Search Results (182)

Exploring the spatial correlation network (SCN) characteristics and influencing factors of the eco-efficiency of cultivated land use (ECLU) in Xinjiang’s counties is crucial for clarifying inter-regional spatial mechanisms and supporting cross-regional collaborative governance. Using a panel dataset covering 85 counties across Xinjiang, this study adopts the super-efficiency SBM model, revised gravity model, social network analysis and QAP model to quantify ECLU, and further investigate its spatial network features as well as driving mechanisms. The results showed that: (1) ECLU exhibited a fluctuating upward trend with significant regional differentiation, and northern Xinjiang performed notably better than southern Xinjiang. (2) SCN remained connected overall, but network density was low, average path length was long, and spatial transmission efficiency was relatively low. (3) Regional differences in economic levels, labor productivity, and industrial structure all had positive effects on the formation of the SCN throughout the study period. Regional differences in fiscal support for agriculture had positive effects only in 2014 and 2017, while differences in the soil and water coordination ratio had a negative effect in 2021. Future policies for sustainable cultivated land use should be differentiated and zone-specific, based on each county’s role within the correlation network, to promote coordinated improvement of ECLU across counties. Full article

The central challenge of the energy transition lies in whether an economy possesses the institutional capacity to systematically regulate its own energy inputs and carbon emissions. Drawing upon social metabolism theory, this study constructs an indicator of carbon–energy metabolic control capacity (CMCC). Building on business ecosystem theory, it conceptualizes the intelligent economic ecosystem (IEE) and incorporates industrial chain intellectual property empowerment (IP) into a causal framework of institutional provision → ecosystem development → enhancement of metabolic control capacity. Using panel data from 30 provincial-level administrative regions in China covering the period 2010–2022, this study employs a spatial Durbin difference-in-differences (SDID) model and a double machine learning (DML) framework for empirical analysis. The results indicate that industrial chain intellectual property empowerment significantly enhances carbon–energy metabolic control capacity and generates positive spatial spillover effects on neighboring regions through the public diffusion of patent information. Furthermore, intelligent economic ecological development serves as a significant partial mediator between intellectual property empowerment and carbon–energy metabolic control capacity, with the indirect effect accounting for more than one-third of the total effect. This mediating mechanism remains robust after replacing machine learning algorithms, altering sample-splitting ratios, controlling for concurrent innovation policies, and excluding the impact of the COVID-19 pandemic. Path-specific mediation analysis further reveals that computing power acquisition and value transformation together with digital substrate robustness constitute the dominant transmission channels, whereas innovation metabolic flux contributes a relatively smaller mediating effect due to the long gestation period required for translating fundamental research into practical applications. Heterogeneity analysis further demonstrates that the transmission mechanism exhibits full mediation in the dimension of metabolic structure, indicating that the contribution of industrial chain intellectual property empowerment to the clean substitution of energy structures depends almost entirely on the mediating role of the intelligent economic ecosystem. These findings provide clear actionable guidelines for three specific policy-making domains to advance low-carbon transitions. First, intellectual property authorities should transition from quantity-driven patent creation to establishing cross-regional patent navigation and industrial chain IP pooling. Second, digital economy and industry regulators need to prioritize computing power value conversion (CCV) over raw infrastructure expansion to mitigate energy rebound effects. Third, energy and environmental agencies ought to integrate real-time algorithmic dispatching with green finance incentives. Ultimately, this study demonstrates that achieving deep low-carbon transformation requires leveraging institutional public goods to catalyze digital ecosystems, which in turn enable precise, dynamic carbon–energy metabolic control. Full article

