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Abstract Developing light yet strong aluminum (Al)-based alloys has been attracting unremitting efforts due to the soaring demand for energy-efficient structural materials. However, this endeavor is impeded by the limited solubility of other lighter components in Al. Here, we propose to surmount this challenge by converting multiple brittle phases into a ductile solid solution in Al-based complex concentrated alloys (CCA) by applying high pressure and temperature. We successfully develop a face-centered cubic single-phase Al-based CCA, Al55Mg35Li5Zn5, with a low density of 2.40 g/cm3 and a high specific yield strength of 344×103 N·m/kg (typically ~ 200×103 N·m/kg in conventional Al-based alloys). Our analysis reveals that formation of the single-phase CCA can be attributed to the decreased difference in atomic size and electronegativity between the solute elements and Al under high pressure, as well as the synergistic high entropy effect caused by high temperature and high pressure. The increase in strength originates mainly from high solid solution and nanoscale chemical fluctuations. Our findings could offer a viable route to explore lightweight single-phase CCAs in a vast composition-temperature-pressure space with enhanced mechanical properties.

Enhancing the corona resistance of epoxy resin (EP) is crucial for ensuring the reliable operation of electrical equipment and power systems, and the incorporation of inorganic nanofillers into epoxy resin has shown significant potential in achieving this. In this study, functionalized graphene oxide (KHGO) was synthesized via a sol-gel method to enhance the corona resistance of EP with electrochemical impedance spectroscopy (EIS) used to assess the properties of KHGO/EP composites. Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) verified the successful grafting of epoxy groups onto the GO surface. The thermal conductivity and stability of the KHGO/EP composite initially increased with KHGO content but declined when the content exceeded 1.2 wt.%. Positron annihilation lifetime spectroscopy (PALS) indicated that KHGO improved interfacial compatibility with EP compared to GO, with agglomeration occurring when KHGO content exceeded a threshold value (1.2 wt.%). EIS analysis revealed that the corona resistance of the KHGO/EP composite was optimal at a filler content of 0.9 wt.%. After corona treatment, the saturation water uptake of the 0.9 wt.% KHGO/EP composite decreased by 15% compared to pure EP with its porosity reduced to just 1/40th of that of pure EP. This study underscores that well-dispersed KHGO/EP composite exhibits excellent corona resistance property suggesting the potential for industrial applications in high-voltage equipment insulation.

Abstract This study proposes a self‐supervised method to reconstruct 3D limbic structures from 2D landmarks extracted from a single view. The loss of self‐consistency can be reduced by performing a random orthogonal projection of the reconstructed 3D structure. Thus, the training process can be self‐supervised by using geometric self‐consistency in the reconstruction–projection–reconstruction process. The self‐supervised network mainly consists of graph convolution and Transformer encoders. This network is called the SS‐Graphformer. By adding a discriminator, the SS‐Graphformer is used as a generator to form a Wasserstein Generative Adversarial Network architecture with a Gradient Penalty to improve the accuracy of the reconstruction. It is experimentally demonstrated that the addition of the 2D structure discriminator can significantly improve the accuracy of the reconstruction.

Al-based foams have drawn increasing attention from industry due to their integration of structure and functional properties. However, large-sized Al-based foams still cannot be homogeneously strengthened by long-time aging due to their low thermal conductivity. In this study, we proposed an age-hardening approach that was applied in large-sized Al-0.16Sc-0.17Zr (wt.%) foams via micro-alloying with Zr and Ti compared with Al-0.21Sc foams; it not only achieved homogeneous strength by long-term aging but also reduced the cost of the alloy by substituting Zr and Ti for the more expensive Sc content. The results show that the Al3(Sc, Zr, Ti) phase with a core–shell structure as a crucial precipitation strengthening phase by micro-alloying with Zr and Ti was less prone to coarsening after a prolonged aging heat treatment. Therefore, the yielding strength of Al-Sc foam micro-alloying with Zr and Ti remained almost unchanged after a maximum aging time of 1440 h due to less coarsening precipitate, which is consistent with the results of mechanical experiments. These findings provide a new way for the heat treatment strengthening of large-sized Al-based foams, thus promoting their industrial applications.

