links. Secondly, the model extracts the feature information (entry time, departure time, weather and land use) from different stay points and extracts the space feature from clustering links. Finally, the cluster links with feature information is introduced into long short-term memory (LSTM) network for customization to implement the personal location prediction capability. By using a 23-day trajectory location data of more than 3 million volunteer users in Shenzhen city, China. The results show that the location prediction F-score of our proposed model is better than variable order Markov model (about 5.5% performance gains) and the traditional N-order Markov model (about 7% performance gains). The model also introduces approximately 6.6% performance gains by utilizing the temporal and weather features of stay points. The model shows the capability of utilizing travel stay point feature for personal next location prediction.

This study analyses the performance of US Mutual Funds, from the perspective of Long Memory (LM), exploring if the returns of MFs are systematic due to their active management or they are random. The sample was 200 US equity MFs, from four categories, Large Cap, Middle Cap, Small Cap and World Stock, both 1- and 5-stars rating funds according to Morning Star rating. The time period was starting between 1981 and 2006 and ending 2016. Rescaled Range Analysis (R/S) employed for the Hurst exponent estimation, so to detect LM. Using Surrogate Data Analysis (SDA), the study was extended to Hurst exponent estimation for surrogate time series. The findings suggest that the selection of a MF presents a lot of complexity for investors. The 5-star MFs, with high qualified, and so expensive managers, tend to achieve random returns, while the returns of 1-star MFs, are more systematic. These MFs have higher fees than the 5-star MFs, but the management fees paid are quite inferior. This leads to the conclusion, that it might be preferable to pay for gaining an almost the same, but systematic return than to pay for the ties of the manager.

This dataset is originated from 12 types of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors during 2220 minutes. The dataset is formatted in "xlsx" file. Each sheet represents one beef cut which is contained columns as follows: Minute: time in minute TVC: continuous label in the total viable count Label: discrete label, 1,2,3,4 denote “excellent”,”good”,”acceptable”, and “spoiled”, respectively. MQ_: the resistant value of gas sensors. In addition, the file "partition.zip" contains a dataset partition for training (50%), testing (25%), and validation data (25%). The first column shows the class label and the rest are features.