Search Results (17,753)

The digital economy has changed the way agricultural production is organized and how rural households access markets, jobs, and information. Yet it remains unclear whether these changes translate into higher income for rural residents, especially in major agricultural regions. This study examines the income effect of digital infrastructure development by using the rollout of the Broadband China policy as a quasi-natural experiment. The analysis draws on panel data for 77 prefecture-level administrative units in the Yellow River Basin, one of China’s major agricultural regions, from 2009 to 2021. A staggered difference in differences model is used to estimate the policy effect. The results show that digital infrastructure development significantly increases rural residents’ income. Under the log income specification, the baseline coefficient indicates an average income increase of about 8.33%. The mechanism analysis shows that innovation capacity and nonfarm employment both serve as positive partial transmission channels, with innovation capacity explaining a larger share of the total effect. The heterogeneity results suggest that the income effect is stronger in regions with higher GDP and larger population size. These findings indicate that digital infrastructure can support rural income growth when it is linked with local innovation capacity, employment opportunities outside agriculture, and rural development policies suited to local conditions. Full article

In the context of revitalizing rural development, farmer entrepreneurship has emerged as a significant driver of rural economic growth. However, existing research has not sufficiently examined the specific mechanisms or heterogeneous effects through which digital skills influence farm household entrepreneurial behavior. This gap is the focus of the present study. Utilizing micro-level survey data collected from 936 farm households across Shandong, Shaanxi, and Henan provinces in 2021, we construct a digital skills index using factor analysis. We then employ a Probit model and an Interaction term model to examine the impact of digital skills on entrepreneurial behavior among Chinese rural households and its underlying mechanisms. Additionally, we explore heterogeneity across different household types. The results show that digital skills are positively associated with entrepreneurial decision-making. Further analysis provides suggestive evidence that this relationship may operate through three channels: shaping risk preferences, expanding relational networks, and improving access to credit. Heterogeneity tests reveal that the promoting effect of digital skills is stronger among disadvantaged households, households with a head younger than 45, and those engaged in opportunity-driven or online entrepreneurship. Theoretically, this study contributes by empirically validating a multi-pathway mechanism framework and identifying relevant boundary conditions. Practically, it offers targeted insights for policymakers to design skill-based interventions and foster inclusive entrepreneurial ecosystems in rural areas. Full article

This study addresses second-home ownership not merely as a form of tourism accommodation or real estate investment, but as a home-building process intersecting with local life, belonging, daily practices, and sustainable destination governance. While the economic, environmental, and community impacts of second-homes have been extensively discussed in the literature, how individuals perceive their primary and secondary homes differently in terms of the bodily, material, vibrant, imaginary, and emotional dimensions of home has been examined in a limited number of studies. This research analyzes paired data obtained through a two-stage online questionnaire from 223 British participants who own a secondary home in the Mugla–Ortaca region and a primary home in the United Kingdom. The 18-item Home Scale was used as the measurement tool. Confirmatory factor analysis, reliability–validity analyses, measurement invariance, and paired-samples t-tests were applied. The findings show that the bodily home difference was not statistically significant at the conventional 0.05 threshold, whereas primary-home scores were significantly higher in the material, vibrant, imaginary, and emotional home dimensions. The small to small-medium effect sizes suggest that the results should be interpreted cautiously as an asymmetrical home-building process rather than as evidence of a hierarchical superiority of the primary home. The study proposes a planning approach that does not view second home owners as merely transient consumers in sustainable coastal–rural destinations, but rather considers social sustainability, service planning, seasonality management, and local community engagement channels together. Full article

