Search

COVID-19 is the most widespread infectious disease in the world. There is an incubation period in the early stage of infection. At present, there are some difficulties in the diagnosis of COVID-19. Medical image analysis based on computed tomography (CT) images is an important tool for clinical diagnosis. However, the lesion size of COVID-19 is smaller, and the lesion shape of COVID-19 is more complex. The effect of the aided diagnosis model is not good. To solve this problem, an aided diagnostic model of COVID-ResNet was proposed based on CT images. Firstly, an improved attention ResNet model was designed based on CT images to focus on the focal lesion area. Secondly, the SE-Res block was constructed. The squeeze excitation mechanism with the residual connection was introduced into the ResNet. The SE-Res block can enhance the correlation degree among different channels and improve the overall accuracy of the model. Thirdly, MFCA (multi-layer feature converge attention) blocks were proposed, which extract multi-layer features. In this model, coordinated attention was used to focus on the direction information of the lesion area. Different layer features were concatenated so that the shallow layer and deep layer features were fused. The experimental results showed that the model could significantly improve the recognition accuracy of COVID-19. Compared with similar models, COVID-ResNet has better performance. On the COVID-19 CT dataset, the accuracy, recall rate, F1 score, and AUC value could reach 96.89%, 98.15%,96.96%, and 99.04%, respectively. Compared with the ResNet model, the accuracy, recall rate, F1 score, and AUC value were higher by 3.1%, 2.46%, 3.0%, and 1.16%, respectively. In ablation experiments, the experimental results showed that the SE-Res block and MFCA model proposed by us were effective. COVID-ResNet transfers the shallow features to the deep, gathers the features, and makes the information complementary. COVID-ResNet can improve the work efficiency of doctors and reduce the misdiagnosis rate. It has a positive significance for the computer-aided diagnosis of COVID-19. [ABSTRACT FROM AUTHOR]

Within the background of carbon rebalancing, this paper introduces the absorption input of goods and the natural environment rebalancing input to study an endogenous growth model driven by green behavior, divides goods into three different types of green low-carbon goods based on their different absorption proportion of carbon, and introduces the environmental function, the rebalancing capacity of the natural environment, the absorption input of goods, carbon reduction research and development, and scientific and technological progress as special production factors into the endogenous growth model, and then studies a production model driven by special production factors. Through system analysis and optimization, this paper determines effective carbon rebalancing regulations for the economic system and obtains new endogenous growth laws. Empirical analysis based on the China Statistical Yearbook finds that the boundary number of the necessary conditions for absorption input at the economic level is 3.47848. When the absorption input is greater than or equal to the boundary number, regulations promoting carbon rebalancing are fully effective. In order to realize green development, it is important to strengthen the absorption input of goods and the rebalancing input of the natural environment. [ABSTRACT FROM AUTHOR]

COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism; Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19. [ABSTRACT FROM AUTHOR]

During the compaction of silty soil subgrade, different filling methods are adopted, which will significantly impact the subgrade performance, but few studies have been applied to quantify this influence. To explore the influence mechanism of dry density and sample preparation method (compaction and static compression method) on the shear strength of silty soil, the consolidated undrained shear test (CU test), dynamic triaxial test, and nuclear magnetic resonance microscopic test on silty soil were carried out in this study. The test results show that the shear strength of the sample is positively correlated with the dry density. The influence of the sample preparation method on shear strength is mainly reflected in the cohesion. The pore size distributions obtained by different sample preparation methods had smaller differences before the CU test. However, significant differences were observed after the CU test, indicating that the influence of the sample preparation method on the shear strength of the sample is not on the initial pore distribution but on the residual stress and overall uniformity. The dynamic triaxial tests show that a differential settlement may occur when multiple sample preparation (soil-filling method in subgrade practice) methods are adopted. [ABSTRACT FROM AUTHOR]

