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Differential code bias (DCB) of satellite is an error that cannot be ignored in precise positioning, timing, ionospheric modeling, satellite clock correction, and ambiguity resolution. The completion of the third generation of BeiDou Navigation Satellite System (BDS-3) has extended DCB to multichannel code bias observations and observable-specific signal bias (OSB). In this paper, the DCB and OSB products provided by the Chinese Academy of Sciences (CAS) are analyzed and compared. The DCB parameters for the BDS satellites are applied in both single- and dual-frequency single point positioning (SPP), and the results are intensively investigated. Based on the satellite DCB parameters of the BDS, the performance of precise point positioning (PPP) with different frequency combinations is also analyzed in terms of positioning accuracy and convergence time. The standard deviations (STDs) of DCBs at each signal pair fluctuate from 0.2 ns to 1.5 ns. The DCBs of BDS-2 are slightly more stable than those of BDS-3. The mean values and STDs of C2I and C7I OSBs for BDS-2 are at the same level and are numerically smaller than their counterparts for the C6I OSBs. The mean OSBs for each signal of the BDS-3, excluding C2I, fluctuate between 12.35 ns and 12.94 ns, and the STD fluctuates between 2.11 ns and 3.10 ns. The DCBs and OSBs of the BDS-3 of the IGSO satellites are more stable than those of the MEO satellites. The corrections for TGD and DCB are able to improve the accuracy of single-frequency SPP by 44.09% and 44.07%, respectively, and improve the accuracy of dual-frequency SPP by 6.44% and 12.85%, respectively. The most significant improvements from DCB correction are seen in single-frequency positioning with B1I and dual-frequency positioning with B2a+B3I. DCB correction can improve the horizontal and three-dimensional positioning accuracy of the dual-frequency PPP of different ionosphere-free combinations by 13.53% and 13.84% on average, respectively, although the convergence is slowed. [ABSTRACT FROM AUTHOR]

Functional principal component analysis (FPCA) has been widely used to capture major modes of variation and reduce dimensions in functional data analysis. However, standard FPCA based on the sample covariance estimator does not work well if the data exhibits heavy‐tailedness or outliers. To address this challenge, a new robust FPCA approach based on a functional pairwise spatial sign (PASS) operator, termed PASS FPCA, is introduced. We propose robust estimation procedures for eigenfunctions and eigenvalues. Theoretical properties of the PASS operator are established, showing that it adopts the same eigenfunctions as the standard covariance operator and also allows recovering ratios between eigenvalues. We also extend the proposed procedure to handle functional data measured with noise. Compared to existing robust FPCA approaches, the proposed PASS FPCA requires weaker distributional assumptions to conserve the eigenspace of the covariance function. Specifically, existing work are often built upon a class of functional elliptical distributions, which requires inherently symmetry. In contrast, we introduce a class of distributions called the weakly functional coordinate symmetry (weakly FCS), which allows for severe asymmetry and is much more flexible than the functional elliptical distribution family. The robustness of the PASS FPCA is demonstrated via extensive simulation studies, especially its advantages in scenarios with nonelliptical distributions. The proposed method was motivated by and applied to analysis of accelerometry data from the Objective Physical Activity and Cardiovascular Health Study, a large‐scale epidemiological study to investigate the relationship between objectively measured physical activity and cardiovascular health among older women. [ABSTRACT FROM AUTHOR]

In an effort to enhance the precision of wind power prediction, this study proposes a wind power prediction model with a secondary-weighted attention mechanism, which is based on feature selection. During the pre-processing stage, wavelet denoising is employed on the original dataset to eliminate noise and enhance the convergence rate of the model. As for model improvement, a secondary-weighted time attention mechanism-LSTM (STAM-LSTM) model is proposed. Additionally, the random forest algorithm is employed to analyse the feature correlation, leading to the best feature combination for the construction of the final input vector. In the comparative experiments, the STAM-LSTM model shows good performance and stability compared to the other nine models and three reference methods. In addition, to validate the effectiveness of feature selection, different combinations of features are entered for prediction. The results show that the model metrics reach a better level after feature selection. Finally, the effects of the STAM mechanism and the Random Forest feature screening assistance strategy, on the model are analysed through ablation experiments. [ABSTRACT FROM AUTHOR]

Deep learning has substantially pushed forward remote sensing image change detection through extracting discriminative hierarchical features. However, as the increasingly high-resolution remote sensing images have abundant spatial details but limited spectral information, the use of conventional backbone networks would give rise to blurry boundaries between different semantics among hierarchical features. This explains why most false alarms in the final predictions distribute around change boundaries. To alleviate the problem, we pay attention to feature refinement and propose deep learning networks that deliver improved separability (ISNet). Our ISNet reaps the advantages from two strategies applied to refining bitemporal feature hierarchies: 1) margin maximization that clarifies the gap between changed and unchanged semantics and 2) targeted arrangement of attention mechanisms that direct the use of channel attention (CA) and spatial attention (SA) for highlighting semantic and positional information, respectively. Specifically, we insert CA modules into share-weighted backbone networks to facilitate semantic-specific feature extraction. The semantic boundaries in the extracted bitemporal hierarchical features are then clarified by margin maximization modules, followed by SA modules to enhance positional change responses. A top–down fusion pathway makes the final refined features cover multiscale representations and have strong separability for remote sensing image change detection. Extensive experimental evaluations demonstrate that our ISNet achieves state-of-the-art performance on the LEVIR-CD, SYSU-CD, and Season-Varying datasets in terms of overall accuracy (OA), Intersection-of-Union (IoU), and F1 score. Code is available at https://github.com/xingronaldo/ISNet. [ABSTRACT FROM AUTHOR]

Background: Alopecia has become an exceedingly prevalent dermatological disorder. Etiologically, infection (bacterial and fungal infection), inflammation, and immune dysregulation are the main causes of immune-mediated hair loss. Treating hair loss has remained challenging as the available therapies are limited. Exosomes from adipose-derived stem cells (ADSC-Exos) have been used for treating neurodegenerative diseases and autoimmune diseases and in wound-healing treatments. However, the function and mechanism of ADSC-Exos in alopecia treatment remain unclear. This study is aimed at investigating the effects of ADSC-Exos on hair growth in vitro and in vivo for potentially treating immune-mediated alopecia and further exploring the underlying mechanism.Methods: Cell proliferation, migration, and apoptosis of dermal papilla cells (DPCs) that were treated with ADSC-Exos were detected using the cell counting kit-8 (CCK-8) assay, scratch wound-healing assay, and flow cytometry assay, respectively. A C57BL/6 hair-depilated mouse model was established in vivo; then, ADSC-Exos were subcutaneously injected alone or in combined with minoxidil. The effects of ADSC-Exos on hair growth, pathological changes, and the related mechanism were investigated by HE staining, quantitative real-time PCR (qRT-PCR), western blotting, and RNA sequencing (RNA-seq).Results: ADSC-Exos significantly promoted DPC proliferation and migration while also reducing apoptosis. In addition, compared with the control group, ADSC-Exos-treated mice had better hair growth, more hair follicles (HFs) and thicker dermis. RNA-seq revealed that the miR-22 and TNF-α signaling pathways were markedly downregulated in DPCs after ADSC-Exos treatment. In addition, according to qRT-PCR and western blotting results, the Wnt/β-catenin signaling pathway was activated in the skin of ADSC-Exos-treated mice.Conclusion: ADSC-Exos therapy positively affected the promotion of hair regrowth by regulating miR-22, the Wnt/β-catenin signaling pathway, and the TNF-α signaling pathway, implying that ADSC-Exos could be a promising cell-free therapeutic strategy for immune-mediated alopecia. [ABSTRACT FROM AUTHOR]

