Explore the vast range of titles available.

The electronic quality of graphene has improved significantly over the past two decades, revealing novel phenomena. However, even state-of-the-art devices exhibit substantial spatial charge fluctuations originating from charged defects inside the encapsulating crystals, limiting their performance. Here, we overcome this issue by assembling devices in which graphene is encapsulated by other graphene layers while remaining electronically decoupled from them via a large twist angle (~10–30°). Doping of the encapsulating graphene layer introduces strong Coulomb screening, maximized by the sub-nanometer distance between the layers, and reduces the inhomogeneity in the adjacent layer to just a few carriers per square micrometre. The enhanced quality manifests in Landau quantization emerging at magnetic fields as low as ~5 milli-Tesla and enables resolution of a small energy gap at the Dirac point. Our encapsulation approach can be extended to other two-dimensional systems, enabling further exploration of the electronic properties of ultrapure devices. While the electronic quality of graphene has significantly improved during the last two decades, charged defects inside encapsulating crystals still limit its performance. Here, the authors overcome this limitation and report the enhanced electronic quality of graphene enabled by tuneable Coulomb screening inside large-angle twisted bilayer and trilayer graphene devices, showing Landau quantization at magnetic fields down to ~5 mT.

Using the bulk g-C3N4 as a precursor, four g-C3N4 nanosheets were further prepared by ultrasonic, thermal, acid, and alkali exfoliation. The structures of these materials were characterized by various techniques such as X-ray powder diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy. The synergistical Fenton catalysis of these materials with Cu2+ was evaluated by using rhodamine B as a simulated organic pollutant. The results showed that there existed a significant synergistical Fenton catalysis between Cu2+ and g-C3N4. This synergistic effect can be observed even when the concentration of Cu2+ was as low as 0.064 mg L−1. The properties of g-C3N4 strongly influenced the catalytic activity of the Cu2+/g-C3N4 system. The coexistent of Cu2+ and the alkali exfoliated g-C3N4 showed the best catalytic activity. Hydroxyl radicals as oxidizing species were confirmed in the Cu2+/g-C3N4 system by electron paramagnetic resonance spectra. The synergistic catalysis may be attributed to the easier reduction of Cu2+ adsorbed on the g-C3N4. This study provided an excellent Fenton catalytic system, and partly solved the rapid deactivation of heterogeneous Fenton catalysts caused by the leaching of metal ions.

BackgroundCompared with the general population, patients with lupus nephritis (LN) are still at high risk of adverse pregnancy outcomes (APOs), including fetal loss, preterm birth and intrauterine growth retardation (IUGR). Umbilical artery is particularly important for placental perfusion and fetal development. Increased umbilical artery resistance could be traced by Doppler velocimetry, which could be used as a screening tool for placenta-related diseases. However, the predictive value in HDP of lupus pregnancies has not been widely assessed.ObjectivesTo examine the predictive value of the fetal umbilical artery Doppler on adverse pregnancy outcomes (APOs) in pregnant women with lupus nephritis (LN).MethodsThe clinical data of 158 LN pregnant patients from the First Affiliated Hospital of Sun Yat-sen University were analysed retrospectively.ResultsTotally, 119 were diagnosed before pregnancy and 39 were newly diagnosed during pregnancy. One or more APOs occurred in 74.7% of patients with LN and 40 (25.3%) were without any APOs. Fifty-four of pregnancies (12.2%) resulted in fetal loss. A total of 55 pregnancies were preterm birth, 24 were IUGR, 14 were fetal distress and 9 were neonatal lupus. Doppler pulsatility index (PI), resistance index (RI) as well as S/D value were significantly higher in the APOs groups than in the patients without APOs (p<0.05). The area below the receiver operating characteristic (ROC) curve for PI, RI and S/D all was 0.7 (95%CI 0.6~0.8). PI with cut-off value of 0.9 indicated the highest risk of APOs, with sensitivity of 28.3% and specificity of 97.2%. Regarding 0.6 as the cut-off value for RI to predict APOs, the sensitivity was 45.7% and the specificity was 80.6%. The optimal cut-off value for S/D was 2.5, at which sensitivity (39.1%) and specificity (90.0%) had the best combination.ConclusionsPregnancies in LN were more prone to pregnancy loss and preterm birth. Umbilical artery Doppler was a useful monitoring measure for APOs in pregnancies of LN.Disclosure of InterestNone declared