Objectives: The present study mainly focused on the assessment of developmental toxicity induced by exposure to brodifacoum (BDF) in zebrafish at early life stages. Material and Methods: Zebrafish embryos were exposed to 0.2, 0.4, and 0.8 mg/L of BDF from 6 to 96 hr post‐fertilization (hpf), and the toxic effects of BDF on early embryonic development were investigated in terms of morphological changes, oxidative stress, and alterations in heart development‐related genes. Results: The experimental results showed that BDF significantly decreased the heart rate, survival rate, body length, and spontaneous movements of zebrafish embryos at 0.8 mg/L, and the morphological developmental abnormalities were also observed at 96 hpf. In addition, exposure to BDF significantly increased oxidative stress levels in zebrafish embryos by increasing the enzymatic activities of catalase (CAT), superoxide dismutase (SOD), and malondialdehyde (MDA) levels, and decreased glutathione (GSH) levels. Furthermore, BDF treatment‐induced alterations in the expression levels of the heart development‐related genes (gata4, sox9b, tbx2b, and nppa). Conclusion: Results from this study indicated that exposure to BDF could lead to marked growth inhibition and significantly alter the activities of antioxidant enzymes in zebrafish embryos. Moreover, BDF exposure exhibited severe cardiotoxicity and significantly disrupted heart development‐related genes. The results indicated that BDF could induce developmental and cardiac toxicity in zebrafish embryos. [ABSTRACT FROM AUTHOR]

According to the reality of China, we divide Internet finance into four parts as Internet payment services, Internet financing services, Internet financial management services, and online banking business of traditional commercial banks, to discuss the impact of Internet finance on the profit efficiency of commercial banks. Our theoretical analysis shows that the external impact of Internet finance will reduce the profit of commercial banks, but Internet financing and Internet financial management have less influence, and the financial Internet business can improve the profit efficiency of commercial banks to some extent. An empirical test of Chinese data provides support for these predictions, it suggests that the estimated impact is significant and robust. Furthermore, differences in ownerships and profit efficiency of commercial banks will be at work. [ABSTRACT FROM AUTHOR]

Phosphor-converted light-emitting diodes (pc-LEDs) have important applications in security surveillance and food testing. However, developing new broadband near-infrared phosphors remains an important issue. Herein, the high-temperature solid-state reaction method was applied to synthesize a new type of Cr3+ doped Mg2Al4Si5O18 broadband NIR phosphor which matches a blue LED chip. It exhibits a broadband near-infrared (NIR) phosphor emission with a focus of 856 nm and a full width at half maximum >200 nm after excitation at 453 nm. The optimum concentration of Cr3+ was established to contain 2 mol%. At 398 K, the luminescence intensity is 45.2% of that at 298 K. The power output of NIR pc-LEDs fabricated by incorporating a 450 nm chip and the Mg2Al4Si5O18:0.02 Cr3+ phosphor is 19.69 mW at 300 mA current. Finally, the developed NIR pc-LED shows significant potential for use in night-vision imaging. [ABSTRACT FROM AUTHOR]

Enhancing the moisture resistance of Mn4+-doped fluoride phosphors is crucial for solid-state lighting (SSL) and liquid crystal display (LCD). Herein, the deteriorated K2AF6:Mn4+ (A = Ti, Si) phosphors were restored with Na2SO3 alkaline reductant, and optimal treatment conditions were systematically determined. The kinetic process for luminescence degradation and restoration, as well as the corresponding reparation mechanism were investigated in detail. These results showed that SO32− anion can reduce Mn4+ to Mn2+, rapidly remove the dark-brown layer of the degradation product, and restore the luminescence intensity of K2AF6:Mn4+ to more than 95%. Therefore, the restored K2TiF6: Mn4+ and K2SiF6: Mn4+ samples exhibit excellent moisture resistance, maintaining to be 66 and 76% of the initial intensity after immersing in deionized water for 300 min. Furthermore, fabricated white light-emitting device using the restored K2TiF6: Mn4+ phosphor also possess color rendering index of 94.2 and correlated color temperature of 3270 K. Our results provide a feasible strategy to significantly improve the moisture resistance of fluoride phosphors, demonstrating a great promise in phosphor-converted white-LED toward lighting and display field. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has attracted significant attention in partial discharge (PD) pattern recognition of gas‐insulated switchgear (GIS). However, the existing DL method extracts numerical information and ignores spatial correlation information. In addition, once the sample size is reduced, model performance is severely degraded. To address these issues, a novel capsule deep graph convolutional network (CDGCN) is employed for GIS PD pattern recognition. First, the GIS PD data is transformed to graph data. Then, the CDGCN is established for GIS PD pattern recognition. The introduction of a ChannelSortPooling layer enriches the graph representation information and filters out the feature values with higher information content. In addition, the multi‐view graph representation is converted to a capsule form, and a capsule network with stronger feature extraction capability is introduced to improve the accuracy of the final graph representation. The results reveal that the proposed CDGCN can effectively diagnose the GIS PD and is more accurate than the traditional DL method. The proposed CDGCN can handle small samples pattern recognition problems and provides a reliable reference for GIS PD pattern recognition. [ABSTRACT FROM AUTHOR]

