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Abstract Interconnecting multiple microgrids (MGs) to form a MG cluster (MGC) is significant to improve the MG power supply reliability and increase the new energy accommodation capacity. However, for the traditional MGC in complex operational conditions, the following technologies need to be further researched, such as mutual power support among MGs, voltage support and power quality improvement and so on. Focusing on the above problems, a novel virtual synchronous port control (VSPC) strategy for MGC is proposed. Firstly, a multi‐port soft open point (SOP) is used to interconnect each sub‐MG in the MGC, to achieve a better flexible interconnection among sub‐MGs. Secondly, on this basis, the VSPC strategy is proposed. By adopting the DC voltage control of multi‐port SOP and the virtual synchronisation control of interconnecting ports, multiple voltage source converters (VSCs) are cluster controlled to provide the sub‐MGs with superordinate ports with virtual synchronisation characteristics, which ensures the dynamic balance of the power inside the MGC and achieves better voltage and frequency support capability. Finally, the example results verify the feasibility and effectiveness of the proposed VSPC strategy.

Abstract Worldwide, myocardial infarction (MI) is the leading cause of death and disability-adjusted life years lost. Recent researches explored new methods of detecting biomarkers that can predict the risk of developing myocardial infarction, which includes identifying genetic markers associated with increased risk. We induced myocardial infarction in mice by occluding the left anterior descending coronary artery and performed TTC staining to assess cell death. Next, we performed ChIP assays to measure the enrichment of histone modifications at the promoter regions of key genes involved in mitochondrial fission. We used qPCR and western blot to measure expression levels of relative apoptotic indicators. We report that miR-181a inhibits myocardial ischemia-induced apoptosis and preserves left ventricular function after MI. We show that programmed cell death protein 4 (PDCD4) is the target gene involved in miR-181a-mediated anti-ischemic injury, which enhanced BID recruitment to the mitochondria. In addition, we discovered that p53 inhibits the expression of miR-181a via transcriptional regulation. Here, we discovered for the first time a mitochondrial fission and apoptosis pathway which is controlled by miR-181a and involves PDCD4 and BID. This pathway may be controlled by p53 transcriptionally, and we presume that miR-181a may lead to the discovery of new therapeutic and preventive targets for ischemic heart diseases.

The structure of plant communities and their response to temperature variations are an essential basis for evaluating the ecological structure and function of tea plantations. However, field surveys and quantitative evaluation of plant communities and ecotea plantations remain challenging. In this study, a novel strategy was proposed for rapid surveillance of plant community structure and its response to changes in weather conditions in tea plantations. This strategy aims to construct the normalized tea plantation ecological index (NTEI) by synergizing environmental parameters with multisource remote sensing data; establish the fitting and inversion model of NTEI by cascading the Fourier function with the convolutional neural networks gate recurrent unit (CNN-GRU) network; and evaluate the variability of the plant community in tea plantations by analyzing the variation characteristics of the NTEI and the measured temperature. The study revealed the following: First, the NTEI can objectively characterize the plant communities of tea plantations, and its variation characteristics were consistent with the changes in vegetation phenology and temperature; second, the Fourier function has the potential to quantify NTEI, and it is fitting R2 for the NTEI of nine plant communities ranged from 0.840 to 0.921; third, the CNN-GRU has the most advantage in establishing the prediction model of NTEI, and its prediction accuracy was Rp2 = 0.955 and RMSEP = 0.314; and fourth, the plant communities with high species richness increased regional ecological stability, had a strong buffering capacity against temperature changes, and had less variability in NTEI. The results provide significant guidance for building plant community structures and improving the ecological benefits of tea plantations.

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models—AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2—are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method.