BackgroundMyocardial fibrosis, as quantified by late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR), provides valuable prognostic information for patients with myocarditis. However, due to the low incidence rate of fulminant myocarditis (FM) and accordingly small sample size, the knowledge about the role of LGE to patients with FM is limited.Methods and resultsA total of 44 adults with viral-FM receiving the Chinese treating regimen were included in this retrospective study. They were divided into the low LGE group and the high LGE group according to the ratio of LGE to left ventricular mass (LGE mass%). CMR exams and LGE were performed after hemodynamic assistance at discharge in all patients with FM. Routine echocardiography parameters and global longitudinal strain (GLS) at discharge and at 2-year follow-up were obtained and then compared. Both left ventricular ejection fraction (LVEF) and GLS showed no significant difference in both groups at discharge, whereas significant differences were observed at 2-year follow-up between two groups. Moreover, there were significant improvements of LVEF and GLS in the low LGE group, but not in the high LGE group during the 2-year period. Furthermore, LGE mass% was negatively correlated with GLS and LVEF.ConclusionsThere were two distinct forms of LGE presentation in patients with FM. Moreover, the cardiac function of patients with low LGE was significantly better than those with high LGE at 2-year follow-up. LGE mass% at discharge provided significant prognosis information about cardiac function of patients with FM.

Valleytronics uses valleys, a novel quantum degree of freedom, to encode information. It combines other degrees of freedom, such as charge and spin, to produce a more comprehensive, stable, and efficient information processing system. Valleytronics has become an intriguing field in condensed matter physics due to the emergence of new two-dimensional materials in recent years. However, in nonmagnetic valleytronic materials, the valley polarization is transient and the depolarization occurs once the external excitation is withdrawn. Introduction of magnetic field is an effective approach to realizing the spontaneous valley polarization by breaking the time-reversal symmetry. In hexagonal magnetic valleytronic materials, the inequivalent valleys at the K and – K ( K ′) Dirac cones have asymmetric energy gaps and Berry curvatures. The time-reversal symmetry in nonmagnetic materials can be broken by applying an external magnetic field, adding a magnetic substrate or doping magnetic atoms. Recent theoretical studies have demonstrated that valleytronic materials with intrinsic ferromagnetism, now termed as ferrovalley materials, exhibit spontaneous valley polarization without the need for external fields to maintain the polarization. The coupling of the valley and spin degrees of freedom enables stable and unequal distribution of electrons in the two valleys and thus facilitating nonvolatile information storage. Hence, ferrovalley materials are promising materials for valleytronic devices. In this review, we first briefly overview valleytronics and its related properties, the ways to realize valley polarization in nonmagnetic valleytronic materials. Then we focus on the recent developments in two-dimensional ferrovalley materials, which can be classified according to their molecular formula and crystal structure: MX _2 ; M(XY) _2 , M(XY _2 ) and M(XYZ) _2 ; M _2 X _3 , M _3 X _8 and MNX _6 ; MNX _2 Y _2 , M _2 X _2 Y _6 and MNX _2 Y _6 ; and the Janus structure ferrovalley materials. In the inequivalent valleys, the Berry curvatures have opposite signs with unequal absolute values, leading to anomalous valley Hall effect. When the valley polarization is large, the ferrovalleys can be selectively excited even with unpolarized light. Intrinsic valley polarization in two-dimensional ferrovalley materials is of great importance. It opens a new avenue for information-related applications and hence is under rapid development.

In this work, to explore potential substitutions for the Co binder phase, ultrafine-grain WC-based cermets with various binder phases of Co, Ni and AlCoCrNiFeCu HEA were prepared using the SPS method. Based on SPS, WC-based cermets were fabricated at higher speed, showing fine carbide particles less than 410 μm. The microstructure, mechanical properties and wear properties were systematically evaluated. By comparison, the grain size of WC was the lowest for WC-10Co, while WC-10 HEA cermet held the coarsest WC particles. The hardness and fracture toughness of WC-10 HEA were the best among all three samples, with values of 93.2 HRA and 11.3 MP·m1/2. However, the bending strength of WC-10HEA was about 56.1% lower than that of WC-10Co, with a value of 1349.6 MPa. The reduction in bending strength is attributed to the lower density, formation of a newly Cr-Al rich phase and coarser WC grains. In dry sliding wear conditions, WC-10 HEA showed the lowest wear rate (0.98 × 10−6 mm3/(N·m)) and coefficient of friction (0.19), indicating the best wear resistance performance. This reveals that WC-based cermet with a HEA binder phase has superior wear performance due to the higher hardness and good self-lubricating effect of the wear products.