In industrial quality control, the precise identification of severe structural weld defects is paramount. Traditional 2D image-based detection methods are susceptible to illumination and texture interference, while high-precision 3D laser scanning solutions are costly and impractical for large-scale deployment. To achieve reliable geometric defect detection at low cost, this paper proposes a detection framework based on monocular depth estimation and a dual-attention point cloud network. First, YOLOv8 is employed for rapid region of interest extraction, and an advanced monocular depth estimation model generates 3D pseudo-point clouds containing geometric information. Secondly, addressing the challenge of distinct spatial orientation features in missed weld defects that are prone to confusion, this paper introduces a dual-attention-enhanced point cloud classification network named DA-PointNet++. This model embeds dual-attention modules within the PointNet++ backbone network, enhancing key feature representation in both the channel and spatial dimensions. Experimental results demonstrate that this approach achieves an accuracy of 93.67% and a recall rate of 90.51% in a unified binary classification task for general weld defect detection, effectively identifying both normal welds and complex missed weld defects. Compared to PointConv, Dynamic Graph Convolutional Neural Network (DGCNN), and mainstream Point Cloud Transformer, this method significantly reduces false negative rates while maintaining low computational costs, offering a cost-effective solution for industrial automation. Full article

Contaminated gas flows are encountered in most industrial processes and require efficient removal of fine dispersed particles of various types and characteristics. Conventional cyclones are widely used under harsh operating conditions; however, their separation efficiency for fine particulate fractions remains relatively low. In this study, next-generation cyclones with a multichannel design featuring cylindrical and spiral casings are investigated, enabling particle collection efficiencies of approximately 90% for particles with a diameter of 2 µm. Under harsh microclimatic conditions—particularly at high humidity levels of 70% or higher and elevated temperatures of 50 to 200 °C—such technology is prone to clogging, necessitating complex regeneration procedures. Recent research has focused on improved channel geometries incorporating secondary peripheral flows, adapted for gas cleaning in harsh environments. Experimental results demonstrate effective removal of fine-dispersed glass and clay particles up to 20 µm in size at initial concentrations of 0.5–15 g/m³. The theoretical assessment of the influence of harsh gas flow conditions includes analyses of critical flow characteristics and the mechanical forces acting on fine particles under varying temperature and humidity conditions. The results indicate a maximum purification efficiency of up to 87.3% with an aerodynamic pressure drop of 440 Pa. The impact of harsh microclimatic conditions is most pronounced in the magnitudes of the centrifugal and drag forces: with an increase in the gas flow temperature by every 50 °C within the range from 0 to 200 °C, these forces increase by factors of 7.3–32.7 and 4–6.3, respectively. Full article

Oblique detonation engines have been proposed for hypersonic propulsion because detonation-based heat addition can, in principle, provide rapid energy release with reduced total-pressure penalties. We investigate non-premixed injection/mixing of an RP-3 aviation-kerosene surrogate in a constrained supersonic channel and its impact on oblique-detonation initiation, stabilization, and static pressure gain. Numerical simulations are performed for a Mach 8 inflow representative of a 30 km altitude condition using an OpenFOAM v7-based reacting-flow solver. We analyze the pressure-gain process following detonation onset, quantify the effects of the inducer-ramp angle, and qualitatively assess the predicted initiation/stabilization trends against direct-connect hot-fire experiments. The results show that non-premixed injection into a supersonic crossflow yields limited mixing over the available mixing length and results in a strongly stratified inflow to the combustor. In the constrained passage, a train of reflected shocks forms and progressively reduces the total-pressure recovery factor along the mixing section, which asymptotically approaches ~0.49. In the combustor, the inducer-ramp angle controls whether and how a stabilized oblique detonation can be established. For a 25° ramp, no self-sustained ODW is obtained under the present conditions, whereas stabilized ODWs are observed for 30° and 35° ramps, exhibiting abrupt and smooth topologies, respectively. These initiation thresholds and stabilized morphologies show qualitative consistency with the direct-connect observations. Due to fuel stratification, pressure gain varies among streamlines but consistently follows a “primary compression–plateau–secondary pressure rise” sequence; the secondary stage contributes approximately 17.54–27.98% of the static pressure rise. Full article

