Search Results (1,126)

Although HyFlex teaching has been studied for decades and has become part of the teaching norm since the 2020 pandemic, studies have generally not investigated the learning experiences of students with disabilities in HyFlex classrooms. This study compared the basic psychological needs (BPN) and engagement of undergraduate students who did (SwA) and did not (SwoA) request academic disability accommodations in an introductory, active learning, human-centered design thinking course, a core component of engineering technology education. Data were collected from 3748 primarily first-year undergraduate engineering technology students between fall 2021 and spring 2024, 126 of whom requested disability accommodation through the disability office. The data sources consisted of an end-of-course survey, in which students reported their basic psychological satisfaction level on a Likert scale and described their BPN experiences and engagement in response to open-ended survey questions. As a novel contribution, this study integrates the descriptive analysis of Likert-scale measures with textual- and word-level sentiment analysis, advancing conceptual understanding of reported BPN satisfaction and engagement and revealing divergent patterns across analytic approaches. While the SwA group reported lower scores across all BPN constructs compared to their counterparts, the highest number of them provided positive feedback statements across all BPN domains. Conversely, the SwoA group reported higher BPN scores across all constructs, yet the highest number of them used negative sentiments in their responses across all BPN constructs. The majority of SwA provided positive feedback on autonomy satisfaction, while the majority of SwoA’s positive feedback was on relatedness to the instructor. Future directions for advancing engineering technology education and disability data collection in higher education are provided. Full article

This work presents INTELLECTUM as a reference architecture and design-time evaluation framework for multi-entity XR–AI–digital twin systems. Rather than optimizing a specific implementation, the paper formalizes architectural invariants, event semantics, and coordination mechanisms that precede and inform system realization. INTELLECTUM provides a conceptual framework for structuring interactions across physical and virtual environments, emphasizing human-centered design, immersive digital twins, and collaborative extended-reality workspaces. The technical specification defines core architectural components, human integration modalities via WebXR and heterogeneous sensor networks, and representative usage scenarios within smart city ecosystems. By enabling AI-assisted urban planning, interactive simulation, and multi-actor coordination, INTELLECTUM positions itself as an XR-based architectural foundation for next-generation smart city platforms. Full article

The appearance of antibiotic-resistant strains of Helicobacter pylori (H. pylori) is leading to a decreased eradication rate of H. pylori infection. There is an urgent need to find new agents with antimicrobial mechanisms different from those of antibiotics, with therapeutic potential to clear colonization of H. pylori in the stomach. Some antimicrobial peptides (AMPs) possess bactericidal activity by enhancing the permeability of the outer membrane and damaging the integrity of the cell membrane. Bacteria are not susceptible to drug resistance through this antimicrobial mechanism. In this study, 28 short peptides containing 12 amino acid residues were designed based on nine amino acid fragments (KRIVQRIKD) from human cathelicidin LL-37, which is stable in gastric juice, and 3 amino acids were added at the C-terminus of the peptide. These designed peptides were not digested and degraded by pepsin at low pH values. The peptides were predicted using the online tool platform. Then, the strongest antimicrobial peptide, named SAMP-12aa (KRIVQRIKDVIR), was screened from 28 short peptides. Further studies found that SAMP-12aa retained anti-H. pylori activity after incubation in simulated gastric juice. The MIC and MBC of SAMP-12aa were 8 μg/mL and 32 μg/mL, respectively. SAMP-12aa showed good bactericidal kinetics. SAMP-12aa was found to have cell selectivity, penetrating and damaging bacterial cell membranes and exhibiting almost no toxicity to human cells at a relatively high concentration (128 μg/mL). Regulatory T (Treg) cells express CD25^High with immunosuppressive activity that induces immune tolerance in response to H. pylori. Molecular docking prediction revealed that SAMP-12aa could target the active center of Foxp3. Flow cytometry analysis revealed that SAMP-12aa can inhibit Foxp3 activity and downregulate CD25 protein expression on CD4⁺ T cells, thereby reducing the development and differentiation of CD4⁺Foxp3⁺CD25^High Treg cells with immunosuppressive effects. Further research revealed that the levels of the cytokine interferon-γ (IFN-γ), which activates CD8⁺ T-cell activity, were significantly elevated, and the levels of transforming growth factor-β (TGF-β), which inhibits CD8⁺ T-cell activity, were significantly reduced. The results of this study reveal that SAMP-12aa not only possesses antibacterial activity but also has immunomodulatory effects. Full article

