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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Radiosonde is extensively used for understanding meteorological parameters in the vertical direction. Four typhoon events, including three landfalls (MERANTI, NEPARTAK, and MEGI) and one non-landfall (MALAKAS), were chosen in analysing the precipitable water vapour (PWV) characteristics in this study. The spatial distribution of the three radiosonde stations in Zhejiang province does not meet the requirement in analysing changes in PWV during typhoon event. Global position system (GPS) observations are an alternative method for deriving the PWV. This enables improvements in the temporal–spatial resolution of PWV computed by the radiosonde measurements. The National Centers for Environmental Prediction (NCEP) re-analysed data were employed for interpolating temperature and atmosphere pressure at the GPS antennas height. The PWV computed from GPS observations and NCEP re-analysed data were then compared with the true PWV. The maximum difference of radiosonde and GPS PWV was not more than 30 mm at Taiz station. The Root-Mean-Square (RMS) of PWV differences between radiosonde and GPS was not more than 5 mm in January, February, March, November, and December. It was slightly greater than 5 mm in April. High RMS in May, June, July, August, September, and October implies that differences in GPS and radiosonde PWVs are evident in these months. Correlation coefficients of GPS and radiosonde PWVs were more than 0.9, indicating that the changes in GPS and radiosonde PWVs are similar. Radiosonde calculated PWVs were used for GPS PWV calibration for understanding the PWV changes during the period of a typhoon event. The results from three landfall typhoons show that the average PWV over Zhejiang province is increasing and approaching China mainland. In contrast, MALAKAS did not make landfall and shows a decreasing PWV trend, although it was heading to China mainland. Generally, the PWV change can be used to predict whether the typhoon will make landfall in these cases. PWV spatial distribution of MERANTI shows that PWV peaks change along the typhoon epicenter over Zhejiang province. [ABSTRACT FROM AUTHOR]

Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm. Thus, we name the obtained feature deep BOW (DBOW). The learning process consists of two parts, including image descriptor learning and feature construction. First, to explore the complex contents within the RS image, we extract the image descriptor in the image patch level rather than the whole image. In addition, instead of using the handcrafted feature to describe the patches, we propose the deep convolutional auto-encoder (DCAE) model to deeply learn the discriminative descriptor for the RS image. Second, the k-means algorithm is selected to generate the codebook using the obtained deep descriptors. Then, the final histogrammic DBOW features are acquired by counting the frequency of the single code word. When we get the DBOW features from the RS images, the similarities between RS images are measured using L1-norm distance. Then, the retrieval results can be acquired according to the similarity order. The encouraging experimental results counted on four public RS image archives demonstrate that our DBOW feature is effective for the RSIR task. Compared with the existing RS image features, our DBOW can achieve improved behavior on RSIR. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

A series of myricetin derivatives containing amide, thioether, and 1,3,4-thiadiazole moieties were designed and synthesized, and their antiviral and antibacterial activities were assessed. The bioassays showed that all the title compounds exhibited potent in vitro antibacterial activities against Xanthomonas citri (Xac), Ralstonia solanacearum (Rs), and Xanthomonas oryzae pv. Oryzae (Xoo). In particular, the compounds 5a, 5f, 5g, 5h, 5i, and 5l, with EC50 values of 11.5–27.3 μg/mL, showed potent antibacterial activity against Xac that was better than the commercial bactericides Bismerthiazol (34.7 μg/mL) and Thiodiazole copper (41.1% μg/mL). Moreover, the in vivo antiviral activities against tobacco mosaic virus (TMV) of the target compounds were also tested. Among these compounds, the curative, protection, and inactivation activities of 5g were 49.9, 52.9, and 73.3%, respectively, which were better than that of the commercial antiviral Ribavirin (40.6, 51.1, and 71.1%, respectively). This study demonstrates that myricetin derivatives bearing amide, thioether, and 1,3,4-thiadiazole moieties can serve as potential alternative templates for the development of novel, highly efficient inhibitors against plant pathogenic bacteria and viruses. [ABSTRACT FROM AUTHOR]