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This study explores the deposition of an Fe-MnCrSi/TiC coating on 45 steel surfaces using high-velocity arc spraying technology, examining the microstructure and hot corrosion behavior of the resultant layer. The microstructure of the FeMnCrSi/TiC coating primarily consists of an α-Fe (BCC) solid solution, composed of Fe, Mn, Cr, Si, C, and other elements, with a minor presence of β-Fe (FCC) solid-solution phase and unmelted TiC particles. Following 100 h of cyclic 900 °C hot corrosion, Mn on the coating surface preferentially oxidizes, forming a manganese-rich oxide layer. This process reduces the oxygen partial pressure (O2) within the coating, prompting the formation of a dense Cr2O3 layer on the inner side of the oxide layer. Concurrently, the rapid diffusion of Mn and Cr elements triggers the generation of Mn- and Cr-deficient regions at the metal/oxide layer interface, inducing the transformation of the coated metal primary matrix from an FCC + BCC dual phase to an α-Fe (BCC) single phase. After the reaction, the hot corrosion weight gain of the coating reached 12.43 mg/cm2, approximately one-fourteenth of the weight gain of the 45 steel substrates. This weight gain adheres to the parabolic law, suggesting that the FeMnCrSi/TiC coating exhibits excellent corrosion resistance under the given conditions. [ABSTRACT FROM AUTHOR]

The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) scene classification has always attracted much attention as an elemental and hot topic in the RS community. In recent years, many methods using convolutional neural networks (CNNs) and other advanced machine-learning techniques have been proposed. Their performance is excellent; however, they are disabled when there are noisy labels (i.e., RS scenes with incorrect labels), which is inevitable and common in practice. To address this problem, some specific RS classification models have been developed. Although feasible, their behavior is still limited by the complex contents of RS scenes, excessive noise filtering schemes, and intricate noise-tolerant learning strategies. To further enhance the RS classification results under the noisy scenario and overcome the above limitations, in this paper we propose a multiscale information exploration network (MIEN) and a progressive learning algorithm (PLA). MIEN involves two identical sub-networks whose goals are completing the classification and recognizing possible noisy RS scenes. In addition, we develop a transformer-assistive multiscale fusion module (TAMSFM) to enhance MIEN's behavior in exploring the local, global, and multiscale contents within RS scenes. PLA encompasses a dual-view negative-learning (DNL) stage, an adaptively positive-learning (APL) stage, and an exhaustive soft-label-learning (ESL) stage. Their aim is to learn the relationships between RS scenes and irrelevant semantics, model the links between clean RS scenes and their labels, and generate reliable pseudo-labels. This way, MIEN can be thoroughly trained under the noisy scenario. We simulate noisy scenarios and conduct extensive experiments using three public RS scene data sets. The positive experimental results demonstrate that our MIEN and PLA can fully understand RS scenes and resist the negative influence of noisy samples. [ABSTRACT FROM AUTHOR]

Mangroves have extremely high economic and ecological value. Through remote sensing, the spatial distribution of and spatiotemporal changes in mangroves can be accurately obtained, providing data support for the sustainable development of coastal wetlands. However, due to the cloudy and rainy conditions in the growing areas of mangroves, there are relatively few high-quality image data available, resulting in a time difference between regional mosaic images, with a maximum difference of several months, which has a certain impact on accuracy when extracting the spatial distribution of mangroves in some regions. At present, most regional mangrove research has ignored the impact of the time difference between mosaic images, which not only leads to inaccurate monitoring results of mangroves' spatial distribution and dynamic changes but also limits the frequency of monitoring of regional mangrove dynamic changes to an annual scale, making it difficult to achieve more refined time scales. Based on this, this study takes the coastal mangrove distribution area in China as the research area, uses Landsat 8 and MODIS images as basic data, reconstructs the January 2021 images of the research area based on the FSDAF model, and uses a random forest algorithm to extract the spatial distribution of mangrove forests and analyze the landscape pattern. The results showed that the fused image based on the FSDAF model was highly similar to the validation image, with an R value of 0.85, showing a significant positive correlation, indicating that the fused image could replace the original image for mangrove extraction in the same month. The overall accuracy of the spatial distribution extraction of mangroves based on the fused image was 89.97%. The high sample separation and spectral curve changes highly similar to the validation image indicate that the fused image can more accurately obtain the spatial distribution of mangroves. Compared to the original image, the fused image based on the FSDAF model is closer to the validation image, and the fused image can reflect the changes in mangroves in time series, thus achieving accurate acquisition of dynamic change information in a short time span. It provides data and methodological support for future monitoring of dynamic changes in large-scale mangroves. The total area of mangroves in China in January 2021 based on the fused image was 27,122.4 ha, of which Guangdong had the largest mangrove area, with 12,098.34 ha, while Macao had the smallest mangrove area of only 16.74 ha. At the same time, the mangroves in Guangdong and Guangxi had a high degree of fragmentation and were severely disturbed, requiring strengthened protection efforts, while the mangroves in Hong Kong, Zhejiang, and Macao had regular shapes, benefiting from local active artificial restoration. [ABSTRACT FROM AUTHOR]

Eucalyptus grows rapidly and can grow up to 10 m per year, making them an important source of wood supply. Guangxi is a typical karst terrain, with hills and plains being the main growth areas for Eucalyptus. The differences in terrain can affect the seasonal growth of Eucalyptus plantations at different forest ages, which in turn affects the stocking of Eucalyptus. Currently, indiscriminate logging and management not only cause resource waste but also bring economic losses to operators. In this study, an unmanned aerial vehicle (UAV) was used to obtain multi-temporal stereo images to investigate the seasonal growth differences in structural parameters of individual Eucalyptus trees under different forest ages and slopes, providing data support for the precise management of Eucalyptus resources. The results showed that the tree height, crown width, diameter at breast height (DBH), and aboveground biomass (AGB) of individual trees, extracted based on UAV stereo images, are highly correlated with the field measured data, with an R2 of 0.99, 0.78, 0.75, and 0.92, and RMSE of 0.21 m, 0.16 m, 1.17 cm, and 3.79 kg/tree, respectively. The growth of Eucalyptus structural parameters varies in different seasons, with faster growth in spring and autumn, accounting for 76.39%, 73.75%, 73.65%, and 73.68% of the total annual growth, respectively. The growth of different structural parameters of individual trees is closely related to forest age, with tree height, crown width, and DBH gradually slowing down with the increase in forest age, while AGB shows a trend of first increasing and then decreasing. The differences in terrain also have a certain impact on the growth of individual Eucalyptus tree structural parameters. For individual 1-year-old and 3-year-old Eucalyptus trees, those located on gentle slopes grow faster in spring and autumn than those located on flat land. However, those located on flat land grow faster in summer and winter than those located on gentle slopes. For tree height, crown width, DBH, and AGB, the maximum annual growth differences between Eucalyptus trees on gentle slopes and flat are 3.17 m, 0.26 m, 1.9 cm, and 9.27 kg/tree, respectively. The results indicate that based on UAV stereo images, the individual tree structural parameters in Eucalyptus plantations under different forest ages and slopes can be extracted, as well as seasonal growth can be monitored, providing data support for logging, planting, and management of Eucalyptus plantations. [ABSTRACT FROM AUTHOR]

