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In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model's ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions. [ABSTRACT FROM AUTHOR]

The preparation of self‐healing polyurethane elastomers (PUEs) incorporating dynamic bonds is of considerable practical significance. However, developing a PUE with outstanding mechanical properties and high self‐healing efficiency poses a significant challenge. Herein, this work has successfully developed a series of self‐healing PUEs with various outstanding properties through rational molecular design. These PUEs incorporate m‐xylylene diisocyanate and reversible dimethylglyoxime as hard segment, along with polytetramethylene ether glycol as soft segment. A significant amount of dynamic oxime‐carbamate and hydrogen bonds are formed in hard segment. The microphase separated structure of the PUEs enables them to be colorless with a transparency of >90%. Owing to the chemical composition and multiple dynamic interactions, the PUEs are endowed with ultra‐high tensile strength of 34.5 MPa, satisfactory toughness of 53.9 MJ m−3, and great elastic recovery both at low and high strains. The movement of polymer molecular chains and the dynamic reversible interactions render a self‐healing efficiency of 101% at 70 °C. In addition, this self‐healing polyurethane could still maintain high mechanical properties after recycling. This study provides a design strategy for the preparation of a comprehensive polyurethane with superior overall performance, which holds wide application prospects in the fields of flexible displays and solar cells. [ABSTRACT FROM AUTHOR]

To optimize electron energy for in situ imaging of large biological samples up to 10 μm in thickness with nanoscale resolutions, we implemented an analytical model based on elastic and inelastic characteristic angles. This model has been benchmarked by Monte Carlo simulations and can be used to predict the transverse beam size broadening as a function of electron energy while the probe beam traverses through the sample. As a result, the optimal choice of the electron beam energy can be realized. In addition, the impact of the dose-limited resolution was analysed. While the sample thickness is less than 10 μm, there exists an optimal electron beam energy below 10 MeV regarding a specific sample thickness. However, for samples thicker than 10 μm, the optimal beam energy is 10 MeV or higher depending on the sample thickness, and the ultimate resolution could become worse with the increase in the sample thickness. Moreover, a MeV-STEM column based on a two-stage lens system can be applied to reduce the beam size from one micron at aperture to one nanometre at the sample with the energy tuning range from 3 to 10 MeV. In conjunction with the state-of-the-art ultralow emittance electron source that we recently implemented, the maximum size of an electron beam when it traverses through an up to 10 μm thick bio-sample can be kept less than 10   n m . This is a critical step toward the in situ imaging of large, thick biological samples with nanometer resolution. [ABSTRACT FROM AUTHOR]

High‐resolution structures of biomolecules can be obtained using single‐particle cryo‐electron microscopy (SPA cryo‐EM), and the rapidly growing number of structures solved by this method is encouraging more researchers to utilize this technique. As with other structural biology methods, sample preparation for an SPA cryo‐EM data collection requires some expertise and an understanding of the strengths and limitations of the technique in order to make sensible decisions in the sample‐preparation process. In this article, common strategies and pitfalls are described and practical advice is given to increase the chances of success when starting an SPA cryo‐EM project. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification plays a crucial role in remote sensing image analysis by classifying pixels. However, the existing methods require more spatial–global information interaction and feature extraction capabilities. To overcome these challenges, this paper proposes a novel model for hyperspectral image classification using an orthogonal self-attention ResNet and a two-step support vector machine (OSANet-TSSVM). The OSANet-TSSVM model comprises two essential components: a deep feature extraction network and an improved support vector machine (SVM) classification module. The deep feature extraction network incorporates an orthogonal self-attention module (OSM) and a channel attention module (CAM) to enhance the spatial–spectral feature extraction. The OSM focuses on computing 2D self-attention weights for the orthogonal dimensions of an image, resulting in a reduced number of parameters while capturing comprehensive global contextual information. In contrast, the CAM independently learns attention weights along the channel dimension. The CAM autonomously learns attention weights along the channel dimension, enabling the deep network to emphasise crucial channel information and enhance the spectral feature extraction capability. In addition to the feature extraction network, the OSANet-TSSVM model leverages an improved SVM classification module known as the two-step support vector machine (TSSVM) model. This module preserves the discriminative outcomes of the first-level SVM subclassifier and remaps them as new features for the TSSVM training. By integrating the results of the two classifiers, the deficiencies of the individual classifiers were effectively compensated, resulting in significantly enhanced classification accuracy. The performance of the proposed OSANet-TSSVM model was thoroughly evaluated using public datasets. The experimental results demonstrated that the model performed well in both subjective and objective evaluation metrics. The superiority of this model highlights its potential for advancing hyperspectral image classification in remote sensing applications. [ABSTRACT FROM AUTHOR]

Alleviating cracks induced by the temperature rise in mass concrete has always been a research hotspot and engineering problem. In this study, a type of energy storage phase change low‐temperature rising concrete was designed and prepared to reduce the cracking risk of mass concrete. First, a type of energy storage coarse aggregate (ESA) was prepared using a semi‐dry method with cement and fly ash as matrix materials, mixed with carbon black (CB), carbon fiber (CF), and phase change material (PCM). Furthermore, the influence of these materials on the mechanical properties, microstructure, and pore structure of the ESA was studied. Subsequently, ESA was used to prepare phase change low‐temperature rising concrete, and its mechanical properties, thermal conductivity (TC), and internal temperature rise were investigated. The research results show that, except for the compressive strength of PCM‐15‐ESA, which is only 7% lower than that of the control, PC‐P0, other samples have no mechanical property deterioration. The double‐layer protection measures can effectively avoid the mechanical property deterioration of concrete induced by PCM leakage. The maximum internal temperature rise of concrete is reduced by 9.8°C, which greatly reduces the risk of cracking of concrete. [ABSTRACT FROM AUTHOR]

In recent years, convolutional neural networks (CNNs) have been introduced for pixel-wise hyperspectral image (HSI) classification tasks. However, some problems of the CNNs are still insufficiently addressed, such as the receptive field problem, small sample problem, and feature fusion problem. To tackle the above problems, we proposed a two-branch convolutional neural network with a polarized full attention mechanism for HSI classification. In the proposed network, two-branch CNNs are implemented to efficiently extract the spectral and spatial features, respectively. The kernel sizes of the convolutional layers are simplified to reduce the complexity of the network. This approach can make the network easier to be trained and fit the network to small sample size conditions. The one-shot connection technique is applied to improve the efficiency of feature extraction. An improved full attention block, named polarized full attention, is exploited to fuse the feature maps and provide global contextual information. Experimental results on several public HSI datasets confirm the effectiveness of the proposed network. [ABSTRACT FROM AUTHOR]