Gastric cancer, one of the most common malignant tumors that can affect the digestive system, poses a serious threat to human life. The survival rate of gastric cancer patients depends on early detection and treatment. The widespread adoption of endoscopy has improved the detection rate of early gastric cancer. Accurate preoperative diagnosis of early gastric cancer is key to developing individualized treatment strategies. Here, nonlinear optical microscopy (NLOM) is used to differentiate between normal gastric mucosae and those with early gastric cancer. Furthermore, the quantitative relationship between submucosal infiltration depth and collagen signals in early gastric cancer is explored. First, the two-dimensional collagen direction angle was measured as an indicator to identify cancerous tissue. The orientation indexes of collagen fibers in normal and cancerous tissues were found to be 0.8511 ± 0.0839 and 0.6466 ± 0.07429 (P < 0.0001), respectively, indicating a significant decrease for the cancerous site. The backscattered second harmonic generation (SHG) signal corresponding to the collagen content and the three-dimensional collagen fiber orientation were then studied for early gastric cancer at different infiltration depths. The backscattered collagen SHG signal (a.u.) in the infiltrated lamina propria, muscularis mucosa, and submucosa were found to be 0.1850 ± 0.0393, 0.0870 ± 0.0189, and 0.0435 ± 0.0163, respectively. The 3D directional variance of collagen corresponding to the three infiltration depth sites were 0.6108 ± 0.0707, 0.6794 ± 0.0610, and 0.8200 ± 0.0618 (P < 0.05), respectively. Significant differences between the early gastric cancer collagen signals at different infiltration depths were observed. Our results indicate that NLOM can differentiate cancerous tissue from normal tissue, and thus diagnose early gastric cancer based on infiltration depth. NLOM provides a new evaluation method for the real-time in situ diagnosis of early gastric cancer and has important clinical significance for preparing accurate individualized treatment guidance. [ABSTRACT FROM AUTHOR]

Tumor angiogenesis is a complex process that is unamenable to intravital whole‐process monitoring, especially on microscopic assessment of tumor microvessel and quantifying microvascular hemodynamics before and after the nanotherapeutics, which hinder the understanding of nanotheranostics outcomes in tumor treatment. Herein, a new photoacoustic (PA) imaging‐optical coherence tomography angiography (OCTA)‐laser speckle (LS) multimodal imaging strategy is first proposed, which is not only able to precisely macro guide the thermo‐chemotherapy of tumor by monitoring blood oxygen saturation (SaO2) and hemoglobin content (HbT), but also capable of long‐term microscopic investigating the microvessel morphology (microvascular density) and hemodynamics changes (relative blood flow) before and after the nanotherapeutics in vivo. Moreover, to realize the tumor thermo‐chemotherapy treatment based on this novel multimodal imaging strategy, a 2D 5‐fluorouracil silicon nanosheets (5‐Fu‐Si NSs) therapeutic agent is designed. Furthermore, 2D high‐resolution tumor microvascular images in different stage display that tendency of the thermo‐chemotherapy effect is closely associated with tumor angiogenesis. Taken together, the investigations establish the fundamental base in theory and technology for further tailoring the novel specific diagnosis and treatment strategy in tumor. More importantly, this technique will be beneficial to evaluate the tumor microvascular response to nanotherapeutics at microscale. [ABSTRACT FROM AUTHOR]

Tumor angiogenesis is a complex process that is unamenable to intravital whole‐process monitoring, especially on microscopic assessment of tumor microvessel and quantifying microvascular hemodynamics before and after the nanotherapeutics, which hinder the understanding of nanotheranostics outcomes in tumor treatment. Herein, a new photoacoustic (PA) imaging‐optical coherence tomography angiography (OCTA)‐laser speckle (LS) multimodal imaging strategy is first proposed, which is not only able to precisely macro guide the thermo‐chemotherapy of tumor by monitoring blood oxygen saturation (SaO2) and hemoglobin content (HbT), but also capable of long‐term microscopic investigating the microvessel morphology (microvascular density) and hemodynamics changes (relative blood flow) before and after the nanotherapeutics in vivo. Moreover, to realize the tumor thermo‐chemotherapy treatment based on this novel multimodal imaging strategy, a 2D 5‐fluorouracil silicon nanosheets (5‐Fu‐Si NSs) therapeutic agent is designed. Furthermore, 2D high‐resolution tumor microvascular images in different stage display that tendency of the thermo‐chemotherapy effect is closely associated with tumor angiogenesis. Taken together, the investigations establish the fundamental base in theory and technology for further tailoring the novel specific diagnosis and treatment strategy in tumor. More importantly, this technique will be beneficial to evaluate the tumor microvascular response to nanotherapeutics at microscale. [ABSTRACT FROM AUTHOR]

Forests play an important role in global ecosystems, but natural disasters such as drought and disease and pest-induced damages often affect growth of trees and even lead to tree death. There have been various vegetation indices (VIs) developed to detect the damages of trees using remote sensing technologies. However, developing an effective and accurate VI for detecting forest damages at an earlier stage is still challenging. In this study, a total of 32 target trees and 17 reference trees were selected from one of Chinese fir plantations that account for 24% of the afforested area in China. The barks of the target trees at 20 cm and 70 cm above the ground were respectively peeled off in August of 2016 to let the trees die. The hyperspectral data were collected from the selected branches with leaves using a high-performance spectrometer HR-1024i (Spectra Vista Corporation) with 1024 bands covering the region of wavelengths from 350 nm to 2500 nm. The spectral data collection was conducted from both the target and reference trees by non-imaging monthly from August of 2016 to February of 2017 until the target trees died. Based on statistical analysis, a Green to Red Region Spectral anGle Index (GRRSGI) was proposed and compared with other ten widely used VIs to detect the early dying process of the damaged trees. The results showed that compared with the reference trees, the damaged trees reduced the reflectance peaks of green bands and the absorption valleys of red bands and the frequency distributions of spectral reflectance from the damaged trees turned from statistically normal to non-normal in the region of 550–640 nm. This indicated that the spectral bands in the region of green to red bands were sensitive to the dying process of the target trees. Moreover, the performance of the VIs for detecting the dying process of the damaged trees varied depending on the time period after the trees damaged. The proposed GRRSGI based on the spectral bands of 550–640 nm led to the performance similar to all other VIs after three months of the damages but significantly better than them in the first two months after the damages. Except for GRRSGI, the Green-Red Spectral Area Index (GRSAI) also performed better than other VIs. Compared with GRSAI, however, the proposed GRRSGI performed slight better when the spectral data of 550–640 nm were re-sampled using the spectral intervals of 5–40 nm with an increase of 5 nm and significantly better when the re-sampling of the hyperspectral data was conducted in the region of 550–670 nm. This implied the proposed GRRSGI provided the greater potential of detecting the early dying process of the damage trees. [ABSTRACT FROM AUTHOR]