Loop-closure detection (LCD), which aims to recognize a previously visited location, is a crucial component of the simultaneous localization and mapping system. In this paper, a novel appearance-based LCD method is presented. In particular, we propose a simple yet surprisingly useful feature matching algorithm for real-time geometrical verification of candidate loop-closures, termed as local relative orientation matching (LRO). It aims to efficiently establish reliable feature correspondences based on preserving local topological structures between the query image and candidate frame. To effectively retrieve candidate loop closures, we introduce the aggregated selective match kernel framework into the LCD task, which can effectively represent images and reduce the quantization noise of the traditional bag-of-words framework. In addition, the SuperPoint neural network is employed to extract reliable interest points and feature descriptors. Extensive experimental results demonstrate that our LRO can significantly improve the LCD performance, and the proposed overall LCD method can achieve much better performance over the current state-of-the-art on six publicly available datasets. [ABSTRACT FROM AUTHOR]

Summary: The stability and toxicity of perovskite solar cells are still preventing full industrialization. Therefore, lead‐free all‐inorganic double perovskite materials have become the focus of research in recent years. Our group proposed a new narrow bandgap lead‐free double perovskite solar cell using a high‐quality Cs2PtI6 film. In this paper, for the purpose of exploring the trend in the bandgap and stability of the perovskite‐derived Cs2PtI6 substituted by halogen ions, we performed a first‐principles investigation based on density functional theory to study the structural and electronic properties of Cs2PtI6−yCly, Cs2PtI6−yBry, Cs2PtBr6−yCly (y = 0, 1, 2, 3, 4, 5, 6). The calculated structural parameters revealed a good mechanical stability for all these compounds, and the trend in the formation energy indicated that the substitution of I− with Cl− and Br− can significantly reduce the energy and improve the thermodynamic stability. Through a comparative analysis of the electronic properties of Cs2PtX6 (X = Cl, Br, I), we found that the substitutional doping of halogen ions can effectively adjust the bandgap and show an obvious change trend. The present study may demonstrate an effective theoretical guidance for preparing lead‐free all‐inorganic perovskite solar cell materials with appropriate bandgap and excellent stability. [ABSTRACT FROM AUTHOR]