Gabor filter is widely used to extract spatial texture features of hyperspectral images (HSI) for HSI classification; however, a single Gabor filter cannot obtain the complete image features. In the paper, we propose an HSI classification method that combines the Gabor filter (GF) and domain-transformation standard convolution (DTNC) filter. First, we use the Gabor filter to extract spatial texture features from the first two principal components of the dimensionality-reduction HSI with PCA. Second, we use the DTNC filter to extract spatial correlation features from HSI in all bands. Finally, the Large Margin Distribution Machine (LDM) uses the linear fusion of the two kinds of spatial features to classify HSI. The experimental results show that the classification accuracy of Indian Pines, Pavia University, and Kennedy Space Center data sets is 96.64, 98.23, and 98.95% with only 4, 3, and 6% training samples, respectively; and these accuracies are 2–20% higher than the other tested methods. Compared with the hyperspectral information based on SVM, EPF, IFRF, PCA-EPFs, LDM-FL, and GFDN method, the proposed method, GFDTNCLDM, significantly improves the accuracy of HSI classification. [ABSTRACT FROM AUTHOR]

Based on a simple system composed of a base and oxygen, an efficient and direct conversion scheme of β-O-4 and β-1 lignin model compounds was developed under mild conditions, and benzoic acid and phenol compounds were obtained. [ABSTRACT FROM AUTHOR]

To monitor, diagnose, and suppress the inter-turn short-circuit faults (ITSCFs) of a submersible motor, an approach for the on-line identification of its winding faults has been proposed based on monitoring the stator current. First, an ITSCF model with the global leakage referred to the stator is given. With this model, the detection parameter, which is equal to the ratio of the turns of the fault windings to the total turns of the windings in the healthy phase, can be derived. Second, a faulty model described by the fourth order state-space equation of the motor with a winding fault has been given. Based on sampled stator voltage and stator current, the detection parameter has been solved and used to estimate the location and the turns of inter-turn short-circuit windings of the motor in real time. The accuracy and the robustness of the proposed approach has been illustrated with a 1.5 kW motor that is fed by a 10 kW inverter. Experiment shows that the identification accuracy in terms of the number of the ITSCF windings of the motor stator is less than 3. It can give a reference for the on-line diagnose the ITSCFs of the stator windings of a submersible motor that works in 2 km deep well. [ABSTRACT FROM AUTHOR]

Government environment is the main determinant in attracting private investment into public–private partnership (PPP) projects, especially in developing countries. Red tape, an indicator of a government's efficiency, plays a critical role in private investment in PPP projects. Reasonable levels of red tape can enhance government transparency and promote private investment, while excessive red tape usually represents low governance efficiency and imposes further risk on private investors. This article explores how developing countries’ red tape affects private investment in PPP projects by examining the moderating effect of corruption. Analyzing a database of 308 PPP projects in 111 developing countries, the study reveals an inverted U-shaped relationship between red tape and private investment. Corruption weakens the positive relationship between red tape and private investment at low levels of red tape and mitigates their negative relationship at high levels. The study integrates the inconsistent results of previous research that postulated either a positive or negative relationship between red tape and private investment by proposing a nonlinear model. It also theorizes the moderating effect of corruption based on real management practice and illustrates its mechanism in absorbing private investment in PPP projects. [ABSTRACT FROM AUTHOR]

In this article, in order to improve the power quality and decrease the consumed power of an induction motor (IM), which is supplied by an alternate battery-powered system, an algorithm that can be used to save its energy consumption has been proposed. The control mechanism is used to reduce the wasted energy caused by IMs start-up current based on bounding its optimal starting voltage. The proposed algorithm includes two parts: First, use branch-and-bound theory to derive the control rule of the IMs start voltage and give corresponding offline calculation procedure framework. Second, according to the control rule, adjust online voltage of the zinc–bromine flow battery, which is used to feed the IM. Due to the proposed approach, the minimum start-up voltage of the battery-powered system can be predicted and by which one the IMs surge current has been suppressed online. Moreover, the relationship of the output voltage of the battery and inverter supply to the load current has been derived by modeling the battery-powered system. The experiments demonstrate that compared with before optimization, the amount of energy consumption of the IM supplied by zinc–bromine flow battery decreases by 8.5% and corresponding indicator parameters of the power quality can reach the standard IEC TS62749-2015. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification technology is important for the application of hyperspectral technology. Support vector machines (SVMs) work well in supervised classifications of hyperspectral images; however, they still have some shortcomings, and their use of a parallel decision plane makes it difficult to conform to real hyperspectral data distribution. The improved nonparallel support vector machine based on SVMs, i.e., the bias constraint additional empirical risk minimization nonparallel support vector machine (BC-AERM-NSVM), has improved classification accuracy compared its predecessor. However, BC-AERM-NSVMs have a more complicated solution problem than SVMs, and if the dataset is too large, the training speed is significantly reduced. To solve this problem, this paper proposes a least squares algorithm, i.e., the least square bias constraint additional empirical risk minimization nonparallel support vector machine (LS-BC-AERM-NSVM). The dual problem of the LS-BC-AERM-NSVM is an unconstrained convex quadratic programming problem, so its solution speed is greatly improved. Experiments on hyperspectral image data demonstrate that the LS-BC-AERM-NSVM displays a vast improvement in terms of solution speed compared with the BC-AERM-NSVM and achieves good classification accuracy. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) always contain abundant spectral and spatial information. Most of the existing deep learning-based hyperspectral anomaly detection methods consider spectral differences between the background and anomalies, and the local spatial information is usually ignored. To make complete use of the spatial–spectral information, this article proposed a local spatial–spectral information-integrated semisupervised two-stream network (LS3T-Net) for hyperspectral anomaly detection. The two-stream network comprises an adaptive convolution and fully connected network and a variational autoencoder (VAE). The adaptive convolution and fully connected network is used to extract the local spatial features of patches, while VAE is trained to learn spectral information close to the background pixels. Furthermore, the detection maps from the two-stream network are incorporated through a process combining the benefits of spatial learning and spectral learning. This enhances the ability to separate the background and anomalies and suppress the false alarm. The experimental results for six real HSI datasets reveal that LS3T-Net can produce more accurate detection results than seven popular benchmark methods. [ABSTRACT FROM AUTHOR]

The very high spectral resolution in hyperspectral images (HSIs) offers an opportunity to detect subtle land-cover changes. However, the availability of HSIs acquired from different platforms requires the development of change detection (CD) methods capable of processing HSIs with different spatial resolutions. In this article, we propose a general end-to-end subpixel convolution-based residual network (SPCNet) to accomplish the CD task between high spatial resolution (HR) and low spatial resolution (LR) HSIs. To effectively tackle the resolution matching issue, a super-resolution (SR) block with an efficient subpixel convolution layer is introduced to upscale the LR feature maps into HR maps. The subpixel convolution layer can fully explore the subpixel context information by learning an array of upscaling filters. Moreover, the designed SPC module is embedded into the LR branch to generate more discriminative representations. More importantly, the SPC module as a plug-and-play unit has the potential to be embedded into other baseline networks to enhance the feature learning capability. Experimental results on four HSI datasets demonstrate the effectiveness of the proposed SPCNet. [ABSTRACT FROM AUTHOR]

The production of aromatic compounds from lignin is an essential issue in the transformation of biomass. Herein, a simple method has been presented for the synthesis of benzanilides and phenols from aniline involving the oxidative cleavage of β-O-4 lignin models using H2O2 as an oxidizing agent without any metal catalysts and under relatively mild conditions. [ABSTRACT FROM AUTHOR]