In this study, the aging process of a surface-functional titanium dioxide nanosheet (f-TNS) composited room-temperature-vulcanized silicone rubber (RTV) composite coating was simulated in a NO2 generation device, and then the electrochemical impedance spectroscopy (EIS) of the aged composite coating was tested in a 3.5% NaCl solution. The water permeation process was analyzed by the changes in the impedance modulus, porosity, and breakpoint frequency of the composite coating. The experimental results show that the water permeability of aged RTV decreases first and then increases with the increase in the composite proportion of f-TNS. When the composite proportion of TNS was 0.3 wt.%, the composite sample had the minimum water permeability and the best resistance to NO2 corrosion. The effect of TNS on the NO2 aging resistance of RTV composites and its mechanism were studied by SEM, FT-IR, and XPS. The impedance modulus and porosity of the aged 0.3% f-TNS/RTV, respectively, were 1.82 × 107 Ω cm2 and 0.91 × 10−4%, which increased by 2.23 times and decreased by 0.37 times, respectively, compared with the values of aged pure RTV sample. In addition, the breakpoint frequency of the aged 0.3% f-TNS/RTV also significantly reduced to 11.3 Hz, whereas it was 35 Hz in aged pure RTV.

High-resolution range profile (HRRP), characterized by its high availability and rich target structural information, has been extensively studied. However, HRRP-based target recognition methods using closed datasets exhibit limitations when it comes to identifying new classes of targets. The scarcity of samples for new classes leads to overfitting during the deep learning process, and the similarity in the scattering structures of different ships, combined with the significant structural differences among samples of the same ship, contribute to a high level of confusion among targets. To address these challenges, this paper proposed Task-Specific Mate-learning (TSML) for few-shot HRRP. Firstly, a Task-Adaptive Mixed Transfer (TAMT) strategy is proposed, which combines basic learning with meta-learning, to reduce the likelihood of overfitting and enhance adaptability for recognizing new classes of ships. Secondly, a Prototype Network is introduced to enable the recognition of new classes of targets with limited samples. Additionally, a Space-Adjusted Meta Embedding (SAME) is proposed based on the Prototype Network. This embedding function, designed for HRRP data, modifies the distances between samples in meta-tasks by increasing the distances between samples from different ships and decreasing the distances between samples from the same ship. The proposed method is evaluated based on an actual measured HRRP dataset and the experimental results prove that the proposed method can more accurately recognize the unknown ship classes with a small number of labels by learning the known classes of ships. In addition, the method has a degree of robustness to the number of training samples and a certain generalization ability, which can produce improved results when applied to other backbones.

In this study, silicone rubber (SiR) with 0, 90, and 180 parts of aluminum hydroxide (Al(OH)3, ATH) contents prepared in the laboratory was treated in a certain concentration of NO2 for 0, 12, 24, and 36 h. Fourier transform-infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and thermogravimetry (TG) were used to study the changes in the surface structure and thermal stability of SiR, as well as the influence of Al(OH)3 on the properties of SiR. According to AFM, the root-mean-square roughness of ATH-90 SiR was 192 nm, which was 2.7 times of ATH-0 SiR. With the incorporation of ATH, the surface of SiR became more susceptible to corrosion by NO2. According to FT-IR and XPS, with the increase in aging time, the side chain Si-CH3 of polydimethylsiloxane (PDMS) was oxidized gradually and a few of nitroso -NO2 groups were formed. According to TG, the incorporation of ATH caused the maximum decomposition rate temperature of PDMS to advance from 458.65 °C to 449.37 and 449.26 °C, which shows that the thermal stability of SiR degraded by adding ATH. After NO2 aging, a new decomposition stage appeared between 75 and 220 °C (stage Ⅰ), and this decomposition trend was similar to aluminum nitrate, which was proven to reduce the thermal stability of PDMS. The effects of NO2 on the surface structure and thermal stability of different ATH contents of silicone rubber were preliminarily clarified by a variety of characterization methods, which provided ideas for the development of silicone rubber resistant to NO2 aging.