Building lighting has a significant impact on occupant health and well-being, energy efficiency, spatial perception, and visual comfort. Many current building lighting systems, however, continue to be insufficiently responsive to changing environmental conditions and human-centric demands, leading to ineffective energy use, poor visual quality, and disruption of the circadian rhythm. This disparity highlights the need for modern buildings to incorporate integrated, intelligent, and sustainable lighting design strategies. This review offers a methodical examination of current, emerging and future developments in building lighting research in six related fields within an architectural scope of building design and performance. To assess lighting effectiveness, it first examines both qualitative and quantitative performance metrics, including illuminance, luminance distribution, glare, color quality, and user comfort. Second, it examines lighting control systems that use tunable light sources that can dynamically change the spectral composition and intensity in response to task demands, occupancy patterns, and daylight availability. Third, the study examines circadian-centric lighting strategies, focusing on digital modeling and simulation approaches that capture real-world lighting conditions and biological reactions. Fourth, the function of virtual reality and sophisticated visualization tools is examined, emphasizing their role in design decision-making and pre-implementation assessment. Fifth, a critical evaluation is conducted of the expanding use of machine learning and data-driven techniques in adaptive lighting control, prediction, and optimization. Limited real-time adaptability, inadequate personalization, disjointed simulation frameworks, and poor integration of human-centric metrics with intelligent control systems are some of the major research gaps. Sustainable Development Goal (SDG) 7, SDG 11, and SDG 3 are in line with the review, which ends with a summary of future paths toward intelligent, energy-efficient, and human-centered building lighting systems. Full article

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

This paper provides a comprehensive survey of Human–Robot Interaction (HRI) for indoor mobile robots operating in human-centered environments such as hospitals, laboratories, offices, and homes. We review interaction modalities—including speech, gesture, touch, visual, and multimodal interfaces—and examine key user experience factors such as usability, trust, and social acceptance. Implementation challenges are discussed, encompassing safety, privacy, and regulatory considerations. Representative case studies, including healthcare and domestic platforms, highlight design trade-offs and integration lessons. We identify critical technical challenges, including robust perception, reliable multimodal fusion, navigation in dynamic spaces, and constraints on computation and power. Finally, we outline future directions, including embodied AI, adaptive context-aware interactions, and standards for safety and data protection. This survey aims to guide the development of indoor mobile robots capable of collaborating with humans naturally, safely, and effectively. Full article

Emotional and workload-related states unfold dynamically during immersive virtual reality (VR) experiences, yet reliable physiological modeling in such environments remains challenging. We investigated whether multi-channel facial electromyography (fEMG), combined with spatio-temporal deep learning, can (i) accurately classify calibrated facial expressions across participants and (ii) transfer to spontaneous, task-elicited behavior in immersive VR. Twelve adults completed a calibration phase involving four intentional expressions (smile, frown, raised eyebrow, neutral), followed by VR scenes designed to elicit emotional, cognitive, physical, and dual task demands. After participant-level physiological normalization, a single shared Convolutional Neural Network–Temporal Convolutional Network (CNN–TCN) model was trained and evaluated using leave-one-participant-out (LOPO) validation. The model achieved strong cross-participant performance (Macro-F1 = 0.88 ± 0.13; ROC-AUC = 0.95 ± 0.06). When applied to unlabeled spontaneous VR task-elicited fEMG recordings, the trained model generated continuous expression classes. Derived static and temporal expression features showed scene-dependent modulation and False Discovery Rate (FDR)-surviving associations, primarily with perceived physical demand (NASA-TLX). The observed muscle activation patterns were physiologically plausible and aligned with Facial Action Coding System (FACS)-based interpretations of underlying muscle activity. These findings demonstrate that end-to-end spatio-temporal modeling of raw fEMG enables facial expression sensing in immersive VR using a single shared model following physiological normalization. The proposed framework bridges calibrated expression learning and spontaneous task-elicited behavior, supporting privacy-preserving, continuous and physiologically grounded monitoring in human-centered VR applications. Full article

The accelerated integration of intelligent agents in user-centered digital environments has intensified research in the field of Human–Robot Interaction, especially regarding mechanisms for adaptive, intuitive, and cognitively aligned communication. The present study develops and empirically examines a structural model of BCI-inspired adaptive agents designed to support coordinated interaction in HRI contexts. The study analyzes users’ perceptions of standardized hypothetical interaction scenarios involving BCI-inspired adaptive digital agents, where BCI inspiration is conceptual and refers to adaptive architectures interpreting behavioral cues rather than direct neural signal acquisition. The proposed model integrates four main constructs—perceived technological innovation, user involvement, agent adaptivity, and digital synergy—and examines their associations with user satisfaction in digital collaborative environments. Data were collected through an anonymous questionnaire (N = 268) and analyzed using structural equation modeling with the PLS-SEM method. The structural model demonstrates substantial explanatory power, accounting for 66.8% of the variance in user satisfaction (R² = 0.668). The study contributes by empirically supporting a scenario-based structural evaluation framework suitable for early-stage adaptive HRI system design. The results highlight the role of digital synergy in aligning innovation, engagement, and adaptive behavior in BCI-inspired adaptive HRI systems, providing directions for the design of adaptive robotic agents oriented toward coordinated interaction, user-centered integration, and responsible use in collaborative digital ecosystems. Full article