Deep learning-based methods (e.g., convolutional neural network (CNN)-based methods) have shown increasing potential in hyperspectral image (HSI) change detection (CD). However, the recent advances in CNN-based methods in HSI CD tasks are mostly devoted to designing more complex architectures or adding additional hand-designed blocks. This increases the number of parameters making model training difficult. In this article, we propose an end-to-end residual self-calibrated network (RSCNet) to increase the accuracy of HSI CD. To fully exploit the spatial information, the proposed RSCNet method adaptively builds interspatial and interspectral dependencies around each spatial location with fewer extra parameters and reduced complexity. Moreover, the introduced self-calibrated convolution (SCConv) helps to generate more discriminative representations by heterogeneously exploiting convolutional filters nested in the convolutional layer. The designed RSC module can explicitly incorporate richer information by introducing response calibration operation. The experiments on four bitemporal HSI datasets demonstrated that the proposed RSCNet method is more accurate than ten widely used benchmark methods. [ABSTRACT FROM AUTHOR]

Studies on the classification of hyperspectral images (HSIs) based on deep learning are in full swing, especially the spectral–spatial dependent global learning (SSDGL) framework, which is both efficient and robust. However, the global convolutional long short-term memory (GCL) module under this framework fails to take full consideration of the spectral characteristics contained in HSIs, and the hierarchically balanced (H-B) sampling strategy introduced in this framework prevents the training process from converging smoothly. In this article, we develop a novel regularized spectral–spatial global learning (RSSGL) framework. Compared with SSDGL, the proposed framework mainly makes three improvements. Above all, aiming at the problem that the GCL module used in SSDGL cannot fully tap the local spectral dependence, we apply 3-D convolution to the gated units of long short-term memory (LSTM) as an alternative to the GCL module for adjacent and nonadjacent spectral dependencies learning. Furthermore, to extract the most discriminative features, an improved statistical loss regularization term is developed, in which we introduce a simple but effective diversity-promoting condition to make it more reasonable and suitable for deep metric learning in HSI classification. Finally, to effectively address the performance oscillation caused by the H-B sampling strategy, the proposed framework adopts an early stopping strategy to save and restore the optimal model parameters, making it more flexible and stable. Experiments conducted on three representative datasets show that the proposed RSSGL has superior classification performance compared with the existing relatively excellent research methods. The source code is released at https://github.com/swiftest/RSSGL. [ABSTRACT FROM AUTHOR]

Remote sensing image object detection is widely used in civil and military fields. The important task is to detect objects such as ships, planes, airports, harbours and so on, and then it can obtain object category and position information. It is of great significance to use remote sensing images to observe the densely arranged and directional targets such as cars and ships parked in parking lots and harbours. The object detection task mainly includes object localization and classification. Remote sensing images contain large number of small objects and dense scenes due to the long shooting distance and wide coverage. Small objects occupy few pixels in the image, and they are easily miss‐detected. In dense scenes, the overlapping part of each object is large, so it is easy to detect objects repeatedly. The traditional small object detection methods deliver low accuracy and take long time. Therefore, object detection is very challenging. We put forward a novel deep learning‐based single shot multibox detector (SSD) model for object detection. First, we propose an improved inception network to optimize SSD to strengthen the small object feature extraction ability (FEA) in the shallow network. Second, the feature pyramid network is modified to enhance the fusion effect. Third, the deep feature fusion module is designed to improve the FEA of the deep network. Finally, the extracted image features are matched with candidate boxes with different aspect ratios to perform object detection and location with different scales. Experiments on DOTA show that the proposed algorithm can adapt to the remote sensing object detection in different backgrounds, and effectively improve the detection effect of remote sensing objects in complex scenes. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) has a good effect in the supervised classification of hyperspectral images. In view of the shortcomings of the existing parallel structure SVM, this article proposes a non-parallel SVM model. Based on the traditional parallel boundary structure vector machine, this model adds an additional empirical risk minimization term to the original optimization problem by adding the least square term of the sample and obtains two non-parallel hyperplanes, respectively, forming a new non-parallel SVM algorithm to minimize the additional empirical risk of non-parallel SVM (Additional Empirical Risk Minimization Non-parallel Support Vector Machine, AERM-NPSVM). On the basis of AERM-NPSVM, the bias constraint is added to it, and AERM-NPSVM (BC-AERM-NPSVM) is further obtained. The experimental results show that, compared with the traditional parallel SVM model and the classical non-parallel SVM model, Twin Support Vector Machine (TWSVM), the new model, has a better effect in hyperspectral image classification and better generalization performance. [ABSTRACT FROM AUTHOR]

Due to the limitations of hardware technology and budget constraints, there always exists a tradeoff between spatial and spectral resolutions in a hyperspectral image (HSI). Because of the limited spatial resolution, mixed pixels are a common issue in HSIs, and consequently, some targets appear as subpixels. The effectiveness of hyperspectral target detection is affected greatly by the subpixel targets, especially when the size of the targets is small. In this article, we proposed a double dictionary-based nonlinear representation model for hyperspectral subpixel target detection (DDNRTD). DDNRTD represents HSIs with a nonlinear model based on background and target dictionaries, which fully considers the spatial property of background and targets and can separate background and targets reliably, especially for small-sized subpixel targets. In addition, we designed an over-completed background dictionary construction strategy to represent the background part more effectively, which integrates spectral angle distance (SAD) with sparse representation. Experiments on two simulated and five real HSI datasets showed that the proposed DDNRTD method produced more accurate detection results than six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Investigating the electric power generated by wave energy converters (WECs) has attracted increasing attention along with the developments of wave energy converting technologies in recent years. Electric power estimation currently depends on wave-to-wire model-based numerical analysis and experimental studies of scale-down prototypes. However, traditional numerical simulations are time-consuming and expensive for power orediction of a full-scale WECs operated in real sea. More importantly, they cannot accurately predict WECs' power performance in real time, thereby cannot be used for prediction-based real-time control. To address those problems, four machine learning techniques are employed as an alternative to traditional, complex physical modelling processes to evaluate and predict the generated electric power, based on historical experimental data of a two-body hinge-barge WEC. Comparative results of a Backpropagation (BP) Neural Network algorithm, a Long Short-Term Memory (LSTM) algorithm, a Support Vector Machine (SVM) algorithm and a Radial Basis Neural Network (RBFF) algorithm indicate that machine-learning-based approaches can accurately predict the electric performance of this complex multi-degree-of-freedom WEC, where the coefficient of determination (R 2) of the BP Neural Network on the test dataset is up to 0.9987. • Four machine learning techniques are employed to evaluate and predict the electric power. • The machine learning techniques can be used to predict WECs' power performance under new sea state. • The coefficient of determination (R2) of the BP Neural Network is up to 0.9987. [ABSTRACT FROM AUTHOR]