Data-driven artificial intelligence methods, especially convolutional neural networks (CNNs), have achieved excellent performance in high-voltage circuit breaker (HVCB) fault diagnosis. However, CNN relies heavily on massive data. When the amount of data decreases, the fault diagnosis performance drops severely. To settle these problems, a few-shot transfer learning (FSTL) with attention mechanism (AM) to realize the mechanical fault diagnosis of HVCBs is proposed. First, a one-dimensional CNN with AM is used to extract the fault features of HVCBs. The introduction of the AM makes CNN pay more attention to the interesting part of the fault signal to extract discriminative features. Then, domain adaptive transfer learning is used to realize a reliable diagnosis of HVCBs in small samples.The subdomain adaptation is adopted to adjust the distribution of related subdomains under the same category. The proposed subdomain adaptation can not only align the global distribution well but also effectively align the distribution of the same category of subdomains. Experimental results show that the FSTL proposed can achieve highly accurate and robust fault diagnosis of HVCBs with few-shot on-site. Compared with the traditional methods, the FSTL is obvious and provides a reliable reference for the diagnosis of HVCBs. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to propose a novel algorithm for slab boundary artifact correction in both single‐band multislab imaging and simultaneous multislab (SMSlab) imaging. Theory and Methods: In image domain, the formation of slab boundary artifacts can be regarded as modulating the artifact‐free images using the slab profiles and introducing aliasing along the slice direction. Slab boundary artifact correction is the inverse problem of this process. An iterative algorithm based on convolutional neural networks (CNNs) is proposed to solve the problem, termed CNN‐enabled inversion for slab profile encoding (CPEN). Diffusion‐weighted SMSlab images and reference images without slab boundary artifacts were acquired in 7 healthy subjects for training. Images of 5 healthy subjects were acquired for testing, including single‐band multislab and SMSlab images with 1.3‐mm or 1‐mm isotropic resolution. CNN‐enabled inversion for slab profile encoding was compared with a previously reported method (i.e., nonlinear inversion for slab profile encoding [NPEN]). Results: CNN‐enabled inversion for slab profile encoding reduces the slab boundary artifacts in both single‐band multislab and SMSlab images. It also suppresses the slab boundary artifacts in the diffusion metric maps. Compared with NPEN, CPEN shows fewer residual artifacts in different acquisition protocols and more significant improvements in quantitative assessment, and it also accelerates the computation by more than 35 times. Conclusion: CNN‐enabled inversion for slab profile encoding can reduce the slab boundary artifacts in multislab acquisitions. It shows better slab boundary artifact correction capacity, higher robustness, and computation efficiency when compared with NPEN. It has the potential to improve the accuracy of multislab acquisitions in high‐resolution DWI and functional MRI. [ABSTRACT FROM AUTHOR]

We report a "multidentate polymer microreactor" method for the creation of secondary structures of colloidal nanocrystals. Using NaYF4:Yb,Er as an example, we demonstrate that the use of sodium polyacrylate (PAAS) as a "multidentate polymer microreactor" allows the controllable growth of primary nanocrystals and induces aggregation of the nanocrystals into well-defined mesoporous clusters. [ABSTRACT FROM AUTHOR]

In the grouted soil nailing system, the bonding strength of cement grout-soil interface offers the required resistance to maintain the stability of whole structure. In practice, soil nailing applications are often placed at unsaturated conditions, such as soil slopes, shallow foundations, retaining walls and pavement structures. In these cases, the water content in the soil nail zone may increase or decrease due to rain water or dry weather, and even cannot become saturated during their design service life. In this study, the effect of water content (degree of saturation) on the shear strength of interface between cement grout and sand are experimentally investigated by means of direct shear test. Meanwhile the water retention curve was determined and interface microstructure was observed. Experimental results show that the shear strength of interface changes non-monotonously with degree of saturation when the interface was prepared, due to the non-monotonousness of the cohesiveness between soil particles. The less the cohesiveness between sand particles, the more grout was observed been penetrated into the voids, and thus the larger the interface shear stress.

