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With the popularity of edge computation, object detection applications face challenges of limited data volume and data privacy. To address these, we propose a federated few-shot object detection framework, F[sup.2]SOD. It involves three steps: collaborative base model training with base class data, novel data augmentation via an improved diffusion model, and collaborative base model fine-tuning for novel model using augmented data. Specifically, we present a data augmentation method based on diffusion models with a twofold-tag prompt construction and object location embedding. In addition, we present distributed framework for training base and novel models, where the base model integrates the Squeeze-and-Excitation attention mechanism into the feature re-weighting module. Experiments on public datasets demonstrate that F[sup.2]SOD achieves efficient few-shot object detection, outperforming State-of-the-Art methods in both accuracy and efficiency.

The p-electron-dominated main-group metals (Sb, Se, In, etc.) have recently been reported to possess excellent oxygen reduction reaction (ORR) activity by means of heteroatom doping into graphene. However, on these main group metal surfaces, other approaches especially the distance effect to modulate catalytic activity are rarely involved. In this work, the origin of excellent 2e[sup.−] ORR catalytic activity of graphene-supported InN[sub.4] moiety by tuning the distance between metallic In atoms is thoroughly investigated by employing the first-principles calculations. Our DFT calculations show that the 2e[sup.−] ORR catalytic activity strongly depends on the crystal orbital Hamilton population (COHP) between In and O atoms. This work is useful for the rational design of main group metal single atom electrocatalysts.

Herein, a WO[sub.3]@TCN photocatalyst was successfully synthesized using a self-assembly method, which demonstrated effectiveness in degrading organic dyestuffs and photocatalytic evolution of H[sub.2]. The synergistic effect between WO[sub.3] and TCN, along with the porous structure of TCN, facilitated the formation of a heterojunction that promoted the absorption of visible light, accelerated the interfacial charge transfer, and inhibited the recombination of photogenerated electron–hole pairs. This led to excellent photocatalytic performance of 3%WO[sub.3]@TCN in degrading TC and catalyzing H[sub.2] evolution from water splitting under visible-light irradiation. After modulation, the optimal 3%WO[sub.3]@TCN exhibited a maximal degradation rate constant that was twofold higher than that of TCN alone and showed continuous H[sub.2] generation in the photocatalytic hydrogen evolution. Mechanistic studies revealed that •O[sub.2] [sup.−] constituted the major active species for the photocatalytic degradation of tetracycline. Experimental and DFT results verified the electronic transmission direction of WO[sub.3]@TCN heterojunction. Overall, this study facilitates the structural design of green TCN-based heterojunction photocatalysts and expands the application of TCN in the diverse photocatalytic processes. Additionally, this study offers valuable insights into strategically employing acid regulation modulation to enhance the performance of carbon nitride-based photocatalysts by altering the topography of WO[sub.3]@TCN composite material dramatically.

Closed-loop geothermal systems (CLGSs) avoid groundwater production and offer stable deep heat supply, but their long-term performance hinges on reliable coupling between the wellbore, the near-well interface and the surrounding formation. Using the D22 well in the Xiongan New Area (deep carbonate reservoir), we built a three-domain thermo-hydraulic framework that updates CO[sub.2] properties with temperature and pressure and explicitly accounts for wellbore-formation thermal resistance. Two geometries (U-tube and single-well coaxial) and two working fluids (CO[sub.2] and water) were compared and optimized under field constraints. With the coaxial configuration, CO[sub.2] delivers an average thermal power of 186.3 kW, exceeding that of water by 44.9%, while the fraction of wellbore heat loss drops by 3–5%. Under field-matched conditions, the predicted outlet temperature (76.8 °C) agrees with the measured value (77.2 °C) within 0.52%, confirming the value of field calibration for parameter transferability. Long-term simulations indicate that after 30 years of continuous operation the outlet temperature decline remains <8 °C for CO[sub.2], outperforming water and implying better reservoir utilization and supply stability. Sensitivity and Pareto analyses identify a practical operating window, i.e., flow velocity of 0.9–1.1 m s[sup.−1] and depth of 3000–3500 m, favoring the single-well coaxial + CO[sub.2] scheme. These results show how field-calibrated modeling narrows uncertainty and yields implementable guidance on geometry, operating conditions, and wellbore insulation strategy. This study provides quantitative evidence that CO[sub.2]-CLGSs in deep carbonate formations can simultaneously increase thermal output and limit long-term decline, supporting near-term engineering deployment.