The interactions between power quality in the AC-DC distribution network segments contribute to the distributed propagation of power quality anomalies throughout the entire network. Focusing on the photovoltaic multifunctional grid-connected inverter (PVMFGCI), this study deeply explores a collaborative governance strategy for optimizing regional power quality. Initially, the analysis dissects the DC ripple generation mechanism corresponding to harmonics and asymmetry in AC subnetwork voltages. Subsequently, a strategy is proposed for partitioning comprehensive control regions for AC-side power quality, taking into account photovoltaic governance resources based on insights from photovoltaic control realms and power quality classifications. Further, a collaborative allocation model for governance resources incorporating active optimization potentials of grid-connected converters is established based on the governance capabilities and residual capacities of PVMFGCI. Finally, the effectiveness of the proposed approach is validated through a MATLAB-based example analysis. [ABSTRACT FROM AUTHOR]

As one of the three fastest-growing tree species in the world, eucalyptus grows rapidly, with a monthly growth rate of up to 1 m and a maximum annual growth rate of up to 10 m. Therefore, ways to accurately and quickly obtain the aboveground biomass (AGB) of eucalyptus in different growth stages at a low cost are the foundation of achieving eucalyptus growth-change monitoring and precise management. Although Light Detection and Ranging (LiDAR) can achieve high-accuracy estimations of individual eucalyptus tree biomasses, the cost of data acquisition is relatively high. While the AGB estimation accuracy of high-resolution images may be affected by a lack of forest vertical structural information, stereo images obtained using unmanned aerial vehicles (UAVs) can not only provide horizontal structural information but also vertical structural information through derived point data, demonstrating strong application potential in estimating the biomass of eucalyptus plantations. To explore the potential of UAV stereo images for estimating the AGB of individual eucalyptus trees and further investigate the impact of stereo-image-derived features on the construction of biomass models, in this study, UAVs equipped with consumer-grade cameras were used to obtain multitemporal stereo images. Different features, such as spectral features, texture, tree height, and crown area, were extracted to estimate the AGB of individual eucalyptus trees of five different ages with three algorithms. The different features extracted based on the UAV images had different effects on estimating AGB in individual eucalyptus trees. By estimating eucalyptus AGB using only spectrum features, we found that tree height had the greatest impact, with its R2 value increasing by 0.28, followed by forest age. Other features, such as spectrum, texture, and crown area, had relatively small effects. For the three algorithms, the estimation accuracy of the CatBoost algorithm was the highest, with an R2 ranging from 0.65 to 0.90, and the normalized root-mean-square error (NRMSE) ranged from 0.08 to 0.15. This was followed by the random forest algorithm. The ridge regression algorithm had the lowest accuracy, with an R2 ranging from 0.34 to 0.82 and an NRMSE value ranging from 0.11 to 0.21. The AGB model that we established with forest age, TH, crown area, and HOM-B feature variables using the CatBoost algorithm had the best estimation accuracy, with an R2 of 0.90 and an NRMSE of 0.08. The results indicated that accurately estimating the AGB of individual eucalyptus trees can be achieved based on stereo images obtained using UAVs equipped with affordable, consumer-grade cameras. This paper can provide methodological references and technical support for estimating forest biomass, carbon storage, and other structural parameters based on UAV images. [ABSTRACT FROM AUTHOR]

Image-based geo-localization focuses on predicting the geographic information of query images by matching them with annotated images in a database. To facilitate relevant studies, researchers collect numerous images to build the datasets, which explore many challenges faced in real-world geo-localization applications, significantly improving their practicability. However, a crucial challenge that often arises is overlooked, named the cross-time challenge in this paper, i.e., if query and database images are taken from the same landmark but at different time periods, the significant difference in their image content caused by the time gap will notably increase the difficulty of image matching, consequently reducing geo-localization accuracy. The cross-time challenge has a greater negative influence on non-real-time geo-localization applications, particularly those involving a long time span between query and database images, such as satellite-view geo-localization. Furthermore, the rough geographic information (e.g., names) instead of precise coordinates provided by most existing datasets limits the geo-localization accuracy. Therefore, to solve these problems, we propose a dataset, MTGL40-5, which contains remote sensing (RS) satellite images captured from 40 large-scale geographic locations spanning five different years. These large-scale images are split to create query images and a database with landmark labels for geo-localization. By observing images from the same landmark but at different time periods, the cross-time challenge becomes more evident. Thus, MTGL40-5 supports researchers in tackling this challenge and further improving the practicability of geo-localization. Moreover, it provides additional geographic coordinate information, enabling the study of high-accuracy geo-localization. Based on the proposed MTGL40-5 dataset, many existing geo-localization methods, including state-of-the-art approaches, struggle to produce satisfactory results when facing the cross-time challenge. This highlights the importance of proposing MTGL40-5 to address the limitations of current methods in effectively solving the cross-time challenge. [ABSTRACT FROM AUTHOR]

Due to unbalanced load growth among different regions and the increasing integration of distributed generators (DGs), distribution station areas (DSAs) currently face issues such as voltage violations, curtailment of renewable energy generation, and imbalanced load rates among DSAs. Interconnecting DSAs to form an AC–DC hybrid distribution network (DN) can not only address the aforementioned problems but also provides more efficient interfaces for DC devices. In order to coordinate the controllable devices within the flexible interconnected DSAs and achieve an optimal operational state, centralized optimal dispatch strategies are mainly used, which requires the deployment of an additional central controller and entails heavy communication and computation burdens. To overcome the drawbacks of centralized dispatch, a fully decentralized optimal dispatch scheme based on the alternating direction method of multipliers (ADMM) is proposed. Based on the network partitioning results, the introduction of auxiliary variables that replicate the coupling variables between areas further eliminates the need for a coordinating center in the standard ADMM, achieving a fully decentralized optimal dispatch. Additionally, two network partitioning methods are proposed for implementing decentralized dispatch. Both partitioning methods can achieve the goals of load rate balance and voltage profile improvement when implementing decentralized dispatch. Their key distinction lies in their effectiveness in improving the voltage profiles on the DC side. The partitioning method that treats the entire DC side as a separate area, resulting in higher investment, achieves better results in improving the DC voltage profiles than the other one. The choice of partitioning method can be based on practical engineering requirements. [ABSTRACT FROM AUTHOR]

