N
-methyladenosine (m
A) methyltransferase Mettl3 is involved in conventional T cell immunity; however, its role in innate immune cells remains largely unknown. Here, we show that Mettl3 ...intrinsically regulates invariant natural killer T (iNKT) cell development and function in an m
A-dependent manner. Conditional ablation of Mettl3 in CD4
CD8
double-positive (DP) thymocytes impairs iNKT cell proliferation, differentiation, and cytokine secretion, which synergistically causes defects in B16F10 melanoma resistance. Transcriptomic and epi-transcriptomic analyses reveal that Mettl3 deficiency disturbs the expression of iNKT cell-related genes with altered m
A modification. Strikingly, Mettl3 modulates the stability of the Creb1 transcript, which in turn controls the protein and phosphorylation levels of Creb1. Furthermore, conditional targeting of Creb1 in DP thymocytes results in similar phenotypes of iNKT cells lacking Mettl3. Importantly, ectopic expression of Creb1 largely rectifies such developmental defects in Mettl3-deficient iNKT cells. These findings reveal that the Mettl3-m
A-Creb1 axis plays critical roles in regulating iNKT cells at the post-transcriptional layer.
The trend of diversified, multi-dimensional, and complex development of power operation and maintenance data has led to the traditional processing methods for massive operation and maintenance data ...being unable to meet the current needs and development requirements of the power system. This paper studies an intelligent processing system for massive power operation and maintenance data based on fuzzy correlation, and combines the application of data label system in data centers to deeply mine data features, Discovering the interconnection relationship between basic data labels and deep data labels, forming fuzzy data labels and fault prediction labels on this basis, facilitating the storage and analysis of a large amount of operation and maintenance data, reducing system load rate, and achieving data interconnection between different systems.
Research on the optical characteristics of water color constituents in Chagan Lake of Jilin Province,Northeast China was carried out in order to investigate the variability of the spectra absorption ...parameters as inputs to bio-optical models and remote sensing algorithms for converting observed spectral signals into water quality information.Samples of total particulates,non-algal particles and colored dissolved organic matter （CDOM） were first prepared by quantitative filter technique （QFT） and then absorption coefficients of these color producing agents were determined by spectrophotometry.Spectral characteristics of absorption coefficients by total particulate matter,spectral specific absorption dependency on chlorophyll concentration （Chl-a） of phytoplankton,spectral absorption slopes variation for CDOM and non-algal particles and their corresponding reasons were examined and clarified over five months of 2009 and 2010 in this study.Results suggest that total particulate spectral absorption in Chagan Lake is mainly dominated by non-algal particles in most cases,but phytoplankton could be the dominant contributor when chlorophyll concentration is high （up to 84.48 mg/m3 in autumn 2010）.The specific absorption coefficients of phytoplankton particulate （a＊ph（λ）） dependency on Chl-a is significantly variable due to relative contributions of package effect and accessory pigments,and the parameters of power function are clearly biased on a long time span.The sources of variability in spectral absorption slopes of CDOM and non-algal particles are mainly attributed to the changing proportions of high molecular weight humic acids and mineral suspended sediments in waters,respectively.
With the rapid development of deep learning techniques, deep convolutional neural networks (DCNNs) have become more important for object detection. Compared with traditional handcrafted feature-based ...methods, the deep learning-based object detection methods can learn both low-level and high-level image features. The image features learned through deep learning techniques are more representative than the handcrafted features. Therefore, this review paper focuses on the object detection algorithms based on deep convolutional neural networks, while the traditional object detection algorithms will be simply introduced as well. Through the review and analysis of deep learning-based object detection techniques in recent years, this work includes the following parts: backbone networks, loss functions and training strategies, classical object detection architectures, complex problems, datasets and evaluation metrics, applications and future development directions. We hope this review paper will be helpful for researchers in the field of object detection.
