Search Results (428)

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

Traditional Agent-based Models (ABMs) often struggle to capture the nuance of adaptive human decision-making during complex crises due to their reliance on static, predefined rules. Large Language Models (LLMs) offer a transformative solution by acting as cognitive engines that empower agents with human-like common-sense reasoning. In this paper, we introduce an LLM-driven Multi-Agent Simulation framework to investigate coupled epidemic–economic dynamics, incorporating a Perception-Deliberation-Action (PDA) loop. Agents, acting as heterogeneous cognitive entities, utilize Chain-of-Thought processes to autonomously balance health risks against economic necessities. This approach endogenously generates adaptive behaviors without explicit scripting. Extensive experiment results across diverse LLM backends confirm the framework’s robustness, revealing divergent socio-economic trajectories under distinct macroscopic conditions and effectively quantifying the trade-offs between public health and economic stability. This approach establishes a high-fidelity computational laboratory for investigating complex scenarios under distinct macroscopic conditions, effectively bridging the gap between micro-level cognition and macro-level societal outcomes. Full article

A Hybrid-Frequency Sampling Tactile Sensing System Based on a Flexible Piezoresistive Sensor Array is presented for reliable and real-time tactile perception under dynamic loading conditions. While recent studies have developed multi-channel tactile arrays, most systems remain limited by time-dependent drift in channel responses, inconsistent dynamic behavior, or insufficient temporal resolution under simultaneous loading. In this work, a system-level design integrating a flexible piezoresistive sensor array with a real-time data acquisition module is developed, incorporating a hybrid-frequency sampling strategy to reduce system complexity while preserving reliable dynamic response in key sensing channels. Register-Transfer Level (RTL) simulation verified that the hardware scheduler rigorously executed the deterministic scanning logic, demonstrating a strict one-to-one correspondence with the physical hardware signals. The array consists of 34 piezoresistive sensing nodes embedded in an elastomeric substrate. Under the implemented hybrid-frequency sampling scheme, the system achieves an overall effective acquisition bandwidth of approximately 36.9 kHz, while maintaining a repeatability better than 4.9% and robust mechanical durability under cyclic bending deformation. Dynamic loading validation was performed using a self-developed pressure comparison platform for measuring the normal contact force applied on the tactile surface, serving as ground-truth data to verify that the voltages acquired by the proposed system accurately correspond to the actual applied force. Quantitative analysis shows a strong linear correlation (R² ≈ 0.98) between the e-skin outputs and the reference forces. The recorded responses exhibit clear intensity-dependent trends and good temporal correspondence among sensing nodes, successfully distinguishing tactile stimuli such as gentle tapping, moderate pressing, and firm contact. The system also captures dynamic tactile responses during finger stroking, showing characteristic multi-unit activation patterns under spatiotemporally varying contact conditions. Compared with previously reported tactile systems typically operating below 100 Hz, the proposed design achieves an approximately 10× enhancement in effective sampling capability while significantly reducing system complexity through hybrid-frequency sampling, thereby supporting reliable dynamic tactile sensing in multi-unit arrays. These results demonstrate that the proposed system provides a practical and scalable hardware platform for dynamic tactile sensing in robotics, human–machine interaction, and wearable tactile systems. Full article

Piezoresistive pressure sensing has broad application prospects in wearable fields such as human–machine interaction, physiological signal detection, and electronic skin. As a high-performance conductive filler, multi-walled carbon nanotubes (MWCNTs) have demonstrated extensive application potential across various domains. However, polymer composites filled with MWCNTs exhibit complex behavior during the printing process, which increases the difficulty of applying extrusion-based 3D printing technology. To this end, this study systematically investigated the extrusion 3D printing process of MWCNTs/polydimethylsiloxane (PDMS) composites. In this research, MWCNTs/PDMS composites with MWCNTs mass fractions of 1 wt%, 2 wt%, 3 wt%, and 4 wt% were prepared. The printability of the materials at each ratio was systematically explored, and rational printing process parameters were determined. On this basis, the influence of MWCNTs mass fraction on sensor performance was analyzed through tensile testing. Finally, three sets of experiments, including palm gesture recognition and gripping tests, elbow joint motion monitoring, and continuous pressure monitoring, successfully verified the feasibility of the fabricated sensors in human motion monitoring. The results demonstrate that the sensors made of this composite material via extrusion 3D printing possess excellent application potential in the field of flexible wearable electronics. Full article

