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A high-spatial-resolution hyperspectral image (HR-HSI) can be obtained by fusing a hyperspectral image (HSI) and a multispectral image (HSI-MSI) since it takes the advantage of combing the low-spatial-resolution HSI (LR-HSI) and the high-spatial-resolution multispectral image (HR-MSI). Currently, most HSI-MSI fusion methods decompose HSIs in both spatial and spectral domains, which destroys the correlation of the two domains and results in poor fusion results. To further explore the correlation of spatial and spectral information, an unsupervised blind spectral–spatial cross-super-resolution network (CSRNet) is proposed for HSI-MSI fusion. The network transforms the fusion into two super-resolution (SR) reconstructions for a HR-HSI to avoid the decomposition of HSIs and achieves interaction with the two reconstructions to provide strong constraints for each other. The network uses the following procedure. First, the HSI-MSI fusion is transformed into a spectral–spatial cross-SR model, which consists two branches: one for spatial and the other for spectral SR (SpeSR) reconstructions. As the basic blocks of the two branches, spatial SR (SpaSR) and the SpeSR blocks in the new network are designed via iterations unfolding from half quadratic splitting (HQS). Then their corresponding spatial constraint (SpaC) and spectral constraint (SpeC) modules are established for the mutual constraints and interactions between the two branches. The SpaSR/SpeSR and SpaC/SpeC modules are alternately connected to form the above SpaSR/SpeSR branches for reconstructing a HR-HSI. Both visual and quantitative results of experiments based on both simulated and real observation datasets showed that the proposed method outperformed seven commonly used methods, suggesting that the new method is effective for preserving the correlation of spatial and spectral information in HR-HSIs.

Efficient oil/water separation tackles various issues in occasions of oil leakage and oil discharge, such as environmental pollution, recollection of the oil, and saving the water. Herein, a compact superhydrophobic/superoleophilic graphitic carbon nitride nanolayer coated on carbon fiber networks (CNBA/CF) is designed and synthesized for efficient gravity-driven oil/water separation. The CNBA/CF shows excellent oil absorption and an impressive oil/water filtration separation performance. The flux reaches the state-of-art value of 4.29 × 105L/m2/h for dichloromethane with separation efficiency up to 99%. Successive oil absorption tests, long-term filtration separation, and harsh conditions experiments confirm the remarkable separation and chemical structure stability of the CNBA/CF filter. Besides, the CNBA/CF demonstrates good photocatalytic antifouling ability thanks to the extended visible light absorption and improved charge separation. This work combines the material surface wettability modulation with a photocatalytic self-cleaning property in the fabrication of efficient oil/water separation materials while overcoming the filter fouling issue.

Owing to the limitations of hyperspectral optical imaging, hyperspectral images (HSIs) have a dilemma between spectral and spatial resolutions. The hyperspectral and multispectral image (HSI–MSI) fusion, which combines a low-spatial-resolution HSI (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI), can generate a high-spatial-resolution HSI (HR-HSI). In existing methods for hyperspectral and multispectral fusion, correlation between spectral and spatial domains in HSIs is mostly neglected. To address this issue, an unsupervised encoder–decoder network under spatial and spectral guidance for the HSI–MSI fusion (uEDSSG) was proposed in this study. To learn more accurate abundances of an LR-HSI and an HR-MSI, multihierarchical encoders under spatial and spectral guidance were designed to extract multihierarchical fused features from the LR-HSI and HR-MSI with the guidance of the HR-MSI and LR-HSI, respectively. In the new method, deep coupling of the point spread function (PSF) or spectral response function (SRF) and edge of the HSIs was designed to maintain the spatial and spectral details of the HR-HSI; a spatial–spectral constraint was constructed to establish the relationship of the HSIs. Both visual and quantitative evaluation results of experiments based on both synthetic and real datasets showed that the proposed method outperformed seven common methods. The results suggest that the new method by maintaining the correlation between spectral and spatial domains can improve the result of HSI–MSI fusion.

Tumor necrosis factor (TNF) induces systemic inflammatory response syndrome (SIRS), and severe SIRS can serve as a model for studying animal death caused by organ failure. Through strategic cecectomy, we demonstrate that necroptosis in the cecum initiates the death process in TNF-treated mice, but it is not the direct cause of death. Instead, we show that it is the cardiac dysfunction downstream of cecum damage that ultimately leads to the death of TNF-treated mice. By in vivoand ex vivophysiological analyses, we reveal that TNF and the damage-associated molecular patterns (DAMPs) released from necroptotic cecal cells jointly target cardiac endothelial cells, triggering caspase-8 activation and subsequent cardiac endothelial damage. Cardiac endothelial damage is a primary cause of the deterioration of diastolic function in the heart of TNF-treated mice. Our research provides insights into the pathophysiological process of TNF-induced lethality.

