We present a novel Fractional Power Iterative regularization method for solving Caputo-Hadamard fractional inverse source problems arising in anomalous diffusion applications. The proposed method addresses the inherent ill-posedness of reconstructing unknown source terms from noisy boundary observations by combining fractional power regularization operators with iterative refinement strategies. Rigorous convergence analysis establishes superlinear convergence rates and optimal error bounds under appropriate smoothness assumptions, with spectral analysis revealing exponential decay characteristics that significantly outperform classical Tikhonov regularization. Comprehensive numerical experiments demonstrate the method’s superiority across multiple performance metrics, showing substantial improvements in reconstruction accuracy and optimal linear scaling behavior with noise levels, while maintaining exceptional spectral preservation capabilities even under challenging noise conditions. Environmental applications to groundwater contamination transport modeling demonstrate the practical significance of fractional diffusion frameworks, where the Caputo-Hadamard operator captures memory effects critical for accurate prediction of contaminant plume evolution in heterogeneous aquifers that classical models significantly underestimate in terms of cleanup timeframes and barrier performance requirements. The research establishes this approach as the current state-of-the-art for fractional inverse source problems, providing essential tools for environmental engineering applications including remediation design, exposure assessment, and long-term monitoring strategies in complex groundwater systems.
Globalization of trade brings many benefits to consumers but simultaneously poses many barriers. In that context, animosity (AN) and cosmopolitanism (CO) represent barriers and motivations in global consumer behavior. Then, this study aims to explore the impact of AN and CO on the purchase intention towards imported Chinese domestic home appliances (PI) through brand image (BI), product judgment (PJ), perceived product quality (PQ) and endorser's credibility (EC). A sample of 506 Vietnamese consumers was collected using a non-probability sampling method. The data were analyzed using quantitative research methods, applying structural equation modelling (SEM) through SmartPLS. The results showed that AN negatively affected BI, PJ and PQ, while CO positively affected BI, PJ and PQ. At the same time, BI, PJ and PQ mediated the correlations between AN, CO and PI. In addition, EC significantly moderate the correlations between BI-PI and PJ-PI. Based on these findings, the study proposed managerial implications to help businesses improve consumer buying behavior towards imported Chinese domestic home appliances, and at the same time, provided some theoretical implications, limitations and suggestions for further research.
The rapid growth of information dissemination on social networks such as Facebook and Twitter, along with the widespread of mobile devices, has intensified the need for early yet accurate rumor detection methods. While existing hybrid machine learning models integrating natural language processing (NLP) and graph neural networks (GNNs) show promising results but often lack effective integration of diverse features and temporal modeling. To overcome these limitations, we introduce MTAS, a novel framework designed to detect rumors effectively by generating a multiview of social data encompassing semantic, inner, global, and temporal features. Specifically, MTAS models social network data into a comprehensive graph structure representing the global relationship among all tweets, words, and users. Through this structure, the framework processes and analyzes propagation patterns, semantic content, and temporal features. Additionally, it employs a subgraphlevel attention mechanism to combine the extracted representations. By explicitly modeling temporal dynamics and enhancing feature fusion, MTAS achieves superior performance. Extensive experiments on two datasets collected from Twitter, Twitter15 and Twitter16, demonstrate that MTAS outperforms state-of-the-art methods, achieving accuracies of 92.9% and 94.6%, respectively, compared to 91.1% and 93.7% for existing best-performing models. Notably, MTAS excels in early-stage rumor detection, achieving over 90% accuracy within the first 8 h of rumor propagation, a crucial step for mitigating the spread of misinformation.
Thermoelectric materials offer a promising route for sustainable energy harvesting by directly converting waste heat into electricity, enabling compact, solid-state, and environmentally friendly energy solutions. Among them, bismuth telluride (Bi2Tes) stands out as the benchmark material for near-room-temperature applications due its excellent electronic transport properties and commercial maturity. However, achieving high-performance bulk or thick-film Bi2Tes remains a formidable challenge. Conventional strategies such as doping, alloying, and nanoinclusion, while successful in thin films, often fail to translate effectively to bulk systems due to issues like pore collapse, poor uniformity, and degraded electrical connectivity. These limitations hinder the formation efficient phonon-scattering architectures without compromising charge transport, resulting in limited improvement in the thermoelectric figure of merit (ZT). In this study, we present a novel and scalable nano engineering strategy that applies metal-assisted chemical etching (MACE) to fabricate nanoporous surface layers on bulk Bi2Tes for the first time. Unlike conventional nanostructuring techniques, MACE enables the formation oriented nanostructures via a simple wet-chemical process, offering high tunability, low cost, and compatibility with large-area substrates. To reduce interfacial resistance, nickel was subsequently electrodeposited onto the nanostructured surface, forming a conformal contact layer that improves charge extraction and output performance. By systematically tuning the MACE duration, the optimized nanostructured Bi2Tes sample exhibited a 2.3 fold improvement compared to the pristine bulk sample. Furthermore, due to the increased surface area from the nanoporous architecture, the internal resistance and output power of the nanostructured Bi2Tes devices demonstrated 25-fold and 5.8-fold improvments, respectively, relative to the untreated sample. These remarkable improvements are attributed to the synergistic effect of enhanced phonon scattering within the nanoporous layer and improved charge transport enabled by the conformal nickel coating. This work not only introduces powerful nanostructuring route for Bi2Tes but also establishes a practical platform for high-performance, thick film thermoelectric devices. The findings offer deep insight into the structure, property, and performance relationships governing thermoelectric efficiency and pave the way toward the scalable fabrication of next generation thermoelectric modules for real-world applications such as industrial waste heat recovery and self powered electronics.
We propose a Crank-Nicolson finite difference scheme to simulate a 2D perturbed soliton interaction under the framework of coupled (2+1)D nonlinear Schrodinger equations with saturable nonlinearity and nonlinear damping. We rigorously demonstrate that the proposed numerical scheme achieves a second-order convergence rate in both the discrete H-0(1) and L-2 norms, relative to the time step and spatial mesh size. We establish the boundedness of discrete energies to prove the existence and uniqueness of the solutions derived from the Crank-Nicolson scheme. The validity of the analysis is confirmed through numerical simulations that apply to the corresponding coupled (2+1)D saturable nonlinear Schr & ouml;dinger equations with damping terms.