Agent-based modeling (ABM) is a powerful tool for simulating building users’ dynamic behavior in demand response (DR) programs. However, ABM faces several challenges, particularly in encoding building users’ natural language features and common sense into rules or mathematical equations. To overcome these limitations, this paper proposes an agent framework based on large language models (LLMs) to simulate building users’ air-conditioning setpoint adjustment behavior under DR. This framework leverages LLMs’ natural language processing capabilities to replicate building users’ reasoning and decision-making processes. It consists of five modules: persona, perception, decision, reflection, and memory. Agents are assigned diverse personas through natural language descriptions based on empirical survey data. LLMs drive agents to reason and make decisions based on incentive prices and historical experiences. The results show that the LLM-based agent has common sense derived from natural language-defined personas and exhibits human-like irrational characteristics. This demonstrates the feasibility of replacing rules with natural language in ABM. The LLM-based agent can more effectively model hard-to-parameterize human features and provide decision explanations through LLM outputs. The results show that the inclusion of reflection and memory modules enables the agent to learn from previous decisions and reduce unreasonable choices. Full article

In human–robot collaboration (HRC) scenarios with direct physical contact, accurately estimating human intentions and adjusting robot behaviors based on multimodal information is the core factors that restrict the efficiency and precision of current HRC tasks. To enhance the performance of human–robot collaboration under physical contact conditions, we propose a joint network model named ACT_force_cooperative (AFC). This model leverages force sensing information as a representation of human intent to achieve human intent prediction during physical interaction, while simultaneously capturing visual information and robot state data, thereby enabling more efficient execution of human–robot collaborative tasks with multimodal information processing. Existing HRC methods often ignore humans’ collaborative experience in the environment and fail to recognize the uniqueness of interactive force in expressing human intentions. Focusing on the special role of interactive force among various types of data in physical interaction environments, the proposed model predicts humans’ future behavioral intentions and adopts a Transformer model to realize the fusion and representation of multimodal information, thus accomplishing more accurate and effective HRC tasks. Experimental results demonstrate that, through the processing of force sensing information and fusion of multimodal data, the proposed model reduces the motion error by 44.9% and increases the effective collaborative time ratio by 20.2% compared with the baseline Action Chunk Transformer (ACT) model. This not only improves the motion accuracy of the robot in collaborative tasks but also enhances the collaborative experience of human operators. Full article

Heparin (Hep) and its clinical antidote protamine (PRO) play essential yet antagonistic roles in anticoagulant therapy, necessitating reliable analytical tools to monitor their levels and interactions. Herein, we report that coptisine chloride (COP), a natural isoquinoline alkaloid, acts as an aggregation-induced emission (AIE) sensor enabling dual-responsive fluorescence modulation toward Hep and PRO. Owing to its rigid polycyclic and intrinsically twisted molecular framework, COP displays typical AIE behavior. In a DMSO/PBS mixture (PBS fraction = 99%, v/v), COP forms strongly emissive aggregates with Hep through electrostatically driven complexation, allowing sensitive Hep detection with a limit of detection (LOD) of 0.70 μg/mL. Subsequent competitive binding of PRO to Hep disrupts the COP–Hep aggregates, giving rise to fluorescence quenching and reversible PRO sensing (LOD: 0.49 μg/mL). Theoretical calculations together with multiple characterization techniques reveal an aggregation–disaggregation mechanism governing the dual fluorescence modulation. Moreover, COP achieves accurate Hep quantification in spiked diluted human serum, affording satisfactory linearity and recoveries (LOD = 0.71 μg/mL; recoveries 98.3–101.6%). These results demonstrate that COP, a structurally simple natural AIE luminogen, serves as a sustainable, biocompatible, and accessible tool for reversible Hep and PRO analysis in complex media. Full article

Fungi regularly occur on spacecrafts, posing a serious risk to humans and equipment. In this study, we characterized how the model organism Aspergillus nidulans responds to low-intensity, short-duration proton irradiation designed to simulate a solar particle event, a common stress factor in space. The oxidative stress-sensitive ∆atfA mutant exhibited a lower survival rate than the wild-type strain. Pretreatment of the wild-type strain with menadione sodium bisulfite (MSB), which activates oxidative stress defense mechanisms, increased tolerance to proton beam radiation. These data are consistent with the idea that oxidative defense contributes to cellular responses to ionizing radiation. Unexpectedly, the applied radiation decreased the tolerance to MSB. To understand this unusual behavior, we compared the transcriptomes of the irradiated and non-irradiated mycelia. As expected, proton beam irradiation upregulated many genes involved in DNA repair but downregulated a large number of antioxidant enzyme genes. The downregulation of three key antioxidant genes—prxA (thioredoxin peroxidase), trxB (thioredoxin reductase), and gsh1 (γ-glutamylcysteine synthase)—was further confirmed by RT-qPCR analysis. One possible explanation is that, due to the rapid elimination of reactive oxygen species generated by water radiolysis, the effects of radiolysis-derived electrons could transiently dominate redox signaling. This shift may interfere with redox sensing in the fungus, resulting in reduced antioxidant gene expression and increased sensitivity to oxidative stress. Oxidative stress sensitivity caused by proton radiation may be the Achilles heel of cells that can survive this stress. Full article