The challenge of analyzing microarray datasets is significantly compounded by the curse of dimensionality and the complexity of feature interactions. Addressing this, we propose a novel feature selection algorithm based on maximum conditional mutual information (MCMI) to identify a minimal feature subset that is maximally relevant and non-redundant. This algorithm leverages a greedy search strategy, prioritizing both feature quality and classification performance. Experimental results on high-dimensional microarray datasets demonstrate our algorithm’s superior ability to reduce dimensionality, eliminate redundancy, and enhance classification accuracy. Compared to existing filter feature selection methods, our approach exhibits higher adaptability and intelligence.

Urban tailwater typically has a low carbon-to-nitrogen ratio and adding external carbon sources can effectively improve the denitrification performance of wastewater. However, it is difficult to determine the dosage of additional carbon sources, leading to insufficient or excessive addition. Therefore, it is necessary to prepare solid slow-release carbon source (SRC) materials to solve the difficulty in determining the dosage of carbon sources. This study selected two SRCs of slow-release carbon source 1 (SRC1) and slow-release carbon source 2 (SRC2), with good slow-release performance after static carbon release and batch experiments. The composition of SRC1 was: hydroxypropyl methylcellulose/disodium fumarate/polyhydroxy alkanoate (HPMC/DF/PHA) at a ratio of 3:2:4, with an Fe3O4 mass fraction of 3%. The composition of SRC2 was: HPMC/DF/PHA with a ratio of 1:1:1 and an Fe3O4 mass fraction of 3%. The fitted equations of carbon release curves of SRC1 and SRC2 were y = 61.91 + 7190.24e−0.37t and y = 47.92 + 8770.42e−0.43t, respectively. The surfaces of SRC1 and SRC2 had a loose and porous morphological structure, which could increase the specific surface area of materials and be more conducive to the adhesion and metabolism of microorganisms. The experimental nitrogen removal by denitrification with SRCs showed that when the initial total nitrogen concentration was 40.00 mg/L, the nitrate nitrogen (NO3−-N) concentrations of the SRC1 and SRC2 groups on the 10th day were 2.57 and 2.66 mg/L, respectively. On the 20th day, the NO3−-N concentrations of the SRC1 and SRC2 groups were 1.67 and 2.16 mg/L, respectively, corresponding to removal efficiencies of 95.83% and 94.60%, respectively. The experimental results indicated that SRCs had a good nitrogen removal effect. Developing these kinds of materials can provide a feasible way to overcome the difficulty in determining the dosage of carbon sources in the process of heterotrophic denitrification.

Abstract The reasonable power quality assessment model of microgrid is significant to the planning and management for a microgrid. In the power quality assessment, how to extract and integrate the implicative information in different evaluation indicators, to realize the comprehensive power quality assessment, are needs to be further studied. To solve the above problems, a microgrid power quality assessment model based on multivariate Gaussian distribution and local sensitivity analysis is proposed in this paper. Firstly, the probability density function is used to integrate the individual assessment indicators, and the correlation between the assessment indicators is reflected by the covariance matrix, and then the above characteristics of evaluation indicators are quantized to be as the indicator weights by using the local sensitivity. Comparing with the traditional power quality assessment method, the proposed evaluation model is more suitable for microgrid. Finally, an example is developed to verify that the overall situation of microgrid power quality can be reasonably reflected by the proposed evaluation model.

In light of the limited research on latent damages during the construction of large-span suspension arches, this study introduced a method to simulate structural damage utilizing random porosity. Initially, based on data from real-world engineering projects, the most susceptible areas within the arch structure were pinpointed. Subsequently, multiple test arch simulation models were constructed. Employing Python, commands for random porosity were implemented within ABAQUS and distinct mesh modules were devised to depict structures under varying degrees of damage. The current investigation delved into the structural responses of these susceptible areas under different damage rates, shedding light on damage progression patterns. Notably, our findings demonstrated that concealed damages on the top plate of the arch foot profoundly influenced structural integrity, whereas damages at the arch hance were comparatively minimal and predominantly manifest at the arch base. The pronounced localized damage at both the arch base and hance initiated and intensified at sectional corners, necessitating enhanced anti-crack measures in these regions. Moreover, depending on the stresses of the arch structure, diverse reinforcement strategies could be employed, optimizing the balance between load-bearing efficiency and cost considerations.