Silicon carbide particle-reinforced aluminum matrix (SiC p /Al) composites have attracted considerable interest as potential materials due to their excellent engineering properties. Many research works have been done associated with turning SiC p /Al in the past. However, it still lacks of experience on milling of SiC p /Al composites. This paper presents an exploratory study on precision milling of SiC p /Al composites with higher volume fraction (SiC p , 65 %) and larger particle size. The experiments were conducted on a Kern MMP 2522, high-precision micromilling machine center. A single flute monocrystalline diamond end mill was used to mill straight grooves with cutting parameters in a few micros. The machined surface quality including surface roughness and surface topography were studied. The cutting mechanisms of SiC particle and tool wear characters were also investigated. The results showed that mirror-like surface with surface roughness around 0.1 μm Ra can be achieved by precision milling with small parameters in the range of a few micros. Most of the SiC reinforcements were cut in partial ductile way with microfractures and cracks on the machined surface; tool wear included chipping and cleavage on monocrystalline diamond edge. A large flank wear on tool bottom face was observed and suspected to be caused by coaction of chemical transition and mechanical abrasion.

Vegetation detection aims to find the area which should be attributed with the labels of vegetation on the captured images, such as forest, grass land etc., and nowadays it is a key research topic in the field of remote sensing information processing and application. Over the last few years, the deep learning method based on convolutional neural network (CNN) has become the mainstream method for vegetation detection. However, due to the peculiarities of the underlying encoding and decoding structures, it is common for some CNN methods to loss some boundary details of vegetation when employing high-resolution images with rich details and clear boundaries. In order to improve the boundary localization capability of vegetation, this paper proposes a hybrid solution, i.e., an MLP (MultiLayer Perceptron)-based high-resolution image adaptive superpixels vegetation detection method. Compared with the traditional watershed transform algorithm, this method adopts the two-step boundary marching criterion to generate superpixels with more adherent boundary and compact regularity which contains adaptive neighborhood information by design. Based on the generated superpixels with boundary detail information, this paper applies MLP for binary predictions, i.e., vegetation or non-vegetation. The experimental results show that our method has more precise vegetation boundary localization and higher accuracy compared with several state-of-the-art methods on the UAV image data set and ISPRS data set.

Pixel-wise land cover classification is a fundamental task in remote sensing image interpretation, aiming to identify planimetric features (e.g., trees, waters, buildings etc.) from earth's surface. Recently, deep learning methods based on fully convolutional neural networks (FCN) become the mainstream approach for land cover classification, thanks to their superior performance in the image context perception and features learning. However, for high-resolution remote sensing images with huge quantity of object details, some deep learning based methods often ignore many important details by nature, specially, in the procedure of pooling operation and stacking convolutions in conventional FCN, it can leads to ambiguous classification of adjacent objects. To refine lost details caused by the stacking convolutions, we propose a position-sensitive attention (PSA) based on skip connections for land cover classification with high-resolution remote sensing images, which designs to deliver a weight that is sensitive to the spatial details in remote sensing images, the PSA module is able to improve pixel-level details scattered across spatial positions. Experimental results demonstrate that our method can be feasible to existing FCN-based models, 1% improvement in F1-score is obtained on 2021 \"Shengteng Cup\" competition dataset after using PSA, when comparing to several state-of-the-art methods, similar or even better performance is achieved on the ISPRS Vaihingen 2D dataset, but with less parameters.