The incorporation of MAX phase interface layers into silicon carbide (SiC) composites has been shown to significantly enhance mechanical properties, particularly under irradiation conditions. However, conventional Ti-based MAX phases suffer from thermal instability and tend to decompose at high temperatures. In this work, an Sc[sub.2]SnC coating was successfully synthesized onto the surface of SiC fibers (SiC[sub.f]) via an in situ reaction between metals and pyrolytic carbon (PyC) in a molten salt environment. The PyC layer, pre-deposited by chemical vapor deposition (CVD), served as both a carbon source and a structural template. Characterization by SEM, XRD, and Raman spectroscopy confirmed the formation of Sc[sub.2]SnC coatings with a distinctive hexagonal flake-like morphology, accompanied by an internal ScC[sub.x] intermediate layer. By turning the Sc-to-Sn ratio in the molten salt, coatings with varied morphologies were achieved. ScC[sub.x] was identified as a critical intermediate phase in the synthesis process. The formation of numerous defects during the reaction enhanced element diffusion, resulting in preferential growth orientations and diverse grain structures in the Sc[sub.2]SnC coating.

The variation of the in situ stress state is closely related to various factors. In situ stress state is also an important indicator to guide mining production. The study of in situ stress measurement and its distribution characteristics has always been a basic and very important work in mine production. In this study, the deep mines of Linyi Mining Area were considered as the research object. In this regard, the stress distribution law of each mine was studied. We found that the relationship between principal stresses was σH > σv > σh, which belongs to the strike-slip stress regime. In this stress regime, the lateral Earth pressure coefficient was greater than one, and the magnitude of the three principal stresses all showed an increasing trend with the increase of depth. The maximum horizontal stress direction of the Gucheng Coal Mine, Guotun Coal Mine, and Pengzhuang Coal Mine was NW-SE under the influence of regional geological structure, while the maximum horizontal stress direction of Wanglou Coal Mine was NE-SW under the influence of local geological structure. Besides, the relationship between mine in situ stress and mine geological structure, the impact of original rock stress on stope stability, and the effect of original rock stress on floor water inrushing were also investigated. We believe that the research results are beneficial to mine disaster prevention and safety production.

Revealing the charge transfer path is very important for studying the photocatalytic mechanism and improving photocatalytic performance. In this work, the charge transfer path turned by the piezoelectricity in Ag-BaTiO[sub.3] nanofibers is discussed through degrading methyl orange. The piezo-photocatalytic degradation rate of Ag-BaTiO[sub.3] is much higher than the photocatalysis of Ag-BaTiO[sub.3] and piezo-photocatalysis of BaTiO[sub.3], implying the coupling effect between Ag nanoparticle-induced localized surface plasmon resonance (LSPR), photoexcited electron-hole pairs, and deformation-induced piezoelectric field. With the distribution density of Ag nanoparticles doubling, the LSPR field increases by one order of magnitude. Combined with charge separation driven by the piezoelectric field, more electrons in BaTiO[sub.3] nanofibers are excited by plasmon-induced resonance energy transfer to improve the photocatalytic property.

In this study, a dual-band dual-beam shared-aperture reflector antenna based on a Cassegrain configuration is designed using a frequency-selective surface (FSS) subreflector. The antenna generates two shaped beams that operate at different frequencies and can spatially overlap. One beam contour can be independently optimized by properly designing the shape of the main reflector. The contour of the second beam is defined by optimizing the unit cell and geometry of the FSS-based subreflector once the shape of the main reflector is set. The reflector antenna design is cast as the optimization of a suitably defined cost function aimed at yielding the desired directivity performance in the regions of coverage. In order to validate the proposed solution, a set of numerical experiments was conducted using most of China and Shaanxi province as benchmark examples.

Ship image classification identifies the type of ships in an input image, which plays a significant role in the marine field. To enhance the ship classification performance, various research focuses on studying multi-modal ship classification, which aims at combining the advantages of visible images and infrared images to capture complementary information. However, the current methods simply concatenate features of different modalities to learn complementary information, which neglects the multi-level correlation between different modalities. Moreover, the existing methods require a large amount of labeled ship images to train the model. How to capture the multi-level cross-modal correlation between unlabeled and labeled data is still a challenge. In this paper, a novel semi-supervised multi-modal ship classification approach is proposed to solve these issues, which consists of two components, i.e., multi-level cross-modal interactive network and semi-supervised contrastive learning strategy. To learn comprehensive complementary information for classification, the multi-level cross-modal interactive network is designed to build local-level and global-level cross-modal feature correlation. Then, the semi-supervised contrastive learning strategy is employed to drive the optimization of the network with the intra-class consistency constraint based on supervision signals of unlabeled samples and prior label information. Extensive experiments on the public datasets demonstrate that our approach achieves state-of-the-art semi-supervised classification effectiveness.

The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time.