A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution

Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to generate a high-resolution (HR) HSI with higher spectral and spatial fidelity from its low-resolution (LR) counterpart. The generative adversarial network (GAN) has proven to be an effective deep learning framework for image super-resolution. However, the optimisation process of existing GAN-based models frequently suffers from the problem of mode collapse, leading to the limited capacity of spectral-spatial invariant reconstruction. This may cause the spectral-spatial distortion on the generated HSI, especially with a large upscaling factor. To alleviate the problem of mode collapse, this work has proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can map the generated spectral-spatial features from the image space to the latent space and produce a coupling component to regularise the generated samples. Essentially, we treat an HSI as a high-dimensional manifold embedded in a latent space. Thus, the optimisation of GAN models is converted to the problem of learning the distributions of high-resolution HSI samples in the latent space, making the distributions of the generated super-resolution HSIs closer to those of their original high-resolution counterparts. We have conducted experimental evaluations on the model performance of super-resolution and its capability in alleviating mode collapse. The proposed approach has been tested and validated based on two real HSI datasets with different sensors (i.e. AVIRIS and UHD-185) for various upscaling factors and added noise levels, and compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR, BAGAN, SR- GAN, WGAN).Comment: 18 pages, 10 figure

A Biologically Interpretable Two-Stage Deep Neural Network (BIT-DNN) for Vegetation Recognition From Hyperspectral Imagery

Spectral-spatial-based deep learning models have recently proven to be effective in hyper-spectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of ``black-box'' model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model--a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-knowledge (i.e., biophysical and biochemical attributes and their hierarchical structures of target entities)-based spectral-spatial feature transformation into the proposed framework, capable of achieving both high accuracy and interpretability on HSI-based classification tasks. The proposed model introduces a two-stage feature learning process: in the first stage, an enhanced interpretable feature block extracts the low-level spectral features associated with the biophysical and biochemical attributes of target entities; and in the second stage, an interpretable capsule block extracts and encapsulates the high-level joint spectral-spatial features representing the hierarchical structure of biophysical and biochemical attributes of these target entities, which provides the model an improved performance on classification and intrinsic interpretability with reduced computational complexity. We have tested and evaluated the model using four real HSI data sets for four separate tasks (i.e., plant species classification, land cover classification, urban scene recognition, and crop disease recognition tasks). The proposed model has been compared with five state-of-the-art deep learning models. The results demonstrate that the proposed model has competitive advantages in terms of both classification accuracy and model interpretability, especially for vegetation classification

Post-collisional ultramafic complex in the northern North China Craton: implications for crust-mantle interaction

The post-collisional ultramafic intrusions within the northern margin of the North China Craton (NCC) preserve important imprints of crust-mantle interaction. Here we investigate ultramafic intrusions from the Luojianggou complex composed of pyroxenite and hornblendite associated with serpentine, with a view to gain insights into the nature of orogenic lithospheric mantle in this major continental collision zone. Zircon UPb data from the ultramafic suite define different age populations, with Paleoproterozoic ages (>2.2 Ga and 1.82 Ga) representing xenocrystic grains captured from the basement. The magmatic zircon grains range in age from 872 Ma to 458 Ma, and are possibly related to multiple magma emplacement associated with the Paleo-Asian Ocean closure. The youngest age of ca. 230 Ma is related to a period of post-collisional extension in the northern margin of the NCC, and this inference is further supported by apatite UPb ages of ~207 Ma. Phase equilibrium modeling suggests temperatures of 700–800 °C and pressures of 11–13 Kbar. The pyroxenite and hornblendite show similar geochemical features and REE patterns, indicating the same magma source and formation through differentiation and accumulation. The arc-like geochemical features of the rocks with enrichment of LILE (Rb, Th and La) and LREE, but depletion of HFSE (Nb, Zr and Hf), possibly formed at the boundary of sub-continental lithospheric mantle (SCLM) and crust through metasomatic reaction of peridotite with felsic melts derived from subduction-related components. The arc-like features, zircon rare earth element patterns and obvious positive Pb anomaly in primitive mantle-normalized trace element spidergrams also indicate the mixing of continental materials in the magma source. The post-collisional extensional setting is correlated to the tectonics associated with the assembly of the Mongolian arc terranes within the northern NCC during the Triassic

Tunable hysteresis effect for perovskite solar cells

Perovskite solar cells (PSCs) usually suffer from a hysteresis effect in current–voltage measurements, which leads to an inaccurate estimation of the device e fficiency. Although ion migration, charge trapping/ detrapping, and accumulation have been proposed as a b asis for the hysteresis, the origin of the hysteresis has not been apparently unraveled. Herein we reporte d a tunable hysteresis effect based uniquely on open- circuit voltage variations in printable mesos copic PSCs with a simplified triple-layer TiO 2 /ZrO 2 /carbon architecture. The electrons are collected by the compact TiO 2 /mesoporous TiO 2 (c-TiO 2 /mp-TiO 2 )bilayer, and the holes are collected by the carbon layer. By adj usting the spray deposition cycles for the c-TiO 2 layer andUV-ozonetreatment,weachievedhysteresis-norm al, hysteresis-free, and hysteresis-inverted PSCs. Such unique trends of tunable hysteresis are anal yzed by considering the polarization of the TiO 2 /perovskite interface, which can accumulate positive charges reversibly. Successfully tuning of the hysteresis effect clarifies the critical importance of the c-TiO 2 /perovskite interface in controlling the hysteretic trends observed, providing important insights towards the understanding of this rapidly developing photovoltaic technology

Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism

In order to improve the accuracy and applicability of person re-identification(Re-ID),a Re-ID method based on vector attention mechanism GoogLeNet is proposed.Firstly,three groups of images(anchor,positive and negative) are input into the GoogLeNet-GMP network to obtain segmented feature vectors.Then,spatial pyramid pooling(SPP) is used to aggregate the features from different pyramid levels,and attention mechanism is introduced.By integrating the multi-scale pooling regions which represent the visual information of the target,the distinguishable features on multiple semantic levels are obtained.At the same time,the mixed form of two different loss functions is taken as the final loss function.Experiments on Market-15012 and Duke-MTMC3 data set show that the proposed method performs better in Rank-1 and mAP indicators than other excellent methods

Local melting to design strong and plastically deformable bulk metallic glass composites

Recently, CuZr-based bulk metallic glass (BMG) composites reinforced by the TRIP (transformation-induced plasticity) effect have been explored in attempt to accomplish an optimal of trade-off between strength and ductility. However, the design of such BMG composites with advanced mechanical properties still remains a big challenge for materials engineering. In this work, we proposed a technique of instantaneously and locally arc-melting BMG plate to artificially induce the precipitation of B2 crystals in the glassy matrix and then to tune mechanical properties. Through adjusting local melting process parameters (i.e. input powers, local melting positions, and distances between the electrode and amorphous plate), the size, volume fraction, and distribution of B2 crystals were well tailored and the corresponding formation mechanism was clearly clarified. The resultant BMG composites exhibit large compressive plasticity and high strength together with obvious work-hardening ability. This compelling approach could be of great significance for the steady development of metastable CuZr-based alloys with excellent mechanical properties

Improved colonic inflammation by nervonic acid via inhibition of NF-κB signaling pathway of DSS-induced colitis mice

Background: Nervonic acid (C24:1Δ15, 24:1 ω-9, cis-tetracos-15-enoic acid; NA), a long-chain monounsaturated fatty acid, plays an essential role in prevention of metabolic diseases, and immune regulation, and has anti-inflammatory properties. As a chronic, immune-mediated inflammatory disease, ulcerative colitis (UC) can affect the large intestine. The influences of NA on UC are largely unknown. Purpose: The present study aimed to decipher the anti-UC effect of NA in the mouse colitis model. Specifically, we wanted to explore whether NA can regulate the levels of inflammatory factors in RAW264.7 cells and mouse colitis model. Methods: To address the above issues, the RAW264.7 cell inflammation model was established by lipopolysaccharide (LPS), then the inflammatory factors tumor necrosis factor-α (TNF-α), Interleukin-6 (IL-6), Interleukin-1β (IL-1β), and Interleukin-10 (IL-10) were detected by Enzyme-linked immunosorbent assay (ELISA). The therapeutic effects of NA for UC were evaluated using C57BL/6 mice gavaged dextran sodium sulfate (DSS). Hematoxylin and eosin (H&E) staining, Myeloperoxidase (MPO) kit assay, ELISA, immunofluorescence assay, and LC-MS/MS were used to assess histological changes, MPO levels, inflammatory factors release, expression and distribution of intestinal tight junction (TJ) protein ZO-1, and metabolic pathways, respectively. The levels of proteins involved in the nuclear factor kappa-B (NF-κB) pathway in the UC were investigated by western blotting and RT-qPCR. Results: In vitro experiments verified that NA could reduce inflammatory response and inhibit the activation of key signal pathways associated with inflammation in LPS-induced RAW264.7 cells. Further, results from the mouse colitis model suggested that NA could restore intestinal barrier function and suppress NF-κB signal pathways to ameliorate DSS-induced colitis. In addition, untargeted metabolomics analysis of NA protection against UC found that NA protected mice from colitis by regulating citrate cycle, amino acid metabolism, pyrimidine and purine metabolism. Conclusion: These results suggested that NA could ameliorate the secretion of inflammatory factors, suppress the NF-κB signaling pathway, and protect the integrity of colon tissue, thereby having a novel role in prevention or treatment therapy for UC. This work for the first time indicated that NA might be a potential functional food ingredient for preventing and treating inflammatory bowel disease (IBD).National Key Research and Development, China | Ref. 2021YFE0109200Universidade de Vigo/CISUGThe Provincial Major Scientific and Technological Innovation Project of Shandong | Ref. 2022TZXD0029The Provincial Major Scientific and Technological Innovation Project of Shandong | Ref. 2022TZXD0032The Provincial Major Scientific and Technological Innovation Project of Shandong | Ref. 2021SFGC0904The Provincial Major Scientific and Technological Innovation Project of Shandong | Ref. 2021TZX D004The Natural Science Foundation of Shandong | Ref. ZR2020MH401The Natural Science Foundation of Shandong | Ref. ZR2021QH351National Wheat Industry Technology System of China | Ref. CARS-03–2