In this study, functional graphene oxide (f-GO) nanosheets were prepared to enhance the NO2 resistibility of room-temperature-vulcanized (RTV) silicone rubber. A nitrogen dioxide (NO2) accelerated aging experiment was designed to simulate the aging process of nitrogen oxide produced by corona discharge on a silicone rubber composite coating, and then electrochemical impedance spectroscopy (EIS) was used to test the process of conductive medium penetration into silicone rubber. After exposure to the same concentration (115 mg·L−1) of NO2 for 24 h, at an optimal filler content of 0.3 wt.%, the impedance modulus of the composite silicone rubber sample was 1.8 × 107 Ω·cm2, which is an order of magnitude higher than that of pure RTV. In addition, with an increase in filler content, the porosity of the coating decreases. When the content of the nanosheet increases to 0.3 wt.%; the porosity reaches a minimum value 0.97 × 10−4%, which is 1/4 of the porosity of the pure RTV coating, indicating that this composite silicone rubber sample has the best resistance to NO2 aging.

In this study, we propose a reconstruction and optimization neural network (RONN), a novel neural network for nonrigid structure from motion, which is completed by an unsupervised convolution neural network. Compared with the traditional method for directly solving 3D structures, our model focuses on depth information that is lost owing to projection. This mathematical model is developed using a convolutional neural network with three modules for integration, reconstruction, and optimization, as well as two prior-free loss functions. The proposed RONN achieves competitive accuracy on several tested sequences and high visual quality of various real video sequences.

Silicone rubber is widely used as an insulating material. In this article, silicone rubber samples were prepared by varying the content of crosslinker (2,5-bis(tert-butyl-peroxy)-2,5-dimethylhexane, DBPMH), and the free volume holes in the samples were investigated by means of positron annihilation lifetime spectroscopy (PALS) measurement. The surface chemical structure, surface micromorphology and water diffusion of the samples after corona discharge treatment were studied by FTIR, SEM and EIS measurements, respectively. As the crosslinker weight ratio increased from 0.2 wt.% to 1.5 wt.%, the mean free volume hole size first decreased and then remained unchanged. However, the concentration of free volume holes did not vary as the crosslinker weight ratio increased. SEM morphologies show that surface cracks were produced on samples having high crosslinking levels after corona treatment. The water diffusion coefficient of samples after corona treatment increased from 3.13 × 10−10 cm2 s−1 to 17.68 × 10−10 cm2 s−1 in the initial immersion period, as the crosslinker weight ratio increased from 0.2 wt.% to 3.0 wt.%. The results indicated that deterioration of samples with high crosslinking levels were more serious and water repellency more easily lost. The corona resistance ability of low crosslinking level silicone rubber stems from internal low molecular weight molecules.

By performing first-principles calculations, we studied hexagonal-boron-nitride (hBN)-supported graphene, in which moiré structures are formed due to lattice mismatch or interlayer rotation. A series of graphene/hBN systems has been studied to reveal the evolution of properties with respect to different twisting angles (21.78°, 13.1°, 9.43°, 7.34°, 5.1°, and 3.48°). Although AA- and AB-stacked graphene/hBN are gapped at the Dirac point by about 50 meV, the energy gap of the moiré graphene/hBN, which is much more asymmetric, is only about several meV. Although the Dirac cone of graphene residing in the wide gap of hBN is not much affected, the calculated Fermi velocity is found to decrease with the increase in the moiré super lattice constant due to charge transfer. The periodic potential imposed by hBN modulated charge distributions in graphene, leading to the shift of graphene bands. In agreement with experiments, there are dips in the calculated density of states, which get closer and closer to the Fermi energy as the moiré lattice grows larger.

An air pollution detector is proposed based on a tube-shaped single-electron transistor (SET) sensor. By monitoring the flow control component of the detector, each air pollutant molecule can be placed at the center of a SET nanopore and is treated as an island of the SET device in the same framework. Electron transport in the SET was incoherent, and the performances of the SET were sensitive at the single molecule level. Employing first-principles calculations, electronic features of an air pollutant molecule within a tube-shaped SET environment were found to be independent of the molecule rotational orientations with respect to axis of symmetry, unlike the electronic features in a conventional SET environment. Charge stability diagrams of the island molecules were demonstrated to be distinct for each molecule, and thus they can serve as electronic fingerprints for detection. Using the same setup, quantification of the air pollutant can be realized at room temperature as well. The results presented herein may help provide guidance for the identification and quantification of various types of air pollutants at the molecular level by treating the molecule as the island of the SET component in the proposed detector.