Accurate temperature and emissivity separation from thermal infrared radiance is essential for characterizing materials under high-temperature laboratory conditions. Existing temperature and emissivity separation methods have largely been developed for multispectral remote sensing applications, where long atmospheric path lengths require extensive atmospheric compensation. In contrast, the current work considers hyperspectral laboratory measurements acquired over a short optical path, where atmospheric effects are comparatively small but increased measurement uncertainty remains within portions of the measured spectrum. The ABB MR304 FTIR spectrometer used in this study exhibits reduced optical transmission below approximately 2.5 $\mathsf{\mu }$m, producing increased measurement uncertainty within the spectral region containing much of the temperature information. To address these conditions, a modified Gray Body Emissivity method, referred to as the Robust Ratio Linear Fitting method, was developed using robust linear regression, spectral masking, and iterative temperature refinement. The algorithm was validated by comparing the retrieved temperatures with pyrometer measurements and the retrieved spectral emissivities with a high-accuracy spectral emissivity database collected using a SOC-100 hemispherical directional reflectometer. When applied to radiance measurements of a carbon phenolic sample heated using a plasma torch and measured with an ABB MR304 FTIR spectrometer, the algorithm retrieved temperatures with a mean absolute percentage error of 3.05% and spectral emissivities with a mean absolute percentage error of 3.13% relative to the SOC-100 reference measurements. Although the method is ineffective at lower temperatures where the peak of the Planck radiance lies within excluded spectral regions, the results demonstrate that the proposed approach provides accurate temperature and emissivity retrieval for high-temperature laboratory FTIR measurements acquired under these experimental conditions. Full article

Accurate detection of transmission line components is crucial for the stability and security of power grid operations. However, accurate power line inspection is always affected by complex factors, such as multi-scale objects, complex background interference, and object occlusion, etc. To tackle these complexities, this paper leverages the long-range dependency modeling advantages of the Transformer architecture, and an improved real-time end-to-end Detection Transformer (RT-DETR) with multi-scale feature reconstruction, referred to as MFRRT-DETR, for Unmanned Aerial Vehicle (UAV) inspection systems is presented. Specifically, an enhanced attention-based backbone network integrated via an aggregated pixel-focus attention (APFA) module is built which uses a dual-path design with fine-grained and coarse-grained branches to combine pixel-level focus with global perception to enhance the interaction between local and global features, alleviating the limitations of the local receptive field in Convolutional Neural Networks (CNNs). To further overcome the issues of target overlap, occlusion, and foreground–background confusion, a context-guided spatial feature reconstruction feature pyramid network (CGR-FPN) module is proposed which strengthens foreground representation and effectively fuses multi-scale features, improving performance in crowded scenes. Additionally, a Focaler–Shape IoU loss function is introduced to mitigate class imbalance issues and localization errors by focusing on hard samples and optimizing bounding box regression, particularly for long and wide irregular rectangular targets. Experiments show that the proposed MFRRT-DETR significantly outperforms advanced detection models, which effectively validates the detection efficiency and accuracy of the proposed model in complex inspection scenarios, making it a promising solution for UAV-based power line inspection. Full article

Digital transformation has become a central management agenda for higher education institutions seeking to improve teaching quality, organizational resilience, and long-term educational sustainability. However, the managerial pathways through which digital leadership contributes to sustainable teaching innovation remain insufficiently explained. Drawing on digital leadership, organizational learning, and sustainable higher education literature, this study develops and tests a conceptual path model linking digital leadership, organizational learning, faculty professional development, institutional support, and sustainable teaching innovation. A cross-sectional survey was conducted with 300 university faculty members and teaching-related personnel who had experience with digital teaching tools. Composite-score-based path analysis showed that digital leadership positively predicted organizational learning, faculty professional development, and sustainable teaching innovation. Organizational learning and faculty professional development also significantly predicted sustainable teaching innovation and served as indirect transmission mechanisms. Institutional support had a positive direct effect but did not significantly moderate the organizational learning–innovation relationship. The findings indicate that sustainable teaching innovation depends not only on digital infrastructure but also on leadership-enabled learning processes, faculty development, and institutional support. This study contributes an educational management strategy model that reframes sustainable teaching innovation as an organizational capability shaped by leadership, learning, professional development, and support mechanisms. Full article