Individual structural parameters of trees, such as forest stand tree height and biomass, serve as the foundation for monitoring of dynamic changes in forest resources. Individual tree structural parameters are closely related to individual tree crown segmentation. Although three-dimensional (3D) data have been successfully used to determine individual tree crown segmentation, this phenomenon is influenced by various factors, such as the (i) source of 3D data, (ii) the segmentation algorithm, and (iii) the tree species. To further quantify the effect of various factors on individual tree crown segmentation, light detection and ranging (LiDAR) data and image-derived points were obtained by unmanned aerial vehicles (UAVs). Three different segmentation algorithms (PointNet++, Li2012, and layer-stacking segmentation (LSS)) were used to segment individual tree crowns for four different tree species. The results show that for two 3D data, the crown segmentation accuracy of LiDAR data was generally better than that obtained using image-derived 3D data, with a maximum difference of 0.13 in F values. For the three segmentation algorithms, the individual tree crown segmentation accuracy of the PointNet++ algorithm was the best, with an F value of 0.91, whereas the result of the LSS algorithm yields the worst result, with an F value of 0.86. Among the four tested tree species, the individual tree crown segmentation of Liriodendron chinense was the best, followed by Magnolia grandiflora and Osmanthus fragrans, whereas the individual tree crown segmentation of Ficus microcarpa was the worst. Similar crown segmentation of individual Liriodendron chinense and Magnolia grandiflora trees was observed based on LiDAR data and image-derived 3D data. The crown segmentation of individual Osmanthus fragrans and Ficus microcarpa trees was superior according to LiDAR data to that determined according to image-derived 3D data. These results demonstrate that the source of 3D data, the segmentation algorithm, and the tree species all have an impact on the crown segmentation of individual trees. The effect of the tree species is the greatest, followed by the segmentation algorithm, and the effect of the 3D data source. Consequently, in future research on individual tree crown segmentation, 3D data acquisition methods should be selected based on the tree species, and deep learning segmentation algorithms should be adopted to improve the crown segmentation of individual trees. [ABSTRACT FROM AUTHOR]

High-performance Nd-Fe-B-based rare-earth permanent magnets play a crucial role in the application of traction motors equipped in new energy automobiles. In particular, the anisotropic hot-deformed (HD) Nd-Fe-B magnets prepared by the hot-press and hot-deformation process show great potential in achieving high coercivity due to their fine grain sizes of 200–400 nm, which are smaller by more than an order of magnitude compared to the traditional sintered Nd-Fe-B magnets. However, the current available coercivity of HD magnets is not as high as expected according to an empirical correlation between coercivity and grain size, only occupying about 25% of its full potential of the anisotropy field of the Nd2Fe14B phase. For the sake of achieving high-coercivity HD magnets, two major routes have been developed, namely the grain boundary diffusion process (GBDP) and the dual alloy diffusion process (DADP). In this review, the fundamentals and development of the HD Nd-Fe-B magnets are comprehensively summarized and discussed based on worldwide scientific research. The advances in the GBDP and DADP are investigated and summarized based on the latest progress and results. Additionally, the mechanisms of coercivity enhancement are discussed based on the numerous results of micromagnetic simulations to understand the structure–property relationships of the HD Nd-Fe-B magnets. Lastly, the magnetization reversal behaviors, based on the observation of magneto-optic Kerr effect microscopy, are analyzed to pinpoint the weak regions in the microstructure of the HD Nd-Fe-B magnets. [ABSTRACT FROM AUTHOR]

Due to the difference in types of loads between regions and the increasing integration of random elements such as electric vehicles (EVs) and distributed generations (DGs), distribution station areas (DSAs) are facing challenges such as unbalanced load rates and voltage violations. An AC–DC hybrid distribution network formed by interconnecting AC-DSAs using flexible DC technology can not only address these issues, but also offer more efficient interfaces for EV charging piles and DC devices on the DC side. To fully leverage the advantages of the technology and coordinate dispatchable elements within each DSA, this paper proposes an optimal scheduling model, which balances the load rate between DSAs, improves voltage profiles, and considers the control mode of the converter station as a dispatchable element, taking into account its impact on the voltage deviation on the DC side. Simulation results demonstrate the effectiveness of the proposed model in balancing load rate and improving voltage profiles. Moreover, rational decision-making regarding the selection of the control mode for converter stations can effectively mitigate the voltage deviation on the DC side without deteriorating the voltage deviation on the AC side. [ABSTRACT FROM AUTHOR]

The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems. [ABSTRACT FROM AUTHOR]

A majority of mangroves are located in the coastal intertidal zone and are subject to tidal periodic inundation. However, the previous vegetation indices used for extracting the spatial distribution of mangroves were not able to effectively extract submerged mangroves, and the applicability of the vegetation indices used on different spatial resolution images obtained from different sensors was not verified. In this study, a new vegetation index, namely the intertidal mangrove identification indices (IMIIs), was proposed, based on GF-2 images of high and low tide levels. Meanwhile, other commonly used vegetation indices were also extracted. All the vegetation indices were used to extract the spatial distribution of mangroves under tidal inundation, and applicability tests of the vegetation indices were conducted on Sentinel-2 images in three different regions. It was found that the IMIIs proposed based on GF-2 images of high and low tide levels can extract submerged mangroves relatively well, and the spatial distribution extraction results of mangroves are better than those of other vegetation indices, with IMII2 outperforming IMII1. At the same time, IMIIs have good applicability in medium resolution Sentinel-2 images, and there are relatively large differences in the extraction results of mangrove spatial distribution among different vegetation indices in areas with significant impact of tidal inundation. Among all vegetation indices, the extraction results of IMIIs are relatively superior. In most cases, multi variables collaborative application can improve the accuracy of mangrove spatial distribution extraction results. Based on the results of this study, it was concluded that the IMIIs proposed in this study can accurately extract the spatial distribution of mangroves inundated by tides from both medium- and high-resolution images, providing accurate basic data for effective management and scientific protection of mangrove resources. [ABSTRACT FROM AUTHOR]

In this paper, a novel AI-based power reserve control strategy is proposed for photovoltaic (PV) power generation systems participating in the frequency regulation (FR) of microgrids. The proposed strategy uses a frequency response module to determine the target power reserve ratio of the PV system based on microgrid frequency deviation, as well as a power reserve control module to obtain the target duty cycle, which is input to the BOOST converter. The use of artificial neural networks (ANN) in the power reserve control module enables the PV system to work at a specified power reserve ratio, producing appropriate power and mitigating frequency fluctuations in the microgrid. Additionally, a deep reinforcement learning (DRL) algorithm is employed as the decision maker for variable step-size control and initial power reserve ratio determination. Simulations were performed to validate the effectiveness of the proposed method, demonstrating a significant reduction in average frequency deviation by 72.36% when subjected to random variations in irradiance intensity and load conditions. Overall, the proposed AI-based power reserve control strategy has good potential for practical applications in real-world microgrids, promoting the absorption of new energy led by PV and reducing the phenomenon of light abandonment. [ABSTRACT FROM AUTHOR]

China's sour gas reservoir is very rich in reserves, taking the largest whole offshore natural gas field in China-Puguang gas field as an example, its hydrogen sulfide content reaches 14.1%. The use of renewable energy, such as solar energy through photocatalytic technology, can decompose hydrogen sulfide into hydrogen and monomeric sulfur, thus realizing the conversion and resourceization of hydrogen sulfide gas, which has important research value. In this study, a concentration sample database of a hydrogen sulfide leakage scenario in a chemical park is constructed by Fluent software simulation, and then a leakage concentration prediction model is constructed based on the data samples to predict the hydrogen sulfide leakage diffusion concentration in real-time. Several machine learning algorithms, such as neural networks, support vector machines, and deep confidence networks, are implemented and compared to find the model algorithm with the best prediction performance. The prediction performance of the support vector machine model optimized by the sparrow search algorithm is found to be the best. The prediction model ensures the accuracy of the prediction results while greatly reducing the computational time cost, and the accuracy meets the requirements of practical engineering applications. [ABSTRACT FROM AUTHOR]