With the increasing demand for energy, various emerging energy storage/conversion technologies have gradually penetrated human life, providing numerous conveniences. The practical application efficiency is often affected by the slow kinetics of hydrogen or oxygen electrocatalytic reactions (hydrogen evolution and oxidation reactions, oxygen evolution and reduction reactions) among the emerging devices. Therefore, the researchers devote to finding cost-effective electrocatalysts. Non-noble metal catalysts have low cost and good catalytic activity, but poor stability, agglomeration, dissolution, and other problems will occur after a long cycle, such as transition metal oxides and carbides. Transition metal nitrides (TMNs) stand out among all kinds of non-noble metal catalysts because of the intrinsic platinum-like electrocatalytic activities, relatively high conductivity, and wide range of tunability. In this review, the applications of TMNs in electrocatalytic fields are summarized based on the number of metals contained in TMNs. The practical application potentials of TMNs in fuel cell, water splitting, zinc-air battery and other electrochemical energy storage/conversion devices are also listed. Finally, the design strategies and viewpoints of TMNs-based electrocatalyst are summarized. The potential challenges of TMNs-based electrocatalyst in the development of electrocatalytic energy devices in the future are prospected.

China Geodetic Coordinate System 2000 (CGCS2000) has been used for several years as a formal published reference frame. The coordinates of all global navigation satellite system (GNSS) stations in China need to be corrected to align with the CGCS2000 frame. Different strategies can be adopted for the realization of an optimal reference frame. However, different strategies lead to different results, with differences as great as several decimeters when GNSS station coordinates are transformed into CGCS2000-defined coordinates. The two common methods for the coordinate correction of a GNSS station are quasi-stable adjustment under CGCS2000 and plate movement correction, and the differences between their results can be greater than 10 cm. In this study, a statistic method called “supervised clustering” is applied to the selection of GNSS reference stations; a new scheme named “partition spacing” for the grouping of all processed GNSS stations is proposed; and the plate movement correction method is used to correct the coordinates of all GNSS stations from the GNSS epoch to the CGCS2000 epoch. The results from the new partitioning method were found to be significantly better than those from the conventional station-blocking approach. When coordinates from the stations without grouping were used as the standard, the accuracy of all the three-dimensional coordinate components from the new partitioning method was better than 2 mm. The root mean squares (RMSs) of the velocities in the x, y, and zdirections resulting from the supervised clustering method were 0.19, 0.45, and 0.32 mm∙a−1, respectively, which were much smaller than the values of 0.92, 0.72, and 0.97 mm∙a−1that resulted from the conventional approach. In addition, singular spectrum analysis (SSA) was used to model and predict the position nonlinear movements. The modeling accuracies of SSA were better than 3, 2, and 5 mm in the east (E), north (N), and up (U) directions, respectively; and its prediction accuracies were better than 5 mm and 1 cm for the horizontal and vertical domains, respectively.

Targeted analysis of sequential window acquisition of all theoretical mass spectra (SWATH-MS) requires the spectral library, which can be generated by shotgun mass spectrometry (MS) or by the pseudo-spectra files directly obtained from SWATH-MS data. The external library generated by shotgun MS is employed in most SWATH-MS research. However, performance of the internal library, which is constructed by pseudo-spectra files, in the targeted analysis of SWATH-MS has not been systemically evaluated. Here, we show that up to 40% of the peptides detected by the internal library were not overlapped with those detected by the external library for most SWATH-MS data sets. However, the internal library did not identify extra phosphopeptides compared with the external library for phosphoproteomic SWATH-MS data. Therefore, the internal library should be incorporated into the external library for targeted analysis of nonphosphoproteomic SWATH-MS, given that it can significantly increase the number of peptides of SWATH-MS without requiring additional instrument measurement time.