Covert communication has emerged as a promising technique for protecting wireless transmissions by concealing the existence of legitimate communication from malicious wardens. However, achieving end-to-end covert communication in space–air–ground integrated networks (SAGINs) is challenging due to the coupled effects of satellite-to-ground relay access, ground multi-hop forwarding, and cooperative jamming. In this paper, we propose an incentive-aware end-to-end covert routing framework for SAGINs, where a low Earth orbit (LEO) satellite delivers information to a ground destination through a selected relay base station and a self-organizing ground route. We first establish a two-stage SAGIN model and characterize the satellite-to-ground covert capacity under satellite sidelobe interference, as well as the ground-route covert performance in the presence of multiple wardens and cooperative jammers. Since jammers are self-interested and incur power costs when generating artificial interference, we design an incentive mechanism to stimulate cooperative jamming for enhancing ground-route covertness. Specifically, the reward allocation and jamming-power response are jointly derived by considering both the route-dependent covertness gain and the power cost of jammers. Based on the resulting route-dependent utility, the ground routing problem is further transformed into a shortest-weighted path-finding problem. To improve the long-term stability of satellite-to-ground relay access, we model the repeated interaction between the LEO satellite transmitter and the satellite warden as a base-station selection process and develop a zero-determinant strategy to stabilize the long-term expected utility relation under different warden monitoring policies. Simulation results demonstrate that the proposed framework effectively balances satellite-to-ground covert capacity and ground-route utility, outperforms baseline relay selection schemes, and achieves stable long-term covert routing performance against uncertain warden behaviors. Full article

Reliable full-line monitoring of belt-conveyor idlers remains challenging because large numbers of idlers operate under spatially varying structural stiffness and strong industrial vibration. This study develops an ultra-weak fiber Bragg grating distributed acoustic sensing (UWFBG-DAS) method combined with characteristic energy feature analysis for long-distance idler monitoring. The method makes three main contributions. First, a simplified finite-element model identifies the middle crossbeam as an effective vibration-transmission path and guides the deployment of the sensing array. Second, envelope demodulation and variational mode decomposition (VMD) are employed to isolate the fault-sensitive IMF2 component, whose energy is temporally accumulated and evaluated using a zone-specific self-referencing threshold derived from normal-operation data. Third, the method is validated through field deployment and fault-type classification. Approximately 1.2 km of a sensing cable was deployed in a coal-fired power plant, and identifiable characteristic-energy increases were observed in 9 of 10 idler-replacement tests. For three representative fault types, stratified five-fold cross-validation of 300 samples achieved an overall classification accuracy of 90.3%, with a 95% Wilson confidence interval of 86.5–93.2%. These results demonstrate the feasibility of UWFBG-DAS combined with zone-specific characteristic energy analysis for long-distance idler monitoring under spatially heterogeneous industrial conditions. Full article

Persistent inter-provincial market segmentation restrains China’s long-term sustainable domestic circulation and balanced regional development, which conflicts with the country’s SDG-aligned coordinated growth goals. Digital transformation cuts cross-border transaction frictions, while targeted fiscal and tax tools adjust local development incentives to realize sustained, inclusive market integration. Drawing on balanced panel data of 30 Chinese provinces from 2009 to 2024, this paper constructs two multi-dimensional composite indices via entropy weighting. We build a trade-flow theoretical framework embedded with fiscal incentive parameters, then design benchmark, dual mediation, and interaction-moderating panel models. System GMM and lagged variable regressions mitigate endogeneity risks, and a full suite of robustness tests validates the reliability of empirical outputs. The results show digital expansion significantly alleviates market fragmentation and fuels sustainable unified market construction. Information transparency improvement and transportation cost reduction serve as two parallel sustainable transmission paths. Obvious regional differentiation exists in inland provinces with underdeveloped market systems, which harvest larger balanced development dividends from digital upgrades. Fiscal and tax policies exert significant positive moderating effects; standardized fiscal allocation can amplify digitalization’s capacity to deliver long-term coordinated regional circulation. This study supplements institutional sustainability logic for digital-market linkage research and delivers differentiated fiscal and digital policy portfolios to narrow inter-regional development gaps and advance sustainable economic balance. Full article

To prevent the collapse of strip waveguides caused by complete undercut during hydrofluoric acid (HF) release in SOI-based cavity optomechanical accelerometers, we propose using rib waveguides as the on-chip optical transmission medium. Based on a 250-nm-thick SOI wafer, we systematically analyze the photonic crystal (PhC) microcavity, rib waveguide transmission, edge coupling, mode conversion to the PhC waveguide, and evanescent coupling. The PhC microcavity has a quality factor of 2.26 × 10⁵ at 1549.15 nm. The optimized rib waveguide (rib width 500 nm, rib height 220 nm) shows a transmission loss of 0.16 dB over 5000 μm. The rib-to-PhC waveguide coupling efficiency is 94.3% (0.25 dB loss), and a 90-μm-long tapered edge coupler achieves 65% efficiency (1.9 dB loss). The total optical path loss (including two edge couplers, rib waveguide transmission, and rib-to-PhC taper) is 4.21 dB. While maintaining optical performance comparable to strip waveguides, the rib waveguide design significantly improves post-release structural integrity at the design level. This work provides a viable optical circuit design foundation for reliable monolithically integrated cavity optomechanical accelerometers, with device fabrication and full system-level characterization planned as future work. Full article