More than 80% of young people (11–17 years) do not meet recommended levels of physical activity, while excessive sedentary smartphone use increases rapidly, highlighting the need for accessible tools that promote active and kinesthetic learning. This study investigates whether smartphones can function as wearable devices capable of tracking movement, detecting biomechanical errors, and providing real-time corrective feedback. Using a user-centered design approach, we developed a gamified Exertion Trainer in which children practiced a straight punch (boxing jab) while wearing a smartphone on their wrist. Embedded accelerometer data were processed on board to deliver immediate, task-specific feedback on arm orientation, using gravity as a fixed reference frame. A randomized crossover trial was conducted with 40 children, comparing a feedback condition with a no-feedback control across two test orders. Quantitative results showed that real-time feedback produced a statistically significant improvement in punch accuracy (p < 0.001) and reduced performance variability, with the strongest effects observed after initial practice and partial retention following feedback removal. Qualitative findings indicated higher engagement and stronger perceptions of kinesthetic learning when feedback was available. These results demonstrate that smartphones can serve as practical wearable devices for delivering biomechanical guidance and supporting movement skill acquisition in children. Full article

Generative Artificial Intelligence (GenAI), particularly Foundation Models (FMs), has recently become a key component of Industry 5.0. Despite growing interest in integrating these technologies into industrial environments, comprehensive analyses of the socio-technical opportunities and challenges of deploying these emerging AI systems in real-world settings remain limited. This article proposes a socio-technical conceptual perspective, termed Responsible Agentic Robotics (RAR), which structures the lifecycle deployment of agentic AI-enabled robotic systems around three core layers: context, design, and value. Additionally, this article presents a brief review of 21 peer-reviewed studies published between 2023 and 2025 (post-ChatGPT era) on FMs and agentic AI-enabled Human–Robot Collaboration (HRC) in industrial assembly/disassembly environments. The results indicate that existing research remains predominantly technology-centric, with a strong emphasis on enhancing robot autonomy, while comparatively limited attention is devoted to human-centered and responsible practices. Moreover, empirical evaluations of human, social, and sustainability dimensions, such as worker empowerment, human factors, well-being, inclusivity, resource utilization, and environmental impact, are rarely conducted and poorly discussed. This article concludes by identifying key socio-technical gaps, outlining future research directions. Full article

Generative AI (GenAI) challenges traditional technology integration frameworks by introducing agentic systems that actively participate in meaning-making, requiring educators to shift from tool operation to cognitive orchestration. This study introduces the MTA–TPACK Dynamic Collaboration Spiral, a theoretical framework that integrates Meta-Task Awareness (MTA) to explain how static knowledge resources are dynamically activated during human–AI collaboration. We empirically illustrate this framework through a two-phase scientific visualization task concerning typhoon–terrain interactions, utilizing Midjourney for human-led orchestration and GPT-4o for closed-loop refinement. The results demonstrate that successful integration requires translating abstract disciplinary knowledge into precise, AI-intelligible visual constraints rather than relying solely on technical prompting skills. Furthermore, we document how evaluation practices evolve from simple error correction to structured, AI-assisted diagnosis. The resulting visual artifacts embody Visible Pedagogical Thinking (VPT)—externalized cognitive constructs that make expert reasoning inspectable and reusable. By foregrounding evaluation-centered task design, this study provides a transferable, theoretically grounded account of how human–AI collaboration can remain pedagogically meaningful. The model contributes to sustainable pedagogical innovation by offering a roadmap for strengthening teachers’ long-term epistemic agency in AI-mediated design environments. Full article

