Search Results (658)

This study examines how AI-enhanced motion capture (AI-MoCap) mediates the preservation, transmission, and re-creation of Chinese shadow puppetry as performative intangible cultural heritage. Through a state-of-the-art review and comparative analysis of three representative application models—technology-driven, culturally integrated, and entertainment-oriented—the paper explores how AI-MoCap supports the digitization of performative techniques while reshaping modes of cultural presentation and interaction. Cross-case comparison highlights recurring tensions between technical standardization and cultural authenticity while also indicating possibilities for symbolic reconstruction, contextual continuity, and ethically grounded design. Based on this comparison, the paper develops a dual-channel inheritance framework—“perception–symbol” and “design–performance”—and treats cultural resolution and digital ethics as analytical and normative principles for resisting algorithmic homogenization. Rather than functioning only as a digitization tool, AI-MoCap can be understood as a mediating mechanism whose cultural value depends on how it remains embedded in community-based performative logics, symbolic systems, and ethical boundaries. The resulting framework offers transferable guidance for future research, curation, training, and policy discussion in the digital safeguarding of performance-based heritage. Full article

Because of their special optical and electrochemical characteristics, superior biocompatibility, adjustable surface chemistry, and inexpensive, scalable synthesis, carbon dots (CDs), including carbon quantum dots and graphene quantum dots, have become powerful and adaptable nanomaterials for advanced pharmaceutical analysis and other toxicants. The sensitive and selective detection of active pharmaceutical substances, degradation products, contaminants, biomarkers, and therapeutic medication levels in complex matrices has shown great promise in recent years with CD-based nanobiosensors. The development of various sensing platforms, such as electrochemical, optical, and dual-mode biosensors, as well as integration into microfluidic, paper-based, and wearable point-of-care (POC) devices, is made possible by their intrinsic fluorescence, effective electron transfer capacity, and ease of functionalization. With an emphasis on sensing mechanisms, biorecognition techniques, and analytical performance, this study critically reviews current developments in CD-based nanobio/chemosensors for pharmaceutical analysis. It includes a thorough discussion of important applications in drug development, stability research, therapeutic drug monitoring, and drug quality control. Along with new developments like green synthesis, AI-assisted signal processing, and smart sensing platforms, current issues with reproducibility, standardization, biocompatibility, and regulatory validation are highlighted. Lastly, prospects for the industrial application and clinical translation of CD-based nanobiosensors are discussed. Full article

Despite the rapid digitization of intangible cultural heritage (ICH), the complex mechanisms governing how users interact and co-create knowledge in digital spaces remain underexplored. Understanding the internal dynamics and engagement logic of these interactive environments is therefore essential to developing sustainable heritage knowledge ecosystems. Conceptualizing the Zhihu community as such an ecosystem, this study investigates ICH thematic structures, knowledge demands, and user participation. By employing an LLM-refined BERTopic framework, this study identified 36 core topics and mapped them onto a four-layer architecture (Cultural Resource Layer, Action Subject Layer, Social Support Layer, and External Interaction Layer) and five knowledge demand dimensions (Basic Knowledge, Cultural Experience, Professional Development, Protection and Inheritance, and Modern Application) through weighted semantic similarity and Spearman correlation analysis. The results reveal a structural configuration dominated by the External Interaction Layer. A dual-track demand mechanism was identified, comprising a professionalized ability-oriented pathway and an affective experience-driven mode. Furthermore, deep engagement was primarily catalyzed by topics that integrate technology, action, and narrative, rather than structural prominence alone. The ICH knowledge ecosystem was characterized by an outward-looking and emotion-driven orientation. This research study contributes an ecosystem framework to heritage information while providing insights for practitioners to optimize digital ICH information services through multi-dimensional semantic integration and public co-creation. Full article

