Search Results (1,426)

To establish the best approach for conserving a species, it is necessary to understand the biology of that species. To better understand the behavior of American black bears (Ursus americanus), we observed 246 black bears for 7950 h in nature over a 24-year period to quantify how the bears communicated. Black bears communicated using several different behaviors. These included thirteen types of vocalizations, eight olfactory behaviors, eight marking behaviors, sixteen different body postures and gestures constituting their body language, and various emotional expressions. Some behaviors appeared to be automatic, including facial expression, ear movements, some forms of body language, the intensity of various vocalizations, and various moans. Other behaviors appeared to be intentional, including mechanically generated sounds and actions that could be used to bluff or deceive, such as the chomping of teeth, huffing, swatting, false charging, and various vocalizations. The conservation of black bears can be improved by establishing management strategies that take into account the vocal and non-vocal communication of the bears. Conflicts and negative encounters between humans and bears can be reduced through behavioral modifications by humans based on our new understanding of the communication system of bears. Knowledge of the communication system of the black bear provides a basis for improved conservation through the non-lethal management of bears involved in bear–human conflicts. Full article

Background/Objectives: Human papillomavirus (HPV) is a major cause of cervical, penile, anal, and oropharyngeal cancers. HPV vaccination is the most effective public health strategy for its prevention. Understanding the factors influencing vaccination intentions is critical for developing effective public health policies and improving population-level vaccine uptake. Therefore, in this scoping review, we aimed to examine HPV vaccination research conducted in Korea, identify common trends and gaps in study populations and influencing factors, and provide evidence-based recommendations for public health policies. Methods: We systematically searched four Korean databases—Research Information Sharing Service (RISS), DBpia, Korean Studies Information Service System (KISS), and National Digital Science Library (NDSL)—for studies published from their respective inception dates to January 2025, using “human papillomavirus,” “HPV,” “vaccination,” and “intention” as keywords. Thirty-six studies were ultimately included. Study characteristics, populations, theoretical frameworks, and key variables were extracted and analyzed using descriptive statistics and content analysis. Results: Of the included studies, 61.1% and 38.9% targeted vaccination-eligible individuals (adolescents and adults) and parents/guardians, respectively, with 50% focusing exclusively on women. The major factors influencing HPV vaccination intention were attitude (47.2%), subjective norms (38.9%), and perceived behavioral control (30.9%). Attitude and knowledge were critical for vaccination-eligible individuals (Direct group), whereas subjective norms were key for parents/guardians (Indirect group). Conclusions: Korean HPV vaccination intention research has predominantly focused on women and parents, with insufficient attention to adolescents and men. Public health strategies must employ multilevel interventions tailored to each group’s decision-making structures, including school-based programs for adolescents, gender-inclusive policies for men, and community-based approaches to address social norms among parents. These findings provide evidence for policy development aligned with the WHO cervical cancer elimination goals. Full article

We study control synthesis under Signal Temporal Logic (STL) specifications for driving scenarios where strict rule satisfaction is not always feasible and human experts exhibit context-dependent flexibility. We represent such behavior using robustness slackness—learned rule-wise lower bounds on STL robustness—and introduce sub-goals that encode intermediate intent in the state/output space (e.g., lane-level waypoints). Prior learning-based MPC–STL methods typically infer slackness with VAE priors and plug it into MPC, but these priors can underrepresent multimodal and rare yet valid expert behaviors and do not explicitly model intermediate intent. We propose a diffusion-guided MPC–STL framework that jointly learns slackness and sub-goals from demonstrations and integrates both into STL-constrained MPC. A conditional diffusion model generates pairs of (rule-wise slackness, sub-goal) conditioned on features from the ego vehicle, surrounding traffic, and road context. At run time, a few denoising steps produce samples for the current situation; slackness values define soft STL margins, while sub-goals shape the MPC objective via a terminal (optionally stage) cost, enabling context-dependent trade-offs between rule relaxation and task completion. In closed-loop simulations on held-out highD track-driving scenarios, our method improves task success and yields more realistic lane-changing behavior compared to imitation-learning baselines and MPC–STL variants using CVAE slackness or strict rule enforcement, while remaining computationally tractable for receding-horizon MPC in our experimental setting. Full article

