Billiards exhibit rich dynamical behavior, typical of Hamiltonian systems. In the present study, we investigate the classical dynamics of particles in the eccentric annular billiard, which has a mixed phase space, in the limit that the scatterer is p...
Nonlinear time series analysis gained prominence from the late 1980s on, primarily because of its ability to characterize, analyze, and predict nontrivial features in data sets that stem from a wide range of fields such as finance, music, human physi...
Fractal properties in time series of human behavior and physiology are quite ubiquitous, and several methods to capture such properties have been proposed in the past decades. Fractal properties are marked by similarities in statistical characteristi...
This article describes a refinement of recurrence analysis to determine the delay in the causal influence between a driver and a target, in the presence of additional perturbations affecting the time series of the response observable. The methodology...
The fifth generation (5G) of mobile communication technology poses lots of questions while introducing significant improvements compared with previous generations. The most sensitive question is related to the safety of human exposure. The aim of pre...
Time-series classification remains a critical task across various domains, demanding models that effectively capture both local recurrence structures and global temporal dependencies. We introduce a novel framework that transforms time series into im...
Time series similarity measurement is a fundamental task underpinning clustering, classification, and anomaly detection. Traditional approaches predominantly rely on one-dimensional data representations, which often fail to capture complex structural...
Recent advances in time series classification (TSC) have exploited deep neural networks (DNN) to improve the performance. One promising approach encodes time series as recurrence plot (RP) images for the sake of leveraging the state-of-the-art DNN to...
Series arc faults (SAFs) pose a significant threat to the safety of photovoltaic (PV) systems. However, the complex operating conditions of PV systems make accurate SAF detection challenging. To tackle this issue, this article proposes a SAF detectio...
Introduction: Radiotherapy plays a critical role in the management of head and neck squamous-cell carcinoma (HNSCC); however, the influence of overall treatment time on patient outcomes remains an area of ongoing investigation. The use of radiation,...
Clinical evidence regarding risk reduction of repeated bladder recurrence after initial photodynamic diagnosis (PDD)-assisted transurethral resection of bladder tumor (TURBT) is still limited in patients with non-muscle-invasive bladder cancer (NMIBC...
For analyzing recurrent event data, we consider a generalization of the classical accelerated failure time model. In the proposed approach, the general function is no longer assumed to be a singleton but allowed to be time-varying. This is in the sam...
Over the past two decades, substantial efforts have been made to develop survival models for gap times between recurrent events. An emerging approach involves considering rate models derived from a non-homogeneous Poisson process, thus allowing the c...
This paper presents a varying-parameter finite-time recurrent neural network, called a varying-factor finite-time recurrent neural network (VFFTRNN), which is able to solve the solution of the time-varying Sylvester equation online. The proposed neur...
The automatic voltage regulator is an important component in energy generation systems; therefore, the tuning of this system is a fundamental aspect for the suitable energy conversion. This article shows the optimization of a fuzzy automatic voltage...
Background: Recurrent approximation of retention parameters in reversed-phase HPLC is effective for revealing anomalies that are otherwise difficult to detect, namely, the reversible hydration of analytes. This was demonstrated previously for restric...
In this paper, we propose a fully convolutional neural network based on recursive recurrent convolution for monaural speech enhancement in the time domain. The proposed network is an encoder-decoder structure using a series of hybrid dilated modules...
Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth’s surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Pla...
Recurrent neural networks (RNNs) remain challenging, and there is still a lack of long-term memory or learning ability in sequential data classification and prediction. In this paper, we propose a flexible recurrent model, BIdirectional COnvolutional...
Rehabilitation robots can establish a direct connection between the user’s nerve signals and the robot’s actuators by integrating with the human nervous system. However, uncertainties in these systems limit their performance and accuracy....
The detection and classification of engine-based moving objects in restricted scenes from acoustic signals allow better Unmanned Aerial System (UAS)-specific intelligent systems and audio-based surveillance systems. Recurrent Neural Networks (RNNs) p...
Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing durin...
Data scarcity is a significant obstacle for modern data science and artificial intelligence research communities. The fact that abundant data are a key element of a powerful prediction model is well known through various past studies. However, indust...
This paper proposes a learning control framework for the robotic manipulator’s dynamic tracking task demanding fixed-time convergence and constrained output. In contrast with model-dependent methods, the proposed solution deals with unknown man...
Multiple time series forecasting is critical in domains such as energy management, economic analysis, web traffic prediction and air pollution monitoring to support effective resource planning. Traditional statistical learning methods, including Vect...