The magnetic reversal behavior of a ferromagnet (FM) coupled through an FeMn antiferromagnet (AF) to a pinned ferromagnet has been investigated by polarized neutron reflectivity measurements. With FeMn as the AF layer it is found that there exists 90° interlayer coupling through this layer and that this plays a key role in the transfer of the exchange bias (EB) effect from the FM/AF interface to the AF/pinned-FM interface. Combined with Monte Carlo simulations, we demonstrate that the competition between the interlayer coupling and the anisotropy of the AF layer results in a control of the EB effect which has potential for device applications.

Nowadays, Visual SLAM has gained ample successes in various scenarios. For feature-based system, it is still limited when running in an indoor room, as the indoor scene is often with few and simple texture which result in less and unevenly distributed point features. To solve this limitation, line features which are quite rich in an indoor scene are extracted and used. However, not all features can geometrically contribute to pose estimation, specifically, line features that are consistent to the motion direction provide only weak geometric constraint for solving pose parameters. Therefore, this paper proposes a selection method for reasonable line features, in particular, based on the Manhattan World Assumption (MWA), structural line features are firstly extracted instead of normal line features. Then, the structural line features are selected according to the direction information of vanishing points and selected for a stronger geometric constraint on pose estimation. In general, the selected structural lines require that the intersection angle between the corresponding principal direction and the camera motion direction is higher than a threshold, which is extensively investigated in the experiments. The experimental results show that, compared to the original ORB-SLAM2, the localization accuracy after using the proposed method can be improved by around 15%-40% on various public datasets, and the real-time performance can be basically guaranteed even including the extra time spent on the selection procedure.

Heavy metal pollution in soils is a common environmental issue. However, previous studies have primarily investigated the total concentrations of heavy metals, while fraction analysis of heavy metals has rarely been conducted. To bridge this gap, 18 topsoil samples of gray-brown desert farmland soil from the Jinchuan District, Gansu Province, China, were collected to analyze the total concentrations and fractions of Cu, Ni, Cd, Hg, Zn, Pb, and As with the Tessier sequential extraction method that were mainly found in the residual fraction (Pb 50%, As 99%). Ni and Pb were associated with secondary bound to carbonate fractions in most soil samples, while Cd, Hg, and Zn were associated with secondary bound to organic matter fractions. Cu was associated with secondary water-soluble, exchangeable, bound to carbonates, bound to Fe-Mn oxides, and bound to organic matter fractions. The mean values of RAC were in the sequence of Cu > Zn > Cd > Pb > Ni > Hg > As. The results of RSP indicated that the ecological risk levels of Cd, Ni, Cu, Zn, As, and Hg in the soils were low, and Pb was the most significant potential risk factor among all elements. The findings can be used to practice sustainable soil management in the area.

Image retrieval is one of the supporting technologies for (near) real-time photogrammetry and loop closure detection in visual SLAM, the conventional retrieval strategy is to firstly obtain the image features of the query image and database images, and search for the resulted images based on nearest features retrieval. However, the image retrieval method based on traditional hand-crafted features (SIFT, SURF, GIST) are hard to guarantee both the efficiency of time and precision in practical applications. Nowadays, learning-based features have shown superior performance in ample computer vision tasks. Thus, this paper investigates several popular learning-based global features (ResNet101, VGG16+NetVLAD, Yolov3+VGG16+NetVLAD) and local features (SuperPoint), to take care of both time efficiency and precision, we present hierarchical image retrieval solutions that combines these two kinds of features, in which global feature is for accelerating searching speed and local feature is for precision. Specifically, three sets of hierarchical retrieval solutions are designed by various combinations of learning-based global feature and local feature. Their precision and time efficiency are compared on different public benchmarks (one contains more than 10,000 images), the experimental results show that among the proposed solutions, VGG16+NetVLAD+SuperPoint has the best performance in efficiency, but the precision is slightly lower than the solution preprocessed with Yolov3.