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Highlights Simultaneous stealth across multiple infrared bands (short-wave infrared (SWIR), mid-wave infrared (MWIR), long-wave infrared (LWIR)) and microwaves at high temperatures (700 °C) is achieved. At 700 °C, the device features low emissivity of 0.38/0.44/0.60 in MWIR/LWIR/SWIR bands, reflection loss below − 3 dB in the X-band (9.6–12 GHz), and high emissivity of 0.82 in the 5–8 μm range. Under an input power equivalent to Mach 2.2 aerodynamic heating, effective thermal management achieves a 72.4 °C temperature reduction compared to conventional low-emissivity molybdenum. High-temperature stealth is vital for enhancing the concealment, survivability, and longevity of critical assets. However, achieving stealth across multiple infrared bands—particularly in the short-wave infrared (SWIR) band—along with microwave stealth and efficient thermal management at high temperatures, remains a significant challenge. Here, we propose a strategy that integrates an IR-selective emitter (Mo/Si multilayer films) and a microwave metasurface (TiB 2 –Al 2 O 3 –TiB 2 ) to enable multi-infrared band stealth, encompassing mid-wave infrared (MWIR), long-wave infrared (LWIR), and SWIR bands, and microwave (X-band) stealth at 700 °C, with simultaneous radiative cooling in non-atmospheric window (5–8 μm). At 700 °C, the device exhibits low emissivity of 0.38/0.44/0.60 in the MWIR/LWIR/SWIR bands, reflection loss below − 3 dB in the X-band (9.6–12 GHz), and high emissivity of 0.82 in 5–8 μm range—corresponding to a cooling power of 9.57 kW m −2 . Moreover, under an input power of 17.3 kW m −2 —equivalent to the aerodynamic heating at Mach 2.2—the device demonstrates a temperature reduction of 72.4 °C compared to a conventional low-emissivity molybdenum surface at high temperatures. This work provides comprehensive guidance on high-temperature stealth design, with far-reaching implications for multispectral information processing and thermal management in extreme high-temperature environments.

It has been well confirmed ox-LDL plays key roles in the development of atherosclerosis via binding to LOX-1 and inducing apoptosis in vascular endothelial cells. Recent studies have shown ox-LDL can suppress microRNA has-let-7g, which in turn inhibits the ox-LDL induced apoptosis. However, details need to be uncovered. To determine the anti-atherosclerosis effect of microRNA has-let-7g, and to evaluate the possibility of CASP3 as an anti-atherosclerotic drug target by has-let-7g, the present study determined the role of hsa-let-7g miRNA in ox-LDL induced apoptosis in the vascular endothelial cells. We found that miRNA has-let-7g was suppressed during the ox-LDL-induced apoptosis in EAhy926 endothelial cells. In addition, overexpression of has-let-7g negatively regulated apoptosis in the endothelial cells by targeting caspase-3 expression. Therefore, miRNA let-7g may play important role in endothelial apoptosis and atherosclerosis.

The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these challenges, addressing both data volume and privacy issues effectively. Nonetheless, the deployment of FL faces its own set of obstacles, notably the excessive delay and energy consumption caused by the vast number of devices and fluctuating channel conditions. In this paper, by considering the heterogeneity of devices and the instability of the network state, the delay and energy consumption models of each round of federated training are established. Subsequently, we introduce a strategic node selection approach aimed at minimizing training costs. Building upon this, we propose an innovative, empirically driven Double Deep Q Network (DDQN)-based algorithm called low-cost node selection in federated learning (LCNSFL). The LCNSFL algorithm can assist edge servers in selecting the optimal set of devices to participate in federated training before the start of each round, based on the collected system state information. This paper culminates with a simulation-based comparison, showcasing the superior performance of LCNSFL against existing algorithms, thus underscoring its efficacy in practical applications.