Ecological network resilience is an important indicator for evaluating whether regional ecosystems can maintain structural connectivity and functional stability under external disturbance. However, long-term changes in ecological network structure and robustness in the Poyang Lake Eco-economic Zone remain insufficiently understood. Based on land-use data from 1990, 2000, 2010, and 2020, this study integrated Morphological Spatial Pattern Analysis (MSPA), landscape connectivity assessment, the MCR model, complex network analysis, and robustness simulations to evaluate the spatio-temporal evolution of ecological network resilience in the Poyang Lake Eco-economic Zone. The results showed that the area of selected ecological sources in the study area decreased from 39.97% in 1990 to 33.14% in 2020, indicating continuous source-area shrinkage and habitat fragmentation. The ecological resistance surface showed increasing spatial heterogeneity, with relatively low resistance in mountain and lakeside wetland areas and high resistance in the central plain areas affected by agricultural and construction-land expansion. Topological analysis showed that network density increased from 0.10 to 0.15, average path length decreased from 3.04 to 2.53, and network efficiency increased from 0.34 to 0.45, suggesting enhanced local connectivity and transmission efficiency. However, the largest connected component decreased from 37 to 26, indicating a decline in regional-scale connectivity and an increasing risk of network fragmentation. Robustness simulations further showed that the network was relatively tolerant to random disturbance but highly sensitive to the priority failure of key nodes and corridors. Overall, the ecological network evolved from relatively continuous connectivity toward local clustering and residual trunk corridors. These findings suggest that ecological restoration in the Poyang Lake Eco-economic Zone should prioritize major ecological sources, cross-regional corridors, stepping-stone habitats, and critical linkage areas to improve network-level resilience. Full article

Artificial intelligence (AI) technology has become the core force driving industrial transformation in today’s world. In-depth exploration of the spillover effects between artificial intelligence and new energy, as well as high-carbon-emission industries is of great significance for optimizing the industrial structure, preventing systemic risks in the industrial system, and achieving high-quality development. Based on the DY and BK spillover index model under the TVP-VAR framework, this paper analyzes the spillover effects between artificial intelligence and new energy, as well as high-carbon-emission industries from a time–frequency perspective, and constructs a spillover network to analyze the risk spillover transmission path. Finally, it explores the optimal investment portfolio weights and investment hedging strategies in the financial market. The results show that there is a significant static spillover effect between artificial intelligence and new energy, as well as high-carbon-emission industries. The intensity of this effect follows the pattern of “short-term > medium-term > long-term”. Moreover, new energy and some high-carbon-emission industries (such as the non-ferrous metals industry, the petrochemical industry, and the chemical industry) are the net spillover sources, while artificial intelligence and some high-carbon-emission industries (such as the power industry, the building materials industry, and the aerospace industry) are the net receiving parties. The dynamic spillover effect exhibits significant time-varying characteristics, being significantly impacted by major events such as environmental protection policies, the COVID-19 pandemic, and technological innovations. The chemical industry is the largest spillover outputter in all frequency domains, while the building materials industry is the largest receiver. From the perspective of the spillover network, the artificial intelligence industry, as a key node of the spillover network, plays a crucial role in the transmission of risk spillover. From the perspective of investment practice, the minimum connectedness portfolio (MCoP) performs well in terms of risk hedging effectiveness and return performance and may be the best choice for investors to balance risk and return. Full article