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve's future overtemperature state, which is characterized by the valve's cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer–FCM–NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters; (ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared; with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically. [ABSTRACT FROM AUTHOR]

Fluctuations in the output power of a high penetration distributed generation (DG) will result in severe voltage fluctuations/violations in the power distribution station area (DSA). This paper proposes the use of Soft Open Point (SOP) for DSA interconnection to alleviate voltage violations and frequent fluctuations. Firstly, the typical topology of a flexible interconnected DSA is introduced, and then the correlation between active/reactive power fluctuations and voltage fluctuations in DSA is analyzed, according to which a local control strategy is proposed to achieve voltage control by relying only on the local electrical information of the nodes connected to the SOP. Finally, the effectiveness of the proposed control strategy is verified by simulation case. The simulation results show that the voltage fluctuation caused by the DG output power fluctuation can be suppressed within tens of milliseconds through the control strategy. This strategy greatly reduces the communication burden and has good real-time performance. It can be used as an SOP control strategy under conditions of communication failure or to realize a plug-and-play SOP, which has significant engineering importance for solving the voltage violations and frequent fluctuations in DSA and improving the consumption capacity of DGs. [ABSTRACT FROM AUTHOR]

The assessment of changes in the height growth of trees can serve as an accurate basis for the simulation of various ecological processes. However, most studies conducted on changes in the height growth of trees are on an annual scale. This makes it difficult to obtain basic data for correcting time differences in the height growth estimates of trees within a year. In this study, the digital elevation models (DEMs) were produced based on stereo images and light detection and ranging (LiDAR) data obtained by unmanned aerial vehicles (UAVs). Individual tree crowns were segmented by employing the watershed segmentation algorithm and the maximum value within each crown was extracted as the height of each tree. Subsequently, the height growth of each tree on a monthly-scale time series was extracted to simulate the time difference correction of regional tree height estimates within a year. This was used to verify the feasibility of the time difference correction method on a monthly scale. It is evident from the results that the DEM based on UAV stereo images was closely related to the DEM based on UAV LiDAR, with correlation coefficients of R2 = 0.96 and RMSE = 0.28 m. There was a close correlation between the tree height extracted from canopy height models (CHMs) based on UAV images and the measured tree height, with correlation coefficients of R2 = 0.99, and RMSE = 0.36 m. Regardless of the tree species, the total height growth in each month throughout the year was 46.53 cm. The most significant changes in the height growth of trees occurred in May (14.26 cm) and June (14.67 cm). In the case of the Liriodendron chinense tree species, the annual height growth was the highest (58.64 cm) while that of the Osmanthus fragrans tree species was the lowest (34.00 cm). By analyzing the height growth estimates of trees each month, it was concluded that there were significant differences among various tree species. In the case of the Liriodendron chinense tree species, the growth season occurred primarily from April to July. During this season, 56.92 cm of growth was recorded, which accounted for 97.08% of the annual growth. In the case of the Ficus concinna tree species, the tree height was in a state of growth during each month of the year. The changes in the height growth estimates of the tree were higher from May to August (44.24 cm of growth, accounting for 77.09% of the annual growth). After applying the time difference correction to the regional tree growth estimates, the extraction results of the changes in the height growth estimates of the tree (based on a monthly scale) were correlated with the height of the UAV image-derived tree. The correlation coefficients of R2 = 0.99 and RMSE = 0.26 m were obtained. The results demonstrate that changes in the height growth estimates on a monthly scale can be accurately determined by employing UAV stereo images. Furthermore, the results can provide basic data for the correction of the time differences in the growth of regional trees and further provide technical and methodological guidance for regional time difference correction of other forest structure parameters. [ABSTRACT FROM AUTHOR]

Subphthalocyanines (SubPcs) are a kind of tripyrrolic macrocycle with a boron atom at their core. Incorporating different units onto the SubPc periphery can endow them with various unique properties. Herein, a series of novel fluorinated low-symmetry SubPc derivatives containing chlorine groups (F8-Cl4-SubPc, F4-Cl8-SubPc) and methoxy groups (F8-(OCH3)2-SubPc) were synthesized and characterized by spectral methods (MS, FT-IR, 1H, 13C, 11B, and 19F NMR spectroscopy), and the effect of the peripheral substituents on their electronic structure of low-symmetry macrocycle was investigated by cyclic voltammetry, theoretical calculation, electronic absorption, and emission spectroscopy. In contrast to perfluorinated SubPcs, these low-symmetry SubPcs revealed non-degenerate LUMO and LUMO + 1 orbitals, especially F8-(OCH3)2-SubPc, which was consistent with the split Q-band absorptions. The cyclic voltammetry revealed that these SubPcs exhibited two or three reduction waves and one oxidation wave, which is consistent with the reported SubPcs. Finally, an intracellular fluorescence imaging study of these compounds revealed that these compounds could enter cancer cells and be entrapped in the lysosomes, which provides a possibility of future applications in lysosome fluorescence imaging and targeting. [ABSTRACT FROM AUTHOR]

Climate change is one of the biggest threats to human living and health in the 21st century. Whilst a large number of papers have been published addressing the health impact of climate change, there is a lack of comprehensive bibliometric analysis in the crosscutting field. This study evaluated the global scientific output of research in the field of climate change and health between 1990 and 2020, based on the Web of Science Core Collection database. Research themes were identified using a social network analysis technique based on author keywords. Research trends were assessed by the change in overall publication number and the percentage of publications in each research theme. Articles were further categorized by the availability of funding and author affiliation to compare the difference between developed and developing countries. Results showed that the research output in the field of climate change and health has increased dramatically in the past 30 years, mainly dominated by researchers in developed countries. The percentage of research receiving funding was found to be the lowest in those published by developing countries only and the highest in those published by the collaboration of developed and developing countries. A total of nine major research themes was identified. Research related to 'risk assessment and adaptation', 'sustainable development' and 'infectious diseases' were relatively underfunded. A significant research trend was observed between 2006 and 2020, with increased attention on research themes related to 'risk assessment and adaptation', 'sustainable development', 'extreme events' and 'air pollution', and reduced attention on research themes related to 'ocean', 'infectious disease' and 'phenology'. The shift of the research trend was mainly driven by research in developed countries. Suggestions, recommendations and future priorities identified by experts in the field of atmospheric sciences, epidemiology, public health, climate change, environmental sciences, and policy development are also provided to guide future research. It is important to shift our focus from single health aspects to an integrated system (such as One Health framework, which considers environmental health, animal health and human health as a whole), with future research focusing more on the systemic impact of climate change in order to achieve better, more effective and efficient risk governance. More funding should be mobilized to support the research capacity building in developing countries and to support climate change adaptation strategies for sustainable development. [ABSTRACT FROM AUTHOR]