Within the context of sustainable educational workforce development, enhancing the retention intention of early childhood educators is essential for ensuring educational quality and long-term talent sustainability. This study surveyed 200 early childhood educators in Taiwan and developed a parallel mediation model to examine how workplace friendship influences retention intention through workplace well-being and job embeddedness. Confirmatory factor analysis and structural equation modeling were conducted using AMOS 24.0. The results indicate that workplace friendship does not exert a direct effect on retention intention; however, it significantly enhances workplace well-being and job embeddedness, which in turn fully mediate the relationship. In line with the JD-R framework, workplace well-being is conceptualized as a core psychological resource, while job embeddedness reflects a structural resource shaping employees’ attachment to their organization. These findings suggest that workplace friendship must be transformed into a psychological and structural resource in order to promote retention. By identifying workplace friendship as an initial social resource that fosters well-being and embeddedness, this study contributes to sustainable human resource management and supports the stable development of the early childhood education system. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

Forensic anthropology, a specialized branch of biological anthropology, plays a crucial role in the identification of human remains, particularly when conventional methods such as fingerprinting are not applicable. In Ecuador, its relevance has increased in response to challenges such as intentional deaths, forced disappearances, violence, mass fatalities, and migration-related deaths. Despite its growing importance, the field faces significant limitations, including restricted access to advanced technologies, limited training opportunities for local forensic anthropologists, and insufficient resources for research and the application of advanced methodologies for victim identification. This article examines the development and current state of forensic anthropology in Ecuador, emphasizing the urgent need for population-specific standards, the establishment of a national osteological collection, and stronger institutional support. It also highlights the contributions of bioarchaeological research and its potential to enhance forensic practices. By analyzing the challenges of identifying skeletonized human remains and other instances of human rights violations, the study underscores the necessity of advancing forensic anthropology in the country. The article further discusses how interdisciplinary efforts have contributed to forensic knowledge in Ecuador and concludes by emphasizing the importance of ethical guidelines, technological integration, and improved infrastructure to strengthen forensic anthropology as both a scientific discipline and a humanitarian tool. Full article

The exoskeleton rehabilitation robot is a structural robot that uses the actuator to control, so as to construct a human–robot collaborative rehabilitation training system to realize the perception and decoding of patients and promotes the recovery of limb function and neural remodeling. This review focused on the synergistic advancement of physical and information interaction in exoskeleton rehabilitation robotics. This review systematically retrieved literature related to the synergistic advancement of physical and information interaction in exoskeleton rehabilitation robotics. Publications from 2011 to 2025 were searched for across the EI, IEEE Xplore, PubMed, and Web of Science databases. The included studies mainly covered the period from 2018 to 2025, reflecting recent technological progress. This article summarizes the collaborative progress of physical and informational interaction in exoskeleton rehabilitation robots. The physical and information interaction is manifested in the bionic structure, physiological information detection and information processing technology to identify human movement intention. The bionic structural design is fundamental to realize natural coordination between human and robot to improve the following of movements. The active participation and movement intention recognition accuracy are enhanced based on multimodal physiological signal detection and information processing technology, which provides a clear direction for the development of intelligent rehabilitation technology. Full article

With the increasing prominence of sustainable consumption and the rising influence of Generation Z in the fashion market, secondhand fashion e-commerce platforms have become essential carriers of green fashion. Although AI-assisted recommendation mechanisms are widely embedded in these platforms, their psychological and behavioral effects on users’ continuous use and social engagement remain insufficiently examined. To address this gap, this study incorporates the Stimulus–Organism–Response (SOR) framework to investigate the psychological reaction pathways and behavioral intentions of Generation Z users within Human-AI Collaboration-enabled green e-commerce environments. Three AI-driven service stimuli—Human-AI Collaborative Recommendation Perception, AI Interaction Transparency, and Perceived Personalization—were conceptualized as stimulus variables; Psychological Immersion, Emotional Triggering, Cognitive Engagement, and Platform Trust were modeled as organism variables; and Continuous Use Intention and Social Sharing Intention served as behavioral response variables. Based on 498 valid samples analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results demonstrate strong empirical support for all proposed hypotheses. Specifically, AI-driven stimuli significantly and positively influence psychological responses, which subsequently strengthen users’ continuous usage and social sharing intentions. This research provides theoretical insights for developing Human-AI Collaboration-enabled service systems that balance efficiency and emotional resonance on green e-commerce platforms, and offers practical implications for promoting sustainable fashion values among younger consumers. Full article