Novel phthalonitrile resins (BPAPNs) were prepared by blending phthalonitrile-terminated oligomeric polyaryl ether nitrile, 4,4′-(1,3-phenylenebis(oxy))diphthalonitrile (PN), and 4-amino-4-(3,4-dicyanophenoxy)phenyl. The curing behavior of the blends was studied by differential scanning calorimetry, Fourier-transform infrared spectroscopy, and rheology measurements. Additionally, quartz fiber-reinforced composites were prepared by hot-pressing process. Thermal stability and mechanical properties of cured phthalonitrile resins and composites were characterized and discussed. Compared with that of pristine PN, the impact strength of cured BPAPNs increased by 60%, while the thermal properties were not sharply compromised. Additionally, the maximum bending strength of BPAPNs composites reached 855 MPa, which was 56% higher than that of pristine PN. The current method shed light on the toughening of phthalonitrile resins, and the corresponding phthalonitrile resins could be used as the matrix for high-performance composites. [ABSTRACT FROM AUTHOR]

• A new DDES-Tr, based on the three-equation k-ω-γ transition/turbulence integrated model, was proposed through the similar way with the typical DDES. • A modification of the length scale was introduced to ensure the models worked in their own interest regions. • DDES-Tr model can not only resolve the massive separation on the leeward side, but also model the transition on the windward side. The hypersonic flow at high angle of attack past the Orion capsule was predicted by a delayed detached eddy simulation (DDES), based on a three-equation k-ω-γ transition/turbulence integrated model (Tr). The computational results of the mean and instantaneous heat rates by DDES-Tr are compared with the available measurements and DDES-FT (full turbulence). As expected, the massive separation on the leeward side can be well predicted by both DDES models, but DDES-FT cannot model the transition and over-predicts the heat flux on the windward side. Fortunately, the DDES-Tr model can not only resolve the massive separation on the leeward side, but also model the transition on the windward side. Furthermore, the contributions of the first, second, and crossflow modes on the transition are quantitatively explored. Although the freestream is hypersonic, the second Mack mode is very weak owing to the low supersonic speed after the strong bow shock wave. The transition is mainly dominated by the first and rarely by the crossflow mode. [ABSTRACT FROM AUTHOR]

Abstract Urban redevelopment practices have received substantial attention in urban planning. Remotely sensed thermal infrared monitoring of urban heat island (UHI) is a well-documented topic, however, there is a lack of understanding of the influence on UHI change caused by urban redevelopment process. The objectives of this study was to investigate the urban redevelopment-induced efforts incorporating remote sensing of UHI and land use change. Multi-temporal ASTER thermal infrared images were employed to characterize UHI change; and high resolution Worldview images were employed to perform land use classification. UHI dynamic was quantified with urban heat island ratio index. Analysis of urban redevelopment-induced land use change with response to UHI were carried out. Landscape ecology methods were employed to quantitatively identify land use change with landscape metrics. Result demonstrated that UHI effect had a trend of mitigation during urban redevelopment process in the study area. Urban heat island intensity could be significantly eliminated or weakened by changes of land use composition and spatial configuration. These phenomena were closely related to redevelopment practices such as industrial relocation, buildings demolition, and brownfield transformation. This article presented a case study for characterizing urban redevelopment process with remote sensing monitoring, quantifying the change with urban heat island ratio index and landscape ecology analysis, research findings could be utilized as indicators for urban planning. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Novel phthalonitrile copoly-oligomers were synthesized by an improved method. • Oligomers exhibited good processability for prepreg and hot-press manufacture. • Glass transition temperatures of cured polymers were higher than 450 °C. • Bending strength reached 942 MPa, capable as structural composite matrices. Abstract A novel series of phthalonitrile terminated fluorene based copoly-oligomers (BPNs) were designed and synthesized by an improved method and could be cured in the presence of bis[4-(4-aminophenoxy)phenyl]sulfone. The structure of oligomers was characterized, while their curing behavior was systematically studied. The oligomers exhibited good solubility, which facilitated the preparation of prepregs. Later quartz fiber reinforced composites were prepared by hot-pressing process, and their thermal and mechanical properties were characterized. The glass transition temperatures of the composites were higher than 450 °C, and the bending strength reached 942 MPa. The combination of excellent thermal and mechanical performance indicated BPNs could be potentially applied in high performance composites or as universal modifiers for heat resistant resins. [ABSTRACT FROM AUTHOR]

Abstract The global navigation satellite system (GNSS) is presently a powerful tool for sensing the Earth's ionosphere. For this purpose, the ionospheric measurements (IMs), which are by definition slant total electron content biased by satellite and receiver differential code biases (DCBs), need to be first extracted from GNSS data and then used as inputs for further ionospheric representations such as tomography. By using the customary phase-to-code leveling procedure, this research comparatively evaluates the calibration errors on experimental IMs obtained from three GNSS, namely the US Global Positioning System (GPS), the Chinese BeiDou Navigation Satellite System (BDS), and the European Galileo. On the basis of ten days of dual-frequency, triple-GNSS observations collected from eight co-located ground receivers that independently form short-baselines and zero-baselines, the IMs are determined for each receiver for all tracked satellites and then for each satellite differenced for each baseline to evaluate their calibration errors. As first derived from the short-baseline analysis, the effects of calibration errors on IMs range, in total electron content units, from 1.58 to 2.16, 0.70 to 1.87, and 1.13 to 1.56 for GPS, Galileo, and BDS, respectively. Additionally, for short-baseline experiment, it is shown that the code multipath effect accounts for their main budget. Sidereal periodicity is found in single-differenced (SD) IMs for GPS and BDS geostationary satellites, and the correlation of SD IMs over two consecutive days achieves the maximum value when the time tag is around 4 min. Moreover, as byproducts of zero-baseline analysis, daily between-receiver DCBs for GPS are subject to more significant intra-day variations than those for BDS and Galileo. [ABSTRACT FROM AUTHOR]

Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions. Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning. Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT). The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively. The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%. [ABSTRACT FROM AUTHOR]

After considering the local effects of nose bluntness on the transition, a modification for the timescale of the second mode in the three-equation k-ω-γ transition model has been proposed to improve the hypersonic transition past cones. The ratio of local unit Reynolds number over freestream unit Reynolds number ( Re local / Re ∞ ) is introduced to delay and reduce the development of second mode instability, according to the results of linear stability theory. The hypersonic transitions past a cone with three nose radii at the angle of attack of 0° were simulated to calibrate the new model and its constants. Then, the improved model is extended to predict the hypersonic transitions past the cones with larger radius and at non-zero angles of attack. Despite the original model can reflect the transition trend, the improved model performs much better than the original one when comparing with the available measurements. [ABSTRACT FROM AUTHOR]

The objects in remote sensing images are normally densely packed, arbitrarily oriented, and surrounded by complex backgrounds. Great efforts have been devoted to developing oriented object detection models to accommodate such data characteristics. We argue that an effective detection model hinges on three aspects: feature enhancement, feature decoupling for classification and localization, and an appropriate bounding box regression scheme. In this article, we instantiate the three aspects on top of the classical Faster R-CNN, with three novel components proposed. First, we propose a weighted fusion and refinement (WFR) module, which adaptively weighs multi-level features and leverages the attention mechanism to refine the fused features. Second, we decouple the RoI (region of interest) features for the subsequent classification and localization via a lightweight affine transformation-based feature decoupling (ATFD) module. Third, we propose a post-classification regression (PCR) module for generating the desired quadrilateral bounding boxes. Specifically, PCR predicts the precise vertex location on each side of a predicted horizontal box, by simply learning the following: (i) classify the discretized regression range of the vertex, and (ii) revise the vertex location with an offset. We conduct extensive experiments on the DOTA, DIOR-R, and HRSC2016 datasets to evaluate our method. [ABSTRACT FROM AUTHOR]