Wave energy is a practical option for the sustainable powering of the drifting buoy beyond solar energy. However, efficient harvesting of wave energy from a complex and dynamic ocean environment for a drifting buoy is significantly challenging, since the buoy's global location and orientation are always in spatiotemporal variation. Herein, we propose a novel multi-DOF wave energy harvesting mechanism that integrates three sets of frequency-tunable harvesting systems into an enclosed drifting buoy, which can greatly enhance the energy harvesting ability of a long-term working drifting buoy that encounters direction-varying and frequency-varying waves. A time-averaged electric power up to 9.18 mW was achieved by a prototype that has a diameter of 45 cm, which is 32.76 % higher than the single-DOF case in sea trials. Additionally, laboratory dry test results show that the prototype provided a power up to 288.76 mW in one direction. This work provides an effective strategy for sustainably generating electricity in the offshore environment for drifting buoys. • A built-in multi-DOF wave energy harvester for a drifting buoy was presented. • Two different-sized prototypes were designed, assembled and tested in the open sea. • A time-averaged electric power of up to 9.18 mW was achieved in real sea tests. • The prototype generated a power of up to 288.76 mW in one direction in laboratory dry test. [ABSTRACT FROM AUTHOR]

• Cryogenic throttle valves for superfluid helium system were developed and numerically simulated. • Influences of cone angle, unilateral minimum clearance, structure type and working medium type on flow field distribution and flow characteristics of valve are studied. • Increasing the cone angle and unilateral minimum clearance enhances valve flow capacity, with the cone angle having a more pronounced impact. • Altering the spool type significantly alters the flow characteristics, whereas changing the working fluid type only affects the velocity and pressure within the valve. As one of the important methods to obtain superfluid helium, throttling has been widely used due to its simple equipment. The cryogenic throttle valve, serving as a vital component in the throttling process, directly impacts the performance of the superfluid helium system. In this study, velocity and pressure field inside valve is numerically simulated by using computational fluid dynamics (CFD), and the flow characteristics of the valve are analyzed by theoretical calculations and experiments. Moreover, influences of cone angle, unilateral minimum clearance, structure type and working medium type on flow field distribution and flow characteristics of valve are studied by altering structural parameters of spool and working medium. The results demonstrate that increasing cone angle and unilateral minimum clearance can enhance flow capacity of valves, but influence of cone angle is more obvious. Changing the spool type will significantly alter the flow characteristics of the valve, while changing the working fluid type will only affect the velocity and pressure inside the valve. [ABSTRACT FROM AUTHOR]

With the growing worldwide attention to marine environment protection, marine resource exploration, and so on, unmanned surface vehicles (USVs) have become one of the critical mobile marine observation equipment. However, USVs generally rely on limited onboard batteries to provide electricity, and thus, they have low endurance and poor load capacity. The lack of a long-term power supply is currently a great challenge for the further application and development of USVs. Harvesting energy from the working environment of USVs is a promising solution to address that problem. To demonstrate the possibility of this scheme, a physical prototype of a novel USV with a built-in vibration energy harvester has been fabricated and tested in an open sea for the first time. Moreover, a full-coupled mathematical model is developed to further optimize the power performance of the USV based on the Taguchi method. Experimental results show that this prototype can generate a maximum instantaneous electric power of 277.71 mW , 112.95 mW and 101.27 mW when the USV is in a status of towage, propulsive and free-floating, respectively. Simulation results present that the optimized USV can achieve a mean electric power of 246.14 mW , which is improved by 49. 88 % compared with the unoptimized results of the developed prototype. • A novel USV with a built-in vibration energy harvester is developed. • A physical prototype is designed, fabricated and tested in an open sea. • A maximum instantaneous electric power of 277.71 mW is achieved in open sea tests. • The vibration energy harvester is numerically optimized based on the Taguchi method. [ABSTRACT FROM AUTHOR]

With the growing worldwide attention to marine environment protection, marine resource exploration, and so on, unmanned surface vehicles (USVs) have become one of the critical mobile marine observation equipment. However, USVs generally rely on limited onboard batteries to provide electricity, and thus, they have low endurance and poor load capacity. The lack of a long-term power supply is currently a great challenge for the further application and development of USVs. Harvesting energy from the working environment of USVs is a promising solution to address that problem. To demonstrate the possibility of this scheme, a physical prototype of a novel USV with a built-in vibration energy harvester has been fabricated and tested in an open sea for the first time. Moreover, a full-coupled mathematical model is developed to further optimize the power performance of the USV based on the Taguchi method. Experimental results show that this prototype can generate a maximum instantaneous electric power of 277.71 mW , 112.95 mW and 101.27 mW when the USV is in a status of towage, propulsive and free-floating, respectively. Simulation results present that the optimized USV can achieve a mean electric power of 246.14 mW , which is improved by 49. 88 % compared with the unoptimized results of the developed prototype. • A novel USV with a built-in vibration energy harvester is developed. • A physical prototype is designed, fabricated and tested in an open sea. • A maximum instantaneous electric power of 277.71 mW is achieved in open sea tests. • The vibration energy harvester is numerically optimized based on the Taguchi method. [ABSTRACT FROM AUTHOR]

Deep learning methods, especially convolutional neural network (CNN)-based methods, have shown promising performance for hyperspectral image (HSI) change detection (CD). It is acknowledged widely that different spectral channels and spatial locations in input image patches may contribute differently to CD. However, they are treated equally in existing CNN-based approaches. To increase the accuracy of HSI CD, we propose an end-to-end Siamese CNN (SiamNet) with a spectral–spatial-wise attention (SSA-SiamNet) mechanism. The proposed SSA-SiamNet method can emphasize informative channels and locations and suppress less informative ones to refine the spectral–spatial features adaptively. Moreover, in the network training phase, the weighted contrastive loss function is used for more reliable separation of changed and unchanged pixels and to accelerate the convergence of the network. SSA-SiamNet was validated using four groups of bitemporal HSIs. The accuracy of CD using the SSA-SiamNet was found to be consistently greater than for ten benchmark methods. [ABSTRACT FROM AUTHOR]

Hard nitride coatings have the prospect of acting as protective layers for cutting tools because of their excellent mechanical properties and wear resistance. To enhance the mechanical, tribological, and high-temperature oxidation properties, the structural design of the coating is important. In this study, TiSiN, CrAlN/TiSiN bilayer and CrAlN/TiSiN multilayer coatings were deposited on the surface of high-speed steel by multi-arc ion plating technology. The microstructure, phase composition, mechanical properties and high-temperature tribological properties of prepared coatings were analyzed. Results indicated that the TiSiN and CrAlN/TiSiN bilayer coatings contain TiN phases and the CrAlN/TiSiN multilayer coating contains three main phases of TiN, AlN and CrN. The CrAlN/TiSiN multilayer coating possesses a denser microstructure than the other two coatings. The multilayer structure improves the cohesive and bonding strength of the coating by reducing the shear stress. The CrAlN/TiSiN multilayer coating exhibits a stable coefficient of friction and excellent wear resistance during high-temperature friction. The formation of TiO2 and Al2O3 oxide layers on the surface of the coating in a high-temperature environment contributes to the desired tribological properties of the coatings. [ABSTRACT FROM AUTHOR]