All-optical memristors possess light-sensing and storage capabilities while simultaneously simulating human synaptic functions, demonstrating immense potential in the field of brain-inspired computing for realizing bionic synapses and brain-like intelligence. In this work, we successfully produced ε-Ga₂O₃ films, ε/β-Ga₂O₃ mixed-phase films, and β-Ga₂O₃ films via chemical vapor deposition (CVD). The optical output and optical response characteristics of the thin films are investigated under 254 nm and 365 nm lasers. The CVD-grown ε-Ga₂O₃ is found to process a small amount of defects and insignificant memristive properties and the β-Ga₂O₃ obtained from the annealing of ε-Ga₂O₃ exhibits superior crystal quality but lacks memristive properties, while the ε/β-Ga₂O₃ mixed-phase films grown directly by CVD contain a fair amount of defects and demonstrate persistent resistance retention exceeding 104 s. Based on the excellent memristive properties of ε/β-Ga₂O₃ mixed-phase films, we conducted experiments simulating optical synapses. By adjusting optical pulse parameters (intensity, repetition rate, and duration), we successfully modeled the short-term plasticity (STP) and long-term plasticity (LTP) observed in biological synapses. Experiments confirm that light stimulation can effectively induce synaptic behaviors, such as the progressive conversion of short-term memory (STM) into long-term memory (LTM), and further fully reproduce the neuroplasticity process of “learning-forgetting-relearning.” This study demonstrates a photoconductive synapse memristor based on the wide-bandgap material gallium oxide, exhibiting exceptional air stability with sustained photoconductivity maintained for over a year. This study provides new insights into the practical application feasibility of all-optical artificial synapses based on gallium oxide. Full article

Standardized operating procedures are essential for ensuring safety and reproducibility in chemical laboratory experiments. However, real-time monitoring of manual laboratory operations, such as pipetting, remains challenging due to complex human–tool interactions, temporal dependencies between procedural steps, and operator variability. In this study, we propose a vision-based deep learning framework that leverages spatiotemporal features for automated monitoring of pipetting operations using non-contact visual sensing. Briefly, human poses and pipette interactions are extracted from video recordings using a YOLO-based perception model, while temporal execution patterns are captured through bidirectional long short-term memory networks. Experimental results demonstrate that the proposed approach can reliably distinguish between standard and non-standard pipetting behaviors across multiple predefined error categories and shows improved robustness compared with static or frame-level analysis. Overall, this work demonstrates the feasibility of vision-based AI systems for objective and scalable monitoring of laboratory pipetting operations, with potential applicability to other manual laboratory procedures. Full article

With the rapid development of artificial intelligence and wearable electronics, there is an increasing demand for skin-like, flexible, and self-powered sensors capable of continuously perceiving mechanical stimuli and human motions. Triboelectric nanogenerator (TENG)-based sensors incorporating stretchable conductive gels represent a promising approach to meet these requirements by combining soft mechanical compliance with efficient electromechanical signal transduction. However, conventional metallic or composite electrodes often suffer from mechanical mismatch with soft skin-like systems, motivating the exploration of intrinsically soft and stretchable conductive gels. In this review, we present a comprehensive and structured overview with comparative perspectives of stretchable skin-like conductive gel-based triboelectric devices. First, different classes of conductive gels, including hydrogels, organogels, ionogels, and other emerging gel systems, are systematically summarized and compared in terms of their composition, crosslinking strategies, conductivity, and mechanical characteristics. Next, the pivotal role of conductive gels in bridging skin-like sensing functions and triboelectric applications is elucidated, highlighting how their intrinsic softness, stretchability, self-healing capability, and interfacial conformability enable intimate skin contact and reliable electromechanical coupling. The key performance attributes of gel-based skin-like triboelectric sensors, including stretchability, self-healing behavior, optical and thermal tolerance, electrical durability, and environmental stability, are critically discussed with representative examples and comparative analysis. Typical device configurations, such as thin-film, fiber-shaped, and textile-based architectures, are further reviewed to illustrate structure–function relationships and application-oriented design strategies. Finally, current challenges, limitations, and future research directions for stretchable conductive gel-based triboelectric systems are outlined, aiming to provide practical guidelines and insights for the rational design of high-performance skin-like triboelectric sensors based on conductive gels. Full article