Insulators are critical insulation components in power transmission lines; however long-term exposure to adverse environmental conditions may threaten the safety and stability of power delivery. Existing studies primarily emphasize detection accuracy, while deployment efficiency and inference speed have received insufficient attention, limiting their applicability to CPU-based edge computing devices. To address these limitations, this paper proposes PEBL-YOLO, a lightweight model for insulator defect detection. The proposed model retains the external C3k2 structure of YOLOv11 while simplifying its internal bottleneck module, in which PConv is embedded to improve spatial feature extraction and fusion efficiency. In the neck, the original Path Aggregation Feature Pyramid Network (PAFPN) is reconstructed by integrating a Bidirectional Feature Pyramid Network (BiFPN) with Efficient Channel Attention (ECA), enabling more effective aggregation of multi-scale features and stronger focus on defect-related regions with minimal parameter increase. Moreover, a lightweight shared decoupled detection head is designed to decouple classification and regression branches. By combining parameter sharing with Group Normalization (GN) the detection head further reduces model complexity while maintaining accurate localization capability. Experimental results show that PEBL-YOLO contains only 1.68 M parameters. It achieves Precision, Recall, mAP@0.5, and mAP@0.5:0.95 of 95.0%, 92.1%, 94.4%, and 53.6%, respectively. These results demonstrate that PEBL-YOLO achieves a favorable trade-off between detection accuracy and parameter efficiency, providing a practical solution for lightweight insulator defect detection in edge computing scenarios. Full article

External intrusion hazards such as construction machinery pose serious threats to the safe operation of transmission lines. However, reliable detection in transmission corridors remains challenging because hazardous targets usually exhibit articulated and elongated structures, while monitoring images are dominated by complex backgrounds and rare hard-negative samples. To address these challenges, this paper proposes CMHdet, a morphology-aware and hard-negative-optimized detection framework for external intrusion monitoring in transmission corridors. First, a Dynamic Deformable Transformer module is embedded into the feature extraction backbone to adaptively adjust spatial sampling positions and enhance the representation of irregular machinery structures under viewpoint changes and occlusion. Second, a dual-path multi-scale aggregation network with shifted-window attention is designed to preserve local structural details while strengthening cross-region contextual interaction for small and large-span targets. Third, a hard-negative-aware optimization strategy is developed by combining Gradient Harmonized Mining loss with a false-alarm-guided dynamic copy-paste augmentation mechanism, enabling the model to learn from confusing background regions frequently encountered in long-term monitoring. Experiments on a real-world transmission corridor dataset demonstrate that CMHdet achieves 93.4% mAP, outperforming the YOLOv10L baseline by 5.7 percentage points, with notable improvements under long-distance, occluded, and adverse-weather conditions. The results indicate that the proposed framework provides a reliable solution for intelligent external intrusion monitoring in transmission corridors. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

A structurally simple three-layer optical absorber is proposed and systematically investigated, consisting of a continuous Ti ground plane, a SiO₂ dielectric spacer, and a Ti tetrahedral nanostructure. The absorber is constructed on a periodic square unit cell, where the lateral dimension directly determines the base width and sidewall inclination angle of the tetrahedral structure, thereby enabling effective modulation of the optical response. Full-wave electromagnetic simulations performed using COMSOL Multiphysics (version 6.0) are employed to evaluate the influence of geometric parameters on broadband absorption behavior. The optimized structure achieves a near-unity absorptivity of 0.9999 at 200 nm and maintains an effective absorption bandwidth (absorptivity > 0.9) spanning 200–3000 nm, covering the ultraviolet, visible, and near-infrared spectral regions. Parametric analysis reveals that the tetrahedral height primarily governs long-wavelength extension through enhanced optical path length, graded-index transition, and improved electromagnetic field confinement, while the unit cell width strongly influences impedance matching and localized field localization. In contrast, the Ti ground layer thickness exhibits minimal influence once it exceeds the optical skin depth, confirming its primary role as a transmission-blocking reflective substrate. Impedance retrieval analysis shows that the real part of the normalized impedance remains close to unity and the imaginary part approaches zero over most of the operating range, demonstrating that the ultrabroadband absorption behavior is dominated by effective impedance matching rather than isolated narrowband resonances. Furthermore, electric and magnetic field distribution analyses reveal that electromagnetic energy dissipation is concentrated near the tetrahedral apex and metal–dielectric interfaces, indicating the coexistence of localized plasmonic modes, cavity-assisted absorption, and multi-scale optical confinement. Full article