In order to protect the high-sensitivity optical lens of the “magnetic field and velocity field imager” in extreme deep space environments, this paper proposes a new type of dual redundant planetary differential lens cover drive mechanism. In view of the critical vulnerability that traditional single-motor direct drive is prone to sudden mechanical jamming and catastrophic single-point failure (SPF) in severe tasks such as Jupiter exploration, this study constructs a “dual input single output (DISO)” rigid decoupling architecture from the perspective of physical topology. Through theoretical analysis and kinematic modeling, the adaptive decoupling mechanism of the two-degree-of-freedom (2-DOF) system under unilateral mechanical stalling is revealed. Dynamic analysis shows that in the nominal dual-motor synergy mode, the system shows a significant “kinematic load-sharing effect”, thus greatly reducing the sliding friction and gear wear rate. In addition, under the severe dynamic fault injection scenario (maximum gravity deviation and sudden jam superposition of a single motor), the cold standby motor is activated and the dynamic takeover is quickly performed. The high-fidelity transient simulation based on ADAMS verifies that although the fault will produce transient global torque spikes and pulsed internal gear contact forces at the moment, all extreme dynamic loads remain well within the structural safety margin. The output successfully achieved a smooth transition, which is characterized by a non-zero-crossing velocity recovery. This research provides an innovative theoretical basis and a practical engineering paradigm for the design of high-reliability fault-tolerant mechanisms in deep space exploration. Full article

Accurate permittivity characterization at terahertz frequencies is important for material analysis and device design, yet it remains challenging for small-volume samples and compact test structures. In this work, a terahertz permittivity sensor based on a spoof surface plasmon polariton (SSPPs) transmission line coupled to a backside split-ring resonator (SRR) is proposed and numerically studied. The SSPPs line is patterned on the top side of the substrate, while the SRR is etched on the backside, with the sample loaded into the SRR gap. The SSPPs mode penetrates through the substrate and excites the SRR, producing a pronounced transmission notch. Changes in the sample permittivity modulate the effective capacitance of the resonator, resulting in a monotonic shift in the notch center frequency. For relative permittivities from 1 to 8, the notch center frequency decreases from 152.1 GHz to 117.8 GHz, corresponding to a total shift of 34.3 GHz and an average sensitivity of about 4.90 GHz/ε_r. The minimum S₂₁ remains within approximately −23.80 to −21.56 dB, while the Q-factor stays in the range of 94.33–108.23, indicating good spectral readability. Tolerance analysis further shows that the resonance frequency is sensitive to critical structural dimensions and layer alignment, and practical implementation is therefore more suitable for single-device calibrated frequency-shift sensing. These results demonstrate the feasibility of the proposed dual-layer SSPPs–SRR configuration for compact permittivity sensing in the terahertz regime. Full article

A micro-cavity based on phase-change material is a very important strategy for the realization of tunable absorption and conversion of terahertz waves. In this work, a tunable terahertz metamaterial absorber based on the phase-change material germanium–antimony–tellurium (GST) is demonstrated. The device features a metal–insulator–metal triple-layer structure, where the dynamic switching of absorption characteristics is achieved via thermally controlled GST phase transition. In the amorphous state, the absorber exhibits a single absorption peak at 7.7 THz. Upon crystallization, the absorption switches to dual peaks at 5.1 THz and 8.3 THz, achieving near-perfect absorption in both states. Full-wave electromagnetic simulations and theoretical analysis based on a multiple-reflection interference model indicate that this performance tuning originates from the GST-phase-transition-induced change in the equivalent optical cavity length. This corresponds to a switch between two resonant modes: coupled inner–outer ring resonance and independent outer ring resonance. These results provide a foundation for developing dynamically tunable terahertz devices with promising applications in terahertz communications, imaging, and sensing. Full article