The permeability of coal seam is one of the main factors restricting the safe and efficient production of Coal Bed Methane (CBM). In order to improve the extraction efficiency of CBM and reduce the probability of coal and gas outburst, it is of great significance to adopt artificial measures to enhance the fracture network of coal seam. In this paper, a field test of air cannons after hydraulic punching was carried out. The results showed that the gas concentration and gas flow rate were greatly improved, which indicates that the pores and fractures of the coal reservoir are effectively connected. The pure gas extraction volume increased by about 0.7–2.2 times than of non-blasting. According to the experimental results, the paper studies the action mechanism of air cannons, mainly including three aspects of action. The first is the continuous impact of air cannons, which gradually breaks the coal body around the hole, and forms a fracture network of different sizes in the coal body. Air cannon blasting performs secondary treatment on the punched holes, accelerates and promotes the creep of the coal body around the borehole to the free surface, makes the cracks develop deeper, and expands the pressure relief range. Third, it has the function of pulse gas injection to displace the CH4, which broke the original adsorption balance, promoted the conversion of the adsorbed gas to the free state. The role of three aspects ultimately achieves efficient gas drainage. [ABSTRACT FROM AUTHOR]

This paper presents a systematic methodology to optimise the geometry of a three-body hinge-barge wave energy converter, to maximise the energy extraction of the device in given sea states and in site-specific wave climates. To that end, a 5-degree-of-freedom mathematical model is proposed to describe the system dynamics in two-dimensional space and a two-layer optimisation is designed to find the optimal design and control variables. The inner-layer optimisation is used to find the optimal control parameters of the power take-off system and the outer-layer optimisation, which uses a genetic algorithm, is employed to find the optimal design parameters of barge lengths and of optimal ballast positioning. In the case study, this methodology is applied to a 1:20 scale prototype of the McCabe Wave Pump device. Numerical results indicate that the optimal dimensions of the device, under given sea states, can be found efficiently and accurately, and that there appears to be no obvious benefit in the use of three barges, over a two-barge system. • Geometric optimisation of a 3-barge hinge barge device (no comparable work to date). •Combined optimisation of geometry AND optimal positions for ballast masses. •2-level optimisation, updating the control for each geometry/ballast specification. •The analysis points to an optimal 2-barge configuration. [ABSTRACT FROM AUTHOR]

The co-production of ethylene glycol and methanol via ethylene carbonate hydrogenation derived from CO 2 has attracted great concerns because of the promising chemical utilization of CO 2 in large-scale. Copper-based catalysts are widely concerned in hydrogenation of ester due to the high catalytic efficiency and low cost, but the stability of copper-based catalyst is poor and needs to be further improved. In this study, the modification Cu–C@SiO 2 -R catalyst with graphite oxide was prepared by using Cu 3 (BTC) 2 as the precursor and ammonia evaporation method, and was applied in ethylene carbonate hydrogenation to synthesis ethylene glycol and methanol. Furthermore, the catalysts were characterized in detail. The results showed that the Cu–C@SiO 2 -R catalyst was modified with graphite oxide, the average size of Cu particles was 2.9 nm and Cu particles had good dispersion. In addition, both Cu–C@SiO 2 -R and Cu@SiO 2 -R catalysts had similar ratio of Cu+/(Cu0+Cu+). In a batch reactor, under 453 K, 5 MPa, 4 h, the catalytic efficiency was 80.0% EC conversion 92.2% EG and 70.8% MeOH selectivity showing excellent catalytic performance capability of Cu–C@SiO 2 -R catalyst. In long-term experiment, the Cu–C@SiO 2 -R catalyst showed excellent stability after using for 264 h. The activity was 0.63 g EC g cat −1 h−1, and 100.0% EC conversion 99.9% EG and 85.8% MeOH selectivity could be achieved in a fixed bed. After the long-term experiment, the Cu+/(Cu++Cu0) ratio in Cu–C@SiO 2 -R catalyst kept at around 0.48. In contrast, the Cu+/(Cu++Cu0) ratio in Cu@SiO 2 -R catalyst decreased sharply from 0.48 to 0.38. The stability of the structure and the balance of valence of Cu were considered to be responsible to the stability of Cu–C@SiO 2 -R catalyst, because the graphite oxide not only kept the Cu+/(Cu0+Cu+) ratio stability, but also restrained the aggregation of Cu particles and loss of copper. This work provides an in-depth understanding of the stabilization mechanism of Cu and can be a reference for the industrial application of ethylene carbonate hydrogenation. Graphite oxide modified Cu–C@SiO 2 -R catalyst was successfully prepared by using Cu 3 (BTC) 2 as the precursor and ammonia evaporation method, exhibited a high activity and stability for the hydrogenation of ethylene carbonate to co-produce methanol and ethylene glycol. [Display omitted] •The modification Cu–C@SiO 2 catalyst by graphite oxide was successfully prepared. •The Cu–C@SiO 2 catalyst shows superior catalytic performance. •Long-term experiment shows the Cu–C@SiO 2 catalyst has excellent catalytic stability. •The modification of graphite oxide is beneficial to keep the balance of copper species in the catalyst. [ABSTRACT FROM AUTHOR]

Calcium silicate hydrate (C‐S‐H) synthesis follows multistep nucleation processes ranging from amorphous globules to nanocrystals. This type of nonclassical nucleation pathway attracts extensive interest and yet related molecular processes are mostly unexplored. Herein, atomic‐level structure evolution during this process is unveiled. From inside to outside of the globule, the atomic arrangement evolves from amorphous configuration to layered stacking of calcium silicate layers with layers spaced by 1.1 nm. Numerous cations surrounding the globules provide extra calcium ions to form nano C‐S‐H foils at the edge of the globule. The polymerization to form calcium silicate chains has almost been completed before 5 min reaction, and the C‐S‐H structure formation is basically based on the self‐assembly to be calcium silicate layers and then lamellar crystal inside the globule. Silicate chain defects inhibit a‐ and c‐axes crystallization, but promote b‐axes one, leading to anisotropic growth of crystalline C‐S‐H. The defects lead to the crystallization pathways conversion from Si–O–Si bonds formation to Ca2+ electrostatic linking. [ABSTRACT FROM AUTHOR]

To explore the prognostic factors of patients with low-grade optic pathway glioma (OPG) and the optimal treatment to reduce the incidence of postoperative hydrocephalus. This single-center study retrospectively analyzed data from 66 patients with OPGs who underwent surgery. The patients were followed, and overall survival (OS) and progression-free survival (PFS) were determined. The effects of different treatments on the hydrocephalus of patients were compared. Postoperative hydrocephalus was identified as a factor to increase the risk of mortality by 1.99-fold (p =.028). And, 5-year survival rate was significantly lower among patients with postoperative hydrocephalus (p =.027). The main factors leading to preoperative hydrocephalus in patients are large tumor volume and invasion into the third ventricle. Gross total resections (GTR) could reduce the risk of long-term hydrocephalus (p =.046). Age younger than 4 years (p =.046) and tumor invasion range/classification (p =.029) are the main factors to reduce the five-year survival rate. Postoperative radiotherapy (RT) and chemotherapy (CT) had no significant effects on OS. Extraventricular drainage (EVD) was not associated with perioperative infection (p =.798 >.05) and bleeding (p =.09 >.05). Compared with 2 stage surgery (external ventricular drainage or ventriculoperitoneal shunt (VPS) was first placed, followed by tumor resection), 1 stage surgery (direct resection of tumor) had no complication increase. Postoperative hydrocephalus is mostly obstructive hydrocephalus, and it is an important factor that reduces the OS of patients with low-grade OPGs. Surgery to remove the tumor to the greatest extent improves cerebrospinal fluid circulation is effective at reducing the incidence postoperative hydrocephalus. For patients whose ventricles are still dilated after surgery, in addition to considering poor ventricular compliance, they need to be aware of the persistence and progression of hydrocephalus. [ABSTRACT FROM AUTHOR]

