Search Results (4,500)

Background/Objectives: Research comprehensively analyzing the psychological characteristics and factors related to smartphone addiction in Korean children and adolescents at risk for depression remains scarce. This study utilized large-scale cohort data to examine the differences in psychological characteristics between an at-risk group for depression and a control group, and to identify the specific factors influencing smartphone addiction within the at-risk group. Methods: Data were obtained from the school-based cohort of internet and smartphone users conducted by the National Center for Mental Health (NCMH), involving a total of 2294 children and adolescents (1009 in the at-risk for depression group and 1285 in the control group). Assessment tools included the Children’s Depression Inventory (CDI), State-Trait Anxiety Inventory for Children (STAIC/TAIC), Self-Esteem Scale (SES), Self-Control Scale, Aggression Questionnaire (K-AQ), School Bullying (SB) scale, and the Smartphone Addiction Scale-Short Form (SAS-SV). Results: Analysis of Covariance (ANCOVA) revealed that the at-risk group exhibited significantly higher levels of anxiety, aggression, involvement in school bullying, and smartphone addiction compared to the control group, while showing lower levels of self-esteem and self-control. Furthermore, multiple regression analysis indicated that higher anxiety and lower self-control were significant predictors of increased smartphone addiction levels. Conclusions: These findings support the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, which posits that emotional vulnerability and deficits in executive functions lead to addictive behaviors. The results suggest that reducing anxiety and enhancing self-control are critical factors in the prevention of smartphone addiction among children and adolescents. Full article

In the existing research on information propagation modeling in social networks, hypergraphs have been widely applied to characterize the high-order interaction relationships involving multiple nodes. However, most models are still based on the assumption of undirected connections, which leads to certain limitations in depicting the information flow direction and the structural characteristics of propagation chains. To address the above problems, a social hypernetwork propagation model with directional constraints is constructed in this paper by introducing the directed hypergraph structure and combining it with the improved SEIR model. The strength of social relationships is measured by intimacy in the model, and a comprehensive characterization of the information propagation process is achieved by integrating the threshold mechanism of the directed hypergraphs with the attenuation function of information timeliness. In addition, the effectiveness of the proposed model is verified by taking the event of “imposing additional tariffs” as an example, and the evolutionary characteristics of propagation in different network structures, as well as the impacts of user confidence and information timeliness, are analyzed using simulation experiments. The results indicate that the model is applicable to characterizing the information propagation trends and dynamic characteristics in real social networks, and can provide theoretical references and methodological support for the prediction and regulation of network public opinion. Full article

In response to the redundant spatio-temporal modeling and insufficient adaptation to dynamic decision-making in eye-tracking interaction of smart home interfaces, a smart home interface eye-tracking response optimization model based on spatio-temporal Transformer and gate control cross-attention is proposed. It adapts the physiological characteristics of eye-tracking jumps through dynamic sparse attention gating to compress computational redundancy and combines multi-objective reinforcement learning attention modulation to construct a closed-loop decision-making mechanism, optimizing interface parameters in real-time. Experiments showed that the model reduced eye-tracking trajectory prediction error by 23.7% compared to advanced benchmarks, increased the success rate of adapting to dynamic mutation scenarios to 89.2%, and controlled performance fluctuations within 2.3% under noise interference. In high-fidelity user testing, the accuracy of cross-task gaze transfer reached 93.4%, the failure rate of glare interference was optimized to 2.4%, and the user cognitive load index was reduced by 27.9%. Its resource consumption and energy consumption were reduced by 26.7% and 44.9%, respectively, while its posture deviation tolerance remained at 3.5°. The sparse spatio-temporal modeling of the spatio-temporal adaptive Transformer module and the enhanced gating mechanism of the hierarchical gated cross-attention module work together to break through the limitations of traditional methods in computational efficiency and dynamic feedback, providing high-precision and low-latency eye-tracking interaction solutions for smart home interface systems, and promoting the practical evolution of personalized human–machine collaborative control. Full article