ABSTRACT Spring vegetation phenology reflects the dynamics of ecosystems and the status of vegetation growth. Earlier spring phenology can promote vegetation growth by extending the length of the vegetation growing season, thus improving the productivity and carbon sink function of terrestrial ecosystems. However, the stage‐specific effects of spring phenology and climate change on vegetation growth are yet to be effectively explained. Taking the Cross‐border Region of China, Democratic People's Republic of Korea, and Russia (CRCDR) as an example, we utilized the Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation growth and extracted the start date of the growing season (SOS) from NDVI to characterize spring phenology and explored the relative importance of SOS and climate factors on vegetation growth. Results indicate that from 2001 to 2020, the SOS in the CRCDR region advanced at a rate of 0.22 days per year, and vegetation growth increased significantly at a rate of 2.3 × 10−3 per year. However, the drivers of vegetation growth varied across different stages of the growing season. In the early growing season, an advanced SOS significantly promoted vegetation growth in forest and grassland, but this facilitative effect gradually diminished and turned inhibitory during the peak and late stages, during which warming became the primary driver of vegetation growth. Notably, the positive influence of SOS persisted until the fifth month of the growing season in forest but only until the fourth month in grassland. The results of this study supplement those of studies on vegetation growth in the CRCDR, elucidate the effects of the SOS on the dynamic process of vegetation growth, and offer insights into vegetation ecosystems. During the early growing season, the advanced SOS significantly promoted vegetation growth, but this promoting effect weakened and gradually shifted to a suppressive effect during the peak and late growing seasons. At the peak and late of the growing season, warming becomes the primary driver of increased vegetation growth.

With the advent of smart cities, the significance of the Internet of Things (IoT) is gaining greater prominence. At the same time, the safety early warning system in the IoT has a significant impact on real-time monitoring and the response to potential risks. Despite the advancements made in edge-assisted IoT deployments, several challenges and constraints persist. Given the potential threat to life posed by safety-related messages, ensuring the authenticity of messages in the edge-assisted IoT safety warning system is crucial. However, considering the identity privacy of devices participating in the edge-assisted Internet of Things system, directly verifying the identity of the sending device is undesirable. To address this issue, in this work, we design a linkable group signature scheme that allows devices to anonymously send safety-related messages to edge nodes, defending against Sybil attacks while ensuring the traceability of malicious device identities. Then, we present a high-efficiency conditional privacy-preserving authentication (CPPA) scheme based on the designed group signatures for the safety warning system in edge-assisted IoT. This scheme effectively protects device identity privacy while providing a reliable authentication mechanism to ensure the credibility and traceability of alert messages. The proposed scheme contributes to the field of safety warning systems in the context of edge-assisted IoT, providing a robust solution for privacy preservation and authentication.

Hydrogen production via water electrolysis offers a sustainable pathway to decarbonize energy systems, yet the development of cost‐effective, efficient bifunctional electrocatalysts for overall water splitting (OWS) still remains a critical challenge. Current catalysts often rely on complex multiphase heterostructures to optimize oxygen and hydrogen evolution reactions (OER/HER), but their intricate designs increase costs and hinder scalability. Here, we present a single‐phase bifunctional electrocatalyst, CaCu3Co2Ru2O12 (CCCRO), which exhibited exceptional performance for OWS in alkaline conditions, specifically, 1.536 V at 10 mA cm−2 and 1.629 V at 100 mA cm−2, along with 500 h of operational stability at a current density of 100 mA cm−2. In situ x‐ray absorption spectroscopy (XAS) revealed the valence‐state transition from Cu2+/Co3+/Ru5+ to Cu2+/Co3.5+/Ru5.5+ during OER, but both valence state reduction and structural reconstruction into a CuCoRu nanoalloy occurred under HER conditions. Density functional theory (DFT) calculations indicated that synergistic effects among Cu, Co, and Ru ions enhance catalytic activities for both OER and HER. This work demonstrates that structurally simple yet compositionally tuned oxides can surpass complex catalysts in both the efficiency and durability of OWS, offering a scalable design paradigm for advancing green hydrogen technologies. Y. Fan and his colleagues present a single‐phase bifunctional perovskite catalyst that exhibits superior overall water splitting performance. In situ x‐ray absorption spectroscopy reveals the structural changes during the HER, while only changes in valence state are observed during the OER. This single‐phase bifunctional catalyst achieves a current density of 10 mA cm−2 for overall water splitting at an applied voltage of only 1.536 V, demonstrating promising potential for industrial applications.

