Search Results (1,617)

In the era of digital intelligence, digital media landscapes increasingly influence cultural tourism consumption. Consumerism capitalizes on tourists’ superficial aesthetic commonalities, constructing a homogenized media imagination that leads to collective convergence in travel decisions, which obscures aspects of local culture, poses safety risks, and results in fleeting local tourism booms. In this study, semistructured interviews were conducted with 36 tourists, and NVivo12.0 was used for three-level node coding in a qualitative analysis to explore the digital media attributions of conformist travel behavior. The findings indicate that digital media landscapes exert a “digital persuasion” effect by reconstructing self-experience models, directing the individual gaze, and projecting idealized self-images. These mechanisms drive tourists to follow digital traffic trends and engage in imitative behaviors, ultimately shaping the phenomenon of “group roaming”, grounded in the psychological effect of herd behavior. Full article

The design and planning of Western-style constructions during the early Qing Dynasty in China constituted a significant multicultural encounter that fused technological advancement with aesthetic innovation. This cultural interplay is particularly evident in the imperial garden and pavilion projects commissioned by the Qing court, which served as physical and symbolic sites of cross-cultural dialogue. Influenced by the intellectual and artistic movements of the European Renaissance, Western architectural concepts gradually found their way into the spatial and visual language of Chinese architecture, especially within the royal gardens and aristocratic buildings of the time. These structures were not simply imitative but rather represented a selective adaptation of Western ideas to suit Chinese imperial tastes and principles. This article examines the architectural language that emerged from this encounter between Chinese and European cultures, analysing symbolic motifs, spatial design, ornamental aesthetics, the application of linear perspective, and the integration of foreign architectural forms. These elements collectively functioned as tools to construct a unique visual discourse that communicated both political authority and cultural hybridity. The findings underscore that this architectural phenomenon was not merely stylistic imitation, but rather a dynamic convergence of technological knowledge and artistic vision across cultural boundaries. Full article

Vision-based end-to-end navigation systems have shown impressive capabilities, especially when combined with Imitation Learning (IL) and advanced Deep Learning architectures, such as Transformers. One such example is CIL++, a Transformer-based architecture that learns to map navigation states to vehicle controls based on expert demonstrations only. Nevertheless, reliance on experts’ datasets limits generalization and can lead to failures in unknown circumstances. Deep Reinforcement Learning (DRL) can address this issue by fine-tuning the pretrained policy, using a reward function that aims to improve its weaknesses through interaction with the environment. However, fine-tuning with DRL can lead to the Catastrophic Forgetting (CF) problem, where a policy forgets the expert behaviors learned from the demonstrations as it learns to optimize the new reward function. In this paper, we present CILRLv3, a DRL-based training method that is immune to CF, enabling pretrained navigation agents to improve their driving skills across new scenarios. Full article

In complex dynamic environments, robotic arms face multiple challenges such as real-time environmental changes, high-dimensional state spaces, and strong uncertainties. Trajectory planning tasks based on deep reinforcement learning (DRL) suffer from difficulties in acquiring human expert strategies, low experience utilization (leading to slow convergence), and unreasonable reward function design. To address these issues, this paper designs a neural network-based expert-guided triple experience replay mechanism (NETM) and proposes an improved reward function adapted to dynamic environments. This replay mechanism integrates imitation learning’s fast data fitting with DRL’s self-optimization to expand limited expert demonstrations and algorithm-generated successes into optimized expert experiences. Experimental results show the expanded expert experience accelerates convergence: in dynamic scenarios, NETM boosts accuracy by over 30% and safe rate by 2.28% compared to baseline algorithms. Full article

In the context of the growing emphasis on sustainable development and building safety performance, wooden architecture will attract increasing attention due to its low-carbon characteristics and excellent seismic resistance. In this study, the bibliometric software Citespace is used for data visualization analysis based on the literature related to Chinese wooden architecture in the China National Knowledge Infrastructure (CNKI) and the Web of Science (WOS) databases, aiming to construct an analytical framework that integrates quantitative visualization and qualitative thematic interpretation which could reveal the current status, hotspots, and frontier trends of research in this field. The results show the following: Research on Chinese wooden architecture has shown a steady growth trend, indicating that it has received attention from an increasing number of scholars. Researchers and institutions are mainly concentrated in higher learning and research institutions in economically developed regions. Research hotspots cover subjects such as seismic performance, mortise–tenon structures, imitation wood structures, Dong architecture, Liang Sicheng, and the Society for the Study of Chinese Architecture. The research process of Chinese wooden architecture can be divided into three stages: the macro stage, the specific deepening stage, and the inheritance application and interdisciplinary integration stage. In the future, the focus will be on interdisciplinary research on wooden architecture from ethnic minority cultures and traditional dwellings. Full article