The growing diversity of wireless standards and complex real-world channel effects render automatic modulation classification (AMC) increasingly challenging for spectrum monitoring and edge intelligence. However, most competitive deep-learning-based AMC networks still require ${10}^5$–${10}^6$ parameters, exceeding the memory available on resource-constrained edge platforms. We propose LGP-Net, a lightweight gated-fusion network that pairs a physics-informed expert branch with a compact temporal encoder built from depthwise separable convolution (DSConv), squeeze-and-excitation (SE) attention, and a single-layer gated recurrent unit (GRU). Specifically, unlike other dual-branch structures that directly concatenate the outputs of both pathways, this work designs a lightweight gating unit that requires no external signal-to-noise ratio (SNR) labels and adaptively reweights the two pathways according to signal-quality degradation. With fewer than 40 K parameters, a peak activation footprint of 26.00 KB and an amortised inference latency of 9.7 $\mathsf{\mu }$s per sample under GPU acceleration, LGP-Net attains 65.00% overall accuracy on RadioML 2016.10B (91.48% at 0 dB) and 62.76% on RadioML 2016.10A, placing it in a competitive accuracy–efficiency regime relative to architectures consuming 5× to 500× more parameters. These characteristics support deployment-oriented feasibility under memory-constrained edge settings and high-throughput spectrum-monitoring pipelines. Full article

Cloud detection in high-resolution Gaofen-1 (GF-1) imagery is challenging due to the absence of short-wave infrared (SWIR) bands, which prevents the use of physically interpretable indices such as the Normalized Difference Snow Index (NDSI) and often leads to severe cloud–snow confusion. To address this limitation, we propose a unified framework, termed the Cross-Modality Spectral Expansion and Dual-Prior Network (CMSE-DPNet), that integrates cross-modality spectral expansion with physical–semantic dual priors. First, an improved CycleGAN reconstructs 13-band pseudo-Sentinel-2 spectra from four-band GF-1 imagery, enabling the computation of snow-sensitive physical indices. Second, a Snow-Aware Feature Attention Guidance Module (SAFAGM) introduces pixel-level physical priors derived from NDSI, while a Label-Guided Channel Attention Module (LG-CAM) injects scene-level semantic priors inferred from geographic metadata using a large language model. These complementary priors guide the network to better distinguish clouds from spectrally similar backgrounds. Experiments on the GF-1 dataset show that the proposed method achieves an F1-score of 94.41% and an Intersection over Union (IoU) of 89.40%, outperforming several state-of-the-art cloud detection methods. The results indicate that cross-modality spectral expansion combined with physical–semantic prior guidance effectively improves cloud detection performance in complex cloud–snow coexistence scenarios. Full article

Using a global panel of bilateral digitally delivered services exports for 192 economies from 2006 to 2022, together with large-scale international news data, this study examines the impact of international media sentiment on digital service exports, with particular attention to the institutional-barrier channel. To address the temporal aggregation mismatch between high-frequency media sentiment and annual trade flows, as well as potential endogeneity concerns, we employ a Mixed Two-Stage Least Squares (M2SLS) approach. The results show that more favorable international media sentiment has a positive and statistically significant effect on digital service exports. This finding remains robust across a range of measurement checks, placebo tests, alternative instrument constructions, subsample analyses, and Bayesian estimation. Further analysis supports an institutional-barrier interpretation by showing that favorable media sentiment is associated with lower bilateral digital service trade policy heterogeneity. The impact is stronger in trust- and reputation-intensive service sectors and in cultural contexts where reputational signals are more salient, while it weakens or reverses in technical service sectors and in highly secular-rational and institutionally asymmetric trading relationships. Full article