Distributed generation (DG) needs to be connected to the microgrid (MG) through an inverter. The power quality of MG is impacted due to the characteristics of DGs and access to many types of loads. Traditionally, robust control or secondary regulation is used in MG inverters to solve power quality problems. However, there are issues in which the controller order is too high or the design is too complicated. A novel adaptive control strategy based on Narendra theory for voltage source inverter in MGs is proposed in this paper to solve the above problems. This strategy improves the control performance by designing an adaptive law. The MG inverter can adjust the parameters adaptively to change the output voltage quality under complex working conditions. The example shows that the inverter with the proposed adaptive control strategy can maintain good voltage control performance under complex conditions of MG, thus ensure the power quality in the MG.

Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity, and so on. Therefore, it is very necessary to find a method that could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.

Characterizing the permeability variation in fractured rocks is important in various subsurface applications, but how the permeability evolves in the foundation rocks of high dams during operation remains poorly understood. This permeability change is commonly evidenced by a continuous decrease in the amount of discharge (especially for dams on sediment-laden rivers), and can be attributed to fracture clogging and/or hydromechanical coupling. In this study, the permeability evolution of fractured rocks at a high arch dam foundation during operation was evaluated by inverse modeling based on the field time-series data of both pore pressure and discharge. A procedure combining orthogonal design, transient flow modeling, artificial neural network, and genetic algorithm was adopted to efficiently estimate the hydraulic conductivity values in each annual cycle after initial reservoir filling. The inverse results show that the permeability of the dam foundation rocks follows an exponential decay annually during operation (i.e. K/K0 = 0.97e−0.59t + 0.03), with good agreement between field observations and numerical simulations. The significance of the obtained permeability decay function was manifested by an assessment of the long-term seepage control performance and groundwater flow behaviors at the dam site. The proposed formula is also of merit for characterizing the permeability change in riverbed rocks induced by sediment transport and deposition.

Soil moisture is a key parameter affecting crop growth. Gaofen-3 satellite is the first C-band synthetic aperture radar produced by China, which provides full-polarization data sources for soil moisture estimation. This article evaluated the potential of estimating soil moisture via GF-3 SAR over agricultural area using different polarimetric decomposition models, namely, the modified Freeman–Durden model (MFDM), the An model and the FDM. Among them, the MFDM is the first attempt to be used for soil moisture retrieval. After removing the volume scattering, the surface and dihedral scattering component were used complementarily to estimate soil moisture. The results show the performance of each polarimetric decomposition models for soil moisture estimation depends on the crop type, crop growth stages and soil moisture conditions. Soil moisture retrievals exhibit an overall underestimation with a root mean square error of 8–11vol. %. This is mainly because of the random orientation assumption in the volume scattering model, which cannot accurately describe the variability of the crop structure. Due to the application of de-orientation process and power constraint, the MFDM shows the best performance both for corn and wheat, with inversion rates of 39%–45%.

Quorum quenching (QQ) is a novel anti-biofouling strategy for membrane bioreactors (MBRs) used in wastewater treatment. However, actual operation of QQ-MBR systems for wastewater treatment needs to be systematically studied to evaluate the comprehensive effects of QQ on wastewater treatment engineering applications. In this study, a novel QQ strain, Acinetobacter pittii HITSZ001, was encapsulated and applied to a MBR system to evaluate the effects of this organism on real wastewater treatment. To verify the effectiveness of immobilized QQ beads in the MBR system, we examined the MBR effluent quality and sludge characteristics. We also measured the extracellular polymeric substances (EPS) and soluble microbial products (SMP) in the system to determine the effects of the organism on membrane biofouling inhibition. Additionally, changes in microbial communities in the system were analyzed by high-throughput sequencing. The results indicated that Acinetobacter pittii HITSZ001 is a promising strain for biofouling reduction in MBRs treating real wastewater, and that immobilization does not affect the biofouling control potential of QQ bacteria.