Interactive digital commerce environments increasingly rely on influencers as algorithmically amplified intermediaries between brands and consumers. However, the process through which influencer attributes translate into brand trust remains theoretically underdeveloped. Drawing on Social Influence Theory and Source Credibility Theory, this study develops a process-based model in which consumer engagement operates as a psychological mechanism linking influencer characteristics, namely credibility, brand alignment, interactivity, and authenticity, to brand trust. Using survey data from 400 active social media users in Lebanon and partial least squares structural equation modeling (PLS-SEM), the findings reveal that all four influencer attributes significantly enhance consumer engagement, which in turn strongly predicts brand trust. Influencer–brand alignment emerges as the strongest driver of engagement, suggesting that value congruence functions as a heuristic cue in interactive digital commerce contexts. By conceptualizing engagement as a trust-internalization mechanism within platform-mediated environments, this study advances electronic commerce theory and provides context-sensitive insight into digital trust formation in emerging markets. Full article

The security of biometric data is a critical challenge in modern information security due to their uniqueness and non-revocability. Compromise of biometric characteristics leads to irreversible consequences; therefore, storing or transmitting them in plaintext is unacceptable. This paper addresses the confidentiality and integrity of fingerprint data using cryptographic protection methods. Considering the specific nature of biometrics, fingerprint features are used only to generate a cryptographic secret rather than being stored directly. To protect the derived secret, a modified threshold secret-sharing scheme based on non-positional polynomial notation and the Chinese Remainder Theorem is proposed. The method generates a cryptographic secret from fingerprint minutiae described by spatial coordinates and ridge orientation. Concatenating minutiae coordinates and converting them into binary form produces a unique value deterministically linked to a specific user. Compared to the classical Shamir scheme, the modified scheme reduces the computational complexity of secret reconstruction from O($n {log}^{2}n$) to O(k log k), decreases data storage requirements by 30–40% through compact polynomial remainders, and increases successful secret reconstruction by 12–15% in the presence of noise in biometric samples. The results show that the proposed algorithm can be effectively applied in biometric authentication systems to protect personal data in distributed environments. Security analysis confirms resistance to major attack classes and demonstrates practical applicability in real-world systems. Full article

Digital transformation in the interior design industry has opened new opportunities for innovation; however, many cost-conscious homeowners still face difficulties in selecting and customizing design packages that achieve a balance between overall cost and sustainable quality. Existing interior design platforms lack seamless support and often require homeowners to invest considerable time and effort to tailor services to their needs while staying within budget. To address these challenges, this paper explores the use of machine learning to build a predictive modelling framework that supports personalized and value-driven interior design recommendations. The proposed approach uses a hybrid recommendation system that combines content-based and collaborative filtering. It also incorporates lightweight techniques such as TF–IDF (Term Frequency–Inverse Document Frequency) and logistic regression to more effectively capture user preferences, budget limits, and several interior-design service categories. Primary data was collected from small to medium-sized interior design companies. To demonstrate the proposed approach, a user-friendly web application tool is developed to integrate machine learning-enabled recommendation services. The resulting solution provides access to professional interior design services, enhancing customization and customer satisfaction while reducing the time and effort required from homeowners. To validate and compare the performance of the proposed approach, several machine learning models including Random Forest, XGBoost and KNN (K-Nearest Neighbors) were tested using standard metrics such as accuracy, precision, recall, and ROC-AUC (Receiver Operating Characteristic-Area Under the Curve). The proposed logistic regression hybrid model achieved the strongest overall results, with an accuracy of 83.62%. These findings demonstrate the significant contribution of this work to enhancing personalization and accessibility in the interior design sector via machine learning-enabled recommendation systems. The proposed approach bridges the gap between expert-level services and financial limits, making it a practical choice for cost-conscious homeowners. Full article

This study presents a deterministic geospatial methodology for the alignment of directional television receiving antennas using publicly available broadcast-sector parameters. The proposed approach relies exclusively on geometric computations derived from user geolocation (WGS84 coordinates) and transmitter site information, including sector azimuth and beamwidth characteristics. By computing the geodesic bearing between receiver and transmitter locations, the method evaluates angular deviation relative to sector orientation and provides an interpretable alignment assessment framework. The methodology operates without requiring empirical signal measurements, propagation modeling, or machine-learning techniques, thereby ensuring transparency, reproducibility, and low computational complexity. The approach is particularly suitable for scenarios where line-of-sight conditions dominate signal propagation. Under such assumptions, the proposed framework offers a lightweight and explainable solution for antenna pointing and orientation guidance while explicitly acknowledging the limitations imposed by simplified geometric modeling. Full article