To solve the problems such as obvious speckle noise and serious spectral distortion when existing fusion methods are applied to the fusion of optical and SAR images, this paper proposes a fusion method for optical and SAR images based on Dense-UGAN and Gram–Schmidt transformation. Firstly, dense connection with U-shaped network (Dense-UGAN) are used in GAN generator to deepen the network structure and obtain deeper source image information. Secondly, according to the particularity of SAR imaging mechanism, SGLCM loss for preserving SAR texture features and PSNR loss for reducing SAR speckle noise are introduced into the generator loss function. Meanwhile in order to keep more SAR image structure, SSIM loss is introduced to discriminator loss function to make the generated image retain more spatial features. In this way, the generated high-resolution image has both optical contour characteristics and SAR texture characteristics. Finally, the GS transformation of optical and generated image retains the necessary spectral properties. Experimental results show that the proposed method can well preserve the spectral information of optical images and texture information of SAR images, and also reduce the generation of speckle noise at the same time. The metrics are superior to other algorithms that currently perform well.

In this paper, gas chromatography–mass spectrometry (GC–MS) and positron annihilation lifetime spectroscopy (PALS) were used to probe the changes of oligomers and the polydimethylsiloxane (PDMS) network in silicone rubber, after different durations of UVA/UVB irradiation. At the early stage (4-D9 decreases. The o-Ps intensity of the extracted silicone rubber increases in the stage after UVB irradiation. These results indicate the crosslinking of oligomers into the PDMS network. After a long duration (>300 h) of UVB irradiation, D4 was generated and the lifetime of τ3 also increased, indicating the rupture of the Si-O bond in the PDMS network. These two aging processes were termed the post curing process and the chain session process. The new finding was that UVA could only induce the post curing process; UVB causes the rupture of the chemical bond in silicone rubber. Photons of UVB could break the C-H bond, and then trigger the backbiting decomposition of PDMS, breaking the Si-O bond, while the photons of UVA cannot. The fact that D4 was generated after UVB irradiation can be used to evaluate the UVB stability of silicone rubber in the future.

Valley pseudospin, a novel quantum degree of freedom, is expected to show valley Zeeman effect in analogy to real spin in magnetic field B. By performing first-principles calculations, we studied the magnetic effect on valley pseudospin in monolayer WSe2. With the application of B, the time reversal symmetry is broken. Our calculation shows that the valley energy degeneracy is broken and the valley Zeeman splitting varies linearly with B, agreeing well with the experiments. It is found that the valley Zeeman splitting is contributed mainly from the atomic orbital magnetic moment of W atom, but the valley contribution is still appreciable. The Berry curvatures of the two inequivalent valleys of monolayer WSe2 are opposite and their change induced by B also depends linearly on B. The calculated circular dichroism and dielectric function reveal that the optical valley-dependent selection rule is preserved and the original single peak in polarization-resolved photoluminescence spectrum will be split into two peaks after the application of B. Our studies demonstrate the possibility of magnetic manipulation of the valley pseudospin.

In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such as water bodies. One of the current challenges is how to fuse the benefits of both to obtain more powerful classification capabilities. This study proposes a classification model based on random forest with the conditional random fields (CRF) for feature-level fusion classification using features extracted from polarized SAR and optical images. In this paper, feature importance is introduced as a weight in the pairwise potential function of the CRF to improve the correction rate of misclassified points. The results show that the dataset combining the two provides significant improvements in feature identification when compared to the dataset using optical or polarized SAR image features alone. Among the four classification models used, the random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper obtained the best overall accuracy (OA) and Kappa coefficient, validating the effectiveness of the method.

Synthetic aperture radar (SAR) provides rich information about the Earth’s surface under all-weather and day-and-night conditions, and is applied in many relevant fields. SAR imagery semantic segmentation, which can be a final product for end users and a fundamental procedure to support other applications, is one of the most difficult challenges. This paper proposes an encoding-decoding network based on Deeplabv3+ to semantically segment SAR imagery. A new potential energy loss function based on the Gibbs distribution is proposed here to establish the semantic dependence among different categories through the relationship among different cliques in the neighborhood system. This paper introduces an improved channel and spatial attention module to the Mobilenetv2 backbone to improve the recognition accuracy of small object categories in SAR imagery. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method.

