Self-Construction of Efficient Interfaces Ensures High-Performance Direct Ammonia Protonic Ceramic Fuel Cells.

Direct ammonia protonic ceramic fuel cells (PCFCs) are highly efficient energy conversion devices since ammonia as a carbon-neutral hydrogen-rich carrier shows great potential for storage and long-distance transportation when compared with hydrogen fuel. However, traditional Ni-based anodes readily suffer from severe structural destruction and dramatic deactivation after long-time exposure to ammonia. Here a Sr2 Fe1.35 Mo0.45 Cu0.2 O6-δ (SFMC) anode catalytic layer (ACL) painted onto a Ni-BaZr0.1 Ce0.7 Y0.1 Yb0.1 O3- δ (BZCYYb) anode with enhanced catalytic activity and durability toward the direct utilization of ammonia is reported. A tubular Ni-BZCYYb anode-supported cells with the SFMC ACL show excellent peak power densities of 1.77 W cm-2 in wet H2 (3% H2 O) and 1.02 W cm-2 in NH3 at 650 °C. A relatively stable operation of the cells is obtained at 650 °C for 200 h in ammonia fuel. Such achieved improvements in the activity and durability are attributed to the self-constructed interfaces with the phases of NiCu or/and NiFe for efficient NH3 decomposition, resulting in a strong NH3 adsorption strength of the SFMC, as confirmed by NH3 thermal conversion and NH3 -temperature programmed desorption. This research offers a valuable strategy of applying an internal catalytic layer for highly active and durable ammonia PCFCs.

A Network with Composite Loss and Parameter-free Chunking Fusion Block for Super-Resolution MR Image.

MRI is often influenced by many factors, and single image super-resolution (SISR) based on a neural network is an effective and cost-effective alternative technique for the high-resolution restoration of low-resolution images. However, deep neural networks can easily lead to overfitting and make the test results worse. The network with a shallow training network is difficult to fit quickly and cannot completely learn training samples. To solve the above problems, a new end-to-end super-resolution (SR) method is proposed for magnetic resonance (MR) images. Firstly, in order to better fuse features, a parameter-free chunking fusion block (PCFB) is proposed, which can divide the feature map into n branches by splitting channels to obtain parameter-free attention. Secondly, the proposed training strategy including perceptual loss, gradient loss, and L1 loss has significantly improved the accuracy of model fitting and prediction. Finally, the proposed model and training strategy take the super-resolution IXISR dataset (PD, T1, and T2) as an example to compare with the existing excellent methods and obtain advanced performance. A large number of experiments have proved that the proposed method performs better than the advanced methods in highly reliable measurement.

Ubiquitin-proteasome system-based signature to predict the prognosis and drug sensitivity of hepatocellular carcinoma.

Background: Ubiquitin-proteasome system (UPS) is implicated in cancer occurrence and progression. Targeting UPS is emerging as a promising therapeutic target for cancer treatment. Nevertheless, the clinical significance of UPS in hepatocellular carcinoma (HCC) has not been entirely elucidated. Methods: Differentially expressed UPS genes (DEUPS) were screened from LIHC-TCGA datasets. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate regression analysis were conducted to establish a UPS-based prognostic risk model. The robustness of the risk model was further validated in HCCDB18, GSE14520, and GSE76427 cohorts. Subsequently, immune features, clinicopathologic characteristics, enrichment pathways, and anti-tumor drug sensitivity of the model were further evaluated. Moreover, a nomogram was established to improve the predictive ability of the risk model. Results: Seven UPS-based signatures (ATG10, FBXL7, IPP, MEX3A, SOCS2, TRIM54, and PSMD9) were developed for the prognostic risk model. Individuals with HCC with high-risk scores presented a more dismal prognosis than those with low-risk scores. Moreover, larger tumor size, advanced TNM stage, and tumor grade were observed in the high-risk group. Additionally, cell cycle, ubiquitin-mediated proteolysis, and DNA repair pathways were intimately linked to the risk score. In addition, obvious immune cell infiltration and sensitive drug response were identified in low-risk patients. Furthermore, both nomogram and risk score showed a significant prognosis-predictive ability. Conclusion: Overall, we established a novel UPS-based prognostic risk model in HCC. Our results will facilitate a deep understanding of the functional role of UPS-based signature in HCC and provide a reliable prediction of clinical outcomes and anti-tumor drug responses for patients with HCC.

[Seasonal Variations and Source Apportionment of Carbonaceous Components in Luoyang: Implication for Brown Carbon Contribution].

