The European Physical Journal Special Topics最新文献

This study investigates the optimization of heat transfer using a ternary hybrid nanofluid, an innovative advancement in nanofluid technology. The primary objective is to analyze the effects of first-order boundary slip conditions, thermal radiation, porous media, viscous dissipation, and Joule heating on the thermal dynamics of the nanofluid. The ternary hybrid nanofluid, consisting of silver (Ag), titanium dioxide ((TiO_2)), and alumina ((Al_2O_3)) nanoparticles suspended in water ((H_2O)), is selected for its potential to enhance heat transfer and thermal efficiency in various applications, including cooling systems, food processing, and refrigeration. The research employs magneto-hydrodynamics combined with the ternary hybrid nanofluid to improve energy and mass transfer processes. Through a similarity transformation, the governing equations are converted into a set of nonlinear ordinary differential equations, which are then solved numerically using the shooting technique integrated with MATLAB. Graphical representations and tabulated data illustrate the impact of different parameters on velocity and temperature fields, skin-friction coefficient, and local Nusselt number. Key findings indicate that increased values of radiation and magnetic parameters result in a thicker thermal boundary layer. The study also reveals that the velocity of the hybrid nanofluid can be effectively controlled by adjusting the magnetic field, porous media, and nanoparticle volume fraction. Notably, the ternary hybrid nanofluid ((Ag-Al_2O_3-TiO_2/H_2O)) demonstrates superior performance compared to hybrid nanofluids with a single component ((Ag-Al_2O_3/H_2O)). Comparisons with pre-existing data show favorable alignment, underscoring the robustness of the results. This research has significant implications for engineering, healthcare, and biomedical technology.

This study aims to propose type-2 fuzzy pooling in a U-shaped convolutional neural network (CNN) architecture (T2FP_UNet). A CNN consists of convolutional, pooling, a fully connected layer, and activation functions. The pooling layer executes a fuzzy pooling operation, utilizing type-2 fuzzy membership function. In contrast to conventional methods (max and average pooling), the fuzzy pooling operation assigns membership values to pixels before computing fuzzy values, thereby preventing the encoder from losing features. The decoder implements dynamic feature extraction to acquire informative features. This approach improves the robustness and uncertainty handling of semantic image segmentation tasks using a modified U-Net architecture with type-2 fuzzy pooling layer and dynamic feature extraction. This method combines the advantages of the feature-fused U-Net architecture, type-2 fuzzy logic and dynamical feature extraction for handling complex uncertainties in image data. Comparative results are tabulated.

This paper presents a novel neuro-sliding mode observer-based control strategy for addressing disturbances, model uncertainties, and unmodeled dynamics in practical multi-agent systems (MAS). The focus is on achieving consensus tracking in non-linear MAS, specifically in the context of synchronous generators. A distributed protocol based on sliding mode approach is proposed to handle unknown model structures and parameters of follower agents influenced by the dynamics of synchronous generators. To achieve consensus tracking under these conditions, a hybrid radial basis function (RBF) neural network is employed to identify the unmodeled dynamics of the follower agents. The neural network’s update law algorithm is adjusted using the errors from both the observer and the controller. The stability of the proposed method is guaranteed by employing Lyapunov theory, ensuring that the consensus error and the error between the states of the consensus error dynamic and its estimator asymptotically converge to a neighborhood of zero. To validate the theoretical results, Matlab simulations are conducted to assess the effectiveness of the proposed approach, providing evidence of its capability and practical applicability.

In this study, we propose an optimal control strategies for a fractional-order COVID-19 model with time delay. Existence and uniqueness of a solution to the fractional delay model are investigated. We compute the basic reproduction number and establish the local stability analysis of the model under the Caputo derivative. We develop a fractional order delayed optimal control problem based on vaccination and treatment as time-dependent control parameters. We derive the necessary and sufficient condition for optimal control. In MATLAB, the resulting fractional delay optimality system is numerically solved employing the forward–backward sweep method. Our findings suggest that combining fractional-order derivatives with time-delay in the model enhances dynamics while increasing model complexity.

