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(1) Background: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. Vast resources have been allocated to control the pandemic and treat patients. However, little attention has been paid to the adverse impact on mental health or effective mitigation strategies to improve mental health. (2) Purpose: The aim of this study was to assess the adverse impact of the COVID-19 outbreak on Chinese college students’ mental health, understand the underlying mechanisms, and explore feasible mitigation strategies. (3) Methods: During the peak time of the COVID-19 outbreak in China, we conducted longitudinal surveys of sixty-six college students. Structured questionnaires collected information on demographics, physical activity, negative emotions, sleep quality, and aggressiveness level. A mixed-effect model was used to evaluate associations between variables, and the mediating effect of sleep quality was further explored. A generalized additive model was used to determine the dose-response relationships between the COVID-19 death count, physical activity, and negative emotions. (4) Results: The COVID-19 death count showed a direct negative impact on general sleep quality (β = 1.37, 95% confidence interval [95% CI]: 0.55, 2.19) and reduced aggressiveness (β = −6.57, 95% CI: −12.78, −0.36). In contrast, the COVID-19 death count imposed not a direct but an indirect impact on general negative emotions (indirect effect (IE) = 0.81, p = 0.012), stress (IE = 0.40, p < 0.001), and anxiety (IE = 0.27, p = 0.004) with sleep quality as a mediator. Moreover, physical activity directly alleviated general negative emotions (β = −0.12, 95% CI: −0.22, −0.01), and the maximal mitigation effect occurred when weekly physical activity was about 2500 METs. (5) Conclusions: (a) The severity of the COVID-19 outbreak has an indirect effect on negative emotions by affecting sleep quality. (b) A possible mitigation strategy for improving mental health includes taking suitable amounts of daily physical activity and sleeping well. (c) The COVID-19 outbreak has reduced people’s aggressiveness, probably by making people realize the fragility and preciousness of life.

Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture search are faced with many open challenges, searching for optimized neural architectures in the federated learning framework is particularly demanding. This survey paper starts with a brief introduction to federated learning, including both horizontal, vertical, and hybrid federated learning. Then neural architecture search approaches based on reinforcement learning, evolutionary algorithms and gradient-based are presented. This is followed by a description of federated neural architecture search that has recently been proposed, which is categorized into online and offline implementations, and single- and multi-objective search approaches. Finally, remaining open research questions are outlined and promising research topics are suggested.

Electrochemical uranium extraction from nuclear wastewater represents an emerging strategy for recycling uranium resources. However, in nuclear fuel production which generates the majority of uranium-containing nuclear wastewater, fluoride ion (F − ) co-exists with uranyl (UO 2 2+ ), resulting in the complex species of UO 2 F x and thus decreasing extraction efficiency. Herein, we construct Ti δ+ -PO 4 3− ion pair extraction sites in Ti(OH)PO 4 for efficient electrochemical uranium extraction in wastewater from nuclear fuel production. These sites selectively bind with UO 2 F x through the combined Ti-F and multiple O-U-O bonds. In the uranium extraction, the uranium species undergo a crystalline transition from U 3 O 7 to K 3 UO 2 F 5 . In real nuclear wastewater, the uranium is electrochemically extracted with a high efficiency of 99.6% and finally purified as uranium oxide powder, corresponding to an extraction capacity of 6829 mg g −1 without saturation. This work paves an efficient way for electrochemical uranium recycling in real wastewater of nuclear production. Electrochemical uranium extraction from real nuclear wastewater is appealing but challenging. Herein, Lin et al develop a strategy of ion pair site for enhanced binding of dominant uranium fluoride species in real wastewater, achieving efficient recycling of uranium as powder product.

Porosity is a key parameter for evaluating reservoir performance, but high-precision prediction is highly challenging in complex shale reservoirs due to the strong heterogeneity of the formation and the highly nonlinear relationship between logging parameters and porosity. Traditional prediction methods based on experience or physical models often have low generalization ability and accuracy. This study proposes a hybrid model (MABC-LSSVM) that combines a modified artificial bee colony (MABC) optimization algorithm with a least squares support vector machine (LSSVM) model. Inertia weights and acceleration coefficients are utilized to change the hyperparameters of the optimization model to achieve high-precision prediction of shale reservoir porosity using data-driven methods. The model inputs include compensating neutron log (CNL), density log (DEN), photoelectric absorption cross-section index (PE), and gamma ray log (GR) parameters. The proposed model is compared with the LSSVM, gradient boosting decision tree (GBDT), and ABC-LSSVM. The results show that the MABC-LSSVM model exhibits the best predictive performance. Its prediction results are highly consistent with the true porosity curve. The coefficient of determination ( R 2 ) is 0.93, significantly higher than for all comparison models. The findings demonstrate the effectiveness of combining an intelligent optimization algorithm with the LSSVM model. This approach is reliable for predicting the porosity in complex formations and performing reservoir evaluations in oil and gas exploration and development.