Purpose: Supratentorial extraventricular ependymoma (SEE) is a rare subset of ependymomas located in the supratentorial parenchyma, and little is known regarding its management and prognosis. Our study aimed to reveal the prognostic factors in patients with SEE and the roles of programmed death ligand-1 (PD-L1), programmed cell death protein 1 (PD-1), Ki-67, and neural cell adhesion molecule L1 (L1CAM) in predicting these patients' outcomes. Methods: We retrospectively studied the clinical features and prognostic factors in 48 patients with SEE admitted to our center from April 2008 to October 2018. Tissue slides were constructed from patient samples, and PD-L1, PD-1, Ki-67, and L1CAM expression levels were evaluated by immunohistochemistry. Results: Patients with gross total resection (GTR) had better progression-free survival than patients with subtotal resection (STR). Moreover, the recurrence hazard ratios in patients with STR at 3, 5, and 10 years were 8.746, 6.866 and 3.962 times those of patients with GTR, respectively. PD-L1 positivity predicted worse progression-free survival, while the recurrence hazard ratios for patients with PD-L1 positivity at 3, 5, and 10 years were 10.445, 5.539, and 3.949 times those of patients with PD-L1 negativity, respectively. Multivariate analysis revealed that PD-L1 expression and GTR could independently predict outcomes in patients with SEE. Conclusion: PD-L1 expression was an independent and more readily obtained predictor of outcomes, representing a simple and reliable biological prognostic factor for patients with SEE. Further studies are needed to explore PD-L1 inhibitor treatment for patients with ependymoma. Clinical trial registration: No clinical trials were performed in the study. [ABSTRACT FROM AUTHOR]

Due to the influences of imaging conditions, spectral imagery can be coarse and contain a large number of mixed pixels. These mixed pixels can lead to inaccuracies in the land-cover class (LC) mapping. Super-resolution mapping (SRM) can be used to analyze such mixed pixels and obtain the LC mapping information at the subpixel level. However, traditional SRM methods mostly rely on spatial correlation based on linear distance, which ignores the influences of nonlinear imaging conditions. In addition, spectral unmixing errors affect the accuracy of utilized spectral properties. In order to overcome the influence of linear and nonlinear imaging conditions and utilize more accurate spectral properties, the SRM based on spatial–spectral correlation (SSC) is proposed in this work. Spatial correlation is obtained using the mixed spatial attraction model (MSAM) based on the linear Euclidean distance. Besides, a spectral correlation that utilizes spectral properties based on the nonlinear Kullback–Leibler distance (KLD) is proposed. Spatial and spectral correlations are combined to reduce the influences of linear and nonlinear imaging conditions, which results in an improved mapping result. The utilized spectral properties are extracted directly by spectral imagery, thus avoiding the spectral unmixing errors. Experimental results on the three spectral images show that the proposed SSC yields better mapping results than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) are playing a significant role in the marine science and engineering field, e.g., they have been widely used in measuring ocean waves and currents. However, their operation time and range are limited by the energy carried by internal batteries, and this challenge needs to be urgently addressed. In this study, a novel USV concept that can harvest vibration energy excited by ocean waves in the heaving and pitching direction is presented, which provides a sustainable and continuous power supply. A fully coupled wave-to-wire model that considers the viscous damping force is developed to investigate the dynamics and electric power performance of this novel device under site-specific realistic wave climates, with the external electric load optimally tuned for each sea state. Besides, the influence of the forward speed, geometric size, mass ratio and end-stop spring is studied. Numerical results show that the proposed USV in a freely floating situation can efficiently capture and convert harvested energy into electricity with a wave-to-wire efficiency of up to 20. 21 %. Additionally, it can generate an annual mean electric power of 29.73 W, under the wave climates of the national wave energy test site in China. • A novel wave-powered unmanned surface vehicle is designed. • A fully coupled wave-to-wire model is developed. • A high wave-to-wire efficiency up to 20.21% is obtained. • An annual mean electric power of 29.73 W is achieved by the USV. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) have been widely used in various fields credited for their high flexibility and excellent manoeuvrability. However, the limited onboard electricity of USVs restricts their applications in long-term working tasks. A promising option to address that problem is employing a wave-powered USV, where a wave energy converter is integrated into the USV. For a wave-powered USV, it is challenging to achieve a precise estimation of its power generation under complex marine environments. Although there are physics-based models of WECs used for the estimation and prediction of power generation of USVs, they cannot achieve satisfactory accuracy in practice because of the linearization and simplification of those mathematical modelling. To fill up this gap, this paper proposes a data-driven modelling method utilizing feature engineering and ensemble learning to estimate the mean power generation of a wave-powered USV, relying on time-serial measurements obtained from real physical wave-flume experiments under regular waves. Results show that the estimation achieves promising performance, where the minimum RMSEs are 3.359 mW and 6.148 mW for a harvester-to-wire model and a wave-to-wire model, respectively. Additionally, the proposed method shows great potential for real-sea applications, such as online power prediction and control. • A data-driven method for power estimation of a wave-powered USV is proposed. • The robustness of the proposed method is verified with the presence of noise. • The proposed method possesses practical significance, such as data-driven control. [ABSTRACT FROM AUTHOR]

With the diversification of marine monitoring buoy functions, limited onboard batteries have been unable to meet the energy demand of multifarious carrying sensors. Finding a self-sustained power supply for marine monitoring buoys is an urgent problem, while wave energy is an ideal and promising option to address that problem. In this study, two wave-excited vibration energy harvesters (WVEHs) are proposed, which can be installed inside large buoys to provide a sustainable power supply. A full-coupled wave-to-wire model is developed to investigate the influence of blocks' mass, geometric size of buoys, mooring system and WVEH's quantity on the electric power performance of harvesters in realistic sea states. Parametric studies based on the Taguchi method imply that the sea state and the WVEH's quantity have a significant effect on the device's electric power performance. Numerical results indicate that the optimized two harvesters can produce a mean electric power of 777.50 W and 747.43 W , respectively, under a sea state with a peak wave period of 7 s and a significant wave height of 1.6 m. A high wave-to-wire efficiency of 2. 60 % is obtained for a specific system parameter combination. Furthermore, more electric power generation can be achieved by installing multiple harvesting units. • Two wave-excited vibration energy harvesters are proposed for large marine buoys. • A full-coupled wave-to-wire model is developed. • Harvesters are optimized based on the Taguchi method in realistic sea states. • A mean electric power of 777.50 W is achieved under a peak wave period of 7 s. [ABSTRACT FROM AUTHOR]

