Dong-Eui University

Banquet services are a strategically important yet underexplored component of full-service hotel operations, characterized by event-based delivery and complex coordination. Existing service quality frameworks inadequately capture the operational, spatial, and experiential features of banquet contexts. This study developed and validated a banquet-specific service quality measurement scale for full-service hotels. Using a modified DINESERV instrument, data were collected from 216 U.S. respondents who had attended a hotel banquet within the previous 36 months. Exploratory factor analysis identified four dimensions, Facilities & Operations, Service Performance, Guest Care, and Venue Quality, with acceptable-to-strong reliability. The findings conceptualized banquet services as integrated, episodic service systems rather than transactional dining encounters. Managerially, the scale provides a practical tool for prioritizing hygiene, operational reliability, and venue functionality while supporting sustainable service improvement. This study offers an empirically grounded framework that advances banquet service quality research and practice.

The rapid evolution of technology characteristics has significantly influenced various sectors, including fashion, in which technology-enabled platforms have increasingly been utilized to enhance personalization and consumer engagement. This study investigates the effect of these characteristics on consumer behavior within fashion curation platforms. Integrating the task–technology fit and the unified theory of acceptance and use of technology models, this study examines key constructs using structural equation modeling. Data were collected via a week-long survey of 300 Korean consumers using fashion curation platforms. The findings reveal that technology characteristics exert a significant influence on task–technology fit and effort expectancy. Additionally, hedonic motivation, social influence, and facilitating conditions were pivotal in shaping behavioral intention. The novelty of this work lies in the fact that it extends the integrated model framework to a fashion curation context to offer a more nuanced understanding. Moreover, the findings provide practical insights for optimizing technology-enabled fashion platforms to boost user adoption and engagement.

In this study, we examined metal accumulation and biochemical responses of Azolla imbricata (Roxb.) Nakai exposed to water medium (WM) and nutrient medium (NM) under single and combined (co-exposure) treatments with five metals (As, Cd, Cu, Pb, and Zn) at 10% environmentally relevant concentrations. Marked differences between WM and NM highlighted the influence of nutrient availability on plant responses. An inverse relationship was observed, with higher biomass in NM (WM < NM) and greater metal accumulation in WM (WM > NM). Growth inhibition, reflected by reduced photosynthetic pigment contents, was accompanied by elevated stress indicators, including electrolyte leakage, proline, malondialdehyde, and anthocyanins, confirming metal-induced phytotoxicity. Metal interactions under co-exposure were predominantly antagonistic in WM and synergistic in NM. Metal accumulation patterns (mg/kg) varied across media and exposure types: under single exposure, values ranged from 83.8 (As) to 43 881 (Zn) in WM and from 11.7 (Cd) to 12 135 (Cu) in NM; under co-exposure, they ranged from 346 (Cd) to 12 688 (Cu) in WM and from 46 (Cd) to 2859 (Cu) in NM. Accumulation sequences did not correspond to exposure concentrations, indicating metal-specific and media-dependent uptake. Under co-exposure, concurrent accumulation of multiple metals was more frequent in WM than that in NM, suggesting nutrient availability constrained simultaneous uptake. Bioconcentration factor values exceeded common thresholds under certain treatments, indicating strong accumulation potential rather than definitive hyperaccumulation. Overall, these findings highlight the potential of A. imbricata for phytoremediation of metal-contaminated waters while emphasizing cautious application in agroecosystems due to potential soil-crop-food pathway transfer.

Polymer composites, enabled by improvements in matrix performance using fillers, are widely used across diverse fields, yet the molecular-level characteristics of matrix-filler interfaces have not been fully understood. Here, we study in detail the fundamental interfacial molecular characteristics of epoxy/hexagonal boron nitride (h-BN) composites by using molecular dynamics and density functional theory. We investigate the structural, thermodynamic, and thermal/mechanical properties for systems with amounts of cross-linkers and quantify the distributions of microscopic cross-linking architectures at the matrix-filler interface, thereby correlating interfacial molecular behaviors to macroscopic response. Our analysis reveals that an excess of hardener (for a higher average cross-linking density) is likely to induce a less compact network, lowering local packing and the fully cross-linked fraction. We also find that the interfacial interactions of matrix/filler can reorganize the filler-surface morphology and govern cross-plane properties of h-BN, thus indicating that the topology of microscopic cross-link pathways─not the bulk properties alone─significantly influences the interfacial mechanisms in polymer composites. These results highlight that optimization of filler orientation and morphology, along with the interfacial cross-linking architecture, would be essential for designing high-performance epoxy composites.

In Low-PowerWide-Area Networks (LPWANs) such as LoRa, the preamble is essential for detecting highly attenuated signals. Its repetitive pattern allows a receiver to identify the presence of the signal and its precise starting point. However, in ultra-low Signal-to-Noise Ratio (SNR) environments, the preamble becomes undetectable as it is buried in strong noise, causing the entire detection process to fail. Although existing methods, such as those based on preamble symbol energy accumulation or deep learning-based spectrogram restoration, have been proposed, their performance remains limited under these extreme conditions. To address this limitation, this paper proposes a novel two-stage preamble detection scheme. The first stage employs a Convolutional-Transformer Encoder-Deconvolutional network that leverages self-attention to capture the distinct linear patterns of chirp signals even in the presence of severe noise. In the second stage, a classifier determines the presence of the preamble. Experimental results demonstrate that our proposed method significantly outperforms conventional approaches, lowering the minimum required SNR for preamble detection. To validate its performance, we utilized metrics including True Positive Rate (TPR) and F-scores. Under these evaluations, our scheme achieves a detection accuracy of over 90% in the ultra-low SNR range of -21.7 dB to -24.3 dB, confirming its robustness and practical viability.