Remotely sensed data have been widely used for classification of land-use and land-cover (LULC) types. However, classifying different forest types in Northeast China using satellite images is still a great challenge because of the similar spectral reflectances of different tree species. The differences of vegetation phenological characteristics provide the potential of classifying the types using time series of spectral variables derived from images. In this study, time series of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) images obtained in 2012 for Northeast China were used to calculate various phenological metrics and to further derive amplitude and phase information of harmonic components using Fourier transforms. The separability of eight vegetation cover types plus water and built-up areas was then analysed using phenological metrics, and amplitude and phase of harmonic components. Moreover, a phenology-based decision tree classifier was developed to classify the types in this area. Out of 4900 national forest inventory plots, 3700 plots were used to train the decision tree classifier and 1200 plots to assess the accuracy of classification by combining the plots’ observations with the values of a published LULC map that had a higher spatial resolution and accuracy of classification using a window majority rule. In addition, three data sets from different temporal resolution MODIS NDVI and EVI time series and two similarity measures were compared for separability and classification of the types. The results showed that (1) Fourier transforms of NDVI and EVI time series led to the first four harmonic components (including component 0, average annual NDVI, and EVI) that captured the phenological characteristics of the cover types; (2) compared to those using only NDVI, the separability values of the classes using NDVI, amplitude, and phase increased from 1.71 to 1.95, implying the potential improvement of classification; (3) the data set from 10-day NDVI time series had higher classification accuracy than those from 16-day NDVI and EVI time series, although the EVI time series performed slightly better than the NDVI time series at the same temporal resolution; and (4) a classification accuracy of 83.8% with a kappa coefficientof 0.79 was finally obtained. This study implied that this method is applicable for classification of vegetation cover types for large areas. [ABSTRACT FROM AUTHOR]

Accurate spatial estimation of forest carbon stocks and their spatial uncertainties at local, regional, national, and global scales is a critical step in global carbon cycle modeling and management. This study aimed at enhancing the methods that are currently used in this area by combining plot data from the forest inventory and analysis program of the U.S. Forest Service and free landsat thematic mapper image data. Three mapping methods including linear regression, sequential Gaussian co-simulation, and block co-simulation algorithm were compared with respect to the accuracy of forest carbon stock estimates obtained for a study area in Southern Illinois, USA. The results indicated that although the linear regression resulted in smaller prediction errors than the sequential Gaussian co-simulation and the block co-simulation approaches, it also produced both negative and unreasonably large estimates, which is a serious drawback. Moreover, the sequential Gaussian co-simulation and the block co-simulation produced not only accurate carbon predictions, but also uncertainties for the local estimates. In addition, the block co-simulation approach scaled up both forest carbon stocks and the input uncertainties from finer to coarser spatial resolutions as is required for mapping forest carbon at national and global scales. Thus, the co-simulation and block co-simulation algorithms resolved an important current methodological challenge. [ABSTRACT FROM AUTHOR]

The broadcast ionospheric model is mainly used to correct the ionospheric delay error for single-frequency users. Since the BeiDou global ionospheric delay correction model (BDGIM) is a novel broadcast ionospheric model for BDS-3, its performance was analyzed through single point positioning (SPP) in this study. Twenty-two stations simultaneously receiving B1C, B2a, B1I and B3I signals were selected from the International GNSS Service (IGS) and the International GNSS Monitoring and Assessment System (iGMAS) tracking networks for the SPP experiments. The differential code bias (DCB) parameters were used to correct the hardware delays in the signals of B1C and B2a. The results showed that the BDGIM performs the best in high-latitude areas, and can effectively improve the positioning accuracy compared with the Klobuchar model. The average 3D positioning accuracy of the four civil signals can reach 3.58 m in high-latitude areas. The positioning accuracies with the BDGIM in the northern hemisphere are better than those in the southern hemisphere, and the global average 3D positioning accuracy of the four civil signals is 4.60 m. The performance of the BDGIM also shows some seasonal differences. The BDGIM performs better than the Klobuchar model on the days of spring equinox and winter solstice, while the opposite is true on the days of summer solstice and autumn equinox. On the day of winter solstice, the average 3D accuracies with the BDGIM on the signals of B1C, B2a, B1I and B3I are 4.13 m, 5.32 m, 4.40 m and 4.49 m, respectively. Although the SPP accuracies are to some extent affected by the geomagnetic storm, the BDGIM generally performs better and are more resistant to the geomagnetic storm than the Klobuchar model. [ABSTRACT FROM AUTHOR]

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering. This paper develops a Distributed Computing System for Remotely Sensed Images framework (DCSRI) to support distributed and high performance computing for geospatial images. A new algorithm supporting parallel computing with dynamic workload balance for large images, namely Variogram-based Image Texture Extractor (VITE) for extracting image texture from massive and dynamic digital remotely sensed images is presented. The VITE algorithm is used to represent and transform the original data into image textures. The DCSRI framework has the capacity to perform high performance computing on Linux clusters or supercomputers to address the intensive computing challenges arising from large and multiple images. Advanced web technology is also exploited to enable interactive user experience with prompt visual feedback as well as good interoperability. Users can also dynamically steer the visualization and computation process by adjusting the computing parameters on-the-fly. This system leads to a great reduction of image data and provides useful information for knowledge discovery and digital image classification in a user friendly and computing efficient way. [ABSTRACT FROM AUTHOR]

Forest carbon sinks significantly contribute to mitigation of atmospheric concentrations of carbon dioxide. Thus, estimating forest carbon is becoming important to develop policies for mitigating climate change and trading carbon credits. However, a great challenge is how to quantify uncertainties in estimation of forest carbon. This study investigated uncertainties of mapping aboveground forest carbon due to location errors of sample plots for Lin-An County of China. National forest inventory plot data and Landsat TM images were combined using co-simulation algorithm. The findings show that randomly perturbing plot locations within 10 distance intervals statistically did not result in biased population mean predictions of aboveground forest carbon at a significant level of 0.05, but increased root mean square errors of the maps. The perturbations weakened spatial autocorrelation of aboveground forest carbon and its correlation with spectral variables. The perturbed distances of 800 m or less did not obviously change the spatial distribution of predicted values. However, when the perturbed distances were 1600 m or larger, the correlation coefficients of the predicted values from the perturbed locations with those from the true plot locations statistically did not significantly differ from zero at a level of 0.05 and the spatial distributions became random. [ABSTRACT FROM AUTHOR]