Automatic Speaker Verification (ASV) systems are increasingly employed to secure access to services and facilities. However, recent advances in speech deepfake generation pose serious threats to their reliability. Modern speech synthesis models can convincingly imitate a target speaker’s voice and generate realistic synthetic audio, potentially enabling unauthorized access through ASV systems. To counter these threats, forensic detectors have been developed to distinguish between real and fake speech. Although these models achieve strong performance, their deep learning nature makes them susceptible to adversarial attacks, i.e., carefully crafted, imperceptible perturbations in the audio signal that make the model unable to classify correctly. In this paper, we explore adversarial attacks targeting speech deepfake detectors. Specifically, we analyze the effectiveness of Basic Iterative Method (BIM) attacks applied in both time and frequency domains under white- and black-box conditions. Additionally, we propose an ensemble-based attack strategy designed to simultaneously target multiple detection models. This approach generates adversarial examples with balanced effectiveness across the ensemble, enhancing transferability to unseen models. Our experimental results show that, although crafting universally transferable attacks remains challenging, it is possible to fool state-of-the-art detectors using minimal, imperceptible perturbations, highlighting the need for more robust defenses in speech deepfake detection. Full article

In the era of the digital economy, how digital transformation (DT) contributes to economic development has become a topic of growing interest. This study focuses on business model innovation (BMI) driven by DT in the manufacturing sector. From this perspective, we aim to explore how DT can reshape the fundamental connotation of economic development. To this end, we construct a mathematical model grounded in a Multi-Structural Economic System framework and employ econometric models focusing on fixed effects, mediation effects, and moderation effects. We also compile a panel dataset using data from China spanning from 2008 to 2024. The empirical findings reveal that BMI serves as a mediation mechanism between the DT and competitive advantage (CA) of manufacturing enterprises. However, competitive imitation of BMI by peer enterprises partially offsets this effect, weakening the relationship between DT and enhanced CA. These findings offer evidence-based insights into the role of BMI in the digital era. For policymakers and industry regulators, this study provides practical implications for promoting knowledge spillovers from BMI, thereby fostering market dynamism and enabling structural transformation in the manufacturing industry. Full article

In this article, two generalized net models (GNMs) are proposed to study the behavior and effectiveness of intelligent virtual agents (IVA) working in a risky environment under different scenarios and training algorithms. The proposed GNMs allow for the selection of machine learning algorithms such as intensity of characteristics Q-learning (InCh-Q), as well as the modification of multi-plan reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), the generative adversarial imitation learning (GAIL) algorithm, and behavioral cloning (CB). The choice of action, the change in priorities, and the achievement of goals by the IVA are studied under different scenarios, such as fire extinguishing, rescue operations, evacuation, patrolling, and training. Transitions in the GNMs represent the scenarios and learning algorithms. The tokens that pass through the GNMs can be the GNMs of the IVA architecture or the IVA memory model, which are enriched with knowledge and experience during the experiments, as the scenarios develop. The proposed GNMs are formally correct and, at the same time, understandable, practically applicable, and convenient for interpretation. Achieving GNMs that meet these requirements is a complex problem. Therefore, issues related to the design and use of GNMs for the reliable modeling and analysis of the behavior and effectiveness of IVAs operating in a dynamic and risky environment are discussed. Some advantages and challenges in using GNMs compared to other classical models used to study IVA behavior are considered. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

This paper presents a Self-Learning Robotic System (SLRS) for healthcare assistance using Deep Imitation Learning (DIL). The proposed SLRS solution can observe and replicate human demonstrations, thereby acquiring complex skills without the need for explicit task-specific programming. It incorporates modular components for perception (i.e., advanced computer vision methodologies), actuation (i.e., dynamic interaction with patients and healthcare professionals in real time), and learning. The innovative approach of implementing a hybrid model approach (i.e., deep imitation learning and pose estimation algorithms) facilitates autonomous learning and adaptive task execution. The environmental awareness and responsiveness were also enhanced using both a Convolutional Neural Network (CNN)-based object detection mechanism using YOLOv8 (i.e., with 94.3% accuracy and 18.7 ms latency) and pose estimation algorithms, alongside a MediaPipe and Long Short-Term Memory (LSTM) framework for human action recognition. The developed solution was tested and validated in healthcare, with the aim to overcome some of the current challenges, such as workforce shortages, ageing populations, and the rising prevalence of chronic diseases. The CAD simulation, validation, and verification tested functions (i.e., assistive functions, interactive scenarios, and object manipulation) of the system demonstrated the robot’s adaptability and operational efficiency, achieving an 87.3% task completion success rate and over 85% grasp success rate. This approach highlights the potential use of an SLRS for healthcare assistance. Further work will be undertaken in hospitals, care homes, and rehabilitation centre environments to generate complete holistic datasets to confirm the system’s reliability and efficiency. Full article