Modern lifestyles are increasingly plagued by physical inactivity, social disconnection, digital addiction, and excessive time indoors—factors that negatively impact the health and well-being of both humans and their companion dogs (Canis familiaris). Evidence shows that nature exposure, physical activity, and human–animal bond (HAB) each enhance physical, mental, and social well-being, yet these domains have rarely been examined together as an integrated therapeutic triad. We introduce a new conceptual framework of bonded green exercise, defined as shared physical activity between a bonded human and dog in natural environments. Synthesizing existing evidence across human and canine sciences into a testable conceptual integration, we posit that bonded green exercise may plausibly activate evolutionarily conserved, synergistic mechanisms of physiological, behavioural, and affective co-regulation. Four testable hypotheses are proposed: (H1) triadic synergy: combined domains produce greater benefits than additive effects; (H2) heterospecific benefit: parallel health gains occur in both species; (H3) behavioural amplification: dogs acts as catalysts to drive human participation in nature-based activity; and (H4) scalable health promotion: bonded green exercise represents a low-cost, accessible, One Health approach with population-level potential. This framework highlights how intentional, shared physical activity in nature may potentially offer a novel low-cost and accessible model for enhancing health, lifespan, welfare, and ecological stewardship across species. Full article

A growing challenge in mobile robotics is the reliance on complex graphical interfaces and rigid control pipelines, which limit accessibility for non-expert users. This work introduces a latency-aware benchmarking framework that enables natural-language robot navigation by integrating multiple Large Language Models (LLMs) with the Robot Operating System 2 (ROS 2) Navigation 2 (Nav2) stack. The system allows robots to interpret and act upon free-form text instructions, replacing traditional Human–Machine Interfaces (HMIs) with conversational interaction. Using a simulated TurtleBot4 platform in Gazebo Fortress, we benchmarked a diverse set of contemporary LLMs, including GPT-3.5, GPT-4, GPT-5, Claude 3.7, Gemini 2.5, Mistral-7B Instruct, DeepSeek-R1, and LLaMA-3.3-70B, across three local planners, namely Dynamic Window Approach (DWB), Timed Elastic Band (TEB), and Regulated Pure Pursuit (RPP). The framework measures end-to-end response latency, instruction-parsing accuracy, path quality, and task success rate in standardised indoor scenarios. The results show that there are clear trade-offs between latency and accuracy, where smaller models respond quickly but have less spatial reasoning, while larger models have more consistent navigation intent but take longer to respond. The proposed framework is the first reproducible multi-LLM system with multi-planner evaluations within ROS 2, supporting the development of intuitive and latency-efficient natural-language interfaces for robot navigation. Full article

Generative AI is a promising adjunct to blended learning, offering an innovative means to enhance academic performance. Its rapid diffusion has been accompanied by criticism and uncertainty, particularly regarding ethics and the potential displacement of human labor. A review of the existing research reveals persistent gaps in understanding AI use among students. This study therefore aimed to develop an integrated model to explain generative AI adoption across two distinctive time points. Employing a survey-based design, cross-sectional data were collected at two time points from college students at a local tertiary institution in Hong Kong. PLS-SEM Model testing showed that performance expectancy was the strongest and most persistent determinant of both intention to use and actual use across both data collections. Risk propensity had no effect at the outset, but at a longer usage time point, it was significantly related to intention and use through performance expectancy. Social influence exerted a direct and significant effect initially and later demonstrated both direct and indirect significant effects on intention and use via performance expectancy. The findings identify key determinants and enhance our understanding of the complex decision-making process involved in the use of generative AI. Full article