Landsat 9 enhances the radiation resolution of the operational land imager from the 12 bits of Landsat 8 to 14 bits. The higher radiation resolution improves the sensitivity of the sensor to detect many subtler differences, especially in the case of dense forests or water. However, it remains unclear whether the difference in radiation resolution between Landsat 8 and Landsat 9 actually affects the classification results of water and tree species. Accordingly, the spectral reflectance and vegetation indices were extracted in this study, based on Landsat 8 and Landsat 9 images. Then, the classification models of land use and land cover (LULC) and tree species were developed by using a gradient tree boosting algorithm. Subsequently, the results were analyzed to further investigate how the differences in radiation resolution affect the classification results of LULC and tree species. It is shown that the LULC classification results of Landsat 8 and Landsat 9 are relatively favorable in most cases. However, the LULC classification results are relatively poor in test areas with a lower classification accuracy of water. Further analysis, in the case of test areas with poor classification results, indicates that there are significant differences in the water classification results between the two datasets. In other words, Landsat 9 produces better water classification results than Landsat 8 in most test areas. However, a temperature close to zero may lead to inverse water classification results. In addition, it indicates that the difference in forest classification results between the two datasets is small, but the results of forest tree species classification based on Landsat 9 are superior to those based on Landsat 8, with an improvement in overall accuracy of 6.01%. The results demonstrate that the difference in radiation resolution between Landsat 8 and Landsat 9 has little impact on the results of LULC classification in most cases. Nevertheless, in the case of some test areas, Landsat 9 is better suited for enhancing the classification accuracy of water and tree species. [ABSTRACT FROM AUTHOR]

In this paper, a fractional-order model of the gas film is proposed for the dynamic characteristics of an air bearing. Based on the dynamic characteristics common between gas film and viscoelastic body, the idea of the fractional-order equivalent modeling of the dynamic characteristics of the gas film is presented to improve the modeling accuracy. Four fractional-order gas film (FOGF) models are introduced based on generalization of traditional viscoelastic models. The analysis of the characteristics of the FOGF models shows that the FOGF model can capture more complex dynamic characteristics and fit the real dynamic data of the gas film better than traditional models. A genetic algorithm particle swarm optimization (GA-PSO) method is used for parameter identification of the proposed models. The experimental results tested on the air bearing motion platform show that the FOGF models are superior in accuracy to the traditional equivalent models for the gas film. In particular, the fractional-order Maxwell gas film (FOMGF) model has the best capture accuracy compared to the other FOGF models and traditional models. [ABSTRACT FROM AUTHOR]

Coal's green mining and scientific utilization is the key to achieve the national vision of carbon peak by 2030 and carbon neutrality by 2060. Clarifying the CO2 flow of coal production is the core part of decarbonization. This study uses an environmental extended multi-regional input–output (EEMRIO) model to analyze the impact of embodied emissions on the indirect CO2 emission intensity of coal production between China's coal mining sector and 141 countries/regions. It is found that the CO2 emission intensity of China's coal production was 34.14 gCO2/MJ in 2014, while the direct and indirect emission intensities were 16.22 gCO2/MJ and 17.92 gCO2/MJ, respectively. From 2007 to 2014, the direct emission intensity of China's coal production increased by 23%, while the indirect emission intensity decreased by 30%. The key material and service inputs affecting indirect carbon emissions of coal production in China are electricity service, metal manufacturing, chemical products, coal mining, and transport, which accounted for 85.5% of the total indirect emission intensity of coal production in 2014. Globally, a large portion of CO2 from Chinese coal production is emitted to meet foreign direct and indirect demands for material and service inputs. Policy implications related to this outcome are further discussed in the study. [ABSTRACT FROM AUTHOR]

This paper investigates the pursuit problem of out-of-control spacecraft under incomplete-information, and provides new ideas for the disposal of dangerous spacecraft with obstacle avoidance capability. Throughout the pursuit process, the maneuver strategy of the out-of-control spacecraft is unknown, and its possibly unconventional and irregular maneuvers may endanger the safe operation of any other spacecraft on orbit. Based on the differential game theory, complete information game strategy pairs are derived. Then, considering that the control information of the target is unavailable to the pursuer, the target's maneuver is regarded as the disturbance item. The incomplete information game strategy is derived from the unilateral optimal cost function. Furthermore, the disturbance estimator is designed to identify the missing information of the target. The optimal hybrid game strategy is proposed as an approach to compensate the target maneuver strategy. Simulation study has been conducted and the results have validated that the missing information can be effectively estimated using the estimator. The designed hybrid game strategy can achieve rapid approach, while saving fuel consumption for on-orbit service. [ABSTRACT FROM AUTHOR]

The vibration characteristics of a composite fan blade are much more complex than those of a solid titanium fan blade due to the anisotropic material properties and complex excitations coming from unsteady flow and mechanically induced vibration. In this study, the dynamic response measurement of a wide-chord composite fan blade was carried out to study the vibration characteristics using multiple macro fiber composite (MFC) actuators, which can generate complex excitation forces with different frequencies and peak values at different locations. The measured mode shapes and natural frequencies were compared with the finite element simulation results. Based on these results, the responses of the blade under the instantaneous excitation of three MFC actuators with different combinations of several natural frequencies were measured and compared. The results show that the responses of the blade excited by different combinations of MFC actuators with different frequencies were significantly different from those excited by a single MFC actuator. The superposition of different mode shapes may cause the change of the vibration stress state, which indicates that the high cycle fatigue location of the blade under complex excitations may change to an unexpected location. The results will be helpful in understanding the vibration characteristics of the composite blades under complex excitations, and the MFC actuator could be a potential tool in vibration active control. [ABSTRACT FROM AUTHOR]

Change detection, as an important task of remote sensing image processing, has a wide range of applications in many aspects such as land use and natural disaster assessment. Recent change detection methods have achieved good results. However, due to the environmental difference between the bi-temporal images and the complicated imaging condition, there are usually problems such as missing small objects, incomplete objects, and rough edges in the change detection results. The existing change detection methods usually lack attention in these areas. In this paper, we propose a Siamese change detection method, named SMD-Net, for bi-temporal remote sensing change detection. The proposed model uses multi-scale difference maps to enhances the information of the changed areas step by step in order to have better change detection results. Furthermore, we propose a Siamese residual multi-kernel pooling module (SRMP) for high-level features to enhance the high-level change information of the model. For the low-level features of multiple skip connections, we propose a feature difference module (FDM) that uses feature difference to fully extract the change information and help the model generate more accurate details. The experimental results of our method on three public datasets show that compared with other benchmark methods, our network comprises better effectiveness and has a better trade-off between accuracy and calculation cost. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSSs) can provide high-precision positioning services, which can be applied to fields including navigation and positioning, autonomous driving, unmanned aerial vehicles and so on. However, GNSS signals are easily disrupted in complex environments, which results in a positioning performance with a significantly inferior accuracy and lengthier convergence time, particularly for the single GNSS system. In this paper, multi-GNSS precise point positioning (PPP) with tightly integrating ultra-wide band (UWB) technology is presented to implement fast and precise navigation and positioning. The validity of the algorithm is evaluated by a set of GNSS and UWB data. The statistics indicate that multi-GNSS/UWB integration can significantly improve positioning performance in terms of the positioning accuracy and convergence time. The improvement of the positioning performance for the GNSS/UWB tightly coupled integration mainly concerns the north and east directions, and to a lesser extent, the vertical direction. Furthermore, the convergence performance of GNSS/UWB solution is analyzed by simulating GNSS signal interruption. The reliability and robustness of GNSS/UWB solution during GNSS signal interruption is verified. The results show that multi-GNSS/UWB solution can significantly improve the accuracy and convergence speed of PPP. [ABSTRACT FROM AUTHOR]

