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Electrocatalytic urea synthesis is an emerging alternative technology to the traditional energy-intensive industrial urea synthesis protocol. Novel strategies are urgently needed to promote the electrocatalytic C–N coupling process and inhibit the side reactions. Here, we report a CuWO 4 catalyst with native bimetallic sites that achieves a high urea production rate (98.5 ± 3.2 μg h −1  mg −1 cat ) for the co-reduction of CO 2 and NO 3 − with a high Faradaic efficiency (70.1 ± 2.4%) at −0.2 V versus the reversible hydrogen electrode. Mechanistic studies demonstrated that the combination of stable intermediates of *NO 2 and *CO increases the probability of C–N coupling and reduces the potential barrier, resulting in high Faradaic efficiency and low overpotential. This study provides a new perspective on achieving efficient urea electrosynthesis by stabilizing the key reaction intermediates, which may guide the design of other electrochemical systems for high-value C–N bond-containing chemicals. Electrocatalytic urea synthesis is an emerging alternative technology to the traditional urea synthesis protocol. Here, a CuWO 4 catalyst with native bimetallic sites achieves efficient co-reduction of carbon dioxide and nitrate to urea by stabilizing intermediates of *NO 2 and *CO for C–N coupling.

This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

Chinese crop diseases and pests named entity recognition (CCDP-NER) is a critical step in extracting domain-specific information in the field of crop diseases and pests, playing a significant role in promoting agricultural informatization. To address challenges such as noisy data, erroneous annotations, and ambiguous entity boundaries in the crop disease and pest domain, this study proposes a deep learning-based CCDP-NER model. The model employs a bidirectional gated recurrent Unit (BiGRU) to capture long-range semantic dependencies and integrates multi-level dilated convolutional neural networks (DCNNs) to extract local fine-grained features, thereby constructing a global-local collaborative representation. Innovatively, the variational information bottleneck (VIB) technique is introduced to filter noise by constraining mutual information, reducing the impact of input noise on feature extraction while simultaneously enhancing the correlation between extracted features and labels, thereby improving model robustness. Additionally, an entity boundary detection module is incorporated to identify the head and tail positions of entities, enhancing boundary recognition accuracy. Experiments conducted on a constructed crop diseases and pests dataset demonstrate that the proposed model effectively identifies crop disease and pest entities, achieving an F1-score of 90.64%. This research holds significant value for applications such as agricultural knowledge graph construction and agricultural question-answering systems.

Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution.

Radar echo extrapolation based on real-world data is a fundamental problem in meteorological forecasting. Existing extrapolation models typically assume complete radar echo sequences, but in practice, data loss frequently occurs due to equipment failures and communication disruptions. Although traditional solutions can handle missing values through a interpolation-then-prediction pipeline, they suffer from a major limitation: interpolating the missing data and then extrapolating will introduce a cumulative error. To address these issues, we propose MissPred, a radar echo extrapolation model specifically designed for missing data patterns. MissPred employs a dual encoder–decoder architecture. Specifically, the training process involves the sequential execution of interpolation and extrapolation as two distinct serial tasks. In order to circumvent the occurrence of cumulative errors, interpolation and extrapolation are required to share encoder parameters. Furthermore, a missing spatiotemporal feature fusion module (MSTF) that is absent has been designed for the purpose of extracting fine-grained complete spatiotemporal features. Finally, the incorporation of adversarial training is introduced to enhance the authenticity of the prediction results. In order to evaluate the proposed model, case studies are conducted on real radar datasets. Our dataset covers missing rates ranging from 10% to 50%. The experimental results show that the model outperforms the baseline model with the prior interpolation of missing data in the missing mode with stable robustness.

The rapid development of deepfake technologies has led to the widespread proliferation of facial image forgeries, raising significant concerns over identity theft and the spread of misinformation. Although recent dual-domain detection approaches that integrate spatial and frequency features have achieved noticeable progress, they still suffer from limited sensitivity to local forgery regions and inadequate interaction between spatial and frequency information in practical applications. To address these challenges, we propose a novel forgery-aware guided spatial–frequency feature fusion network. A lightweight U-Net is employed to generate pixel-level saliency maps by leveraging structural symmetry and semantic consistency, without relying on ground-truth masks. These maps dynamically guide the fusion of spatial features (from an improved Swin Transformer) and frequency features (via Haar wavelet transforms). Cross-domain attention, channel recalibration, and spatial gating are introduced to enhance feature complementarity and regional discrimination. Extensive experiments conducted on two benchmark face forgery datasets, FaceForensics++ and Celeb-DFv2, show that the proposed method consistently outperforms existing state-of-the-art techniques in terms of detection accuracy and generalization capability. The future work includes improving robustness under compression, incorporating temporal cues, extending to multimodal scenarios, and evaluating model efficiency for real-world deployment.