Expressive imitation in the performing arts is typically trained through animal behavior imitation, aiming not only to reproduce action trajectories but also to recreate rhythm, style and emotional states. However, evaluation of such animal imitation behaviors relies heavily on teachers’ subjective judgments, lacking structured criteria, exhibiting low inter-rater consistency and being difficult to quantify. To enhance the objectivity and interpretability of the scoring process, this study develops a machine learning and structured pose data-based auxiliary evaluation framework for imitation quality. The proposed framework innovatively constructs three types of feature sets, namely baseline, ablation, and enhanced, and integrates recursive feature elimination with feature importance ranking to identify a stable and interpretable set of core structural features. This enables the training of machine learning models with strong capabilities in structured modeling and sensitivity to informative features. The analysis of the modeling results indicates that temporal–rhythm features play a significant role in score prediction and that only a small number of key feature values are required to model teachers’ ratings with high precision. The proposed framework not only lays a methodological foundation for standardized and AI-assisted evaluation in performing arts education but also expands the application boundaries of computer vision and machine learning in this field. Full article

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article

Background/Objectives: Ataxic dysarthria is a speech disorder characterized by the impaired coordination of movement due to cerebellar dysfunction. Despite its clinical relevance, few studies have explored its rehabilitation. This study aimed to evaluate the applicability of IMITAF, an adaptive computer-based clinical treatment protocol originally developed to target aphasia with a novel population comprising individuals with ataxic dysarthria. The approach leverages principles of procedural motor learning. Methods: Ten patients with ataxic dysarthria due to neurodegenerative disease were retrospectively studied. All patients received approximately one month of speech–language (SL) treatment. Among them, (1) three patients (LL, MD, and BoA) adjunctively received the IMITAF treatment, forming the experimental group, and (2) the remaining seven patients did not receive IMITAF, serving as the control group. Dysarthria was assessed using the “Protocollo di Valutazione Disartria e Disfonia” (PVDD). The applicability of IMITAF was assessed through within-session performance and by direct single-case comparisons of total PVDD scores pre- and post-treatment. Additionally, multiple single-case Crawford analyses were conducted using PVDD scores and subscores to compare trained (i.e., directly targeted) and untrained abilities between the experimental and control groups Results: Patients in the IMITAF group showed improvements during exercises, with further increases in total PVDD scores post-treatment. Two patients (LL and BoA) showed significant gains, while MD’s scores remained stable. Compared to the control group, all three experimental patients demonstrated measurable improvements in trained core deficits associated with dysarthria, including phonation, articulation, intelligibility, and prosody (as assessed by PVDD). Conclusions: These findings suggest that IMITAF may offer therapeutic benefits for patients with ataxic dysarthria. By engaging a cortico-subcortical network involved in procedural motor learning, IMITAF may help mitigate speech deficits resulting from cerebellar dysfunction. This preliminary evidence supports the potential of IMITAF as a promising adjunctive tool in the rehabilitation of ataxic dysarthria. Full article

Epigenetic modifications are heritable, reversible alterations that causally reshape chromatin architecture and thereby influence DNA repair without changing nucleotide sequence. DNA methylation, histone modifications and non-coding RNAs profoundly influence DNA repair mechanisms and genomic stability. Aberrant epigenetic patterns in cancer compromise DNA damage recognition and repair, therefore impairing homologous recombination (HR), non-homologous end joining (NHEJ), and base excision repair (BER) by suppressing key repair genes and lowering access to repair sites. Then it is dissected how loss-of-function mutations in Switch/Sucrose non-fermentable, imitation switch and CHD (Chromodomain helicase DNA-binding) chromatin-remodeling complexes impair nucleosome repositioning, preventing effective damage sensing and assembly of repair machinery. Non-coding RNAs contribute to epigenetic silencing at DNA break sites, exacerbating repair deficiencies. This review evaluates recent advances concerning epigenetic dysfunction and DNA repair impairment. It is also highlighted that nanoparticle-mediated delivery strategies are designed to overcome pharmacologic resistance. It is presented how epigenetic dysregulation of DNA repair can guide more effective and drug-resistant cancer therapies. Full article

Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation through an engineering lens, charting the full pipeline from sensing to deployment. We first examine the expanding sensor palette—monocular and multi-camera rigs, stereo and RGB-D devices, LiDAR–camera hybrids, event cameras, and infrared systems—highlighting the complementary operating envelopes and the rise of learning-based depth inference. The advances in visual localization and mapping are then analyzed, contrasting sparse and dense SLAM approaches, as well as monocular, stereo, and visual–inertial formulations. Additional topics include loop closure, semantic mapping, and LiDAR–visual–inertial fusion, which enables drift-free operation in dynamic environments. Building on these foundations, we review the navigation and control strategies, spanning classical planning, reinforcement and imitation learning, hybrid topological–metric memories, and emerging visual language guidance. Application case studies—autonomous driving, industrial manipulation, autonomous underwater vehicles, planetary rovers, aerial drones, and humanoids—demonstrate how tailored sensor suites and algorithms meet domain-specific constraints. Finally, the future research trajectories are distilled: generative AI for synthetic training data and scene completion; high-density 3D perception with solid-state LiDAR and neural implicit representations; event-based vision for ultra-fast control; and human-centric autonomy in next-generation robots. By providing a unified taxonomy, a comparative analysis, and engineering guidelines, this review aims to inform researchers and practitioners designing robust, scalable, vision-driven robotic systems. Full article