Urban micromobility systems are increasingly deployed to support sustainable transportation goals; however, their overall sustainability performance remains inconsistently assessed across environmental, social, economic, and operational dimensions. This study proposes a conceptual framework for evaluating the sustainability of urban micromobility systems, with a particular focus on e-scooters. It clarifies and restructures fragmented indicators into distinct, non-overlapping sustainability dimensions. The framework is structured around five impact areas: Environment, Economy, Users, Transport Performance, and Safety, complemented by two enabling components, namely the legal framework and business model, which are conceptualized as preconditions for system feasibility rather than performance dimensions. Building on existing sustainability assessment literature, the framework consolidates established indicators while introducing micromobility-adapted and context-specific indicators, such as service availability and operational characteristics, to better capture the distinctive features of shared micromobility systems. The resulting framework provides a structured and flexible tool for researchers, planners, and policymakers, emphasizing that micromobility sustainability depends not only on measured impacts, but also on governance, operational design, and local implementation conditions. Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article

Lightweight and slender footbridges exemplify sustainable, material-efficient infrastructure, yet their vibration performance is frequently governed by high dynamic sensitivity, which directly affects serviceability, user comfort, and structural durability. This paper provides a critical review of full-scale experimental investigations and validated finite element simulations addressing the dynamic behavior of various footbridges, focusing on the influence of structural typology, material solutions, and excitation characteristics on vibration-related performance within sustainability-driven design objectives. This article is organized into three core research themes: (1) standards and guidelines in bridge engineering practice, (2) dynamics of footbridges with special structural response to human-induced loading, including walking, running, crowd actions, and higher harmonic contributions, and (3) case studies, based on which the gaps in the current approach are formulated. Based on a synthesis of findings from leading research on structural dynamics and sustainable infrastructure, this paper highlights critical gaps in current vibration serviceability guidance for footbridges. Concluding remarks delineate the principal research challenges and formulate evidence-based, practical recommendations to enhance the resilience, vibration comfort compliance, and sustainability of future footbridge infrastructure. Full article

Pedestrians are amongst the most vulnerable road user groups. Efforts to enhance pedestrian safety have mainly focused on intersections and midblock crossings. This study investigated the effect of bus stop environments on pedestrian safety in Kumasi, an area with a high incidence of pedestrian fatalities in Ghana. Crashes within a 50 m radius of bus stops were extracted using a spatial join. The Negative Binomial regression model was applied to model pedestrian crashes around bus stops as a function of three distinct non-collinear independent variable groups: road design features, bus stop characteristics, and pedestrian exposure measures. Formal bus stops were associated with higher crash rates than informal ones. The presence of medians and crosswalks was associated with lower crash rates, whereas wider carriageways were associated with higher crash rates. Higher crashes were linked to passing pedestrians and waiting pedestrians, while crossing pedestrians were associated with reduced crashes. These findings suggest that the combined effects of infrastructure and behavioural factors influence pedestrian safety at bus stops. Prioritising low-cost safety treatments, such as guard-railed waiting areas, marked crosswalks, medians, and raised crossings, around bus stops will yield substantial safety benefits for resource-constrained contexts and advance sustainable urban mobility. Full article

In this paper, we propose a downlink (DL) channel estimation scheme for frequency-division duplex (FDD) multi-antenna orthogonal frequency-division multiplexing (OFDM) systems, leveraging atomic norm minimization (ANM) and deep neural networks (DNN). Unlike time-division duplex (TDD) systems, where uplink (UL) and DL channels are reciprocal, FDD systems do not share this reciprocity, leading to increased channel training overhead. However, both theoretical analyses and empirical evidence reveal that key channel characteristics—such as angles of arrival and departure, path delays, and the number of propagation paths—exhibit partial reciprocity between UL and DL. Building on this insight, we design a DL channel estimation scheme that exploits frequency-independent UL parameters along with estimated DL channel gains. Our method integrates ANM with DNN to enhance estimation accuracy and efficiency. Specifically, ANM formulates the estimation problem while avoiding the off-grid errors inherent in traditional grid-based methods. To further mitigate performance degradation in clustered-path channels and reduce computational complexity, we introduce a DNN-based architecture that predicts channel parameters. The DNN captures hidden relationships between received pilot signals and frequency-independent channel parameters, enabling accurate estimation with linear time complexity. During training, ANM assists in serving users, ensuring reliable performance. Once the DNN is fully trained, it takes over to balance quality of service (QoS) and latency, providing an efficient and accurate solution for DL channel estimation in FDD-OFDM systems. Full article