Human resource management is a strategic axis for organizations, especially in contexts where artificial intelligence (AI) tools, such as natural language processing (NLP), play a fundamental role. Recruiting external applicants from large CV repositories requires consistent screening. The proposed methodology involves leveraging an existing curriculum vitae (CV) repository, structuring and indexing the data within a vector-based knowledge base, and applying retrieval techniques to identify candidates that satisfy role-specific criteria. Using 5029 CVs as benchmarks, we evaluate 3 queries, 3 variables (Degree, Skills, Experience), and 7 scenarios. Sampling n = 76 CVs for Queries 1–2 and n = 350 CVs for Query 3. The proposed approach achieved consistently high specificity across scenarios and query profiles, while sensitivity showed the largest fluctuations, particularly under single-requirement configurations. Across all queries and scenarios, accuracy ranged 65.79–98.00%, specificity 86.67–100.00%, and sensitivity 0.00–94.92%, while error rates decreased from 34.21% to 2.00% as constraint strictness increased. Sensitivity fluctuated most under single-requirement settings, and Experience-only screening showed the weakest selection behavior. Moreover, the results indicate that the ability to confirm suitable candidates is sensitive to query formulation, since non-standard role naming, experience phrasing, and other lexical variations can reduce the system’s capacity to detect positive evidence. Overall, these findings indicate that a knowledge-base-centered design enables consistent and interpretable requirement-driven candidate screening and provides a quantitative baseline for future improvements in recruitment-oriented retrieval systems. Full article

For logistics facilities at the county level, workforce scheduling is a basic operational concern. Although these facilities are developing rapidly, they still mostly rely on human and semi-automated work. Significant differences in employee productivity and skill levels, along with regular changes in demand, exacerbate this challenge. This study proposes a sustainability-oriented dual-objective optimization model to coordinate operational cost control with employee well-being enhancement. To address this issue, we designed an improved Genetic Algorithm that combines heuristic initialization with specialized repair operators, forming a systematic optimization framework. The effectiveness of the proposed system design and algorithm has been validated through real-world case studies. Experimental results demonstrate that this model not only achieves a balance between cost and employee satisfaction under uncertain demand conditions but also provides county-level logistics centers with sustainable scheduling solutions adaptable to business changes. Management recommendations based on the experimental results are proposed, such as implementing differentiated scheduling strategies, easing restrictions on maximum working hour variations, establishing a progressive optimization mechanism, and optimizing staffing and employee structure in accordance with corporate characteristics. This study provides scientific decision support for county-level logistics systems to achieve sustainable operations and human resource management transformation. Full article

As virtual reality technologies evolve toward widespread adoption in education, industry, and social communication, their increasing complexity exposes new and often overlooked security challenges. Immersive environments collect continuous multimodal data, including motion tracking, gaze, voice, and biometric indicators that extend far beyond traditional computing attack surfaces. This paper synthesizes recent research (2023–2025) on cybersecurity, privacy, and behavioral safety in virtual reality (VR) systems, identifies the main vulnerabilities, and proposes a unified defense architecture: the three-layer VR Security Framework (TVR-Sec). Through comparative review and conceptual integration of 31 peer-reviewed studies, three interdependent protection domains emerged: (1) System Integrity, securing hardware, firmware, and network communications against spoofing and malware; (2) User Privacy, ensuring the ethical management of biometric and behavioral data through federated learning and consent-based control; and (3) Socio-Behavioral Safety, addressing harassment, manipulation, and psychological exploitation in shared virtual spaces. The framework situates VR security as a multidimensional adaptive process that combines technical hardening with human-centered defense and ethical design. By aligning cyber–human protections through an AI-driven monitoring and policy engine, TVR-Sec advances a holistic paradigm for securing future immersive ecosystems. Full article

Accumulation of tetracycline (TC) in aquatic environments poses a significant threat to human health and ecosystems, driving the need for efficient removal technologies. Two-dimensional metal–organic frameworks (2D MOFs) are promising adsorbents due to their tunable structures and abundant active sites. In this work, three 2D MOFs, M₃(HHTP)₂ (M = Cu, Ni, Co), were synthesized via a solvothermal method. Among them, Cu₃(HHTP)₂ exhibited superior TC adsorption with a maximum capacity of 302.84 mg/g. The adsorption process, best described by the Langmuir isotherm and pseudo-second-order kinetic models, indicates chemisorption. Mechanistic investigations reveal that the high-activity coordination sites formed by Cu²⁺ due to Jahn–Teller distortion enable strong coordination with TC. This is identified as the key factor governing the differential adsorption performance among the three MOFs. Simultaneously, the surface functional groups facilitate hydrogen bonding, and the advantageous pore structure of the material itself, together forming a synergistic adsorption. This work not only elucidates the microscopic mechanism behind the efficient adsorption of TC by Cu₃(HHTP)₂ but also, through comparative analysis of isostructural MOFs, confirms the decisive role of metal center electronic structure in modulating the adsorption behavior of 2D MOFs. The insights gained from this study may serve as a reference for the design of 2D high-performance adsorbents. Full article