We propose a monolithically integrated optical frequency comb (OFC) generation platform on thin-film lithium niobate (TFLN), featuring cascaded dual-drive Mach–Zehnder modulators (DDMZM) and a ${\mathrm{S}\mathrm{i}}_3{\mathrm{N}}_4$-assisted spot size converter (SSC). To capture microscopic mode mismatches and spatial phase accumulation often overlooked in idealized scalar simulations, we establish a multi-physics co-simulation framework integrating finite-difference time-domain (FDTD) analysis with macroscopic transmission modeling. Based on this framework, the cascaded modulator architecture generates 25 highly stable comb lines with a dense 2 GHz spacing and an envelope flatness within 2 dB. Tolerance analysis indicates that the comb generation is highly resilient to typical manufacturing and environmental variations, including thermal bias drift, RF phase mismatch, and half-wave voltage ($V_{\pi }$) dispersion. Furthermore, physical-layer modeling shows that the integrated SSC reduces fiber-to-chip coupling loss to 0.55 dB per facet, preserving the necessary optical power budget. To validate the platform’s viability as a multi-wavelength continuous-wave source for spatial-division multiplexed (SDM) interconnects, a parallel transmission over a 20 km standard single-mode fiber is modeled. Using a digital signal processing (DSP)-free 10 Gb/s non-return-to-zero (NRZ) scheme, the 25-channel system maintains a worst-case bit error rate strictly below the forward error correction (FEC) threshold. This work offers a practical, physics-based evaluation framework for high-density co-packaged optics (CPO). Full article

Permanent magnet synchronous motor (PMSM)-driven position servo systems in high-speed flight vehicles face severe challenges from extreme thermal environments, which induce significant parameter variations up to 25% (e.g., motor torque constant) and complex multi-scale disturbances. This paper proposes a novel adaptive robust control strategy integrating three key components: (1) an ultra-local model formulation motivated by physically consistent thermal effect analysis of electromagnetic, mechanical, and tribological parameters; (2) a dual-layer disturbance observer architecture comprising a third-order finite-time convergent extended state observer (FTCESO) for fast-varying disturbances and a σ-modification adaptive estimator for slow-varying thermal drifts; and (3) a global nonlinear integral terminal sliding mode controller with a cycloidal reaching law. Stability analysis based on homogeneous system theory and Lyapunov methods establishes practical finite-time convergence with explicit bounds. The experimental results on a TMS320F28335-based servo platform demonstrate that the proposed method reduces the maximum position deviation by 83–94% compared to PID, LADRC, and conventional SMC controllers under the tested disturbance conditions, achieving settling time reductions exceeding 90%. Under combined thermal drift and external loading, the proposed approach limits the maximum tracking error to below 0.45° while maintaining a steady-state error under 0.08°. Full article

This study proposes an integrated artificial intelligence-based framework for modeling and predicting quay crane productivity and operational delays in conventional container terminals, addressing key limitations in the existing port analytics literature. The research introduces a novel dual-mode machine learning architecture that explicitly separates retrospective prediction (forecast mode) from pre-operational decision support (decision mode), addressing a critical gap in existing literature where predictive models are rarely aligned with real-world informational constraints. The framework is applied to a high-resolution, real-world dataset comprising ship-level operations over a three-year period (2023–2025), incorporating a structured representation of 27 delay types and multiple resource allocation variables. A multi-indicator modeling strategy is employed, simultaneously analyzing four productivity metrics (RQCP, GMPH, WBMPH and NMPH), thus allowing for a systematic comparison of their structural sensitivities to delays, congestion, and equipment utilization. The results reveal a clear hierarchy of predictability and operational behavior: structurally driven indicators such as RQCP and GMPH exhibit high predictive stability, while delay-sensitive indicators such as NMPH display greater variability, reflecting real-time operational disruptions. The consistent model performance in forecasting and decision-making indicates significant predictive value in pre-operational variables, endorsing its utility for uncertain decision-making. Sensitivity analysis reveals a critical nonlinear congestion threshold affecting predictive accuracy under extreme operational strain. Employing a combination of multi-indicator productivity modeling, structured delay classification, and ensemble learning within an integrated analytical framework, this research enhances both methodological and practical insights into port operations, aiding in merging predictive analytics with operational decision-making in container terminals to enhance resource allocation, delay handling, and container terminal efficiency. Full article