Substrate-free epidermal antennas promise imperceptible and long-term wearable sensing, yet their electromagnetic performance is fundamentally constrained by the properties of ultrathin conductors. In this work, gold nanomesh is employed for the first time as the radiating conductor of a substrate-free epidermal tattoo antenna operating in the UHF RFID band. Owing to its RF-thin nature, the nanomesh behavior is governed by sheet resistance rather than skin-depth effects, imposing a strict upper bound on achievable radiation efficiency. By combining surface-impedance modeling, full-wave simulations, and on-body experiments, we demonstrate that ohmic losses set a geometry-independent limit on the realized gain of on-skin antennas. An inductively coupled loop architecture is optimized to approach this bound while ensuring mechanical robustness and impedance stability. Measurements on phantoms and human subjects confirm the predicted performance limits within a few decibels, enabling reliable UHF RFID read ranges up to 30–40 cm under standard regulatory constraints. Full article

Joint acidosis is increasingly recognized as an important determinant of cellular behavior in osteoarthritis (OA). Declines in extracellular pH (pHe) occur across cartilage, meniscus, synovium, and subchondral bone, where they influence inflammation, matrix turnover, and pain. Among proton-sensing G protein-coupled receptors, GPR68 responds to the acidic pH range characteristic of human OA joints. The receptor is activated between pH 6.8 and 7.0, couples to Gq/PLC-MAPK, cAMP-CREB, G12/13-RhoA-ROCK signaling pathways, and is expressed most prominently in articular cartilage, with additional expression reported in synovium, bone, vasculature, and some neuronal populations. These pathways regulate transcriptional programs relevant to cartilage stress responses, inflammation, and matrix turnover. GPR68 expression is increased in human OA cartilage and aligns with regions of active matrix turnover. We previously reported that pharmacologic activation of GPR68 suppresses IL1β-induced MMP13 expression in human chondrocytes under acidic conditions, indicating that increased GPR68 expression may represent a microenvironment-responsive, potentially adaptive signaling response rather than a driver of cartilage degeneration. Evidence from intestinal, stromal, and vascular models demonstrates that GPR68 integrates pH changes with inflammatory and mechanical cues, providing mechanistic context, although these effects have not been directly established in most joint tissues. Small-molecule modulators, including the positive allosteric agonist Ogerin and the inhibitor Ogremorphin, illustrate the tractability of GPR68 as a drug target, although no GPR68-directed therapies have yet been evaluated in preclinical models of OA. Collectively, current data support GPR68 as a functionally relevant proton sensor within the acidic OA joint microenvironment. Full article

To address the pervasive challenges of resource inefficiency and static management in the hospitality sector, this study proposes a novel management framework that synergistically integrates Model Predictive Control (MPC) with Green Human Resource Management (GHRM). Methodologically, the framework establishes a dynamic closed-loop architecture that cyclically links environmental sensing, predictive optimization, plan execution and organizational learning. The MPC component generates data-driven forecasts and optimal control signals for resource allocation. Crucially, these technical outputs are operationally translated into specific, actionable directives for employees through integrated GHRM practices, including real-time task allocation via management systems, incentives-aligned performance metrics, and structured environmental training. This practical integration ensures that predictive optimization is directly coupled with human behavior. Theoretically, this study redefines hospitality operations as adaptive sociotechnical systems, and advances the hospitality energy-saving management framework by formally incorporating human execution feedback, predictive control theory, and dynamic optimization theory. Empirical validation across a sample of 40 hotels confirms the framework’s effectiveness, demonstrating significant reductions in daily average water consumption by 15.5% and electricity usage by 13.6%. These findings provide a robust, data-driven paradigm for achieving sustainable operational transformations in the hospitality industry. Full article

Artificial Intelligence (AI) is increasingly reshaping how cultural heritage institutions design and deliver digital visitor experiences, particularly through adaptive Augmented Reality (AR) applications. However, most existing AR deployments in museums and galleries remain static, rule-based, and insufficiently responsive to visitors’ contextual, behavioral, and emotional diversity. This paper presents ARIA (Augmented Reality for Interpreting Artefacts), a conceptual and architectural framework for AI-supported, adaptive AR experiences in cultural heritage settings. ARIA is designed to address current limitations in personalization, affect-awareness, and ethical governance by integrating multimodal context sensing, lightweight affect recognition, and AI-driven content personalization within a unified system architecture. The framework combines Retrieval-Augmented Generation (RAG) for controlled, knowledge-grounded narrative adaptation, continuous user modeling, and interoperable Digital Asset Management (DAM), while embedding Human-Centered Design (HCD) and Fairness, Accountability, Transparency, and Ethics (FATE) principles at its core. Emphasis is placed on accountable personalization, privacy-preserving data handling, and curatorial oversight of narrative variation. ARIA is positioned as a design-oriented contribution rather than a fully implemented system. Its architecture, data flows, and adaptive logic are articulated through representative museum use-case scenarios and a structured formative validation process including expert walkthrough evaluation and feasibility analysis, providing a foundation for future prototyping and empirical evaluation. The framework aims to support the development of scalable, ethically grounded, and emotionally responsive AR experiences for next-generation digital museology. Full article