A total of 98 samples were collected to analyze the seasonal variation and source apportionment of carbonaceous components, especially brown carbon (BrC), of PM2.5in Luoyang during 2018-2019. The concentrations of organic carbon (OC) and elemental carbon (EC) ranged from (7.04±1.82) µg·m-3to(23.81±8.68) µg·m-3and (2.96±1.4) µg·m-3to (13.41±7.91) µg·m-3, respectively, showing the seasonal variation of being high in winter and low in summer; the carbonaceous fraction and secondary organic aerosol percentages were higher by 8.33%-141.03% and by 0.77%-63.14%, respectively, compared with that in 2015. The light absorption cross section (MAC) values showed different seasonal variations with the concentration of carbonaceous fraction, shown in descending order as autumn (7.67 m2·g-1)>winter (5.65 m2·g-1)>spring (5.13 m2·g-1)>summer (3.84 m2·g-1). The MAC values ranged from 3.84 to 7.67 m2·g-1 at 445 nm, which was lower than that in coal ash. Seasonal variation in light absorption and the contribution of BrC to total light absorption (babs,BrC,405 nm, babs,BrC,405 nm/babs,405 nm) in descending order was winter (31.57 Mm-1, 33%), autumn (11.40 Mm-1, 25%), spring (4.88 Mm-1, 23%), and summer (2.12 Mm-1, 21%). The proportion of carbonaceous components decreased as haze episodes evolved, whereas the contribution of light absorption of BrC increased, highlighting the important contribution of BrC to the total light absorption. The results of PMF and correlation coefficients of babs,BrC,405 nm and PM2.5 components indicated that motor vehicles and secondary nitrate contributed 27.7% and 24.0%, respectively. Our findings have significant scientific implications for the deep controlling of carbonaceous aerosol, especially for BrC, in Luoyang in the future.

An Efficient High-Entropy Perovskite-Type Air Electrode for Reversible Oxygen Reduction and Water Splitting in Protonic Ceramic Cells.

Reversible protonic ceramic electrochemical cells (R-PCECs) are emerging as ideal devices for highly efficient energy conversion (generating electricity) and storage (producing H2 ) at intermediate temperatures (400-700 °C). However, their commercialization is largely hindered by the development of highly efficient air electrodes for oxygen reduction and water-splitting reactions. Here, the findings in the design of a highly active and durable air electrode are reported: high-entropy Pr0.2 Ba0.2 Sr0.2 La0.2 Ca0.2 CoO3- δ (HE-PBSLCC), which exhibits impressive activity and stability for oxygen reduction and water-splitting reactions, as confirmed by electrochemical characterizations and structural analysis. When used as an air electrode of R-PCEC, the HE-PBSLCC achieves encouraging performances in dual modes of fuel cells (FCs) and electrolysis cells (ECs) at 650 °C, demonstrating a maximum power density of 1.51 W cm-2 in FC mode, and a current density of -2.68 A cm-2 at 1.3 V in EC mode. Furthermore, the cells display good operational durabilities in FC and EC modes for over 270 and 500 h, respectively, and promising cycling durability for 70 h with reasonable Faradaic efficiencies. This study offers an effective strategy for the design of active and durable air electrodes for efficient oxygen reduction and water splitting.

EHDFL: Evolutionary hybrid domain feature learning based on windowed fast Fourier convolution pyramid for medical image classification.

With the development of modern medical technology, medical image classification has played an important role in medical diagnosis and clinical practice. Medical image classification algorithms based on deep learning emerge in endlessly, and have achieved amazing results. However, most of these methods ignore the feature representation based on frequency domain, and only focus on spatial features. To solve this problem, we propose a hybrid domain feature learning (HDFL) module based on windowed fast Fourier convolution pyramid, which combines the global features with a wide range of receptive fields in frequency domain and the local features with multiple scales in spatial domain. In order to prevent frequency leakage, we construct a Windowed Fast Fourier Convolution (WFFC) structure based on Fast Fourier Convolution (FFC). In order to learn hybrid domain features, we combine ResNet, FPN, and attention mechanism to construct a hybrid domain feature learning module. In addition, a super-parametric optimization algorithm is constructed based on genetic algorithm for our classification model, so as to realize the automation of our super-parametric optimization. We evaluated the newly published medical image classification dataset MedMNIST, and the experimental results show that our method can effectively learning the hybrid domain feature information of frequency domain and spatial domain.

HWA-SegNet: Multi-channel skin lesion image segmentation network with hierarchical analysis and weight adjustment.

