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Oxygen, one of the most abundant elements on Earth, often forms an undesired interstitial impurity or ceramic phase (such as an oxide particle) in metallic materials. Even when it adds strength, oxygen doping renders metals brittle 1 – 3 . Here we show that oxygen can take the form of ordered oxygen complexes, a state in between oxide particles and frequently occurring random interstitials. Unlike traditional interstitial strengthening 4 , 5 , such ordered interstitial complexes lead to unprecedented enhancement in both strength and ductility in compositionally complex solid solutions, the so-called high-entropy alloys (HEAs) 6 – 10 . The tensile strength is enhanced (by 48.5 ± 1.8 per cent) and ductility is substantially improved (by 95.2 ± 8.1 per cent) when doping a model TiZrHfNb HEA with 2.0 atomic per cent oxygen, thus breaking the long-standing strength–ductility trade-off 11 . The oxygen complexes are ordered nanoscale regions within the HEA characterized by (O, Zr, Ti)-rich atomic complexes whose formation is promoted by the existence of chemical short-range ordering among some of the substitutional matrix elements in the HEAs. Carbon has been reported to improve strength and ductility simultaneously in face-centred cubic HEAs 12 , by lowering the stacking fault energy and increasing the lattice friction stress. By contrast, the ordered interstitial complexes described here change the dislocation shear mode from planar slip to wavy slip, and promote double cross-slip and thus dislocation multiplication through the formation of Frank–Read sources (a mechanism explaining the generation of multiple dislocations) during deformation. This ordered interstitial complex-mediated strain-hardening mechanism should be particularly useful in Ti-, Zr- and Hf-containing alloys, in which interstitial elements are highly undesirable owing to their embrittlement effects, and in alloys where tuning the stacking fault energy and exploiting athermal transformations 13 do not lead to property enhancement. These results provide insight into the role of interstitial solid solutions and associated ordering strengthening mechanisms in metallic materials. Ordered oxygen complexes in high-entropy alloys enhance both strength and ductility in these compositionally complex solid solutions.

Oxygen solute strengthening is an effective strategy to harden alloys, yet, it often deteriorates the ductility. Ordered oxygen complexes (OOCs), a state between random interstitials and oxides, can simultaneously enhance strength and ductility in high-entropy alloys. However, whether this particular strengthening mechanism holds in other alloys and how these OOCs are tailored remain unclear. Herein, we demonstrate that OOCs can be obtained in bcc (body-centered-cubic) Ti-Zr-Nb medium-entropy alloys via adjusting the content of Nb and oxygen. Decreasing the phase stability enhances the degree of (Ti, Zr)-rich chemical short-range orderings, and then favors formation of OOCs after doping oxygen. Moreover, the number density of OOCs increases with oxygen contents in a given alloy, but adding excessive oxygen (>3.0 at.%) causes grain boundary segregation. Consequently, the tensile yield strength is enhanced by ~75% and ductility is substantially improved by ~164% with addition of 3.0 at.% O in the Ti-30Zr-14Nb MEA. Ordered oxygen complexes (OOCs) endow a unique interstitial strengthening mechanism for simultaneously enhancing strength and ductility in HEAs. Here, the authors demonstrate whether such mechanism can be extended to other alloy systems and how the formation of OOCs is tailored.

Change history: In this Letter, owing to a production error, all the data points (except the two points for O-2 and N-2, respectively) were missing in Fig. 1b. The figure has been corrected online.Change history: In this Letter, owing to a production error, all the data points (except the two points for O-2 and N-2, respectively) were missing in Fig. 1b. The figure has been corrected online.

The purpose of this paper is to demonstrate the feasibility of a new technical route based on the characteristic absorption spectra of Marine water bodies based on the current typical semiconductor materials. According to the design and calculation results, it can be concluded that with silicon oxide, silicon nitride, titanium dioxide and gallium phosphide as typical application materials, the multi-medium spacer layer structure is adopted, and the dielectric metassurface-multi-channel narrowband filter constructed in the band of 473~493nm and 635~655nm is divided into four channels. This design fully exploits the advantages of simple, tunable metasurface fabrication, and can adjust the filter’s transmission wavelength by altering its transverse geometric parameters for admittance matching. In general, it is feasible to construct a medium metasurface multi-channel narrow band filter using a multi-medium spacer layer structure, and integrate a variety of transmission-wavelength narrowband filters on the same substrate through a planar graphic process such as lithography, to form an antenna design based on micro and nano arrays.

Based on SEIR (susceptible–exposed–infectious–removed) epidemic model, we propose a modified epidemic mathematical model to describe the spread of the coronavirus disease 2019 (COVID-19) epidemic in Wuhan, China. Using public data, the uncertainty parameters of the proposed model for COVID-19 in Wuhan were calibrated. The uncertainty of the control basic reproduction number was studied with the posterior probability density function of the uncertainty model parameters. The mathematical model was used to inverse deduce the earliest start date of COVID-19 infection in Wuhan with consideration of the lack of information for the initial conditions of the model. The result of the uncertainty analysis of the model is in line with the observed data for COVID-19 in Wuhan, China. The numerical results show that the modified mathematical model could model the spread of COVID-19 epidemics.