Content-based remote sensing (RS) image retrieval (CBRSIR) is a critical way to organize high-resolution RS (HRRS) images in the current big data era. The increasing volume of HRRS images from different satellites and sensors leads to more attention to the cross-source CSRSIR (CS-CBRSIR) problem. Due to the data drift, one crucial problem in CS-CBRSIR is the modality discrepancy. Most existing methods focus on finding a common feature space for various HRRS images to address this issue. In this space, their similarity relations can be measured directly to obtain the cross-source retrieval results straight. This way is feasible and reasonable, however, the specific information corresponding to HRRS images from different sources is always ignored, limiting retrieval performance. To overcome this limitation, we develop a new model for CS-CBRSIR in this paper named dual modality collaborative learning (DMCL). To fully explore the specific information from diverse HRRS images, DMCL first introduces ResNet50 as the feature extractor. Then, a common space mutual learning module is developed to map the specific features into a common space. Here, the modality discrepancy is reduced from the aspects of features and their distributions. Finally, to supplement the specific knowledge to the common features, we develop modality transformation and the dual-modality feature learning modules. Their function is to transmit the specific knowledge from different sources mutually and fuse the specific and common features adaptively. The comprehensive experiments are conducted on a public dataset. Compared with many existing methods, the behavior of our DMCL is stronger. These encouraging results for a public dataset indicate that the proposed DMCL is useful in CS-CBRSIR tasks. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between energy consumption and technological innovation using a dynamic panel data model and regional-level data from China for the period 1995-2012. In contrast to previous studies, it examines the short and long-run bilateral relationship between technological innovation and energy consumption. The results show that in the short run, technological innovation leads to an increase in energy consumption, while energy consumption has no significant effect on technological innovation. In the long run, however, energy consumption is positively and bilaterally related to technological innovation. These findings suggest that although technological innovation does not directly lead to a reduction in energy consumption as mentioned in the extant literature, it could help achieve sustainability through improving energy efficiency and developing energy structure for developing countries. [ABSTRACT FROM AUTHOR]

PPP using Kalman filter typically takes half an hour to achieve high positioning precision, which is required for small movements detection. Many dataset gaps due to temporary GPS receiver signal loss challenge the feasibility of PPP in GPS applications for kinematic precise positioning. Additional convergence time is needed before PPP reaches the required precision again. In this study, Partial parameters were estimated by using the position and ZWD as prior constraint. The solved partial parameters were applied to initialize the Kalman filter for PPP instantaneous re-convergence. A set of bridge GPS data with logging gaps were used to validate the re-convergence performance of improved PPP. The results show that the displacements from position-constrained PPP with initialized variance are 0.14 m, 0.09 m and 0.05 m, which are much better than those from standard PPP. The precision of displacement from position- and ZWD-constrained PPP with initialized variance is slightly improved when compared with that from position-constrained PPP with initialized variance at all 3 surveying points. The bridge experiment verifies that the displacement time series of improved PPP instantaneously converges at the first epoch of all signal reacquired, in contrast, standard PPP deviates by meters. This finding suggests that improved PPP can successfully deal with the GPS data logging gaps for instantaneous convergence. [ABSTRACT FROM AUTHOR]

In this paper, a preconditioned and proximal alternating direction method of multipliers (PPADMM) is established for iteratively solving the equality-constraint quadratic programming problems. Based on strictly matrix analysis, we prove that this method is asymptotically convergent. We also show the connection between this method with some existing methods, so it combines the advantages of the methods. Finally, the numerical examples show that the algorithm proposed is efficient, stable, and flexible for solving the quadratic programming problems with equality constraint. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) plays an important role in retrieving high temporal–spatial resolution precipitable water vapor (PWV) and its applications. The weighted mean temperature (Tm) is a key parameter for the GNSS PWV estimation, which acts as the conversion factor from the zenith wet delay (ZWD) to the PWV. The Tm is determined by the air pressure and water vapor pressure, while it is not available nearby most GNSS stations. The empirical formular is often applied for the GNSS station surface temperature (Ts) but has a lower accuracy. In this paper, the temporal and spatial distribution characteristics of the coefficients of the linear Tm-Ts model are analyzed, and then a piecewise-linear Tm-Ts relationship is established for each GPS station using radiosonde data collected from 2011 to 2019. The Tm accuracy was increased by more than 10% and 20% for 86 and 52 radiosonde stations, respectively. The PWV time series at 377 GNSS stations from the infrastructure construction of national geodetic datum modernization and Crustal Movement Observation Network of China (CMONC) were further obtained from the GPS observations and meteorological data from 2011 to 2019. The PWV accuracy was improved when compared with the Bevis model. Furthermore, the daily and monthly average values, long-term trend, and its change characteristics of the PWV were analyzed using the high-precision inversion model. The results showed that the averaged PWV was higher in Central-Eastern China and Southern China and lower in Northwest China, Northeast China, and North China. The PWV is increasing in most parts of China, while the some PWVs in North China show a downward trend. [ABSTRACT FROM AUTHOR]

Simple Summary: Following changing cellular signals, various genes adjust their activities and initiate transcripts with the right rates. The precision of such a transcriptional response has a fundamental role in the survival and development of lives. Quite unexpectedly, gene transcription has been uncovered to occur in sporadic bursts, rather than in a continuous manner. This has raised a provoking issue of how the bursting transmits regulatory signals, and it remains controversial whether the burst size, frequency, or both, take the role of signal transmission. Here, this study showed that only the burst frequency was subject to modulation by activators that carry the regulatory signals. A higher activator concentration led to a larger frequency, whereas the size remains unchanged. When very high, the burst cluster emerged, which may be mistaken as a large burst. This work thus supports the conclusion that transcription regulation is in a "digital" way. Gene transcription has been uncovered to occur in sporadic bursts. However, due to technical difficulties in differentiating individual transcription initiation events, it remains debated as to whether the burst size, frequency, or both are subject to modulation by transcriptional activators. Here, to bypass technical constraints, we addressed this issue by introducing two independent theoretical methods including analytical research based on the classic two-model and information entropy research based on the architecture of transcription apparatus. Both methods connect the signaling mechanism of transcriptional bursting to the characteristics of transcriptional uncertainty (i.e., the differences in transcriptional levels of the same genes that are equally activated). By comparing the theoretical predictions with abundant experimental data collected from published papers, the results exclusively support frequency modulation. To further validate this conclusion, we showed that the data that appeared to support size modulation essentially supported frequency modulation taking into account the existence of burst clusters. This work provides a unified scheme that reconciles the debate on burst signaling. [ABSTRACT FROM AUTHOR]