Abstract: A great challenge the US military land managers are often faced with is how to optimize road networks in order to maintain roads (including all roads, trails, and paths) for the purpose of military training and reducing negative impacts on environment. In this study, a methodology was developed to identify superfluous roads for being closed in terms of both sustainable military land carrying capacity and environment for Fort Riley. In this method, Geographic Information Systems (GIS), remote sensing, and landscape analysis technologies were combined to derive various spatial data layers of factors that had significant impacts on both military training and environment. The factors included maintenance cost of roads, road access area, military training intensity, soil erosion, water quality, landscape fragmentation, and noise production. The factors were quantified and normalized. A spatial multicriteria decision was then developed to obtain the weights of the factors, combine the data layers, and derive a priority map of all the roads for being closed. This map summarized the negative and positive impacts of the factors on environment and military land carrying capacity and can provide the US military land managers with useful guidelines and tools for determining superfluous roads in terms of both sustainable military training and environment. It is expected this effort can provide a method to quickly ascertain which roads are most cost-effective for being closed without hindering the mission and at the same time with benefits for environmental protection and thus provide the land managers with a comprehensive analysis and assessment of alternatives at their disposal. [Copyright &y& Elsevier]

This study investigates applications and efficiencies of remotely sensed data and the sensitivity of grid spacing for the sampling and mapping of a ground and vegetation cover factor in a monitoring system of soil erosion dynamics by cokriging with Landsat Thematic Mapper (TM) imagery based on regionalized variable theory. The results show that using image data can greatly reduce the number of ground sample plots and sampling cost required for collection of data. Under the same precision requirement, the efficiency gain is significant as the ratio of ground to image data used varies from 1: 1 to 1: 16. Moreover, we proposed and discussed several modifications to the cokriging procedure with image data for sampling and mapping. First, directly using neighbouring pixels for image data in sampling design and mapping is more efficient at increasing the accuracy of maps than using sampled pixels. Although information among neighbouring pixels might be considered redundant, spatial cross-correlation of spectral variables with the cover factor can provide the basis for an increase in accuracy. Secondly, this procedure can be applied to investigate the appropriate spatial resolution of imagery, which, for sampling and mapping the cover factor, should be 90 m × 90 m - nearly consistent with the line transect size of 100 m used for the ground field survey. In addition, we recommend using the average of cokriging variance to determine the global grid spacing of samples, instead of the maximum cokriging variance. [ABSTRACT FROM AUTHOR]

Abstract: Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps. [Copyright &y& Elsevier]

B Thermo-Chemotherapy b A promising biocompatible nanotheranostics agent based on chemotherapeutic drug 5-fluorouracil and 2D silicon nanosheets is developed for synergistic thermo-chemotherapy strategy. The tumor tissue functional parameters, including SaO SB 2 sb , HbT, micro microvascular morphology, and hemodynamics, are dynamically monitored by multimodal PA/OCTA/LS imaging strategy to accurately evaluate overall therapeutic effects. Intravital Whole-Process Monitoring Thermo-Chemotherapy Via 2D Silicon Nanoplatform: A Macro Guidance and Long-Term Microscopic Precise Imaging Strategy (Adv. Sci. 16/2021). [Extracted from the article]

Intravital Whole-Process Monitoring Thermo-Chemotherapy Via 2D Silicon Nanoplatform: A Macro Guidance and Long-Term Microscopic Precise Imaging Strategy (Adv. Sci. 16/2021) B Thermo-Chemotherapy b A promising biocompatible nanotheranostics agent based on chemotherapeutic drug 5-fluorouracil and 2D silicon nanosheets is developed for synergistic thermo-chemotherapy strategy. The tumor tissue functional parameters, including SaO SB 2 sb , HbT, micro microvascular morphology, and hemodynamics, are dynamically monitored by multimodal PA/OCTA/LS imaging strategy to accurately evaluate overall therapeutic effects. [Extracted from the article]

Abstract: We assessed short-term impacts of changes in military training load on vegetative cover at Fort Hood, TX. From 1989 to 1995, permanent field transects were monitored for vegetative cover and land use disturbance using standard army monitoring methods [Land Condition Trend Analysis (LCTA)]. Land use intensity (training load) was quantified and used to develop a model to predict future vegetation cover values. We found that standard Army monitoring methods detected changes in installation resources associated with changes in training load. Increased training loads were associated with increased measures of disturbance, decreased ground cover, and decreased aerial vegetative cover. We found that the spatial pattern of disturbance and vegetation cover remained relatively constant over the study period despite large variations in overall training load. Our model used the consistency in spatial cover patterns over time and the strong relationship between training load and vegetation cover to predict the impact of future training loads on vegetation. [Copyright &y& Elsevier]

Soil erodibility (susceptibility of soil to be lost to erosion) is one of the components of the universal soil loss equation (USLE). In the USLE, erodibility is known as the K factor, which in turn is a function of these soil properties: particle size distribution, organic matter content, structure, and permeability. The traditional approach for estimating soil erodibility does not account for spatial variability of individual soil properties or spatial correlation among those properties. Our objectives in this study were to evaluate the use of joint sequential simulation for mapping soil erodibility, as well as to partition the individual and joint variance contribution of soil properties for predicting soil erodibility. We collected 192 usable soil samples across Fort Hood, Texas in the summer of 1999. For each of those samples, we obtained an estimate of particle size distribution, organic matter content, structure, permeability, and calculated soil erodibility. We carried out both independent and joint sequential simulation to generate spatially explicit predictions and variance of all soil properties as well as covariance between pairs of soil properties for each cell within our simulation area. We used the program GCOSIM3D to conduct those simulations. On average, joint sequential simulation resulted in a K factor variance of less than half the variance obtained from independent simulation. Using the results from joint sequential simulation, we partitioned the contribution of each soil property and pair of properties using first-order Taylor series expansion of the soil erodibility function. Individually, Very-Fine-Sand-and-Silt contributed the most (46.19%), whereas Structure contributed the least (6.53%) to the K factor variance. Jointly, Permeability/Structure contributed the most (9.32%), whereas Sand/Very-Fine-Sand-and-Silt caused the largest reduction (−19.19%) in the K factor variance. We conclude that joint sequential simulation provided approximately twice as much precision as independent simulation for the spatially explicit prediction of soil erodibility. Likewise, first-order Taylor series expansion offered an accurate approach for partitioning the individual and joint contribution of soil properties to soil erodibility variance. This partitioning allowed us to identify large sources of uncertainty and suggest efficient approaches for further improving the precision of K value predictions. [Copyright &y& Elsevier]

In the management of natural resources, multiple variables correlated with each other usually need to be mapped jointly. However, joint mapping and spatial uncertainty analyses are very difficult mainly because of interactions among variables and imperfection of existing methods. There is abundant evidence that considering interactions among variables and spatial information from neighbors can result in improved maps. This study presents a remote sensing-aided method for that purpose. The method is based on the integration of joint sequential co-simulation with Landsat TM image for mapping and polynomial regression for spatial uncertainty analysis. The method was applied to a case study in which ground cover (GC), canopy cover (CC), and vegetation height (VH) were jointly mapped to derive a map of the vegetation cover factor for predicting soil loss. The variance contributions from the variables, their interactions, and the spatial information from neighbors leading to uncertainty of predicted vegetation cover factor were assessed. The results showed that in addition to unbiased maps, this method reproduced the spatial variability of the variables and the spatial correlation among them, and successfully quantified the effect of variation from all the components on the prediction of the vegetation cover factor. [Copyright &y& Elsevier]