With the increase in electricity demand, the ampacity calculation based on the dynamic thermal rating (DTR) technology is increasingly significant for assessing and improving the power transfer capacity of the existing overhead conductors. However, the DTR models now available present some inadequacies in measurement techniques related to wind speed. Therefore, it is essential to propose a new model instead of wind speed measuring in DTR technology. In this paper, the influence analysis of various weather parameters on the conductor ampacity is carried out by using the real weather data. Based on the analysis, it is confirmed that the impact of wind speed is significant, especially in the case of the low wind speed. Moreover, an equivalent heat transfer (EHT) model for DTR technology is proposed instead of wind speed measuring. For this EHT model, the calculation of conductor ampacity is realized through investigating the correlation of heat losses between the heating aluminum (Al) ball and conductor. Finally, combined with the finite element method (FEM), the EHT model proposed in this paper is verified by the Institute of Electrical and Electronic Engineers (IEEE) standard. The results indicate that the error of the EHT model is less than 6% when employing the steady thermal behavior of the Al ball to calculate the ampacity. The EHT model is useful in the real-time thermal rating of overhead conductors. It can increase the utilization of overhead conductors while also avoiding the limitation of the existing measurement techniques related to wind speed.

Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM). We introduce an Affiliated Fusion Conditional Random Field (AF-CRF), which combines the information of visual pictures and DSM, and a multi-scale strategy with attention to improve the segmenting results. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics.

Transmission line corridor (i.e., Right-of-Ways (ROW)) clearance management plays a critically important role in power line risk management and is an important task of the routine power line inspection of the grid company. The clearance anomaly detection measures the distance between the power lines and the surrounding non-power-facility objects in the corridor such as trees, and buildings, to judge whether the clearance is within the safe range. To find the clearance hazards efficiently and flexibly, this study thus proposed an automatic clearance anomaly detection method utilizing LiDAR point clouds collected by unmanned aerial vehicle (UAV). Firstly, the terrain points were filtered out using two-step adaptive terrain filter and the pylons were detected in the non-terrain points following a feature map method. After dividing the ROW point clouds into spans based on the pylon detection results, the power line point clouds were extracted according to their geometric distribution in local span point clouds slices, and were further segmented into clusters by applying conditional Euclidean clustering with linear feature constraints. Secondly, the power line point clouds segments were iteratively fitted with 3D catenary curve model that is represented by a horizontal line and a vertical catenary curve defined by a hyperbolic cosine function, resulting in a continuous mathematical model of the discretely sampled points of the power line. Finally, a piecewise clearance calculation method which converts the point-to-catenary curve distance measurements to minimal distance calculation based on differential geometry was used to calculate the distance between the power line and the non-power-facility objects in the ROW. The clearance measurements were compared with the standard safe threshold to find the clearance anomalies in the ROWs. Multiple LiDAR point clouds datasets collected by a large-scale UAV power line inspection system were used to validate the effectiveness and accuracy of the proposed method. The detected results were validated through qualitatively visual inspection, quantitatively manual measurements in raw point clouds and on-site field survey. The experiments show that the automatic clearance anomaly detection method proposed in this paper effectively detects the clearance hazards such as tree encroachment, and the clearance measurement accuracy is decimeter level for the LiDAR point clouds collected by our UAV inspection system.

Using first-principles calculations within the framework of density-functional theory, we studied the modulation effect of hydrogen/fluorine chemical decoration on the surface work function of BN sheets. We found that the difference in the work function (ΔWBN) between two surfaces of the chair structure varies with the different decoration. Geometric distortion and chemical effects cause opposite modulation effects, and the chemical effect plays a leading role by inducing charge redistribution in the system.

Two-dimensional MoS2-based materials are considered to be one of the most attractive materials for next-generation nanoelectronics. The electrostatic properties are important in designing and understanding the performance of MoS2-based devices. By using Kelvin probe force microscopy, we show that few-layer MoS2 sheets exhibit uniform surface potential and charge distributions on their surfaces but have relatively lower surface potentials on the edges, folded areas as well as defect grain boundaries.