Warming trends of the Laurentian Great Lakes and surrounding areas have been observed in recent decades, and concerns continue to rise about the pace and pattern of future climate change over the world's largest freshwater system. To date, many regional climate models used for the Great Lakes projection either neglected the lake-atmosphere interactions or only coupled with 1-D column lake models to represent the lake hydrodynamics. The study presents the Great Lakes climate change projection that has employed the two-way coupling of a regional climate model with a 3-D lake model (GLARM) to resolve 3-D hydrodynamics important for large lakes. Using the three carefully selected CMIP5 AOGCMS, we show that the GLARM ensemble average substantially reduces the surface air temperature and precipitation biases of the driving AOGCM ensemble average in present-day climate simulations. The improvements are not only displayed from the atmospheric perspective but also evidenced in accurate simulations of lake surface temperature, and ice coverage and duration. After that, we present the GLARM projected climate change for the mid-21st century (2030-2049) and the late century (2080-2099) for the RCP4.5 and RCP8.5. Under RCP 8.5, the Great Lakes basin is projected to warm by 1.3-2.2 °C by the mid-21st century and 4.0-4.9 °C by the end of the century relative to the early-century (2000-2019). Moderate mitigation (RCP 4.5) reduces the mid-century warming to 0.8-1.9 °C and late-century warming to 1.8-2.7 °C. Annual precipitation in GLARM is projected to increase for the entire basin, varying from -0.4 % to 10.5 % during the mid-century and 1.2 % to 28.5 % during the late-century under different scenarios and simulations. The most significant increases are projected in spring and early summer when current precipitation is highest and little increase in winter when it is lowest. Lake surface temperatures (LSTs) are also projected to increase across the five lakes in all of the simulations, but with strong seasonal and spatial heterogeneities. The most significant LST increase will occur in Lake Superior. The strongest warming was projected in spring, followed by strong summer warming, suggesting earlier and more intense stratification in the future. In contrast, a relatively smaller increase in LSTs during fall and winter are projected with heat transfer to the deepwater due to strong mixing and energy required for ice melting. Correspondingly, the highest monthly mean ice cover is projected to be 3-6 % and 8-20 % across the lakes by the end of the century in RCP 8.5 and RCP 4.5, respectively. In the coastal regions, ice duration will decrease by up to 30-50 days. [ABSTRACT FROM AUTHOR]

With the rapid development of NIR pc-LEDs, the development of a novel NIR phosphor with broadband emission, strong luminescence and high thermal stability is urgent. Here, three isotypic garnet fluorides Na3X2Li3F12 (X = Al, Ga, or In) (NXLF) activated by Cr3+ were synthesized. The emission peak shifts from 752 to 778 nm and FWHM expands from 101 to 121 nm. The values of Dq/B indicate that Cr3+ ions undergo a weak crystal field. Among them, NInLF:Cr3+ has the highest IQE of 87.2% and NAlLF:Cr3+ exhibits the best thermal stability, which maintains 82% of its initial intensity at 423 K. The Huang–Rhys factor of NAlLF:Cr3+ exhibits a rather low EPC. The output power and photoelectric conversion efficiency of the packaged NIR pc-LEDs could achieve 149 mW and 14.3% at 350 mA. Under the irradiation of NIR light, clear images of blood vessel distribution of palm and arm were obtained, implying that these phosphors have great application potential in the biomedical field. [ABSTRACT FROM AUTHOR]

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the "black box" problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) phosphor-converted light emitting diode (pc-LED) light sources have broad application prospects in environmental science, biomedical and plant growth fields. However, NIR phosphors still suffer from narrowband emission and low thermal stability. Here, we prepared a novel Lu2CaMg2−xSi3O12:xCr3+ (LCMS:xCr3+, x = 0.005–0.06) phosphor with broadband emission by a high-temperature solid state reaction. Under excitation at 450 nm, the emission spectrum of the LCMS:0.05Cr3+ phosphor shows a broadband emission in the range of 650–1000 nm with a large full width at half maximum (FWHM) of 125 nm and an R-line emission of 692 nm. In addition, the LCMS:0.05Cr3+ phosphor has good thermal stability and can maintain 70% emission intensity at 150 °C relative to that at room temperature. The LCMS:0.05Cr3+ phosphor exhibits a high internal quantum efficiency (IQE) of ∼76%. Using this phosphor, a NIR pc-LED with photoelectric efficiencies of 14.8% at 100 mA and 6.0% at 320 mA and NIR output powers of 59.5 mW at 100 mA and 181.0 mW at 320 mA was obtained. The broadband LCMS:0.05Cr3+ phosphor with a NIR emission peaked at 750 nm can serve as a light source for plant cultivation and has superior application prospects in the agriculture field. [ABSTRACT FROM AUTHOR]