•Hybrid BF and PVAF exhibited an excellent coupling role in improving the mechanical properties and durability of RAC.•The optimal fiber content of RAC was determined based on the tests and response ...surface optimization.•The optimum fiber contents of BF and PVAF were 0.274% and 0.102%, respectively.
With the rapid economic development, a great quantity of concrete structures is facing dismantling and rebuilding. In this process, many recycled aggregates (RA) that will have an adverse effect on the environment will be produced. Fiber-reinforced RA concrete (RAC) (FRRAC) was prepared using single-incorporated basalt fiber (BF), single-incorporated polyvinyl alcohol fiber (PVAF), and double-incorporated BF with PVAF as micro-reinforced materials to improve the environment and the performance of RAC. The influence of fibers on the performance of RAC was analyzed on the basis of workability, mechanical properties and durability along with the desire function to achieve the multi-objective optimization of the comprehensive performance of FRRAC to obtain the optimal content of hybrid fibers. Results showed that the incorporation of fiber improved the mechanical performance and durability of RAC. The double-incorporated BF with PVAF could play an excellent coupling role, that is, BF and PVAF mainly improved the strength and toughness of RAC, respectively. The optimum contents of BF and PVAF for preparing unit volume FRRAC were 0.274% and 0.102%, respectively. This ratio could prepare FRRAC with compressive strength of 54.3 MPa, splitting tensile strength of 3.29 MPa, flexural strength of 3.67 MPa, compressive strength of 49.9 MPa after 200 freeze–thaw cycles and compressive strength of 50.7 MPa after 120 sulfate wetting–drying cycles.
Consumer preferences and government policies are important factors that affect the diffusion of new energy vehicles (NEVs). Based on the complex network evolutionary game theory, this paper ...constructs a R&D diffusion model of NEVs considering the emission trading scheme (ETS), and studies the effect of consumer green preferences and related government policies on the R&D diffusion of NEVs. The simulation analysis shows that: (1) consumer green preferences and the quota system have duality to the R&D diffusion of NEVs, which means that while increasing the proportion of NEV enterprises, they inhibit the R&D diffusion among NEV enterprises. (2) NEV enterprises are more inclined to invest in R&D projects with a low success probability, rather than those with a high success probability. (3) When the carbon price reaches a certain threshold, the ETS will facilitate the R&D diffusion of NEVs. However, with the further increase of the carbon price, the promotional effect will weaken. (4) When the R&D tax incentives reach a certain threshold, the increase in R&D tax incentives will greatly promote the R&D diffusion of NEVs. However, the promotional effect has an upper limit.
•This paper adopts complex network evolutionary game model.•Consumer green preferences have duality to the R&D diffusion of NEVs.•The ETS can promote the R&D diffusion of NEVs under a certain carbon price.•NEV enterprises tend to invest in R&D projects with a low success probability.
Core Ideas
The SHAW model was used to simulate the freeze–thaw process during freeze–thaw periods.
It revealed the effects of soil texture and groundwater table depth on soil freezing and thawing.
...The frost depth and accumulated negative soil surface temperature relationship was determined.
During freeze–thaw periods, the transformation between phreatic water and soil water will change the soil hydrothermal properties and affect the soil freezing and thawing in shallow groundwater areas. The purpose of this study was to determine the effect of four different groundwater table depths (GTDs) and two soil textures on the process of soil freezing and thawing during two successive freeze–thaw periods using the Simultaneous Heat and Water (SHAW) model. The results show that the frost depth was the maximum when the GTD was 1.0 m, and the maximum frost depths of sandy loam and fine sand were 97.6 and 98.9 cm, respectively. When the GTD was larger than 1.5 m, the maximum frost depth decreased with an increase in GTD, and the maximum frost depth of the soil profile was more sensitive to changes in the air temperature. The frost depth of the soil profile was linear with the square root of the accumulated negative soil surface temperature (ANST) under different GTDs. The ANST was influenced by the phreatic evaporation, and the soil freezing rate increased with an increase in GTD under the same ANST. This research is significant for the rational development of soil water and heat resources and the study of soil water–heat transfer in shallow groundwater areas.