The presence of surface trap states (STSs) is one of the key factors to affect the electronic and optical properties of quantum dots (QDs), however, the exact mechanism of how STSs influence QDs remains unclear. Herein, we demonstrated the impact of STSs on electron transfer in CdSe QDs and triplet-triplet energy transfer (TTET) from CdSe to surface acceptor using femtosecond transient absorption spectroscopy. Three types of colloidal CdSe QDs, each containing various degrees of STSs as evidenced by photoluminescence and X-ray photoelectron spectroscopy, were employed. Time-resolved emission and transient absorption spectra revealed that STSs can suppress band-edge emission effectively, resulting in a remarkable decrease in the lifetime of photoelectrons in QDs from 17.1 ns to 4.9 ns. Moreover, the investigation of TTET process revealed that STSs can suppress the generation of triplet exciton and effectively inhibit band-edge emission, leading to a significant decrease in TTET from CdSe QDs to the surface acceptor. This work presented evidence for STSs influence in shaping the optoelectronic properties of QDs, making it a valuable point of reference for understanding and manipulating STSs in diverse QDs-based optoelectronic applications involving electron and energy transfer.

The surface modification of amorphous carbon nanospheres (ACNs) through templates has attracted great attention due to its great success in improving the electrochemical properties of lithium storage materials. Herein, a safe methodology with toluene as a soft template is employed to tailor the nanostructure, resulting in ACNs with tunable surface pores. Extensive characterizations through transmission electron microscopy (TEM), scanning electron microscopy (SEM), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption/desorption isotherms elucidate the impact of surface pore modifications on the external structure, morphology, and surface area. Electrochemical assessments reveal the enhanced performance of the surface-pore-modified carbon nanospheres, particularly ACNs-100 synthesized with the addition of 100 μL toluene, in terms of the initial discharge capacity, rate performance, and cycling stability. The interesting phenomenon of persistent capacity increase is ascribed to lithium ion movement within the graphite-like interlayer, resulting in ACNs-100 experiencing a capacity upswing from an initial 320 mAh g−1 to a zenith of 655 mAh g−1 over a thousand cycles at a rate of 2 C. The findings in this study highlight the pivotal role of tailored nanostructure engineering in optimizing energy storage materials.

The TDICCD aerial camera was developed to study the relationship between the structure and optical system. Based on the camera outputs, integrated analysis and experimental methods were proposed. The proposed method was then used to both study and verify the influence of thermal disturbance on the optical performance and optimal aerial camera design. The nodal displacement of the optical surface under thermal disturbance was calculated via the finite element method. The resulting data were fitted to Zernike polynomial coefficients using the Zernike polynomial. Additionally, a method of calculating rigid body displacement was also proposed to determine the effects of rigid optical system displacement. The method calculates the RMS and PV parameters by fitting the surface distortion data. The fitted Zernike polynomial coefficients were input to ZEMAX software to obtain the optical system response. The influence of thermal disturbance on the optical performance of the aerial camera was analyzed. The analysis results have shown that the low-temperature conditions have a more prominent impact on the optical performance of aerial cameras. The radial and axial lens steady-state temperature range was 2.06 °C in conduction temperature of − 40 °C. At the same time, the aerial camera surface was frosted at − 40 °C to carry out the low-temperature experiment, which verified the results obtained for a large temperature difference environment. Finally, results were verified experimentally.

This article introduces and validates a stealth metasurface that is compatible with visible, infrared, and microwave frequencies, designed to combine structural color for visible camouflage, low infrared emission, and extensive microwave absorption. The complete structure is constructed using two distinct layers: a visible-infrared layer (VIL) for coloration and infrared management, and a microwave absorption layer (MAL) for microwave absorption.. In the design of VIL, frequency selective surfaces (FSS) with SiO 2 /Ag/ZnO/Ag multilayer film structures were designed to achieve selective reflection of visible light for multiple structural colors, low infrared radiation for infrared stealth, and high microwave transmittance simultaneously. For MAL, a synergistic approach of equivalent circuit method (ECM) and particle swarm optimization algorithm (PSO) is adopted to design and optimize square metasurfaces for 8-18GHz microwave absorption. Then the visible-infrared-microwave compatible stealth metasurface can be made by combining VIL and MAL. In conclusion, the metasurface structure presented in this article fulfills the requirements for multiband stealth in contemporary warfare and holds significant potential for widespread application.