Digital camera sensors are designed to capture a wide range of incident illuminants, enabling the creation of high-quality images. However, these sensors lack the capability to differentiate between the color of the source illuminant and the actual color (or original color) of the object being captured. For this reason, the computational color constancy (CCC) was introduced and has been developed over decades. The CCC is an approach to modeling the color perception of the human visual system (HVS) by ensuring accurate object color determination under varying source illuminant conditions. At the core of human visual perception (HVP)-based CCC is attaining higher accuracy in scene illuminant estimation. The emergence of deep convolutional neural networks (DCNNs) was a recent innovation in accurate illuminant estimation, fundamentally transforming the CCC research landscape. Nevertheless, accurate illuminant estimation still remains a huge challenge for both traditional and state-of-the-art (SOTA) approaches. To further advance precision in illuminant estimation, this article presents a novel learning-based illumination color cast estimation approach to HVP-based CCC. Most importantly, the proposed approach is intended to integrate informative features into both channel and spatial regions while preserving long-term dependency feature information with the use of dense skip connections. To achieve these objectives, the proposed Dense Dual Connection Aggregated Transform Network (DDCATNet) architecture is designed to comprise several modules: shallow feature extraction, channel-wise and spatial feature-based Dense Dual Connection (DDC), fusion of the dense channel-wise attention (CA) and spatial attention (SA) branches through a gate mechanism (GM) unit, and aggregate transform. It is worth noting that both the CA blocks and the SA blocks in the DDC module are characterized by dense and cascading connections, meant to preserve long-term feature information and modulate different-level feature information at both global and local scales. The densely connected CA branch (DCA) and the densely connected SA branch (DSA) are also highly effective in securing high-contribution information while suppressing redundant data. The GM unit is integrated at the back of the DDC module, fusing the two DCA and DSA branches to ensure the adaptive merging of useful hierarchical feature information and the extraction of more valuable feature information. As a result, the proposed DDCATNet architecture significantly enhanced precision in illuminant estimation, thereby improving performance. In rigorous experiments on a wide range of datasets, the proposed DDCATNet approach outperformed its SOTA counterparts, validating the efficacy and generalization capabilities, as well as robust camera-invariance, across diverse, single- and multi-illuminant datasets and model architectures. Full article

Drug-induced QT interval prolongation is a critical safety concern in drug development, yet accurate and mechanistically interpretable prediction from chemical structure remains challenging due to the limited substructural resolution of existing approaches. Here, we present a motif-level graph learning framework for interpretable QT risk prediction. In this framework, molecules are decomposed into chemically meaningful motifs, enabling representation at an intermediate structural scale between atoms and predefined structural alerts. Motif features are encoded using a pre-trained chemical language model, and inter-motif relationships are modeled via attention-based graph learning with cross-scale integration. The model is trained and evaluated on two clinically grounded datasets derived from regulatory drug labeling (DIQTA) and real-world pharmacovigilance data (FAERS), achieving strong and consistent predictive performance with robust generalization across data sources. Importantly, motif-level attention reveals that QT liability is associated with the cooperative organization of compact cationic centers and heteroatom-rich, conformationally adaptable scaffolds, rather than isolated functional groups. These patterns are consistent with known determinants of human ether-à-go-go-related (hERG) channel blockade while providing a more structured and chemically specific interpretation beyond conventional structural alerts. Overall, this work establishes a generalizable and interpretable framework for QT risk prediction and highlights motif-level graph learning as an effective strategy for structure-based modeling of adverse drug reactions. Full article

With growing global concern over climate risk, high-carbon enterprises are assuming an increasingly critical role in strengthening climate resilience and fostering low-carbon development. However, how climate risk disclosure shapes their carbon performance—specifically through what mechanisms and pathways—remains a pivotal yet underexplored question. To address this gap, this study constructs a panel dataset comprising Chinese listed high-carbon companies over the period 2006–2022 and employs a two-way fixed-effects econometric model to assess how climate risk disclosure affects carbon performance while investigating the underlying mediating channel. The empirical results provide robust evidence that enhanced climate risk disclosure improves the carbon performance of high-carbon enterprises. Mechanism analysis indicates that this beneficial outcome is mainly achieved through promoting green technological innovation and easing corporate financial constraints. Heterogeneity analysis further shows that the effect is stronger among smaller companies, firms operating in less concentrated industries, and those headquartered in China’s eastern region. The policy implications derived from these findings include establishing and strengthening a mandatory climate risk disclosure framework, introducing targeted incentives for green innovation and transition finance and tailoring climate risk management strategies according to firm-specific characteristics. Overall, this study underscores climate risk disclosure as a crucial factor in supporting the shift toward low-carbon operations among high-carbon enterprises. Full article

