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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.

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.

Shale content ( V sh ) is related to reservoir lithology and physical properties, and accurate shale content prediction models can improve lithology identification efficiency and reduce cost. However, there has been little work on intelligent prediction of shale content. This study focused on mudstone from the geothermal study area of western Gansu Province. CNN-BiGRU-VAE models were selected to predict the shale content using gamma-ray (GR), spontaneous potential (SP), compensated neutron (CNL), resistivity (RESIS), and acoustic time difference (DT) logs. The predicted shale content of the CNN-BiGRU-VAE model was compared with those of traditional Larionov, Clavier and Stieber models. To fairly evaluate these models, the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were introduced. The results show that Larionov, Clavier and Stieber methods have high RMSE, MAE and MAPE values, indicating these methods poorly predicted shale content. These models significantly underestimate the shale content, resulting in evident deviations between the predicted shale content and their real value. Conversely, CNN-BiGRU-VAE provided accurate shale content prediction, with values of RMSE, MAE and MAPE of 0.032, 0.069 and 0.081. The CNN-BiGRU-VAE model can greatly outperform other models by extracting complex relationships between well-log data and shale content values. These findings demonstrate the effectiveness of the new method in shale content prediction when only logging data are available.

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.

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.

A bstract Motivated by the results of Anastasiou et al., we consider the convolutional double copy for BRST and anti-BRST covariant formulations of gravitational and gauge theories in more detail. We give a general BRST and anti-BRST invariant formulation of linearised N = 0 supergravity using superspace methods and show how this may be obtained from the square of linearised Yang-Mills theories. We demonstrate this relation for the Schwarzschild black hole and the ten-dimensional black string solution as two concrete examples.

(1) Patients in a minimally conscious state (MCS) and their families face prolonged emotional distress and psychological challenges due to the uncertainty of recovery and limited means of emotional communication. This study aims to develop an EEG-based emotion visualization system to support affected families by translating patients’ neural activity into perceivable emotional imagery. (2) Using simulated MCS patient EEG data corresponding to different emotional states, we designed a dynamic visual interface via TouchDesigner to convert bio-signals into real-time emotional animations. User tests involving questionnaires and interviews were conducted to evaluate the system’s performance. (3) The results demonstrate that the system accurately conveys emotional states, enhances caregivers’ perception of patients’ internal conditions, and significantly alleviates family members’ anxiety. (4) These findings suggest that EEG-based emotion visualization offers a viable and compassionate tool for supporting MCS families, providing new pathways for interdisciplinary research combining neuroscience and design while establishing a foundation for future clinical and home-care applications.