Phishing emails continue to evade conventional detection systems due to their increasingly sophisticated, multi-faceted social engineering tactics. To address the limitations of single-modality or rule-based approaches, we propose SAHF-PD, a novel phishing detection framework that integrates multi-modal feature extraction with semantic-aware hierarchical fusion, based on large language models (LLMs). Our method leverages modality-specialized large models, each guided by domain-specific prompts and constrained to a standardized output schema, to extract structured feature representations from four complementary sources associated with each phishing email: email body text; open-source intelligence (OSINT) derived from the key embedded URL; screenshot of the landing page; and the corresponding HTML/JavaScript source code. This design mitigates the unstructured and stochastic nature of raw generative outputs, yielding consistent, interpretable, and machine-readable features. These features are then integrated through our Semantic-Aware Hierarchical Fusion (SAHF) mechanism, which organizes them into core, auxiliary, and weakly associated layers according to their semantic relevance to phishing intent. This layered architecture enables dynamic weighting and redundancy reduction based on semantic relevance, which in turn highlights the most discriminative signals across modalities and enhances model interpretability. We also introduce PhishMMF, a publicly released multimodal feature dataset for phishing detection, comprising 11,672 human-verified samples with meticulously extracted structured features from all four modalities. Experiments with eight diverse classifiers demonstrate that the SAHF-PD framework enables exceptional performance. For instance, XGBoost equipped with SAHF attains an AUC of 0.99927 and an F1-score of 0.98728, outperforming the same model using the original feature representation. Moreover, SAHF compresses the original 228-dimensional feature space into a compact 56-dimensional representation (a 75.4% reduction), reducing the average training time across all eight classifiers by 43.7% while maintaining comparable detection accuracy. Ablation studies confirm the unique contribution of each modality. Our work establishes a transparent, efficient, and high-performance foundation for next-generation anti-phishing systems. Full article

Robot-assisted rehabilitation has demonstrated significant efficacy in improving motor function among patients with physical and neurological impairments. The development of effective rehabilitation robots requires careful integration of mechanical design and control systems to ensure safe, compliant, and intention-oriented human–robot interaction while delivering appropriate therapeutic assistance and feedback. Parallel robot manipulators have increasingly gained attention in rehabilitation applications due to their superior precision, structural stiffness, and high load capacity compared to their serial counterparts. This paper presents a scoping review of control strategies specifically implemented in parallel rehabilitation robots between 2015 and 2025. The control strategies include position control, force control, compliance control, adaptive control, intelligent control, and hybrid control. Our analysis showed a progressive shift from traditional position-based control toward more sophisticated adaptive and intelligent strategies that better accommodate patient-specific needs and therapeutic requirements. Full article

Employer-funded education and training (EFET) has gained increasing attention as a strategic human resource practice for developing human capital and enhancing organizational performance. However, empirical evidence on its effectiveness remains limited in emerging economies, particularly within the Kingdom of Saudi Arabia (KSA), where workforce localization and human capital development are central to Vision 2030. This study examines the associations between EFET participation and three key employee outcomes: motivation, retention intention, and productivity. Using a quantitative research design, data were collected from 200 employees and managers across multiple sectors in KSA through a structured questionnaire. Structural Equation Modeling (SEM) was employed to test the hypothesized relationships while controlling for gender, age, sector, and years of experience. The results indicate that EFET participation is positively and significantly associated with employee motivation, retention intention, and self-reported productivity, with the strongest association observed for retention intention. Model fit indices demonstrate an excellent overall fit, supporting the proposed model’s robustness. By integrating Human Capital Theory with empirical evidence from the Saudi context, this study contributes to the literature by extending understanding of how employer-funded education functions within a non-Western labor market. The findings offer practical implications for organizations and policymakers seeking to optimize education and training investments in support of sustainable workforce development and Vision 2030 objectives. Full article

Active music and movement engagement has been widely integrated in human socialization across history and cultures, and is particularly prevalent in early childhood play and learning. For clinical populations, music therapy is known to support social skills and wellbeing for young children. However, there is less evidence for the value of active music engagement for non-clinical populations in terms of supporting social and behavioral wellbeing in the early years. This study reports results from the Rhythm and Movement for Self-Regulation (RAMSR) program delivered by generalist kindergarten teachers in low socioeconomic communities. This randomized control trial involved 213 children across eight preschools in disadvantaged communities in Queensland, Australia. The intervention group received 16 to 20 sessions of RAMSR over eight weeks, while the control group undertook usual preschool programs. Data was collected through teacher report at pre and post intervention, and again six months later once children had transitioned into their first year of school. Robust mixed models accounting for repeated measures and clustering of children within kindergartens (random effects), evidenced significant intervention effects across the three time points for improved prosocial skills (p = 0.04, n_p² = 0.02), and reduced externalizing (p < 0.01, n_p² = 0.03) and internalizing behavior problems (p = 0.04; n_p² = 0.02), with small to moderate effect sizes. These findings highlight the valuable role that intentional active music engagement in universal settings such as preschool can play in terms of social and behavioral wellbeing. The importance of these results lies in the fact that children from lower socioeconomic backgrounds are more likely to experience risks to social and behavioral development, requiring additional supports, yet experience inequities in access to high-quality music and movement programs. Full article