Soil loss is commonly predicted using the revised universal soil loss equation consisting of rainfall–runoff erosivity, soil erodibility, slope steepness and length, cover management, and support practice factors. Because of the multiple factors, their interactions, and spatial and temporal variability, soil erosion varies considerably over space and time. For these reasons, modeling soil loss is very complicated. Decision-makers need local and regional estimates of soil loss as well as their corresponding uncertainties. Neglecting the local and detailed information may lead to improper decision-making. This paper demonstrates a strategy based on a sample data set and a geostatistical method called sequential Gaussian simulation to derive local estimates and their uncertainties for the input factors of a soil erosion system. This strategy models the spatial and temporal variability of the factors and derives their estimates and variances at any unknown location and time. This strategy was applied to a case study at which the rainfall–runoff erosivity R factor was spatially and temporally estimated using a data set of rainfall. The results showed that the correlation between the observations and estimates by the strategy ranged from 0.89 to 0.97, and most of the mean estimates fell into their confidence intervals at a probability of 95%. Comparing the estimates of the R factor using a traditional isoerodent map to the observed values suggested that the R factor might have increased and a new map may be needed. The method developed in this study may also be useful for modeling other complex ecological systems. [Copyright &y& Elsevier]

Global navigation satellite system (GNSS) provides users with all-weather, continuous, high-precision positioning, navigation, and timing (PNT) services. In the operation and use of GNSS, the influence of the space environment is a factor that must be considered. For example, during geomagnetic storms, a series of changes in the Earth's magnetosphere, ionosphere, and upper atmosphere affect GNSS's positioning performance. To investigate the positioning performance of global satellite navigation systems during geomagnetic storms, this study selected three geomagnetic storm events that occurred from September to December 2023. Utilizing the global positioning system (GPS)/Beidou navigation satellite system (BDS) dual-system, kinematic precise point positioning (PPP) experiments were conducted, and the raw observational data from 100 stations worldwide was analyzed. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry Free (GF) cycle-slip detection threshold during geomagnetic storms, which means that the GF is no longer applicable to high-precision positioning services. Meanwhile, there is no significant change in the satellite signal strengths received at the stations during the period of the decrease in positioning accuracy. Analyzing the cycle-slip rates for stations where abnormal accuracy occurred, it was observed that stations experiencing a significant decline in positioning accuracy exhibited serious cycle-slip misjudgments. To improve the kinematic PPP accuracy during magnetic storms, this paper proposes an adaptive threshold for cycle-slip detection and designs five experimental strategies. After using the GF adaptive threshold, the station positioning accuracy improved significantly. It achieved the accuracy level of the quiet period, while the cycle-slip incidence reached the average level. During magnetic storms, the ionosphere changes rapidly, and the use of the traditional GF constant threshold will cause serious cycle-slip misjudgments, which makes the dynamic accuracy in high latitude areas and some mid-latitude areas uncommon, while the use of the GF adaptive threshold can alleviate this phenomenon and improve the positioning accuracy in the high-latitude regions and some of the affected mid-latitude areas during the magnetic storms. [ABSTRACT FROM AUTHOR]

The need for timely, spatially, and thematically accurate information regarding forests is increasing because of the key role of forests in the global carbon balance and sustainable social, economic, ecological, and cultural development. While an increasing number of countries in the world already are conducting statistically sound forest inventories, a few for 100 years, inventories in other countries are still lacking, which makes the global information about forest statistics inaccurate. This implies that remote sensing-based approaches are vulnerable to misclassification and inaccurate estimation of forest parameters and changes, such as the recognition of temporarily unstocked forest areas, forest degradation, and species composition, as well as in strictly following the globally adopted definitions. [Extracted from the article]

Deep learning classifiers exhibit remarkable performance for hyperspectral image classification given sufficient labeled samples but show deficiency in the situation of learning with limited labeled samples. Active learning endows deep learning classifiers with the ability to alleviate this deficiency. However, existing active deep learning methods tend to underestimate the feature variability of hyperspectral images when querying informative unlabeled samples subject to certain acquisition heuristics. A major reason for this bias is that the acquisition heuristics are normally derived based on the output of a deep learning classifier, in which representational power is bounded by the number of labeled training samples at hand. To address this limitation, we developed a feature-oriented adversarial active learning (FAAL) strategy, which exploits the high-level features from one intermediate layer of a deep learning classifier for establishing an acquisition heuristic based on a generative adversarial network (GAN). Specifically, we developed a feature generator for generating fake high-level features and a feature discriminator for discriminating between the real high-level features and the fake ones. Trained with both the real and the fake high-level features, the feature discriminator comprehensively captures the feature variability of hyperspectral images and yields a powerful and generalized discriminative capability. We leverage the well-trained feature discriminator as the acquisition heuristic to measure the informativeness of unlabeled samples. Experimental results validate the effectiveness of both (i) the full FAAL framework and (ii) the adversarially learned acquisition heuristic, for the task of classifying hyperspectral images with limited labeled samples. [ABSTRACT FROM AUTHOR]

With the development of Global Navigation Satellite System (GNSS) and the opening of Application Programming Interface (API) of Android terminals, the positioning research of Android terminals has attracted the attention of GNSS community. In this paper, three static experiments were conducted to analyze the raw GNSS observations quality and positioning performances of the smartphones. For the two experimental smartphones, the numbers of visible satellites with dual-frequency signals are unstable and not enough for dual-frequency Precise Point Positioning (PPP) processing all through the day. Therefore, the ionosphere-constrained single-frequency PPP model was employed to improve the positioning with the smartphones, and its performance was evaluated and compared with those of the Single Point Positioning (SPP) and the traditional PPP models. The results show that horizontal positioning accuracies of the smartphones with the improved PPP model are better than 1 m, while those with the SPP and the traditional PPP models are about 2 m. [ABSTRACT FROM AUTHOR]

To predict complex flow, including transition on the windward side and massive separation on the leeward side, a Reynolds averaged Navier Stokes-Large Eddy Simulation (RANS-LES) hybrid model based on the three-equation k - ω - γ transition model (RANS-LES-Tr) was developed. Then, it was applied to simulate the following three cases, such as the low-speed flow past a cylinder only with the first transition mode, the low-speed prolate spheroid flow at a high angle of attack (AoA) with both the first and crossflow modes; and hypersonic flow past the "Orion" capsule with the first, second and crossflow modes. RANS-LES-Tr shows much better agreements with available measurements, such as skin friction and heat flux on the windward side, than RANS-LES based on the full turbulence model. [ABSTRACT FROM AUTHOR]

Tai, X.; Wang, G.; Grecos, C., and Ren, P., 2020. Coastal image classification under noisy labels. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 151-156. Coconut Creek (Florida), ISSN 0749-0208. Remote sensing coastal images commonly suffer from noisy labels, which tend to reduce the benefits of training coastal image classifiers and thus deteriorate the classification performance. This article explores the feasibility of classifying coastal images under noisy labels. The state-of-the-art co-teaching paradigm in computer vision is exploited for processing remote sensing coastal images that are partially incorrectly labeled. The data with noisy labels are hard to learn and induce large loss values for a classifier. In the light of this observation, the co-teaching paradigm trains two identically structured but randomly initialized convolutional neural network (CNN) classifiers with stochastic optimization. Training of the two CNN classifiers is performed on mini-batches in an interactive and iterative manner. Specifically, the data with small loss values of one CNN classifier are used for updating the other CNN classifier in the next training iteration. The two CNN classifiers turn robust to noisy labels through the iterative process of teaching each other. Experimental evaluations on a coastal image dataset, i.e., the Moye Island remote sensing dataset, validate the effectiveness of the proposed coastal image classification strategy. [ABSTRACT FROM AUTHOR]