This study systematically examines the low-carbon transition challenges faced by the Yellow River Basin, a core strategic energy base in China with a coal-dominated energy system, under the dual carbon goals. Existing studies based on traditional econometric models or single-province analyses are mostly limited to static analysis, failing to simultaneously capture the nonlinear spatiotemporal evolution, cross-regional spillover effects, and long-term changing trends of carbon emissions in the basin. To fill this gap, this study builds an Energy–Economy–Carbon (EEC) analytical framework, and develops an integrated TFT-ASTGCN deep learning framework. Specifically, we employ the Temporal Fusion Transformer (TFT) for high-precision multivariate time-series simulation and peak forecasting, while the Attention-based Spatial–Temporal Graph Convolutional Network (ASTGCN) is used to identify complex spatial dependencies of inter-provincial emissions. The empirical results confirm that: (1) Basin carbon emissions show significant coal-driven carbon lock-in, with initial decoupling between economic growth and emissions. (2) Most provinces will maintain rising emissions under the current development mode, posing severe challenges to carbon peaking. (3) Asymmetric spatial spillover effects are prominent, underscoring cross-regional collaborative governance as a critical pathway for achieving an early and stable carbon peak in the basin. Full article

Previous approaches to flexible-lined channel design typically isolate least-cost cross-section optimization from parameter uncertainty, or restrict reliability analysis to specific cases, limited failure modes, and proprietary codes. This paper presents VegyRap, an open-source QGIS-based plugin with an intuitive graphical user interface that unites these traditionally disjointed, sequential tasks into a single computational framework. The tool guides designers sequentially through: (i) terrain-driven longitudinal profile optimization using dynamic programming; (ii) least-cost cross-sectional optimization for riprap and vegetated linings; and (iii) multi-mode probabilistic reliability analysis coupled with dual risk–cost Pareto optimization. To seamlessly handle the stochastic behavior of uncertain variables, the framework features built-in statistical distributions and allows users to flexibly evaluate up to four distinct failure modes: overtopping, erosion, sedimentation, and near-critical flow oscillation. The framework’s capabilities are demonstrated through nine diverse design examples, incorporating benchmark validations against published studies and a comprehensive real-world case study in Wadi Al-Arja, Saudi Arabia. Results highlight that for vegetated channels, a hierarchical two-phase design logic is essential to satisfy both establishment-phase stability (Class E) and long-term conveyance (Class B). While benchmark comparisons show VegyRap achieves consistent cost reductions of 10–15% over traditional methods, the case study demonstrates that deterministic least-cost solutions can carry non-negligible failure probabilities. By utilizing marginal efficiency analysis to identify cost-effective enhancements, the integrated Pareto-based dual optimization produces transparent trade-off surfaces, empowering practitioners to transition from a single least-cost solution to a defensible, risk-calibrated preferred alternative. Full article

This paper presents a comprehensive experimental assessment of electromagnetic field (EMF) exposure dynamics during the transition from IEEE 802.11ax (Wi-Fi 6) to IEEE 802.11be (Wi-Fi 7). Using a human-centric experimental setup, we evaluate the impact of Wi-Fi 7’s core innovations—4096-QAM modulation, 320 MHz bandwidth, and Multi-Link Operation—under iPerf3-controlled high-traffic conditions. A key contribution of this study is the analysis of multi-client influence, comparing EMF emission profiles when one versus two devices are active. Our results reveal a significant paradigm shift: while Wi-Fi 7 generates higher near-field peaks (up to 955.92 mV/m in MLO mode at 20 cm) to sustain high-order modulation, it exhibits an aggressive spatial decay, with E-field intensity collapsing by up to 76.6% at one meter. We demonstrate that the transition from a single-client to a dual-client configuration significantly alters the stochastic nature of the field, increasing the probability of transient high-power events, as characterized by our Complementary Cumulative Distribution Function (CCDF) framework. The findings confirm that Wi-Fi 7’s performance gains are decoupled from long-range exposure; the high-intensity field remains strictly localized, providing a natural safety buffer. This study provides new experimental vista into how next-generation WLAN systems trade near-field strength for far-field safety, maintaining compliance with international limits while supporting multi-device gigabit connectivity. Full article

