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Scintillation-based X-ray imaging can provide convenient visual observation of absorption contrast by standard digital cameras, which is critical in a variety of science and engineering disciplines. More efficient scintillators and electronic postprocessing derived from neural networks are usually used to improve the quality of obtained images from the perspective of optical imaging and machine vision, respectively. Here, we propose to overcome the intrinsic separation of optical transmission process and electronic calculation process, integrating the imaging and postprocessing into one fused optical–electronic convolutional autoencoder network by affixing a designable optical convolutional metasurface to the scintillator. In this way, the convolutional autoencoder was directly connected to down-conversion process, and the optical information loss and training cost can be decreased simultaneously. We demonstrate that feature-specific enhancement of incoherent images is realized, which can apply to multi-class samples without additional data precollection. Hard X-ray experimental validations reveal the enhancement of textural features and regional features achieved by adjusting the optical metasurface, indicating a signal-to-noise ratio improvement of up to 11.2 dB. We anticipate that our framework will advance the fundamental understanding of X-ray imaging and prove to be useful for number recognition and bioimaging applications.

The pansharpening method based on spatial injection is prone to spectral distortion due to the spatial detail injection amount mismatch. For this reason, this paper proposes a method that combines adaptive intensity component extraction and spatial detail injection optimisation. Firstly, a new method for extracting the I component of multispectral (MS) image is proposed, which decomposes each band of MS image and panchromatic (PAN) image according to the structure-energy mechanism through the joint bilateral filter and then determines the weight coefficient of each band of MS image to extract the I component by solving the minimum optimisation problem of the structure layer of PAN image and the energy layer of MS image. Secondly, a spatial detail extraction method combining local spatial frequency and local energy is proposed, and the method can fully extract the fine detail features of PAN images and MS images. In this paper, many experiments are conducted on QuickBird, GeoEye-1 and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods, and qualitative and quantitative analyses of the experimental results show that the proposed method has certain superiority. [ABSTRACT FROM AUTHOR]

At present, as a research hotspot for time series data (TSD), the deep clustering analysis of TSD has huge research value and practical significance. However, there still exist the following three problems: (1) For deep clustering based on joint optimization, the inevitably mutual interference existing between deep feature representation learning progress and clustering progress leads to difficult model training especially in the initial stage, the possible feature space distortion, inaccurate and weak feature representation; (2) Existing deep clustering methods are difficult to intuitively define the similarity of time series and rely heavily on complex feature extraction networks and clustering algorithms. (3) Multidimensional time series have the characteristics of high dimensions, complex relationships between dimensions, and variable data forms, thus generating a huge feature space. It is difficult for existing methods to select discriminative features, resulting in generally low accuracy of methods. Accordingly, to address the above three problems, we proposed a novel general two-stage multi-dimensional spatial features based multi-view deep clustering method 1DCAE-TSSAMC (One-dimensional deep convolutional auto-encoder based two-stage stepwise amplification multi-clustering). We conducted verification and analysis based on real-world important multi-scenario, and compared with many other benchmarks ranging from the most classic approaches such as K-means and Hierarchical to the state-of-the-art approaches based on deep learning such as Deep Temporal Clustering (DTC) and Temporal Clustering Network (TCN). Experimental results show that the new method outperforms the other benchmarks, and provides more accurate, richer, and more reliable analysis results, more importantly, with significant improvement in accuracy and spatial linear separability. [ABSTRACT FROM AUTHOR]

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. [ABSTRACT FROM AUTHOR]

Conversion‐type tellurium‐based cathodes have attracted great attention in aqueous zinc‐ion batteries (AZIBs) due to potentially high capacity with impressive Te4+/Te0/Te2− conversion. However, impeded by the low‐conductivity Te4+ species and unpreferred kinetics, realizing deep zinc‐tellurium redox remains a major challenge. Herein, energetic NiTe2 nanosheets are first proposed and implemented as preprepared tellurium‐based deep redox cathodes in AZIBs with accelerated electron transport and favorable kinetics. Operando synchrotron X‐ray diffraction and comprehensive ex situ characterizations demonstrate that Te4+ species from direct oxidation of NiTe2 in dilute mild‐aqueous electrolyte undergo a thermodynamic two‐step TeO2→Te→ZnTe discharge process, while the intertwined tellurium oxides and high‐conductivity nickel monomers derived in situ from NiTe2 nanosheets promote a kinetics‐preferred one‐step ZnTe→TeO2 charging conversion with significantly boosted reversibility. Therefore, a high capacity of 510 mAh g−1 at 50 mA g−1 and 93% capacity retention over 400 cycles at 2000 mA g−1 are established. Operando pH tracking and electrode‐electrolyte engineering analyses define that the mild electrolyte environment rich in moisture and electrode conductivity optimization are both critical for achieving reversible tellurium deep redox conversion. [ABSTRACT FROM AUTHOR]