Plant growth behavior is a function of genetic network architecture. The importance of root microbiome variation driving plant functional traits is increasingly recognized, but the genetic mechanisms governing this variation are less studied. Here, we collect roots and rhizosphere soils from nine Populus species belonging to four sections ( Leuce , Aigeiros , Tacamahaca , and Turanga ), generate metabolite and transcription data for roots and microbiota data for rhizospheres, and conduct comprehensive multi-omics analyses. We demonstrate that the roots of vigorous Leuce poplar enrich more Pseudomonas , compared with the poorly performing poplar. Moreover, we confirm that Pseudomonas is strongly associated with tricin and apigenin biosynthesis and identify that gene GLABRA3 ( GL3 ) is critical for tricin secretion. The elevated tricin secretion via constitutive transcription of PopGL3 and Chalcone synthase ( PopCHS4 ) can drive Pseudomonas colonization in the rhizosphere and further enhance poplar growth, nitrogen acquisition, and secondary root development in nitrogen-poor soil. This study reveals that plant-metabolite-microbe regulation patterns contribute to the poplar fitness and thoroughly decodes the key regulatory mechanisms of tricin, and provides insights into the interactions of the plant’s key metabolites with its transcriptome and rhizosphere microbes. Multi-omics analysis reveals that differential plant performance among Populus species is associated with Pseudomonas in the rhizosphere. Further mechanistic investigation identifies GL3 as a regulator of flavone biosynthesis contributing to Pseudomonas recruitment.

Jamming is a big threat to the survival of a radar system. Therefore, the recognition of radar jamming signal type is a part of radar countermeasure. Recently, convolutional neural networks (CNNs) have shown their effectiveness in radar signal processing, including jamming signal recognition. However, most of existing CNN methods do not regard radar jamming as a complex value signal. In this study, a complex-valued CNN (CV-CNN) is investigated to fully explore the inherent characteristics of a radar jamming signal, and we find that we can obtain better recognition accuracy using this method compared with a real-valued CNN (RV-CNN). CV-CNNs contain more parameters, which need more inference time. To reduce the parameter redundancy and speed up the recognition time, a fast CV-CNN (F-CV-CNN), which is based on pruning, is proposed for radar jamming signal fast recognition. The experimental results show that the CV-CNN and F-CV-CNN methods obtain good recognition performance in terms of accuracy and speed. The proposed methods open a new window for future research, which shows a huge potential of CV-CNN-based methods for radar signal processing.

Tropical cyclone (TC)‐induced sea surface temperature cooling (TCC) has both local and basin‐scale effects, but the roles of upper ocean conditions and TC intensity in its long‐term variability remain unclear in the Northwest Pacific (NWP). This study finds that annual cumulative basin‐scale TCC has increased (21.51 ± 6.41°C/yr), driven primarily by enhanced TCC in the western coastal and mid‐latitude regions north of 20°N (39.26 ± 6.67°C/yr), while a decreasing trend is observed south of 20°N (−17.75 ± 4.42°C/yr). At the local scale, TCC has intensified (0.08 ± 0.01°C/decade), with extreme events (top 95%) increasing even more (0.09 ± 0.01°C/decade). The probability of typical TCC (>1°C) has almost doubled over the last four decades, and the TCC distribution has shifted rightward, reflecting stronger TCC. These trends are primarily driven by changes in ocean stratification, with variations in TC intensity playing a secondary role. Plain Language Summary Tropical cyclones (TCs) often induce sea surface temperature (SST) cooling through intense mixing processes. However, the long‐term changes in extreme TC‐induced cooling (TCC) over the past four decades in the Northwest Pacific (NWP) remain relatively understudied. Utilizing multi‐source data sets and robust algorithms, we find that extreme TCC has significantly intensified over the past 40 years. In the last two decades, the TCC distribution has shifted more toward the extreme distribution observed in the previous 20 years. Moreover, basin‐scale trends vary considerably across different latitudinal regions, with a stronger trend observed in the subtropics. By applying the Cooling Inhibition Index (CI) and an improved Potential Intensity (PI) framework, we demonstrate that the CI effectively explains variations in extreme TCC. Furthermore, compared to PI calculations based on SST alone, incorporating subsurface ocean temperatures into PI provides a more accurate representation of the long‐term changes in extreme TCC. Key Points Basin‐scale tropical cyclone‐induced cooling (TCC) exhibits opposite trends across different latitudinal basins Extreme TCC has been intensifying, particularly over the last two decades Long‐term variations in extreme TCC can be well explained by using the Cooling Inhibition index (CI)