As Advanced Persistent Threats (APTs) continue to evolve, constructing a dynamic cybersecurity knowledge graph requires precise extraction of entity–relationship triples from unstructured threat intelligence. Existing approaches, however, face significant challenges in modeling low-frequency threat associations, extracting multi-relational entities, and resolving overlapping entity scenarios. To overcome these limitations, we propose the Symmetry-Aware Prototype Contrastive Learning (SAPCL) framework for joint entity and relation extraction. By explicitly modeling syntactic symmetry in attack-chain dependency structures and its interaction with asymmetric adversarial semantics, SAPCL integrates dependency relation types with contextual features using a type-enhanced Graph Attention Network. This symmetry–asymmetry fusion facilitates a more effective extraction of multi-relational triples. Furthermore, we introduce a triple prototype contrastive learning mechanism that enhances the robustness of low-frequency relations through hierarchical semantic alignment and adaptive prototype updates. A non-autoregressive decoding architecture is also employed to globally generate multi-relational triples while mitigating semantic ambiguities. SAPCL was evaluated on three publicly available CTI datasets: HACKER, ACTI, and LADDER. It achieved F1-scores of 56.63%, 60.21%, and 53.65%, respectively. Notably, SAPCL demonstrated a substantial improvement of 14.5 percentage points on the HACKER dataset, validating its effectiveness in real-world cyber threat extraction scenarios. By synergizing syntactic–semantic multi-feature fusion with symmetry-driven dynamic representation learning, SAPCL establishes a symmetry–asymmetry adaptive paradigm for cybersecurity knowledge graph construction, thus enhancing APT attack tracing, threat hunting, and proactive cyber defense.

In this study, we developed a novel wireless, passive pressure-sensing method functional at cryogenic temperatures (−196 °C). The currently used pressure sensors are inconvenient and complicated in cryogenic environments for their weak low-temperature tolerances and long wires for power supply and data transmission. We propose a novel pressure-sensing method for cryogenic applications by only using low-temperature-tolerant passive devices. By innovatively integrating a magnetoresistor (MR) on a backscattering antenna, the pressure inside a cryogenic environment is transferred to a wirelessly obtainable return loss. Wireless passive measurement is thus achieved using a backscattering method. In the measurement, the pressure causes a relative displacement between the MR and a magnet. The MR’s resistance changes with the varied magnetic field, thus modulating the antenna’s return loss. The experimental results indicate that our fabricated sensor successfully identified different pressures, with high sensitivities of 4.3 dB/MPa at room temperature (24 °C) and 1.3 dB/MPa at cryogenic temperature (−196 °C). Additionally, our method allows for simultaneous wireless readings of multi sensors via a single reading device by separating the frequency band of each sensor. Our method performs low-cost, simple, robust, passive, and wireless pressure measurement at −196 °C; thus, it is desirable for cryogenic applications.

Yaks are typical plateau-adapted animals, however the microvascular changes and characteristics in their lungs after birth are still unclear. Pulmonary microvasculature characteristics and changes across age groups were analysed using morphological observation and molecular biology detection in yaks aged 1, 30 and 180 days old in addition to adults. Results: Our experiments demonstrated that yaks have fully developed pulmonary alveolar at birth but that interalveolar thickness increased with age. Immunofluorescence observations showed that microvessel density within the interalveolar septum in the yak gradually increased with age. In addition, transmission electron microscopy (TEM) results showed that the blood–air barrier of 1-day old and 30-days old yaks was significantly thicker than that observed at 180-days old and in adults ( P  < 0.05), which was caused by the thinning of the membrane of alveolar epithelial cells. Furthermore, Vegfa and Epas1 expression levels in 30-day old yaks were the highest in comparison to the other age groups ( P  < 0.05), whilst levels in adult yaks were the lowest ( P  < 0.05). The gradual increase in lung microvessel density can effectively satisfy the oxygen requirements of ageing yaks. In addition, these results suggest that the key period of yak lung development is from 30 to 180 days.

The effect of an external magnetic field on the evolution of the self-generated electromagnetic field during laser ablation is investigated by using the Vlasov–Fokker–Planck simulations. It is found that the self-generated field is rotated and distorted under an external magnetic field, and for highly magnetized plasma, the rotation of the electric field becomes stable after the laser ablation. The theoretical analysis indicates that the rotation and tortuosity are primarily attributed to the advection of the Nernst effect and the Righi–Leduc (RL) flux. The curl of the self-generated field increases with the Hall parameter χ e and reaches a peak at χ e = 0.075 , then it decreases with the χ e continuous increase. As the Hall parameter increases, the RL flux contributes more than 60% to the rotation of the electric field. Furthermore, the distortion of the electric field continues to rotate after the laser ablation due to the cross-gradient Nernst transport. These findings provide theoretical references for the evolution of the self-generated electromagnetic field in laser-driven magnetized plasmas.