Simple Summary: Apaf-1 is involved in the apoptosis pathway and Bmapaf-1 showed a significant response to BmNPV infection in our previous transcriptome data. In this study, the underlying mechanism of Bmapaf-1 in response to BmNPV infection was studied. To preliminarily determine the relationship of Bmapaf-1 with BmNPV, the expression pattern of Bmapaf-1 was analyzed in different tissues of differentially resistant silkworm strains following virus infection. To further define the role of Bmapaf-1 in BmNPV infection, the alteration of BmNPV infection in BmN cells and the expression patterns of Bmcas-Nc and Bmcas-1 were analyzed following knockdown and overexpression of Bmapaf-1 using siRNA and the pIZT/V5-His-mCherry insect vector, respectively. Furthermore, to analyze whether Bmapaf-1 is involved in BmNPV infection by apoptosis, the inducer NSC348884 and inhibitor Z-DEVD-FMK were used. Discovery of the anti-BmNPV (Bombyx mori nuclearpolyhedrovirus) silkworm strain suggests that some kind of antiviral molecular mechanism does exist but is still unclear. Apoptosis, as an innate part of the immune system, plays an important role in the response against pathogen infections and may be involved in the anti-BmNPV infection. Several candidate genes involved in the mitochondrial apoptosis pathway were identified from our previous study. Bombyx mori apoptosis protease-activating factor-1 (Bmapaf-1) was one of them, but the antiviral mechanism is still unclear. In this study, sequences of BmApaf-1 were characterized. It was found to contain a unique transposase_1 functional domain and share high CARD and NB-ARC domains with other species. Relatively high expression levels of Bmapaf-1 were found at key moments of embryonic development, metamorphosis, and reproductive development. Further, the significant difference in expression of Bmapaf-1 in different tissues following virus infection indicated its close relationship with BmNPV, which was further validated by RNAi and overexpression in BmN cells. Briefly, infection of budded virus with enhanced green fluorescent protein (BV-EGFP) was significantly inhibited at 72 h after overexpression of Bmapaf-1, which was confirmed after knockdown of Bmapaf-1 with siRNA. Moreover, the downstream genes of Bmapaf-1, including Bmnedd2-like caspase (BmNc) and Bmcaspase-1 (Bmcas-1), were upregulated after overexpression of Bmapaf-1 in BmN cells, which was consistent with the RNAi results. Furthermore, the phenomenon of Bmapaf-1 in response to BmNPV infection was determined to be related to apoptosis using the apoptosis inducer NSC348884 and inhibitor Z-DEVD-FMK. Therefore, Bmapaf-1 is involved in the response against BmNPV infection by the mitochondrial apoptosis pathway. This result provides valuable data for clarifying the anti-BmNPV mechanism of silkworms and breeding of resistant silkworm strains. [ABSTRACT FROM AUTHOR]

Semantic segmentation is an important and challenging task in the aerial image community since it can extract the target level information for understanding the aerial image. As a practical application of aerial image semantic segmentation, building extraction always attracts researchers' attention as the building is the specific land cover in the aerial images. There are two key points for building extraction from aerial images. One is learning the global and local features to fully describe the buildings with diverse shapes. The other one is mining the multi-scale information to discover the buildings with different resolutions. Taking these two key points into account, we propose a new method named global multi-scale encoder-decoder network (GMEDN) in this paper. Based on the encoder-decoder framework, GMEDN is developed with a local and global encoder and a distilling decoder. The local and global encoder aims at learning the representative features from the aerial images for describing the buildings, while the distilling decoder focuses on exploring the multi-scale information for the final segmentation masks. Combining them together, the building extraction is accomplished in an end-to-end manner. The effectiveness of our method is validated by the experiments counted on two public aerial image datasets. Compared with some existing methods, our model can achieve better performance. [ABSTRACT FROM AUTHOR]

Monazite ((Ce, La, Nd, Th)PO4) is one of the widely used minerals for U–Th–Pb dating in geochronology. To better understand the possible effects of radiogenic Pb on the in situ dating method, a natural monazite U–Th–Pb standard sample (RW-1) was chemically and structurally characterized down to atomic scales by using the combination of Raman spectrum (RM), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). The experimental results revealed that radiogenic Pb exists as Pb2+ and substitutes for the Ce site in the monazite crystal lattice. Moreover, TEM imaging demonstrated that monazite is well crystalline revealed by an atomic structure in most areas except for a few tiny defects, which are likely attributed to alpha self-healing from an electronic energy loss of α particles. The characterization of the chemical state and occupancy of radiogenic Pb, and the distribution of Pb and Th in monazite at the nanoscale and atomic scale could provide insight for us to understand the mechanisms of the nanogeochronology. [ABSTRACT FROM AUTHOR]

The axial compressive behaviour of an innovative type of square concrete filled steel tube (CFST) column to reinforced concrete (RC) beam joint was experimentally investigated in this paper. The innovative joint was designed such that (i) the steel tubes of the CFST columns were completely interrupted in the joint region, (ii) the longitudinal reinforcements from the RC beams could easily pass through the joint area and (iii) a reinforcement cage, including a series of reinforcement meshes and radial stirrups, was arranged in the joint area to strengthen the mechanical performance of the joint. A two-stage experimental study was conducted to investigate the behaviour of the innovative joint under axial compression loads, where the first stage of the tests included three full-scale innovative joint specimens subjected to axial compression to assess the feasibility of the joint detailing and propose measures to further improve its axial compressive behaviour, and the second stage of the tests involved 14 innovative joint specimens with the improved detailing to study the effect of the geometric size of the joint, concrete strength and volume ratio of the steel meshes on the bearing strengths of the joints. It was generally found from the experiments that (i) the innovative joint is capable of achieving the design criterion of the 'strong joint-weak member' with appropriate designs, and (ii) by decreasing the height factor and increasing the volume ratio of the steel meshes, the axial compressive strengths of the joints significantly increased, while the increase of the length factor is advantageous but limited to the resistances of the joint specimens. Because of the lack of existing design methods for the innovative joints, new design expressions were proposed to calculate the axial compression resistances of the innovative joints subjected to bearing loads, with the local compression effect, the confinement effect provided by the multi-layers of steel meshes and the height effect of concrete considered. It was found that the proposed design methods were capable of providing accurate and safe resistance predictions for the innovative joints. [ABSTRACT FROM AUTHOR]