Wang, M. and Zhou, S., 2020. An adaptive visibility detection method based on a UAV-borne real-time panoramic camera. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 282–288. Coconut Creek (Florida), ISSN 0749-0208. This paper presents an adaptive visibility detection method based on a UAV-borne real-time panoramic camera and designs a target board with strong absorption properties. One real-time panoramic camera and four target boards are carried by five UAVs. The horizontal visibility values in four vertical directions are measured with the brightness contrast method, and the visibility measurement errors are analysed for adaptation. The baseline length should be adjusted to the position where the minimum visibility error occurs to obtain high precision and refined atmospheric horizontal visibility results. This method uses UAV hovering in the near ground or half sky to measure the visibility, which can avoid the influence of background obstacles and uneven sky background, and even can be applied to the measurement of vertical visibility and slant visibility. Moreover, the inherent visual brightness ratio can be by improving the structure of the artificial target plate, the inherent visual brightness ratio can be increased by improving the structure of the artificial target board.

Motion blur in images is usually modeled as the convolution of a point spread function (PSF) and the original image represented as pixel intensities. The knife-edge function can be used to model various types of motion-blurs, and hence it allows for the construction of a PSF and accurate estimation of the degradation function without knowledge of the specific degradation model. This paper addresses the problem of image restoration using a knife-edge function and optimal window Wiener filtering. In the proposed method, we first calculate the motion-blur parameters and construct the optimal window. Then, we use the detected knife-edge function to obtain the system degradation function. Finally, we perform Wiener filtering to obtain the restored image. Experiments show that the restored image has improved resolution and contrast parameters with clear details and no discernible ringing effects.

A Secchi depth is a basic parameter to describe the optical properties of water, which is related to the composition and content of chlorophyll, suspended solids and yellow substances in water, and is also closely related to solar radiation on the surface of water, physical and chemical properties of water and meteorological conditions. The most direct way to obtain the spatiotemporal distribution of water Secchi depth is to use ships to regularly measure the Secchi depth of the base-stations, but this method can only obtain the Secchi depth of the measurement station state, and it is impossible to obtain the Secchi depth characteristics of seawater with large spatiotemporal distribution. As a brand-new observation method, remote sensing technology can obtain the distribution characteristics of ocean parameters in large time and space. In recent years, with the rapid development of remote sensing technology, especially the development of water-colored remote sensors and the improvement of the accuracy of inversion algorithms, many remote sensing products for water Secchi depth have been provided. However, due to the difference in orbit operation and observation parameters of different remote sensing loads, water Secchi depth products are affected by clouds and meteorological climate environment, and there is no same standard for verification and evaluation, which limit the promotion and application of water Secchi depth products. At the same time, data integrated technology has been widely recognized by many disciplines and has been greatly developed in recent years, and pixel-level, feature-level, decision-level integrated technology and development are also popular research directions in the world. Therefore, based on an improved variational optimization algorithm, this article integrates SDD retrieval products on multiple satellite data t such as SNPP, MODIS, and MERIS, and verifies them with ship survey data. The correlation is better than 0.9, proving that this method can improve the accuracy of SDD inversion products.

Background Resistance to the proteasome inhibitor bortezomib is a major obstacle to the treatment of multiple myeloma (MM) and a major cause of relapse and death. Investigating the role of bortezomib resistance genes in MM is crucial. This study aimed to evaluate the potential of bortezomib resistance-related genes (BRGs) as prognostic biomarkers in MM. Methods  The transcriptome data of bortezomib resistant myeloma cell lines, as well as the gene expression and clinical data of patients were downloaded from the Gene Expression Omnibus (GEO) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression models were employed to screen variables and construct a multigene prognostic signature based on BRGs. Single-sample gene set enrichment analysis (ssGSEA) was performed to quantify the relative infiltration levels of immune cells. The pRRophetic algorithm was utilized to assess and infer the sensitivity of anti-MM chemotherapeutics. Results We identified 129 differentially expressed BRGs, with 25 associated with MM prognosis. Using the LASSO Cox regression model, we identified five key genes (IFI16, ARID5B, LTBP1, PNOC, CRIP1) and developed a bortezomib resistance model for risk stratification and prognosis prediction. Multivariate Cox regression analysis revealed that the risk score was an independent prognostic factor for overall survival (OS). Based on pRRophetic results, high-risk patients may be more sensitive to other chemotherapeutic agents, such as doxorubicin and etoposide. Additionally, we constructed a nomogram incorporating patient age, LDH, International Staging System (ISS), and BRGs, which demonstrated robust prognostic prediction capabilities. The receiver operating characteristic (ROC) values for 1-, 3-, and 5-years survival rates were 0.730, 0.734, and 0.775, respectively. We also validated the expression patterns of the five key genes in MM. IFI16 and CRIP1 expression levels were upregulated in relapsed patients, whereas ARID5B expression was decreased. PNOC and LTBP1 showed no significant differences. Notably, lower ARID5B expression was associated with poorer OS in patients. Conclusions The BRGs signature is a reliable biomarker for predicting the prognosis of MM and helps optimize clinical decision-making for treatment, and identifies key gene ARID5B downregulation as an adverse prognostic factor in multiple myeloma.

Conventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.