This study proposes and experimentally validates a multifunctional, ultra-wideband polarization conversion metasurface. The design integrates polarization conversion and electromagnetic scattering functions into a single structure, enabling applications in polarization conversion, beam control, and effective reduction of the radar cross-section (RCS). The metasurface achieves linear-to-circular polarization conversion with an axial ratio (AR) of less than 3 dB across dual-band ranges of 14.6–26.8 GHz and 31–33.5 GHz. Additionally, by adjusting metallic resonant rings within the unit structure, cross-polarization conversion with a polarization conversion ratio (PCR) greater than 0.9 is realized in the 13.6–29.8 GHz frequency range, maintaining excellent stability even at oblique incidence angles up to 50°. Leveraging the phase cancellation principle, various coding arrays are designed to precisely control the scattered beams, reducing the RCS by more than 10 dB. The comparison of simulation and experimental results further validates the wide application potential of this polarization converter in fields such as wireless communication, antenna engineering, and radar stealth.

The catalytic effect of FeCoNiCrMo high entropy alloy nanosheets on the hydrogen storage performance of magnesium hydride (MgH 2 ) was investigated for the first time in this paper. Experimental results demonstrated that 9wt% FeCoNiCrMo doped MgH 2 started to de-hydrogenate at 200°C and discharged up to 5.89wt% hydrogen within 60 min at 325°C. The fully dehydrogenated composite could absorb 3.23wt% hydrogen in 50 min at a temperature as low as 100°C. The calculated de/hydrogenation activation energy values decreased by 44.21%/55.22% compared with MgH 2 , respectively. Moreover, the composite’s hydrogen capacity dropped only 0.28wt% after 20 cycles, demonstrating remarkable cycling stability. The microstructure analysis verified that the five elements, Fe, Co, Ni, Cr, and Mo, remained stable in the form of high entropy alloy during the cycling process, and synergistically serving as a catalytic union to boost the de/hydrogenation reactions of MgH 2 . Besides, the FeCoNiCrMo nanosheets had close contact with MgH 2 , providing numerous non-homogeneous activation sites and diffusion channels for the rapid transfer of hydrogen, thus obtaining a superior catalytic effect.

Hydropower, an important renewable energy source worldwide, is threatened by reservoir sedimentation. Ecological restoration (ER) can mitigate this by reducing upstream sediment, thereby extending hydropower facilities’ lifespan. However, ER may also reduce runoff, potentially diminishing energy generation and complicating its overall impact on hydropower potential. Here, we examine China’s Yellow River, once the world’s most sediment-laden river, using eco-hydrological and reservoir regulation models to assess how large-scale ER influences the hydropower potential of the Xiaolangdi Reservoir, which controls 92.3% of the basin area. Our results indicate that, excluding upstream reservoirs’ operations and socioeconomic water use, Xiaolangdi could generate a total of ~2.7×10 11 kWh of energy before facing diminished flexibility and efficiency caused by the exhaustion of sediment storage—57.3% more than without ER—equating to an additional ~100 billion kWh. This enhancement in hydropower potential primarily arises from the extended lifespan, despite a 6.9% reduction in average annual energy generation. These findings advance our understanding of the ecosystem-water-sediment-energy nexus, offering valuable insights for integrated watershed management globally. By integrating the opposing effects through sediment and runoff changes, this study reveals that ecological restoration in the Yellow River Basin enhances hydropower potential, advancing our understanding of the ecosystem-water-sediment-energy nexus.