Generative adversarial networks (GANs) have been used to obtain super-resolution (SR) videos that have improved visual perception quality and more coherent details. However, the latest methods ...perform poorly in areas with dense textures. To better recover the areas with dense textures in video frames and improve the visual perception quality and coherence in videos, this paper proposes a multiresolution mixture generative adversarial network for video super-resolution (MRMVSR). We propose a multiresolution mixture network (MRMNet) as the generative network that can simultaneously generate multiresolution feature maps. In MRMNet, the high-resolution (HR) feature maps can continuously extract information from low-resolution (LR) feature maps to supplement information. In addition, we propose a residual fluctuation loss function for video super-resolution. The residual fluctuation loss function is used to reduce the overall residual fluctuation on SR and HR video frames to avoid a scenario where local differences are too large. Experimental results on the public benchmark dataset show that our method outperforms the state-of-the-art methods for the majority of the test sets.
The addition of Nb to FeCrAl alloy enhanced its mechanical properties owing to the precipitated-phase strengthening effect. However, the additive amount and its effect on hydrogen embrittlement (HE) ...susceptibility have to be evaluated before implementation, as the hydrogen produced by fission and corrosion in the reactor may affect its mechanical properties. Therefore, hydrogen pre-charged tensile bar specimens were tested, and the HE mechanism was investigated. The results demonstrate that though FeCrAl alloys show low HE susceptibility, the Nb-doped FeCrAl alloys still exhibit a slightly higher HE susceptibility than Nb-free FeCrAl alloys. Their reduced ductility was mainly caused by decohesion and cracking of Fe2(Nb, Mo) precipitates and quasi-cleavage cracking induced by AlN inclusions. The underlying HE mechanisms for the Nb-doped FeCrAl alloys are hydrogen-enhanced localized plasticity (HELP) and HELP-mediated hydrogen-enhanced decohesion (HEDE).
•FeCrAl alloys have low HE susceptibility, slightly increasing for the Nb adding.•AlN and Fe2(Nb, Mo) precipitates are the factors affecting HE susceptibility.•Quasi-cleavage cracking initiated from AlN is a common fracture in the alloy.•Premature cracking and decohesion of Fe2(Nb, Mo) occurred during the strain test.•The HE mechanisms of FeCrAl-xNb alloys are the HELP and HELP-mediated HEDE.
With the abolition of the purchase subsidies for electric vehicles (EVs) in China commencing from 2023, expanding the incentive policy system has become a priority. In the absence of subsidies, ...different types of information from multiple dimensions become important references for EV adoption, which were rarely studied. To fill the gap, we developed an evolutionary model based on multiplex networks to analysis the effectiveness of different types of policies based on consumers' dynamic decisions. Furthermore, different interaction mechanisms were constructed to examine the influence of information dimensions of EV quantity and evaluations. Our results show that: (1) The value retention rate of EVs exerts a significant impact on the adoption rate. (2) Restricting the purchase and driving of traditional vehicles can stimulate the diffusion of EVs. However, due to crowding-out effect, no better policy effect can be achieved when both policies are strictly implemented. (3) The behavioral information possesses a dual impact. Demonstration policies can enhance adoption by improving consumers’ perception of the number of EVs. Education and publicity can promote the adoption by enhancing consumers' evaluation. (4) Compared with the evaluation information, the quantity of EVs is more reliable, which also reflects the subtle influence of the environment.
•A dynamic consumer decision model based on two-layer multiplex networks.•The dynamics between consumer behavior and preference are considered.•Social information in the two dimensions of electric vehicles quantity and evaluation.•Alternative incentive policy systems beyond subsidies.•Policies based on economic value perception and social information interaction.
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