Marine sponges are well known as rich sources of biologically natural products. Growing evidence indicates that sponges harbor a wealth of microorganisms in their bodies, which are likely to be the true producers of bioactive secondary metabolites. In order to promote the study of natural product chemistry and explore the relationship between microorganisms and their sponge hosts, in this review, we give a comprehensive overview of the structures, sources, and activities of the 774 new marine natural products from sponge-derived microorganisms described over the last two decades from 1998 to 2017. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in HSI classification neglect adequately utilizing the strong complementary yet correlated information from each convolutional layer and only employ the last convolutional layer features for classification. In this paper, we propose a novel fully dense multiscale fusion network (FDMFN) that takes full advantage of the hierarchical features from all the convolutional layers for HSI classification. In the proposed network, shortcut connections are introduced between any two layers in a feed-forward manner, enabling features learned by each layer to be accessed by all subsequent layers. This fully dense connectivity pattern achieves comprehensive feature reuse and enforces discriminative feature learning. In addition, various spectral-spatial features with multiple scales from all convolutional layers are fused to extract more discriminative features for HSI classification. Experimental results on three widely used hyperspectral scenes demonstrate that the proposed FDMFN can achieve better classification performance in comparison with several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In the convolutional sparse coding-based image super-resolution problem, the coefficients of low- and high-resolution images in the same position are assumed to be equivalent, which enforces an identical structure of low- and high-resolution images. However, in fact the structure of high-resolution images is much more complicated than that of low-resolution images. In order to reduce the coupling between low- and high-resolution representations, a semi-coupled convolutional sparse learning method (SCCSL) is proposed for image super-resolution. The proposed method uses nonlinear convolution operations as the mapping function between low- and high-resolution features, and conventional linear mapping can be seen as a special case of the proposed method. Secondly, the neighborhoods within the filter size are used to calculate the current pixel, improving the flexibility of our proposed model. In addition, the filter size is adjustable. In order to illustrate the effectiveness of SCCSL method, we compare it with four state-of-the-art methods of 15 commonly used images. Experimental results show that this work provides a more flexible and efficient approach for image super-resolution problem. [ABSTRACT FROM AUTHOR]

Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers' attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have recently shown outstanding capability for hyperspectral image (HSI) classification. In this work, a novel CNN model is proposed, which is wider than other existing deep learning-based HSI classification models. Based on the fact that very deep residual networks (ResNets) behave like ensembles of relatively shallow networks, our proposed network, called multipath ResNet (MPRN), employs multiple residual functions in the residual blocks to make the network wider, rather than deeper. The proposed network consists of shorter-medium paths for efficient gradient flow and replaces the stacking of multiple residual blocks in ResNet with fewer residual blocks but more parallel residual functions in each of it. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art classification methods. [ABSTRACT FROM AUTHOR]

China has been undergoing a rapid process of urbanization. The urbanization rate, increased from 35% in 2000 to 59.58% in 2018, and is expected to increase to 70% by 2030. As Chinese cities consumed approximately 77% of China's total energy and emitted about 81% of all carbon emissions in 2017, it has become increasingly necessary to quantitatively analyze city-level carbon emissions and related issues. The present study adopted single regional and multi-regional input-output (MRIO) models to analyze the features of four Chinese municipalities (Beijing, Tianjin, Shanghai and Chongqing) and calculate their embodied carbon emissions (ECE). In addition, we used ecological relationship concepts to analyze the relationships between those municipalities and other regions based on ECE flows through an ecological network analysis (ENA) model. The results show that all four megacities were net importers of ECE, and their imported ECE typically flowed from nearby geographic regions. In addition, exploitation was the main ecological relationship between these four megacities and China's other regions. Knowing the detailed data related to ECE, ECE flows and the ecological relationships among these megacities could help policymakers establish more comprehensive environment-related policies, which are crucial for achieving sustainable development targets. [ABSTRACT FROM AUTHOR]

While establishing where to preserve in a world heritage site is critical for heritage protection, management and cultural sustainability, the approach to delineating protection scopes under the integrity principle remains less discussed in literature. This study explores a quantitative approach to evaluating the preservation scopes of the Kaiping Diaolou and Villages (KDVs), a world culture heritage in south China. Diaolous are multiple-story tower dwellings, originally built for defense purposes in the villages of Kaiping, with mixed Chinese and Western architectural styles. We investigate 1629 Diaolous with varying architectural and locational attributes and defensive functions for resisting invasion. We use a GIS-based analysis to identify the boundaries of defensive regions and then categorize them into four types, relying on principal component and clustering analysis. Results suggest that current protection scopes may overlook the spatial organization of Diaolous as a defensive system, failing to consider the cultural value of typology and boundary integrity. The delineation of protection scopes thus need to emphasize functional distribution and organization of historical buildings along with their architectural value. [ABSTRACT FROM AUTHOR]

Aerial photographs and satellite images are one of the resources used for earth observation. In practice, automated detection of roads on aerial images is of significant values for the application such as car navigation, law enforcement, and fire services. In this paper, we present a novel road extraction method from aerial images based on an improved generative adversarial network, which is an end-to-end framework only requiring a few samples for training. Experimental results on the Massachusetts Roads Dataset show that the proposed method provides better performance than several state of the art techniques in terms of detection accuracy, recall, precision and F1-score. [ABSTRACT FROM AUTHOR]

Image captioning generates a semantic description of an image. It deals with image understanding and text mining, which has made great progress in recent years. However, it is still a great challenge to bridge the "semantic gap" between low-level features and high-level semantics in remote sensing images, in spite of the improvement of image resolutions. In this paper, we present a new model with an attribute attention mechanism for the description generation of remote sensing images. Therefore, we have explored the impact of the attributes extracted from remote sensing images on the attention mechanism. The results of our experiments demonstrate the validity of our proposed model. The proposed method obtains six higher scores and one slightly lower, compared against several state of the art techniques, on the Sydney Dataset and Remote Sensing Image Caption Dataset (RSICD), and receives all seven higher scores on the UCM Dataset for remote sensing image captioning, indicating that the proposed framework achieves robust performance for semantic description in high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

A series of novel phosphorylated penta-1,4-dien-3-one derivatives were designed and synthesized. The structures of all title compounds were determined by 1H-NMR, 13C-NMR, 31P-NMR, and high-resolution mass spectrometry (HRMS). Bioassay results showed that several of the title compounds exhibited remarkable antibacterial and antiviral activities. Among these, compound 3g exhibited substantial antibacterial activity against Xanthomonas oryzae pv. Oryzae (Xoo), with a 50% effective concentration (EC50) value of 8.6 μg/mL, which was significantly superior to bismerthiazol (BT) (58.8 µg/mL) and thiodiazole-copper (TC) (78.7 μg/mL). In addition, compound 3h showed remarkable protective activity against tobacco mosaic virus (TMV), with an EC50 value of 104.2 μg/mL, which was superior to that of ningnanmycin (386.2 μg/mL). Furthermore, the microscale thermophoresis and molecular docking experiments on the interaction of compounds 3h and 3j with TMV coat protein (TMV CP) were also investigated. Compounds 3h and 3j bound to TMV CP with dissociation constants of 0.028 and 0.23 μmol/L, which were better than that of ningnanmycin (0.52 μmol/L). These results suggest that novel phosphorylated penta-1,4-dien-3-one derivatives may be considered as an activator for antibacterial and antiviral agents. [ABSTRACT FROM AUTHOR]