The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor.

Accurate and effective monitoring of environmental parameters in tea seedling greenhouses is an important basis for regulating the seedling environment, which is crucial for improving the seedling growth quality. This study proposes a tea seedling growth simulation (TSGS) model based on deep learning. The Internet of Things system was used to measure environmental change during the whole seedling process. The correlation between the environmental parameters and the biomass growth of tea seedlings in various varieties was analyzed. A CNN-LSTM network was proposed to build the TSGS model of light, temperature, water, gas, mineral nutrition, and growth biomass. The results showed that: (1) the average correlation coefficients of air temperature, soil temperature, and soil moisture with the biomass growth of tea seedlings were 0.78, 0.84, and −0.63, respectively, which were three important parameters for establishing the TSGS model. (2) For evaluating the TSGS model of a single variety, the accuracy of ZM’s TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.98, RMSEP = 0.14). (3) For evaluating the TSGS model of multiple varieties, the accuracy of TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.96, RMSEP = 0.17). This study provided effective technical parameters for intelligent control of tea-cutting growth and a new method for rapid breeding.

Inflammation plays an important role in aortic dissection (AD). Macrophages are critically involved in the inflammation after aortic injury. Neuraminidases (NEUs) are a family of enzymes that catalyze the cleavage of terminal sialic acids from glycoproteins or glycolipids, which is emerging as a regulator of macrophage-associated immune responses. However, the role of neuraminidase 1 (NEU1) in pathological vascular remodeling of AD remains largely unknown. This study sought to characterize the role and identify the potential mechanism of NEU1 in pathological aortic degeneration. After β-aminopropionitrile monofumarate (BAPN) administration, NEU1 elevated significantly in the lesion zone of the aorta. Global or macrophage-specific NEU1 knockout (NEU1 CKO) mice had no baseline aortic defects but manifested improved aorta function, and decreased mortality due to aortic rupture. Improved outcomes in NEU1 CKO mice subjected to BAPN treatment were associated with the ameliorated vascular inflammation, lowered apoptosis, decreased reactive oxygen species production, mitigated extracellular matrix degradation, and improved M2 macrophage polarization. Furthermore, macrophages sorted from the aorta of NEU1 CKO mice displayed a significant increase of M2 macrophage markers and a marked decrease of M1 macrophage markers compared with the controls. To summarize, the present study demonstrated that macrophage-derived NEU1 is critical for vascular homeostasis. NEU1 exacerbates BAPN-induced pathological vascular remodeling. NEU1 may therefore represent a potential therapeutic target for the treatment of AD.

The voltage control performance of the voltage source inverter (VSI) in a microgrid may change under different load conditions. However, in the case of traditional control strategies, the robustness of VSI is insufficient. In response to the above problems, a novel robust control scheme for VSI in the microgrid based on H∞ hybrid sensitivity is proposed in this study. The grid-side interference during the VSI operation is taken as the variable, and the sensitivity function is designed to build a H∞ robust voltage controller for VSI. In addition, an adaptive virtual impedance group is designed to further improve the voltage control robustness under a variety of operation conditions. Finally, comparative simulation experiments are carried out to verify the anti-interference ability of the proposed control strategy under different working conditions.

The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R2 for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring.

The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation’s accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation’s accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks. PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims.

The power quality assessment provides a reference for power quality management and control of microgrid operation. In terms of reflecting the correlation of power quality indexes and the dynamic changes of microgrid operating conditions, the traditional power quality assessment methods need to be improved. A power quality comprehensive evaluation based on CRITIC and dynamic coefficient is proposed in this paper. In this method, the objective weight of power quality indicators in single node is determined by using the intensity of conflict and contrast firstly. For the node weight calculation, the dynamic coefficient is proposed to reflect the different influence degree of node with different connected load. The proposed method in this paper can reflect both the internal characteristic of data sequence and the relationship between different data sequences. In addition, it also can reflect the dynamic changes of microgrid. Finally, an example is used to verify the feasibility of the proposed method.

