Surface electromyography (sEMG) provides a non-invasive measure of neuromuscular activity, yet its continuous waveforms exhibit substantial inter-subject variability, making muscle activities difficult to compare across individuals. Although existing methods improve cross-subject action recognition, they mainly learn action-category invariance and remain limited in characterizing the correspondence of muscle-activity changes across individuals. Motivated by this gap, we propose a Muscle-State Tokenization Framework that constructs a cross-subject shared muscle-state vocabulary and converts continuous sEMG signals into discrete token sequences. We also introduce a large-scale dataset, ActionEMG-43, comprising 43 diverse actions and sEMG recordings from 16 major muscle groups across the body. Experiments on ActionEMG-43 demonstrate that the proposed tokens achieve high inter-subject consistency (Cohen’s Kappa = 0.82 ± 0.09), provide interpretable muscle-state representations, and outperform sEMG signals across all evaluated models in cross-subject action recognition. These results suggest that muscle-state tokenization can mitigate the impact of inter-subject variability while preserving task-relevant information, providing a robust and interpretable representation layer for sEMG analysis with potential applications in rehabilitation assessment, human–machine interaction, and motor function analysis.
Pain severity in osteoarthritis (OA) often dissociates from the extent of cartilage structural damage, indicating that non-structural mechanisms contribute to symptom persistence. Here, we identify articular chondrocytes as active orchestrators of pathological neurovascular invasion through a pathogenic secretome, rather than merely passive victims of degeneration. Integrated human single-cell transcriptomic analysis reveals that OA chondrocytes undergo phenotypic reprogramming characterized by coordinated activation of angiogenic and neurotrophic programs. Functionally, inflammatory chondrocytes directly promote endothelial angiogenesis and sensory axon outgrowth, linking chondrocyte-derived signals to neurovascular invasion and sustained pain. To therapeutically target this upstream mechanism, we engineered MMP-13-responsive celecoxib-loaded hyaluronic acid methacrylate microspheres (HMPC) for disease-adaptive delivery to degenerative cartilage. HMPC suppresses chondrocyte COX-2 activity and remodels the pathological chondrocyte secretome through restoration of miR-339-5p_R-5 and miR-103-3p_R-2, thereby inhibiting angiopoietin-2 (Angpt2)-mediated angiogenesis and brain-derived neurotrophic factor (Bdnf)-driven sensory neurogenesis. In murine OA models, HMPC markedly alleviates pain-related behaviors, reduces neurovascular infiltration, and preserves joint structure. Collectively, this study supports a chondrocyte-centric framework in which reprogrammed chondrocytes govern neurovascular invasion through microRNA-dependent secretome regulation and introduces a smart biomaterial-enabled strategy with disease-modifying potential for OA pain intervention.
This study focuses on occupant behavior in office buildings and proposes a systematic framework for modeling stochastic occupant behavior based on the Theory of Planned Behavior (TPB) and a fuzzy multi-behavior decision-making (FMB) model. The research initially focuses on the three core components of the TPB: attitude, subjective norm, and perceived behavioral control, and designs a questionnaire for investigating energy-related occupant behaviors and potential influencing factors. The influence of these three factors on energy-related behaviors was identified through structural equation modeling (SEM) of the survey data, leading to the establishment of three key evaluation criteria for the FMB model: comfort, economy, and norm compliance. Based on the TPB this study integrates fuzzy decision methods and survival analysis to predict the priority and duration of building occupants’ multi-equipment operation behaviors. Additionally, Bayesian inference is employed to effectively identify the multi-state choices in shading adjustments. The validation results demonstrated that the proposed model effectively replicates users’ actual energy-use decision-making processes. The findings offer a scientific methodology for modeling occupant behavior in office buildings, contributing to the improvement of occupant behavior modeling accuracy in real-world environments.
In the transmodal seafood cold chain, seafood perishability and uncertainty in consumers' freshness perceptions create a trade-off between freshness preservation and sales revenue. This study considers the channel choice behaviors influenced by consumers' perception of freshness and develops a joint optimization model for fresh-keeping transportation and pricing based on freshness-decay dynamics to determine the fresh-keeping strategy, transportation routing, and pricing scheme with the objective to maximize retailers' sales profit. Seafood freshness indicators are first calculated using kinetic models parameterized with experimental data obtained under controlled time-temperature conditions in existing studies. A back propagation neural network is then adopted to establish the nonlinear mapping between these physicochemical indicators and perception of freshness based on corresponding sensory evaluation data, while a nested logit model is used to capture the price-freshness trade-offs during selling. It is revealed through numerical analysis that: (1) E-commerce platforms will adopt higher transportation temperatures (3.05 degrees C) to reduce costs, enabling aggressive pricing (54.76 CNY/kg), but contribute only about 5.72% of the retailer's total profit; (2) Brick-and-mortar stores will implement stricter temperature control (0.37 degrees C) leveraging location advantages, supporting premium pricing despite incurring fresh-keeping costs about 4.30 times as high as those of the e-commerce platform; (3) AVS is priced higher than SM (129.05 vs. 109.54 CNY/kg), reflecting its added service value as a store-affiliated online-to-home channel; (4) Ambient temperature changes induce coordinated operational adjustments, with total system profit changing by only 0.06% under both low- and high-temperature scenarios, while higher ambient temperatures impose greater fresh-keeping pressure. The model provides actionable transportation protocols: temperature-driven cargo allocation, refrigeration investment benchmarks, and dynamic pricing tariffs aligned with realtime freshness metrics.
This paper is devoted to simultaneously recovering multiple parameters from internal measurements for nonlocal diffusion equations. The uniqueness of the inverse problem is established by employing the asymptotic behavior of solutions, analytic continuation, the Laplace transform, and properties of analytic functions. For numerical reconstruction, we apply the Levenberg-Marquardt method to obtain a stable approximate solution of the inverse problem. Numerical examples are provided to demonstrate the efficiency of the proposed algorithm and to validate our theoretical findings.