Carbon nanotube field-effect transistors (CNT FETs) hold great promise for extending Moore’s Law, yet their performance is critically limited by excessive off-state leakage, caused by band-to-band tunneling (BTBT) in narrow bandgap CNT channels. In this work, we overcome this long-standing bottleneck by introducing a co-design strategy that integrates a small-diameter HiPco CNT channel with a novel asymmetric gate architecture. This approach strategically reshapes the channel electrostatics to simultaneously suppress the gate-induced drain leakage (GIDL) effect and preserve excellent carrier transport. The efficacy of this strategy is rigorously validated through calibrated technology computer-aided design (TCAD) simulations for both NMOS and PMOS operation, demonstrating an ultralow off-current of 10 fA/µm, an on-current of 1.08 mA/µm, and a record on–off ratio of 1.1 × 10¹¹ for back-gated CNTFETs at the 90 nm node. The design exhibits outstanding scalability: at the scaled 28 nm node with a supply voltage of 0.7 V, the PMOS device achieves 3 mA/µm on-current and 6 pA/µm off-current, maintaining an on–off ratio of 5 × 10⁸. This work establishes a scalable pathway toward femtoampere-level CNT CMOS, addressing the static power challenge in future nano-electronics. Full article

In Low Earth Orbit Beam-Hopping Satellite Systems (L-BHSS), co-channel interference among beams severely degrades communication quality. To address the inter-beam co-channel interference avoidance problem, this paper proposes a Parameter-Sharing Multi-Agent Deep Deterministic Policy Gradient-Based Beam Geometry Multi-Parameter Optimization Algorithm (PS-MADDPG-BGMPOA) for the joint optimization of satellite beam geometric parameters. The effects of free-space path loss, atmospheric impairments, and Rician fading are comprehensively considered, and a beam geometric multi-parameter optimization model is formulated with the objective of maximizing the long-term Signal-to-Interference-plus-Noise Ratio (SINR), incorporating beamwidth, beam center offset from the satellite nadir direction angle, inter-beam separation angle, and beam activation states. To tackle the resulting high-dimensional mixed action space, the proposed algorithm employs parameter sharing and grouped decision-making, which alleviates the dimensionality explosion problem and decouples the network scale from the number of beams, enabling efficient cooperative optimization with reduced training complexity. Simulation results show that, under various channel conditions and beam configurations, the proposed method effectively enhances communication quality and spectral efficiency while exhibiting superior real-time performance and stability. Full article

To address the urgent need for electronics operable in extremely high-temperature environments, this paper presents a novel three-terminal, dual-gate, lateral 4H-SiC n-channel depletion-mode junction field effect transistor (JFET) without a backside electrode. Featuring a fully planar electrode layout, the device eliminates the back-gate effect and significantly improves integration compatibility. Experimental results demonstrate stable DC operation up to 500 °C, with an intrinsic gain of 9.79 at room temperature and 6.01 at 500 °C. Comparison with TCAD simulations confirms excellent agreement in the key physical trends of threshold voltage drift and mobility degradation, though quantitative discrepancies are observed and attributed to process-induced parasitic effects such as non-ideal ohmic contacts and interface states. Analysis shows that the new structure broadens the channel depletion layer by optimizing the depletion profile, thereby suppressing channel-length modulation and improving both output resistance and gate control. This work not only provides an effective device platform for high-temperature 4H-SiC analog integrated circuits (ICs) but also deepens the understanding of process-performance correlations, offering clear guidance for process-oriented device optimization. The proposed structure serves as a foundation for developing fully planar, high-temperature 4H-SiC analog ICs with promising potential in aerospace, automotive, and energy exploration systems. Full article