The accelerated growth of e-commerce has intensified the dual challenges of weak infrastructure and carbon emission pressures in last-mile delivery for rural and mountainous regions. As the World Bank calls for integrating carbon market development into national strategies, Truck-Drone Collaborative Delivery (TDCD) has emerged as a critical sustainable solution. However, existing research often overlooks the strict airspace regulations in sensitive border areas. Therefore, this paper proposes a Vehicle Routing Problem with Drones and Mobile Base Stations (VRPDBS) model that explicitly incorporates airspace constraints and mobile hub deployment. We introduce a quantified “Regional Flyability Factor” ($f_k$) to measure the impact of airspace restrictions on routing decisions and solve the problem using a hybrid metaheuristic algorithm. A case study based on real-world data from the Yanbian Korean Autonomous Prefecture reveals that strict airspace compliance imposes an absolute delivery delay of 4–5 h and an operational cost premium of up to 15%, an impact that can be effectively mitigated through a mobile base station mediation strategy. More importantly, multi-scenario sensitivity analysis under carbon price uncertainty indicates that although truck-dominant modes are cost-effective at current low carbon prices, drone-intensive configurations demonstrate superior economic robustness and environmental performance under high carbon price scenarios. This study not only provides a technical framework for green logistics planning in complex airspace but also offers strategic decision support for logistics enterprises to navigate long-term climate policy risks. Full article

Terahertz (THz) metasurface biosensors still encounter difficulties in simultaneously achieving high spectral resolution and stable readout across different refractive-index regimes. In this work, an all-liquid-crystal-polymer (LCP) THz metasensor supporting dual quasi-bound states in the continuum (quasi-BIC) resonances is proposed for regime-dependent refractive-index sensing. By introducing structural asymmetry into a periodic LCP cubic-cluster metasurface, two pronounced resonances are generated with quality factors (Q factors) of 6811 and 2526, respectively. Near-field distributions and multipole decomposition analysis indicate that the two resonances possess distinct electromagnetic features, which result in different responses to surrounding dielectric perturbations. In the low-refractive-index range of 1.0–1.5, the two resonance frequencies exhibit a linear variation with refractive index, yielding sensitivities of 122 GHz/RIU and 179 GHz/RIU, respectively. These dual-mode linear responses further offer a foundation for concentration- and temperature-related evaluation through analyte refractive-index mapping. In the higher-refractive-index range of 1.5–1.8, the intermodal frequency difference shows improved linearity with refractive index compared with the individual resonance frequencies, enabling a differential readout scheme with enhanced robustness against common perturbations. The results demonstrate that the proposed all-LCP dual-quasi-BIC metasensor not only enables high-resolution THz refractive-index sensing, but also establishes a regime-dependent spectral readout approach for different dielectric-response intervals. Full article

Electromagnetic vortex waves carrying orbital angular momentum (OAM) provide an additional modal dimension for electromagnetic scattering analysis, but the resulting OAM mode-purity spectra are highly nonlinear and expensive to characterize through repeated full-parameter simulations. To address this issue, this work proposes a dual-path data-driven surrogate framework for the simulation-level prediction of OAM mode-purity spectra in metallic-target vortex-wave scattering. High-frequency datasets were generated within a prescribed workflow that combined an angular-spectrum formulation of Bessel vortex beams with a facet-based physical-optics method. Five representative metallic targets were considered, namely, Plate, Spiral, Spite, Missile, and Dihedral. In the first surrogate path, a numerical-parameter-based regression model was developed to predict the mode-purity spectrum from physical scattering variables for canonical targets. In the second surrogate path, a phase-map-based regression model was introduced to predict the spectrum directly from scattered-field phase maps without explicit geometric parameterization. The results show that the parameter-based surrogate achieves low prediction errors for canonical targets, while the proposed ConvNeXt + GAM model provides strong regression performance across multiple target categories in the phase-map-based setting. Overall, the proposed framework offers an efficient surrogate approximation of the nonlinear mapping between the scattering conditions and OAM mode-purity spectra under simulated conditions. This study is positioned as a simulation-level surrogate modeling investigation, and extension to experimental measurements or real-scene applications remains as future work. Full article