Neutrino physics has entered into the era of precision measurements. Over the last two decades, significant efforts have been made to measure precise parameters of the PMNS matrix, which describes neutrino oscillation phenomena. The next generation neutrino experiment will prioritize measuring leptonic CP-violation, potentially revealing the matter–antimatter asymmetry of the universe. Technological advancements will enable faster and more precise measurements. This article describes how neutrino experiments, will utilize machine learning techniques to identify and reconstruct different neutrino event topology in detectors. This approach promises unprecedented measurements of neutrino oscillation parameters.

Recently, researchers have used various methods for time-series forecasting based on artificial neural network models. Among these approaches, one of the most effective ones is the Echo State Network (ESN). An ESN is a variant of recurrent neural networks (RNNs) that are used in environmental studies. In this work, we propose models to predict the dynamics of dust particles (PM 2.5) using reservoir computing. The model was based on data on the content of PM 2.5 obtained in Seoul, Republic of Korea, collected between January 2017 and August 2017. Hourly data for this period were averaged over a 6-h interval to reduce variability in the source data. For training, 800 samples of the time series were selected; for the test set, 50 samples (part 1 of the work) and 100 samples (part 2 of the work) were used. Prediction accuracy was assessed using several accuracy indices and a Taylor diagram. The application of the proposed approach demonstrated the effectiveness of reservoir calculations for predicting dust content in megacities. The accuracy and the quality of the models improved from 9 to 67%, depending on the evaluation indicator. It was also found that the accuracy of the model decreased when the predicted time interval exceeded 6% of the training time interval.

In this paper, the authors utilize a linear matrix inequality (LMI) technique for designing a quantum genetic algorithm (QGA)-based memory state feedback control of a nonlinear system. The performance of the proposed model is enhanced using the QGA-based algorithm for finding the control gain matrices as a searching tool. To evaluate the fitness function of QGA, the LMI problem is formulated as a constrained optimization. The more general Lyapunov–Krasovskii (LKFs) functional is selected to analyze the closed-loop system stability and the criterion for its asymptotic stability. Numerical examples are provided to verify the effectiveness of the QGA-based proposed control scheme.

This paper investigates the dynamics of a Hopfield inertial bi-neuron with double memristive synaptic weights. The dynamical behavior of the system is investigated with both numerical and analytical studies to characterize the proposed model, which has up to thirty-nine equilibrium points. In this model, numerical simulations show many behaviors such as chaos, antimonotonicity of periodic and chaotic bubbles, and bursting oscillation (regular and irregular). Moreover, this system showed multiple coexistence of up to six different attractors, with the attractor basins confirming this phenomenon. A ring and star network of Hopfield neurons was also considered. We found interesting spatio-temporal regimes, including chimera and cluster states. Moreover, we showed a striking coexistence of synchronized, chimera, and cluster states in the network. The integration of multiple memristors in neural network systems holds promise for improving our understanding of the brain and developing more sophisticated artificial intelligence technologies that can better mimic human cognitive abilities.

Urban water systems continue to face a major problem with water leakage, which results in substantial waste, shortages, damage to infrastructure, and monetary losses. While deep learning models have been effective in locating and identifying leaks, overfitting may result from their complexity over several training epochs. By including an attention mechanism, prominent features are given priority, improving model performance without compromising simplicity. Furthermore, layer normalization reduces problems in long short-term memory networks such as exploding gradients. Notable F1-scores are achieved by the proposed approach, demonstrating strong performance in both leak detection and localization tasks. Performance analysis under three different conditions for leak detection task such as source adaptation, target adaptation and adversarial simulation have shown an increase with scores of 91.59, 86.25 and 82.51 yielding 8.2%, 8.7% and 6.8% of improvement in F1-score, respectively. Similarly, performance analysis under three different conditions for leak localization task such as source adaptation, target adaptation and adversarial simulation has shown an increase with scores of 89.86, 84.39 and 80.77, yielding 7.4%, 8.5% and 8.6% of improvement in F1-score, respectively. Also, analysis using Wasserstein distance indicates reduced covariate shift through significant increase in accuracy (around 6.5%–9.5%, respectively), which is essential for adapting to varying water demand scenarios. The effectiveness of the proposed approach in urban water management is underscored by these results, emphasizing its potential for enhancing resource conservation and infrastructure sustainability.