Cross-domain data exchange is an important technical approach for realizing the value of data assets. However, lacking a single trusted root CA across domains, cross-domain schemes often encounter difficulties in authentication, controlled data flow, and fine-grained authorization. We propose a cross-domain data sharing scheme that uses decentralized identifiers and threshold proxy re-encryption. This scheme adopts the intra-domain leader node to verify the user identity, and the inter-domain multi-agent nodes collaborate in a threshold manner to handle cross-domain registration requests and re-encryption requests. Through threshold cooperation, the problem of single point of failure is effectively solved. The hash value of cross-domain registration information is stored on the blockchain, leveraging the immutable and traceable characteristics of blockchain to achieve trusted cross-domain data sharing. In addition, we introduce a ciphertext version tag to enable fast updates of re-encryption keys and use zero-knowledge proofs to verify re-encrypted ciphertext correctness. The security analysis indicates that our scheme has IND-CCA2 security under the DBDH assumption and can effectively resist collusion attacks. Performance analysis shows that our scheme is efficient, and can better meet the needs of cross-domain data sharing. Full article

Sustainability in tourism is becoming an increasingly significant challenge given the growing environmental, social, and cultural pressures associated with traditional forms of travel. One tool considered in this context is virtual reality (VR), which enables tourism experiences without the need for physical travel. The aim of this article is to examine how individual characteristics and features of the VR experience relate to user experience and changes in the evaluation of tourist destinations. The empirical study is based on surveys conducted before and after VR sessions in which 215 participants used the “Google Earth VR” application and visited locations of their choice. This paper presents the results of the relationship analysis between different variables. The dataset included evaluation of perceived realism and its components (360° representation, graphical quality, lag/smoothness, freedom of exploration, sound quality, tracking accuracy), engagement, emotion in VR, intuitiveness of VR use and more. In terms of the most important results, participants with a higher interest in travel reported stronger emotional responses and higher engagement during the VR experience, while perceived realism showed a weaker but directionally consistent association. VR showed a somewhat stronger, though still small, association with positive change in destination evaluation among participants with low initial tourism interest, for whom the experience may introduce novelty or reduce psychological distance to the destination. The analyses conducted contribute to a better understanding of the factors associated with the virtual tourism experience and highlight the potential of VR as a tool supporting the development of more sustainable forms of tourism experiences. Full article

This study aims to explore and propose a design-oriented methodology for solar–thermal methanol reforming (ST-MSR) hydrogen production equipment suitable for marine applications. To address key challenges such as the intermittency of solar energy, spatial and environmental constraints on board ships, operational safety, and user experience, a multidisciplinary integrated-design decision-making framework is established. First, the Kano model is employed to systematically analyze the latent needs of target users regarding ST-MSR equipment, while the analytic hierarchy process (AHP) is used to determine the weighting of evaluation criteria. Second, the theory of inventive problem solving (TRIZ) is applied to generate innovative conceptual design solutions. Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) is adopted to conduct a multi-dimensional comprehensive evaluation and optimization-based selection of the conceptual alternatives. The optimal design scheme is thus identified in terms of energy performance, product characteristics, user experience, economic feasibility, and environmental adaptability. The results indicate that the microchannel and phase-change thermal-storage integrated solar–thermal-tracking chemical reactor achieves the highest comprehensive evaluation score among the proposed schemes, demonstrating superior performance in terms of safety, energy efficiency, and adaptability to marine environments. This research provides a systematic industrial design methodology and practical reference for the design and product development of clean energy equipment for ships, contributing to the green and sustainable transformation of the maritime industry. Full article