Predictive maintenance in the car industry is an active field of research for machine learning and anomaly detection. The capability of cars to produce time series data from sensors is growing as the car industry is heading towards more connected and...
This paper addresses the implementation of an algorithm to compute day-ahead forecasts of photovoltaic power production with hourly updates as time progresses. Input data includes meteorological variables and real measurements of the power produced i...
In this paper, the stochastic Takagi–Sugeno fuzzy recurrent neural networks (STSFRNNS) with distributed delay is established based on the Takagi–Sugeno (TS) model and the fixed time synchronization problem is investigated. In order to syn...
In this paper, a real-time implementation of a sliding-mode control (SMC) in a hardware-in-loop architecture is presented for a robot with two degrees of freedom (2DOF). It is based on a discrete-time recurrent neural identification method, as well a...
This study applies Real-Time Recurrent Learning Neural Network (RTRLNN) and Adaptive Network-based Fuzzy Inference System (ANFIS) with novel heuristic techniques to develop an advanced prediction model of accumulated total inflow of a reservoir in or...
Discrete time-varying matrix problems are prevalent in scientific and engineering fields, and their efficient solution remains a key research objective. Existing direct discrete recurrent neural network models exhibit limitations in noise resistance...
Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors shoul...
The concept presented in this paper is based on previous dynamical methods to realize a time-varying matrix inversion. It is essentially a set of coupled ordinary differential equations (ODEs) which does indeed constitute a recurrent neural network (...
Background: Catheter ablation (CA) for atrial fibrillation (AF) is superior to antiarrhythmic drugs in maintaining sinus rhythm. Novel evidence suggests that increasing the time between the first diagnosis of AF and ablation, or diagnosis-to-ablation...
Time series forecasting has played an important role in different industries, including economics, energy, weather, and healthcare. RNN-based methods have shown promising potential due to their strong ability to model the interaction of time and vari...
Accurate and real-time forecasting of the price of oil plays an important role in the world economy. Research interest in forecasting this type of time series has increased considerably in recent decades, since, due to the characteristics of the time...
Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural network (RNN) models boosted the interpre...
Unexpected readmission to intensive care units (ICUs) endangers patients’ lives due to premature patient transfers or prolonged stays at the care units. This can be mitigated by stratification of the readmission risk at discharge times using st...
In multivariate and multistep time series prediction research, we often face the problems of insufficient spatial feature extraction and insufficient time-dependent mining of historical series data, which also brings great challenges to multivariate...
Corporate financial distress typically emerges through a gradual accumulation process, rendering crisis prediction inherently dynamic and path-dependent. However, many existing studies continue to rely on static cross-sectional data or short-term obs...
In this paper, a novel analysis method based on recurrence networks is proposed to characterize the evolution of dynamical systems. Through phase space reconstruction, a time series was transformed into a high-dimensional recurrence network and a cor...
This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand sub...
Background/Objectives: Peritoneal surface metastases (PSMs) from colorectal cancer have high rates of peritoneal recurrence after cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). Prior studies dichotomize peritoneal...
Background: Disease recurrence is the primary cause of death in patients with gastric cancer who have undergone complete surgical resection. No prognostic factors for recurrence, other than the Tumor, Node, and Metastasis stage, have been established...
Acute ischaemic stroke (AIS) has a high risk of recurrence, particularly in the early stage. Our study aimed to assess the clinical characteristics and risk factors of in-hospital ischaemic recurrence in AIS patients in different periods. This study...
Inhibition of angiotensin II synthesis seems to decrease hepatocellular carcinoma recurrence after radical therapies; however, data on the adjuvant role of angiotensin II receptor 1 blockers (sartans) are still lacking. Aim of the study was to evalua...
21 January 2023
During exercise with increasing intensity, the human body transforms energy with mechanisms dependent upon actual requirements. Three phases of the body’s energy utilization are recognized, characterized by different metabolic processes, and se...
Background: Obstructive sleep apnea (OSA) is a major modifiable risk factor for atrial fibrillation (AF), promoting arrhythmogenesis through intermittent hypoxia, autonomic activation, and atrial remodeling. Although continuous positive airway pressu...
Identification of embedding dimension is helpful to the reconstruction of phase space. However, it is difficult to calculate the proper embedding dimension for the financial time series of dynamics. By this Letter, we suggest a new method based on Ma...
Pilonidal sinus disease increasingly affects children and adolescents. While recurrence rates in adults are lower, with a minimum of 2% per year, and are essentially a function of the surgical approach over time, this is totally different in children...
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