Dendrite‐free Zn anode is the key to avoiding battery failure, which is largely determined by the crystal growth during the electrodeposition process. Therefore, controlling the specific crystal orientation growth of Zn is imperative for high‐performing aqueous zinc ion batteries. Herein, an electrocrystallization orientation regulation strategy is proposed to achieve near‐unity stacked Zn (002) texture growth. Featuring the zincophilic nature and high electronegative carboxylate radical, disodium maleate molecules tend to selectively adsorb on the Zn (002) crystal plane, forming a dynamic protection layer. This adsorption layer regulates Zn2+ diffusion along [100] and [101] orientations with the lowest deposition rate on the (002) plane, homogenizes Zn2+ flux, and keeps away water from Zn surface, constructing the flattened and horizontally arranged Zn deposition layer with dominantly Zn (002) texture and inhibited side‐reaction. Consequently, the Zn anode with near‐unity stacked (002) texture exhibits a 40‐fold enhancement in running lifetime beyond 3200 h and improved coulombic efficiency of 99.81% over 3000 cycles than that with bare ZnSO4 electrolyte. Even at harsh plating/stripping conditions of 30 and 30 mAh cm−2, the Zn anode still sustains state‐of‐the‐art stability over 120 h, enabling a substantial advance in the long‐term stability of the battery. [ABSTRACT FROM AUTHOR]

Conversion‐type electrodes offer a promising multielectron transfer alternative to intercalation hosts with potentially high‐capacity release in batteries. However, the poor cycle stability severely hinders their application, especially in aqueous multivalence‐ion systems, which can fundamentally impute to anisotropic ion diffusion channel collapse in pristine crystals and irreversible bond fracture during repeated conversion. Here, an amorphous bismuth sulfide (a‐BS) formed in situ with unprecedentedly self‐controlled moderate conversion Cu2+ storage is proposed to comprehensively regulate the isotropic ion diffusion channels and highly reversible bond evolution. Operando synchrotron X‐ray diffraction and substantive verification tests reveal that the total destruction of the Bi─S bond and unsustainable deep alloying are fully restrained. The amorphous structure with robust ion diffusion channels, unique self‐controlled moderate conversion, and high electrical conductivity discharge products synergistically boosts the capacity (326.7 mAh g−1 at 1 A g−1), rate performance (194.5 mAh g−1 at 10 A g−1), and long‐lifespan stability (over 8000 cycles with a decay rate of only 0.02 ‰ per cycle). Moreover, the a‐BS Cu2+‖Zn2+ hybrid ion battery can well supply a stable energy density of 238.6 Wh kg−1 at 9760 W kg−1. The intrinsically high‐stability conversion mechanism explored on amorphous electrodes provides a new opportunity for advanced aqueous storage. [ABSTRACT FROM AUTHOR]

Zhaotong City in Yunnan Province is one of the largest apple growing bases in China. However, the terrain of Zhaotong City is complicated, and the rainy weather is more frequent, which brings difficulties to the identification of apple orchards by remote sensing. In this paper, an improved spectral angle feature is proposed by combining the Spectral Angle Mapper and Sentinel-1 data. Based on the Google Earth Engine and Sentinel image, a random forest classifier was used to extract apple orchards in the Ganhe Reservoir area, Zhaoyang District, Zhaotong City, which provides a theoretical basis for extracting the spatial distribution and sustainable development of the local apple industry. The classification results show that the improved spectral angle characteristics can improve the overall accuracy and F1 score of apple orchards. The RGB band combined with NDVI, GLCM, and improved spectral angle features obtained the most favorable results, and the F1 score and overall accuracy were 88.89% and 84.44%, respectively, which proved the reliability of the method in identifying apple orchards in Zhaotong City. [ABSTRACT FROM AUTHOR]

BACKGROUND. Vaccines that block human-to-mosquito Plasmodium transmission are needed for malaria eradication, and clinical trials have targeted zygote antigen Pfs25 for decades. We reported that a Pfs25 protein-protein conjugate vaccine formulated in alum adjuvant induced serum functional activity in both US and Malian adults. However, antibody levels declined rapidly, and transmission-reducing activity required 4 vaccine doses. Functional immunogenicity and durability must be improved before advancing transmission-blocking vaccines further in clinical development. We hypothesized that the prefertilization protein Pfs230 alone or in combination with Pfs25 would improve functional activity. METHODS. Transmission-blocking vaccine candidates based on gamete antigen Pfs230 or Pfs25 were conjugated with Exoprotein A, formulated in Alhydrogel, and administered to mice, rhesus macaques, and humans. Antibody levels were measured by ELISA and transmission-reducing activity was assessed by the standard membrane feeding assay. RESULTS. Pfs25-EPA/Alhydrogel and Pfs230D1-EPA/Alhydrogel induced similar serum functional activity in mice, but Pfs230D1-EPA induced significantly greater activity in rhesus monkeys that was enhanced by complement. In US adults, 2 vaccine doses induced complement-dependent activity in 4 of 5 Pfs230D1-EPA/Alhydrogel recipients but no significant activity in 5 Pfs25-EPA recipients, and combination with Pfs25-EPA did not increase activity over Pfs230D1-EPA alone. CONCLUSION. The complement-dependent functional immunogenicity of Pfs230D1-EPA represents a significant improvement over Pfs25-EPA in this comparative study. The rhesus model is more predictive of the functional human immune response to Pfs230D1 than is the mouse model. TRIAL REGISTRATION. ClinicalTrials.gov NCT02334462. FUNDING. Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health., Introduction The world has achieved substantial strides in malaria control, with roughly half of the countries endemic for malaria having eliminated the disease in the past 50 years. However, existing [...]