An artificial neural network (ANN) method for the modeling of global topside ionospheric vertical scale height (VSH) using electron density profiles retrieved from Global Navigation Satellite Systems radio occultation (RO) data is proposed in this study. The data for this study are 80,124 VSHs derived from the events randomly selected from 9 years of Constellation Observing System for Meteorology, Ionosphere, and Climate RO measurements and 144,530 VSHs derived from the events randomly selected from 16 years of topside sounder measurements of both Aluoette‐1/2 and ISIS‐1/2 satellites during 1962–1978 are used for comparison. VSHs from the International Reference Ionosphere are also used for the comparison. Results showed that: (1) the median of the relative residuals of the new ANN regression approach/model (which was based on RO measurements) was 8.5% less than that of the traditional approach/model (which was based on the topside sounder data); (2) the median of the relative residuals of the ANN model when longitude was used as a variable was 1.1% less than the one without longitude; and substantial error in the polar region was shown to be mitigated by taking the variable longitude into consideration; (3) compared to International Reference Ionosphere, the accuracy of the new ANN model was improved by around 14%; (4) the new ANN model outperforms the traditional base vector‐based least squares model by around 10% when incoherent scatter radar measurements are used as a reference; and (5) the characteristics of global VSHs generated from the new model during geomagnetic storms better agree with measurements than that of the base vector‐based least squares. A new approach for vertical scale height modeling from ionospheric radio occultation data using artificial neural network is proposedThe new approach significantly outperforms the traditional model in accuracyThe new approach agrees with the characteristic of VSH in equatorial region better than traditional approach during geomagnetic storm times

As an oxygen reduction reaction (ORR) catalyst, nitrogen-doped carbon (NC) is widely used in zinc-air batteries (ZABs). However, NC catalysts exhibit low conductivity and insufficient exposure of active sites. Therefore, a Co-based deep eutectic solvent (DES) was selected to modify NC catalyst (Co-NC) to improve ORR performances. Density functional theory (DFT) calculation shows that the modification of Co-based DES can change the electronic structure of NC and increase metallic active sites, which is beneficial to the desorption of reaction intermediates on Co-NC, further improving ORR performance. Co-NC shows excellent ORR performances and stability. Impressively, ZABs assembled with the Co-NC manifest a high maximum power density of 177.4 ​mW ​cm−2, a high specific capacity of 726.12 ​mA ​h g−1and a charge-discharge cycle life of 500 ​h. This study can provide practical reference for surface modified carbon-based electrocatalyst with DES to improve ORR performances.

The goal of this study is to present a new model for global topside electron temperature ( Te) using a deep neural network (DNN) that is trained using measurements from incoherent scatter radars (ISRs). This study is also an investigation into whether this model can be used to generate the electron temperature in the topside ionosphere using GNSS ionospheric radio occultation (GNSS‐IRO) data as the input. ISR is one of the most reliable and long‐term sources to measure topside ionospheric electron density and plasma temperature information simultaneously. However, a drawback of ISR databases is the relatively poor spatial coverage due to the low number of ISR stations around the world. In contrast, GNSS‐IRO can be used to measure the global distributed electron density, but Teinformation is not directly detected. The relationship between the electron density and the electron temperature has been investigated by many researchers, but these studies have not explicitly considered the parameters that are known to influence the electron temperature, such as solar and geomagnetic activity level, and the features of electron density profile ( hmF2, NmF2, and scale height). This study uses a DNN technique to create a new global topside electron temperature model from three submodels that have been trained using data from three ISR stations: Arecibo (low latitude), Millstone Hill (midlatitude), and Poker Flat (high latitude). This global model is trained using electron density profile information (e.g., vertical scale height [VSH], hmF2, and NmF2) and solar and geomagnetic activity ( F10.7and Kp, respectively) in addition to traditional spatial and temporal variables (e.g., local time, month, and latitude) as the independent variables. After the Temodel is developed, Teinformation can be generated from the GNSS‐IRO electron density profiles using a newly created Ne−Temodel. This model's outputs are assessed with regard to out‐of‐sample ISR data and compared to the latest International Reference Ionosphere model. It is found that the electron temperature profiles from the DNN have a root‐mean‐square deviation of 259 K in the low‐latitude region (i.e., against Arecibo data), 254 K in the midlatitude region (i.e., against Millstone Hill data), and 314 K in high‐latitude region (i.e., against Poker Flat data), and all of them are smaller than the root‐mean‐square deviation from International Reference Ionosphere. An additional comparison between the model results versus the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs is also conducted. A statistical analysis of the diurnal electron temperature profiles obtained from GNSS‐IRO is shown to agree with the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs. The full codes and outputs can be found at the Zenodo (10.5281/zenodo.3637617). A new global topside Temodel using deep neural network from incoherent scatter radar (ISR) with newly added Ne‐related variables is proposedThe new model agrees better with the out‐of‐sample ISR measurements than IRI‐2016The Teresults predicted from the proposed model and ionospheric radio occultation measurements agree well with TIEGCM in the region with sufficient IRO data

Features Yuxian County, an ancient region located west of Beijing, China. Historical relics that can be found in Yuxian; Natural landscapes; Folk art products.