Convolutional neural networks (CNNs) show excellent performance in accurate medical image segmentation. However, the characteristics of sample with small size and insufficient feature expression, irregular shape of the segmented target and inaccurate judgment of edge texture have always been problems to be faced in the field of skin lesion image segmentation. Therefore, in order to solve these problems, discrete Fourier transform (DFT) is introduced to enrich the input data and a CNN architecture (HWA-SegNet) is proposed in this paper. Firstly, DFT is improved to analyze the features of the skin lesions image, and multi-channel data is extended for each image. Secondly, a hierarchical dilated analysis module is constructed to understand the semantic features under multi-channel. Finally, the pre-prediction results are fine-tuned using a weight adjustment structure with fully connected layers to obtain higher accuracy prediction results. Then, 520 skin lesion images are tested on the ISIC 2018 dataset. Extensive experimental results show that our HWA-SegNet improve the average segmentation Dice Similarity Coefficient from 88.30% to 91.88%, Sensitivity from 89.29% to 92.99%, and Jaccard similarity index from 81.15% to 85.90% compared with U-Net. Compared with the State-of-the-Art method, the Jaccard similarity index and Specificity are close, but the Dice Similarity Coefficient is higher. The experimental data show that the data augmentation strategy based on improved DFT and HWA-SegNet are effective for skin lesion image segmentation.

ILCS: An Improved Lightweight Convolution Structure and Mixed Interactive Attention for Steel Surface Defect Classification.

The classification method of steel surface defects based on deep learning provides a basis for quality control of industrial steel manufacturing. Due to a large number of interference in the steel production area and the limited computing resources of the edge equipment deployed in the production area, it is a challenge to develop a lightweight model to achieve rapid and accurate classification in the case of limited computing resources. In this article, an improved lightweight convolution structure (LCS) is proposed, which combines the separable structure of convolution and introduces depth convolution and point direction convolution instead of the traditional convolutional module, so as to realize the lightweight of the model. In order to ensure the classification accuracy, spatial attention and channel attention are combined to compensate for the accuracy loss after deep convolution and point direction convolution respectively. Further, in order to improve the classification accuracy, a mixed interactive attention module (MIAM) is proposed to enhance the extracted feature information after LCS. The experimental results show that the recognition accuracy of our method exceeds that of the traditional model, and the number of parameters and the amount of calculation are greatly reduced, which realizes the lightweight of the steel surface defect classification model.

Convolutional Neural Network with Multiscale Fusion and Attention Mechanism for Skin Diseases Assisted Diagnosis.

Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous feature extraction. To solve this problem, we propose a novel multiscale feature fusion network (MSFA-Net). MSFA-Net can extract feature information at different scales through a multiscale feature fusion structure (MSF) in the network and then calibrate and restore the extracted information to achieve the purpose of melanoma segmentation. Specifically, based on the popular encoder-decoder structure, we designed three functional modules, namely MSF, asymmetric skip connection structure (ASCS), and calibration decoder (Decoder). In addition, a weighted cross-entropy loss and two-stage learning rate optimization strategy are designed to train the network more effectively. Compared qualitatively and quantitatively with the representative neural network methods with encoder-decoder structure, such as U-Net, the proposed method can achieve advanced performance.

Boron inhibits cadmium uptake in wheat (Triticum aestivum) by regulating gene expression.

Various nutrients (Mg, Zn, Fe, Mn, Si, etc.) can supress cadmium (Cd) uptake and alleviate Cd toxicity, but the mechanisms are not the same. In this study, the molecular mechanism governing the effects of boron (B) on uptake of Cd in hydroponically grown wheat was characterized. As compared to control (0 µM Cd), B concentration per plant decreased by 22% and 29% under 5 µM Cd and 50 µM Cd treatment respectively. In addition, B application decreased Cd concentration and accumulation in whole wheat. Correlation analysis of different elements show that there was a highly negative correlation between concentrations of B and Cd (r = -0.854 with significant correlation) in wheat. Additionally, 16,543 differentially expressed genes (DEGs) (7666 up- and 8877 down-regulated) were detected between 0 and 5 µM Cd treatments in wheat roots by transcriptome sequencing. Gene ontology functional category and Kyoto encyclopedia of genes and genomes pathway analyses indicated that the DEGs were involved in biological process, cellular component, and molecular function. Five highly homologous genes to Cd transporters were identified; these genes were involved in metal ion binding, transmembrane ion transport, and protein transport. According to the qRT-PCR results, expression of all these genes was down-regulated in the 462 µM of B treatment compared with the 46.2 µM of B treatment regardless of the Cd treatments (0.5 or 5 µM Cd). These results suggest that B is an inhibitor of Cd uptake, and the down-regulation of five highly homologous genes could be associated with decreased uptake of Cd after B application.