Spatial injection-based pansharpening methods are prone to spatial or spectral distortions in pansharpening images due to insufficient extraction of spatial details and a mismatch between the amount of spatial detail information injected and the required amount. To this end, this paper proposes a pansharpening method that optimizes spatial detail extraction and injection. Firstly, a method to optimize the amount of spatial detail injection is proposed, that is, to extract the high-frequency information of the image through iterative filtering and determine the optimal number of iterations based on the global analysis of the method. Then, to fully extract and combine the spatial detail information of the source image, the detailed high-frequency image extracted corresponding to the optimal iterative filtering times is decomposed by non-subsampled shearlet transform (NSST), and a new multi-scale sum-modified-Laplacian (NSML) as an external stimulus to a parameter-adaptive pulse-coupled neural network model (PAPCNN). A fusion rule based on multi-scale morphological gradients is designed to extract a small amount of detailed information for the low-frequency subband. The fused spatial detail image can be obtained by combining the fused low-frequency and high-frequency subbands and inverse NSST transformation. Finally, pansharpening can be realized by combining spatial detail image, injection coefficient, and MS image. In this paper, many experiments are carried out on the QuickBird, GeoEye-1, and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods. Experimental results show that the method proposed in this paper can achieve better fusion results. [ABSTRACT FROM AUTHOR]

The further application of promising transition‐metal chalcogenides (TMCs) cathodes in dilute neutral aqueous Zn batteries (AZBs) is mainly plagued by unsatisfactory working voltages (usually <1 V vs Zn2+/Zn) and their conventional cationic redox centers reaching theoretical capacity limit. Hence, to break the confinement, a novel Zn‐Cu2‐xSe battery is developed in dilute neutral‐aqueous electrolyte by introducing a tailored charge‐carrier, which not only alters the intercalation potential of ions embedded into Cu2‐xSe (vs Zn2+/Zn, working voltage from ≈0.4 to ≈1.2 V) but also activates the anionic redox centers of Cu2‐xSe (capacity release from 143.4 to 323.2 mAh g−1 at 0.4 A g−1). In situ synchrotron X‐ray diffraction (SXRD) and substantial ex situ characterizations reveal the multi‐step phase conversion undergone by cathode and triggered additional Se‐based anionic (Sen2−/Se2−) reversible redox reaction. A multi‐electron synergistic transfer process established on the cationic‐anionic redox centers circumvents the slow relaxation of single‐ion charge compensation achieving high‐capacity and enhanced ion diffusion kinetics. As a result, an extraordinary energy density of up to 406.2 Wh kg−1 at 240 W kg−1 is implemented (calculated based on the mass of Cu2‐xSe cathode), which is ≈8.4 times higher than that of conventional Zn‐Cu2‐xSe batteries, representing an advanced development toward energetic AZBs. [ABSTRACT FROM AUTHOR]

During routine bridge maintenance, edge detection allows the partial condition of the bridge to be viewed. However, many edge detection methods often have unsatisfactory performances when dealing with images with complex backgrounds. Moreover, the processing often involves the manual selection of thresholds, which can result in repeated testing and comparisons. To address these problems in this paper, the wavelet transform modulus maxima method is used to detect the target image, and then the threshold value of the image can be determined automatically according to the OTSU method to remove the pseudo-edges. Thus, the real image edges can be detected. The results show that the information entropy and SSIM of the detection results are the highest when compared with the commonly used Canny and Laplace algorithms, which means that the detection quality is optimal. To more fully illustrate the advantages of the algorithms, images with more complex backgrounds were detected and the processing results of the algorithms in this paper are still optimal. In addition, the automatic selection of thresholds saves the operator's effort and improves the detection efficiency. Thanks to the combined use of the above two methods, detection quality and efficiency are significantly improved, which has a good application in engineering practice. [ABSTRACT FROM AUTHOR]