Yang, Q.; Wang, G.; Zhang, X.; Grecos, C., and Ren, P., 2020. Coastal image captioning. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 145-150. Coconut Creek (Florida), ISSN 0749-0208. Coastal images convey immense semantic information of the corresponding coastal areas. This article presents a preliminary approach to coastal image captioning that describes salient semantic information of coastal images with accurate and meaningful sentences. Specifically, this article exploits a state-of-the-art method referred to as self-critical sequence training for coastal image captioning. Firstly, a convolutional neural network (CNN) produces fixed-length features from the coastal images. Secondly, the fixed-length features are fed into a long short-term memory (LSTM) to generate captions, i.e., the accurate and meaningful sentences. Finally, the LSTM is optimized in the context of reinforcement learning. Experimental evaluation on the Moye island remote sensing dataset validates the effectiveness. [ABSTRACT FROM AUTHOR]

Verticillium wilt is a severe plant disease that causes massive losses in multiple crops. Increasing the plant resistance to Verticillium wilt is a critical challenge worldwide. Here, we report that the hemibiotrophic Verticillium dahliae -secreted Asp f2-like protein VDAL causes leaf wilting when applied to cotton leaves in vitro but enhances the resistance to V. dahliae when overexpressed in Arabidopsis or cotton without affecting the plant growth and development. VDAL protein interacts with Arabidopsis E3 ligases plant U-box 25 (PUB25) and PUB26 and is ubiquitinated by PUBs in vitro. However, VDAL is not degraded by PUB25 or PUB26 in planta. Besides, the pub25 pub26 double mutant shows higher resistance to V. dahliae than the wild-type. PUBs interact with the transcription factor MYB6 in a yeast two-hybrid screen. MYB6 promotes plant resistance to Verticillium wilt while PUBs ubiquitinate MYB6 and mediate its degradation. VDAL competes with MYB6 for binding to PUBs, and the role of VDAL in increasing Verticillium wilt resistance depends on MYB6. Taken together, these results suggest that plants evolute a strategy to utilize the invaded effector protein VDAL to resist the V. dahliae infection without causing a hypersensitive response (HR); alternatively, hemibiotrophic pathogens may use some effectors to keep plant cells alive during its infection in order to take nutrients from host cells. This study provides the molecular mechanism for plants increasing disease resistance when overexpressing some effector proteins without inducing HR, and may promote searching for more genes from pathogenic fungi or bacteria to engineer plant disease resistance. [ABSTRACT FROM AUTHOR]

Developing an individual tree diameter increment (ΔDBH) model is the basis of near-natural management of mixed, uneven-aged oak forests. This analysis used remeasurement data (2009–2014) comprising 6154 observations from 112 permanent plots in central China to develop and compare an indicator variable model (IVM) and a mixed-effect model (MEM) to estimate ΔDBH. First, a basic model was estimated using 12 potential explanatory variables. Geographical regions (GR), competition intensities (CI) and species compositions (SC) were introduced into the basal model as indicator variables or mixed effects, step by step, and then the prediction accuracy of IVM and MEM was compared. The results showed that (1) the independent variables significantly affecting ΔDBH included the reciprocal of DBH, basal area, altitude, and mean annual rainfall; (2) the introducing GR could not improve the accuracy of estimating ΔDBH, but the CI and SC could. (3) Compared with the basic model and IVM, the percentage mean absolute deviation of MEM decreased by 2.07% and 1.11%, while the root mean square error decreased by 0.06 and 0.04, respectively. The MEM including CI and SC as a random effect showed the best predictive performance and can be applied to improve the prediction of individual oak trees ΔDBH. [ABSTRACT FROM AUTHOR]

Surface urban heat islands (SUHIs) have been investigated in many regions around the world, but little attention has been given with regard to SUHIs in South America. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data was used to investigate the diurnal, seasonal, and interannual variations in the SUHI intensity (SUHII, the urban LST minus the rural LST) in 44 South American cities in different climate zones and types of rural land. To examine the effects of factors that may influence the SUHII, correlations between the SUHII and the enhanced vegetation index (EVI), urban area, population, altitude, and anthropogenic heat emissions were performed. The results showed that the SUHI effect was obvious in South America. The mean daytime SUHII was higher than the mean night-time SUHII in all areas except for the arid climate zone. In the daytime, the summer displayed a stronger SUHII in the warm temperate climate zone than the other seasons. The night-time SUHII showed less obvious seasonal variations. In addition, the surrounding land cover influenced the SUHII. During the day, the SUHII was therefore stronger in rural areas that were covered by forests than in other types of rural land. Interannually, most cities showed an insignificant temporal trend in the SUHII from 2003 to 2016. The daytime SUHII was significantly and negatively correlated with the ∆EVI (the urban EVI minus the rural EVI) across the 44 cities, but a poor relationship was observed at night. In addition, anthropogenic heat emissions were positively correlated with the night-time SUHII. Urban area, population, and altitude were weakly correlated with the SUHII, which suggested that these factors may not have a significant impact on the spatial variations in the SUHII in South America. [ABSTRACT FROM AUTHOR]

Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most contemporary state estimation methods apply demanding pre-processing steps, such as map-matching. The objective of this paper is to design, train and benchmark a data-driven procedure to forecast crowd movements, which can in real-time predict crowd movement. This procedure entails two steps. The first step comprises of a cell sequence derivation method that allows the representation of spatially continuous GPS traces in terms of discrete cell sequences. The second step entails the training of a Recursive Neural Network (RNN) with a Gated Recurrent Unit (GRU) and six benchmark models to forecast the next location of pedestrians. The RNN-GRU is found to outperform the other tested models. Some additional tests of the ability of the RNN-GRU to forecast illustrate that the RNN-GRU preserves its predictive power when a limited amount of data is used from the first few hours of a multi-day event and temporal information is incorporated in the cell sequences. [ABSTRACT FROM AUTHOR]

Forest canopy cover (CC) directly and indirectly influences various processes of forest ecosystems. Airborne light detection and ranging (LiDAR) can be used to characterize forest spatial structures and further obtain estimates of forest CC. However, nonreturn laser pulses from targets of interest impact the estimation accuracy of forest CC. The objective of this article was to develop a novel method of estimating the nonreturn laser pulses to improve the estimation accuracy of forest CC using LiDAR data. The improved models for estimating forest CC were developed by introducing the loss ratio of laser pulses and a CC coefficient into the original models. The forest CC reference data were collected and used to validate the forest CC estimates. The results show that the loss ratio for forested areas was much higher than that for open ground areas. The range between the sensor and a target was a crucial factor that caused the loss of returns. The relationship between the range and the loss ratio was nonlinear in both open ground and forested areas. Compared with the original models, the improved models combining the loss ratio and the CC coefficient statistically significantly increased the estimation accuracy of the forest CC. Moreover, the forest CC estimates from the canopy height model (CHM) were more accurate than those from the height normalized point cloud (NPC) data. In addition, the simplified models were more generalized than the other models. This article is novel and has great potential to improve mapping of forest CC. [ABSTRACT FROM AUTHOR]