In recent decades, spatial feature extraction has greatly improved the performance of hyperspectral image (HSI) classification. This paper presents an HSI classification method based on multiple spatial features extraction and fusion (MSFs-EF). The method consists of five sequential steps. 1- Principal component analysis is applied for HSI dimensionality reduction. 2- A mean curvature filter is used to extract the spatial texture features from the HSI. 3- The spatial correlation features are obtained using a domain transform normalized convolution filter. 4- Spatial texture features and spatial correlation features are combined. 5- The multiple spatial features are classified using the Large Margin Distribution Machine. Three hyperspectral data sets are used to verify the performance. This method improves the accuracy of HSI classification compared with SVM method, edge-preserving filter, recursive filter method, and deep learning method. In the case of ratios of training samples of 5%, 0.6%, and 5%, the overall accuracy of three data sets reaches 98.23%, 99.17%, and 98.21% respectively, and are about 1.3%∼19%, 0.2%∼13%, and 0.4%∼13% higher than other fourteen algorithms. In the case of ratios of training samples of 10%, 1%, and 10%, the overall accuracy of the three data sets reaches 98.63%, 99.53%, and 98.99%, respectively and still outperform other methods. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) with the margin theory is widely used for the hyperspectral classification. However, the margin model is a single interval and does not represent the complete distribution of hyperspectral image data sets. In addition, the spatial texture information obtained by filtering in recent years has become a hot research topic for improving classification of hyperspectral images, but the spatial correlation information is often lost in the spatial texture information extraction. To solve this problem, this paper proposed an algorithm with large margin distribution machine (LDM) that combined the spatial information obtained by the bilateral filter and linear spatial correlation information (BFLSCI-LDM). First, spatial features were extracted by bilateral filter from hyperspectral image whose dimensionality was reduced by principal component analysis. Next, the linear spatial correlation information was constructed for hyperspectral images. Finally, the spatial information and original spectral information were combined for LDM. The experimental results of actual hyperspectral images indicated that the proposed BFLSCI-LDM method was superior to other classification methods, including the original SVM with the raw spectral features, the dimensionality reduction features, and spatial-spectral information, the method of edge-preserving filter and recursive filter, and the LDM-based method. [ABSTRACT FROM AUTHOR]

Motivation Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio. Results To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset—CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking. Availability and implementation The source code and data for CryoTransformer are openly available at: https://github.com/jianlin-cheng/CryoTransformer. [ABSTRACT FROM AUTHOR]

• The bio-based carbamate was directly synthesized from CO 2 and pentanediamine (PDA). • The performance of the CeO 2 catalyst was influenced by doping a small amount of Zr. • The diethyl carbonate intermediate is generated from the reaction of CO 2 and ethanol, and subsequently transformed into PDC. • The Zr 0.05 Ce catalyst showed good catalytic activity in the carbonylation between PDA and CO 2. Diethyl pentane-1,5-diyldicarbamate (PDC), a bio-based intermediate used for isocyanate and polyurethanes, was synthesized by the direct carbonylation of CO 2 and pentanediamine (PDA) with 2-cyanopyridine (2-CP) over Zr-doped CeO 2. In this study, Zr-doped CeO 2 catalysts with different contents of Zr are successfully synthesized to produce PDC from PDA, CO 2 , and ethanol. Among them, Zr 0.05 Ce exhibits enhanced reaction activity and the productivity reached 6.0 mmol·g−1·h−1. Characterizations based on X-ray photoelectron spectroscopy, Raman, and CO 2 /NH 3 -temperature-programmed desorption prove that the doping of Zr atoms into CeO 2 regulates the acid–base sites and promotes oxygen vacancy formation, which is primarily responsible for the formation of monoethyl carbonate, an intermediate for diethyl carbonate (DEC). In-situ diffuse reflectance Fourier-transform spectrometry and Fourier-transform infrared spectroscopy reveal that PDA reacts with 2-CP to form N, N'-(pentane-1,5-diyl)dipicolinamide, which are transformed into PDC. Finally, the catalytic activity could be regenerated after calcination. This study presents a green route for producing bio-based carbamate by constructing C-N bond using CO 2 as a carbonyl source, which can replace carbamates derived from petrochemical resources in the future. [Display omitted] [ABSTRACT FROM AUTHOR]

Biogas slurry has ammonia (NH 4 +-N) abundance and is applied in a major production region, Southwest China. This study compared the consequent environmental risk of nitrogen (N) leaching in two major soils, yellow soil and calcareous soil, in Southwest China, and investigated underlying microbial mechanisms. Biogas slurry was applied to soil columns packed with yellow soil and calcareous soil at the rates of 0 to 240 kg N/hm2. Soil N leaching, N contents, and N-related bacteria gene abundances were measured, with their responses to slurry application analyzed through partial least squares-structural equation modeling (PLS-SEM). Nitrate (NO 3 −-N) dominated in N leaching as a result of soil NO 3 −-N accumulation and microbial regulation. Nitrification increased yellow soil NO 3 −-N contents and leaching, however, both were inhibited by dissimilatory nitrate reduction to ammonium (DNRA) and assimilatory nitrate reduction to ammonium (ANRA) in the two soils. Further, NO 3 −-N leaching was enhanced by biogas slurry in yellow soil by promoting nitrification and preventing DNRA and ANRA; but it was not affected in calcareous soil. Accordingly, yellow soil has higher NO 3 −-N leaching than calcareous soil. The leaching can be regulated by the DNRA and ANRA in both soils, which were overlooked relative to nitrification but probably reactive to slurry, warranting further exploration. [Display omitted] • Yellow soil has higher nitrate leaching than calcareous soil with slurry application. • Slurry raises yellow soil nitrate leaching via nitrification and nitrate reduction. • Slurry increases calcareous soil nitrate but not the leaching via nitrate reduction. • Nitrification controls yellow soil nitrate leaching but not that in calcareous soil. • Nitrate reduction as DNRA and ANRA regulate both soils' nitrate leaching. [ABSTRACT FROM AUTHOR]

Laser thermal processing (LTP) has been proposed as a means to avoid unwanted transient enhanced diffusion and deactivation of dopants, especially boron and arsenic, during the formation of ultrashallow junctions. Although experimental studies have been carried out to determine the efficacy of LTP for pure Si and lightly B-doped junctions, the effects of high concentrations of dopants (above 2% B) on the thermodynamic and kinetic properties of the regrown film are unknown. In this study, a classical interatomic potential model [Stillinger-Weber (SW)] is used with a nonequilibrium molecular dynamics computer simulation technique to study the laser thermal processing of heavily B-doped Si in the range 2-10 at. % B. We observe only a small effect of boron concentration on the congruent melting temperature of the B :Si alloy, and thus the narrowing of the "process window" for LTP is predicted to be small. No significant tendency for boron to segregate was observed at either the regrowth front or the buried c-Si interface during fast regrowth. The B-doped region regrew as defect-free crystal with full activation of the boron atoms at low boron concentrations (2%), in good agreement with experiments. As the concentration of boron increased, the number of intrinsic Si defects and boron interstitials in the regrown materials increased, with a minor amount of boron atoms in clusters (<2%). An instability limit for crystal regrowth was observed at around 8%-10% boron atoms during fast regrowth; systems with 10% B showed partial amorphization during regrowth. Comparison with tight-binding quantum mechanical calculations showed that the SW model gives similar diffusivities in the liquid and tendency to cluster, but the lifetimes of the SW clusters are considerably too long (> 150 ps, compared to 5 ps in tight binding). The importance of adequate system size is discussed. [ABSTRACT FROM AUTHOR]

Highlights • The fluctuation of the reflected intensity from a group of discrete particles in turbulence atmospheric model. • Log-amplitude covariance function of the reflected waves from two particles at different heights in turbulent atmosphere is deduced. • A fast algorithm to calculated the scintillation index of the received field is developed based on the Mote Carlo method. Abstract The intensity fluctuation of the reflected field from a group of discrete particles illuminated by a laser beam in turbulent atmosphere is investigated here. First, the expression of the correlation function of the received intensity is derived by dividing the illuminated space into a number of sub blocks. Then, the log-amplitude covariance function of the reflected waves from two particles at different heights in a turbulent atmosphere is deduced using the Rytov method. The dimensions of the sub blocks are discussed based on the 3D log-amplitude covariance function, and the normalized variance of the received intensity is calculated and discussed based on the sub blocks. Results show that the normalized variance is mainly affected by the cross section area of the illuminated space, whereas the latter is greatly influenced by the position of the received point and the width of the transmitting beam. [ABSTRACT FROM AUTHOR]

Hyperspectral image feature extraction generally does not consider the optimisation of structure element types, resulting in the loss of spatial correlation information. To solve this issue, this paper proposes a novel adaptive classification method (MPDTNC-SVM), in which spatial information is extracted by Morphological Profile Filter (MPF) and Domain Transform Normalised Convolution Filter (DTNCF). First, MPF extracts the spatial features on multiple principal component analysis (PCA) components of the hyperspectral image, and DTNCF works over all spectral bands to extract spatially correlated features. The two spatial features are then combined and fed into Support Vector Machine (SVM). Second, a two-step optimisation is implemented in the classification process. Specifically, the best structure of MPF is chosen for classification. Next, the optimal parameters of the structural elements are obtained through iterative classification optimisation, with the best classification performance produced in the process. Experimental results of actual hyperspectral images show that the proposed MPF and DTNCF with SVM (MPDTNC-SVM) method is superior to other classification methods, including Edge-Preserving Filter and Recursive Filter method, morphological feature-based methods, and the SVM methods with raw spectral features, reduced-dimensional and spatial-spectral information. [ABSTRACT FROM AUTHOR]

In recent years, the spatial texture features obtained by filtering have become a hot research topic to improve hyperspectral image classification, but spatial correlation information is often lost in spatial texture information extraction. To solve this problem, a spectral-spatial classification method based on guided filtering and by the algorithm Large Margin Distribution Machine (LDM) is proposed. More specifically, the spatial texture features can be extracted by a Guided filter (GDF) from hyperspectral images whose dimensionality is reduced by a Principal Component Analysis (PCA). Spatial correlation features of the hyperspectral image are then obtained using a Domain Transform Interpolated Convolution Filter. The last step is to fuse spatial texture features and correlation features for classification by LDM. The experimental results using the actual hyperspectral image indicate that the proposed GDFDTICF-LDM method is superior to other classification methods, such as the original Support Vector Machine (SVM) with raw spectral features, dimensionality reduction features and spatial-spectral information, methods of edge-preserving filter and recursive filter, and LDM-based methods. [ABSTRACT FROM AUTHOR]

Soy protein hydrolysates (SPH) and their blends with xanthan gum (SPH/XG), produced via a combination of enzymatic hydrolysis and heat-shearing treatment, were used as fat replacers in the production of reduced fat ice cream. The physicochemical properties of soy protein isolate (SPI) and its hydrolysates with 1% (SPH1) and 5% degree of hydrolysis (SPH5) were characterized. The results suggested that enzymatic hydrolysis produced a lower average molecular weight ( M w ), particle size, viscosity, and water holding capacity compared with SPI, but increased the solubility, oil holding capacity, and foaming capacity. The particle size of the SPH5/XG fat replacer was significantly reduced by enzymatic hydrolysis and heat-shearing treatment. In addition, the incorporation of XG could stabilize the composite system, as indicated by rheology measurement. The sensory properties of ice creams were correlated with the physicochemical measurements. The results illustrated that 50% fat-substituted ice cream with SPH5/XG (96:4) had an appearance, taste, and texture similar to that of 10% full fat ice cream, and the overall performance was acceptable among the low-fat ice cream samples. Therefore, SPH/XG treated by enzymatic hydrolysis and microparticulation is an alternative choice as a fat replacer in typical ice cream production. [ABSTRACT FROM AUTHOR]

A series of Mg 3 Al 1−x Ce x O composite oxide catalysts were prepared by co-precipitation for the transesterification of ethylene glycol to ethylene carbonate. The characterization results revealed that the addition of cerium changed the acid-base properties of the catalyst and that the strong basic sites on the catalyst surface played an important role in the reaction. The Mg 3 Al 0.9 Ce 0.1 O catalyst showed the highest catalytic activity (ethylene carbonate yield 98.1%). Compared with pure Mg-Al oxide, the appropriate addition of Ce was conducive to improving the surface basicity and adsorbed oxygen content of the catalyst. Additionally, the reaction process of the formation of ethylene glycol hydroxyl group with the alkoxy anion formed by the catalyst attacking the carbonyl carbon of nucleophilic dimethyl carbonate to form an adsorption intermediate (HEMC) on the catalyst surface for further intramolecular rearrangement to form ethylene carbonate was confirmed by operando infrared spectroscopy. The reaction kinetics shows that the reaction conforms to the second-order kinetic equation, and the activation energy is 53.71 kJ·mol−1. Catalytic stability in a fixed-bed reactor was studied. After a long cycle of use (150 h), the yield of ethylene carbonate was stabilized at about 89.0%. [Display omitted] • A novel Mg 3 Al 1−x Ce x O composite oxide catalyst was prepared by coprecipitate method. The catalyst was used in the carbonylation of ethylene glycol and dimethyl carbonate to synthesize ethylene carbonate. The yield of ethylene carbonate reached 98.1%. • The catalyst was continuously amplified for 150 h using a fixed-bed. The yield of ethylene carbonate is stable at about 89.0%, and the catalyst has good long-term durability and stability. • The interaction between cerium species and Mg-Al oxides and acid-base synergies play an important role in the reaction. • The reaction path and intermediates of ethylene carbonate synthesis were studied by operando IR spectroscopy. [ABSTRACT FROM AUTHOR]