Summary: Background: Liver fibrosis is the strongest histological risk factor for liver‐related complications and mortality in metabolic dysfunction‐associated fatty liver disease (MAFLD). Second harmonic generation/two‐photon excitation fluorescence (SHG/TPEF) is a powerful tool for label‐free two‐dimensional and three‐dimensional tissue visualisation that shows promise in liver fibrosis assessment. Aim: To investigate combining multi‐photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD. Methods: AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy‐confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre‐processed images and test data sets. Multi‐layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts. Results: AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3‐4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3‐4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts. Conclusion: AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD. [ABSTRACT FROM AUTHOR]

Background: Rowell syndrome (RS) is an uncommon condition characterized by erythema multiforme (EM)‐like lesions and lupus erythematosus. It is more common in females, and EM may be the first manifestation of the disease with positive autoantibodies, such as antinuclear antibody (ANA), SSA, SSB and rheumatoid factor. The pathogenesis of RS is unknown and is likely caused by drug induction, ultraviolet exposure and infection. Method: We describe a case of RS from China which presented as characteristic targetoid‐like lesions and chilblain‐like erythema on hands and feet. This is a case of RS in a female patient from the inpatient department of dermatology. Results: A 41‐year‐old female with systemic lupus erythematosus exhibited chilblain‐like erythema and characteristic EM lesions on her extremities. She tested positive for serum ANA (1:320) and anti‐double‐stranded DNA, as well as other autoantibodies. Systemic glucocorticoids and hydroxychloroquine worked effectively for her. Conclusion: The present case met diagnostic criteria of RS. Notably, there was a co‑occurrence of facial butterfly erythema, chilblain‐like erythema and EM lesions distributed on the limbs in this case. [ABSTRACT FROM AUTHOR]

Tropospheric delay (TD) parameter estimation is a critical issue underlying high-precision data processing for global navigation satellite systems (GNSSs). The most widely used TD parameter estimation methods are the random walk (RW) and piece-wise constant (PWC). The RW method can effectively track rapid variations of tropospheric delay, but it may introduce excessive noise. In contrast, the PWC method introduces less noise, but it is less adaptable to cases of large variations of tropospheric delay. To address the problem of how to choose the optimal TD parameter estimation method, this paper investigates the variation patterns of international GNSS service zenith tropospheric delay (IGS ZTD) products and proposes a combined strategy model for TD parameter estimation. Firstly, this paper avoids the day-boundary jumps problem of IGS ZTD products by grouping based on single-day data. Secondly, this paper introduces discrete point areas (DPAs) to measure the magnitude of the ZTD values and uses comprehensive indicators to reflect the variation of ZTD. Next, based on the Köppen-Geiger climate classification, this study selected five different climate classifications with a total of 20 IGS stations as experimental data. The data assessed span from day of year (DOY) 001 to DOY 365 in 2022. This paper then applied 26 different parameter estimation strategies for static precise point positioning (PPP) data processing, and the parameter estimation strategies that were used include the RW and PWC (with the piece-wise constant ranging from twenty minutes to five hundred minutes at twenty-minute intervals). Finally, ZTD and positioning results were obtained using various parameter estimation methods, and a combined strategy model was established. We selected five different climate classifications of IGS stations as validation data and designed three sets of comparative experiments: RW, PWC120, and the combined strategy model, to verify the effectiveness of the combined strategy model. The experimental results revealed that: RW and the combined strategy model have a comparable ZTD accuracy and both are superior to PWC120. The combined strategy model improves the positioning accuracy in the U direction compared to RW and PWC120. In arid (B) and polar (E) regions with a small variation of TD, the PWC120 strategy displayed a better positioning accuracy than the RW strategy; in equatorial (A) and warm-temperate (C) regions, where there are large variations of TD, the RW strategy exhibited a better positioning accuracy than the PWC120 strategy. The combined strategy model can flexibly select the optimal parameter estimation method according to the comprehensive indicator while ensuring ZTD estimation accuracy; it enhances positioning accuracy. [ABSTRACT FROM AUTHOR]

Soluble hyperbranched poly(arylene ether nitrile)s end-capped by either hydroxyl (HBPAEN-OHs) or phthalonitrile groups (HBPAEN-PNs) were synthesized via nucleophilic substitution reactions, where activated carbon supported calcium oxide was used as the catalyst to avoid crosslinking at high conversion. In order to evaluate their potential application in phthalonitrile resins, these hyperbranched polymers were blended with 1,3-bis(3,4-dicyanophenoxy)benzene and 4-(4-aminophenoxy)phthalonitrile, then cured into polymers. Curing mechanisms of different prepolymers were proposed and compared, and processability, thermal and mechanical properties were characterized. The introduction of HBPAEN-OHs was found to postpone the curing, while thermal and mechanical properties were compromised. On the contrary, the usage of HBPAEN-PNs was beneficial to both processability and thermal properties. More importantly, the impact strength for polymers containing HBPAEN-PN could be as high as 12.92 kJ m−2, indicating such resin could be potentially applied as high temperature structural composite matrices. In addition, the toughening mechanism was proposed and discussed. Image 1 • Novel hyperbranched poly(arylene ether nitrile) modifiers were synthesized. • Phthalonitrile prepolymers were obtained by blending with these modifiers. • Prepolymers exhibited processability capable for RTM manufacture. • Toughening was realized at no cost of thermal properties. [ABSTRACT FROM AUTHOR]

As a cross-disciplinary scientific domain, a great deal of research on landscape geochemistry has been conducted since its establishment. However, the used methods lack the ability of revealing the relationships of landscape geochemistry with other sciences, and the obtained knowledge is thus fragmented and isolated. In this study, the state-of-the-art regarding the applications of relatively new geospatial technologies including fractal theory, geographic information system and remote sensing to landscape geochemistry was reviewed and analyzed to provide deep insights of current research and a roadmap for furthering the development of landscape geochemistry as a cross-disciplinary discipline. The results showed that substantial research on the applications of fractal theory, GIS and RS technologies for analyzing the processes and data of landscape geochemistry has been conducted by using the advantages of the geospatial technologies. However, the great challenges still exist when the geospatial technologies were individually utilized due to the limitations of the technologies themselves and the complexity of landscape geochemistry. In the end, opportunities and challenges for advancing the further studies of landscape geochemistry were discussed in detail and new directions of studying landscape geochemistry using multidisciplinary or integrated approaches to enhance understanding the relationships among the relevant disciplines were suggested. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Low melting dual-functional phthalonitrile monomers were synthesized. • Prepolymers exhibited processability capable for RTM manufacture. • Glass transition temperatures of cured polymers were higher than 470 °C. • Impact strength reached 13.63 kJ m−2, capable as structural composite matrices. Abstract Low melting phthalonitrile monomers containing allyl and/or methoxyl groups were designed, synthesized and characterized. In order to evaluate their potential application in phthalonitrile resins, these monomers were blended with 1,3-bis(3,4-dicyanophenoxy)benzene and 4-(4-aminophenoxy)phthalonitrile, then cured into polymers. Curing mechanisms of different prepolymers were proposed and compared, and processability, thermal and mechanical properties were characterized. The introduction of methoxyl units was found to postpone the curing, and thermal properties were slightly compromised. On the contrary, the usage of allyl was beneficial to both processability and thermal properties. More importantly, the impact strength for allyl-containing polymers could be as high as 13.63 kJ m−2, indicating such resin could be applied as high temperature structural composite matrices. [ABSTRACT FROM AUTHOR]