Accurate prediction of food crop yield is of great significance for global food security and regional trade stability. Since remote sensing data collected from unmanned aerial vehicle (UAV) platforms have the features of flexibility and high resolution, these data can be used as samples to develop regional regression models for accurate prediction of crop yield at a field scale. The primary objective of this study was to construct regional prediction models for winter wheat yield based on multi-spectral UAV data and machine learning methods. Six machine learning methods including Gaussian process regression (GPR), support vector machine regression (SVR) and random forest regression (RFR) were used for the construction of the yield prediction models. Ten vegetation indices (VIs) extracted from canopy spectral images of winter wheat acquired from a multi-spectral UAV at five key growth stages in Xuzhou City, Jiangsu Province, China in 2021 were selected as the variables of the models. In addition, in situ measurements of wheat yield were obtained in a destructive sampling manner for prediction algorithm modeling and validation. Prediction results of single growth stages showed that the optimal model was GPR constructed from extremely strong correlated VIs (ESCVIs) at the filling stage (R2 = 0.87, RMSE = 49.22 g/m2, MAE = 42.74 g/m2). The results of multiple stages showed GPR achieved the highest accuracy (R2 = 0.88, RMSE = 49.18 g/m2, MAE = 42.57 g/m2) when the ESCVIs of the flowering and filling stages were used. Larger sampling plots were adopted to verify the accuracy of yield prediction; the results indicated that the GPR model has strong adaptability at different scales. These findings suggest that using machine learning methods and multi-spectral UAV data can accurately predict crop yield at the field scale and deliver a valuable application reference for farm-scale field crop management.

The convolutional neural network (CNN) method has been widely used in the classification of hyperspectral images (HSIs). However, the efficiency and accuracy of the HSI classification are inevitably degraded when small samples are available. This study proposes a multidimensional CNN model named MDAN, which is constructed with an attention mechanism, to achieve an ideal classification performance of CNN within the framework of few-shot learning. In this model, a three-dimensional (3D) convolutional layer is carried out for obtaining spatial–spectral features from the 3D volumetric data of HSI. Subsequently, the two-dimensional (2D) and one-dimensional (1D) convolutional layers further learn spatial and spectral features efficiently at an abstract level. Based on the most widely used convolutional block attention module (CBAM), this study investigates a convolutional block self-attention module (CBSM) to improve accuracy by changing the connection ways of attention blocks. The CBSM model is used with the 2D convolutional layer for better performance of HSI classification purposes. The MDAN model is applied for classification applications using HSI, and its performance is evaluated by comparing the results with the support vector machine (SVM), 2D CNN, 3D CNN, 3D–2D–1D CNN, and CBAM. The findings of this study indicate that classification results from the MADN model show overall classification accuracies of 97.34%, 96.43%, and 92.23% for Salinas, WHU-Hi-HanChuan, and Pavia University datasets, respectively, when only 1% HSI data were used for training. The training and testing times of the MDAN model are close to those of the 3D–2D–1D CNN, which has the highest efficiency among all comparative CNN models. The attention model CBSM is introduced into MDAN, which achieves an overall accuracy of about 1% higher than that of the CBAM model. The performance of the two proposed methods is superior to the other models in terms of both efficiency and accuracy. The results show that the combination of multidimensional CNNs and attention mechanisms has the best ability for small-sample problems in HSI classification.

Abstract The composition and function of the intestinal microbiota play important roles in digestion and degradation of herbal medicines (HMs). However, few studies have examined the relationship between the fecal microbiota and HMs. In this study the effect of unfermented Astragalus (UA) and fermented Astragalus (FA) on growth performance, serum biochemical parameters, and fecal microbiota was evaluated in broiler chickens. In total, 180 one-day-old broiler chickens (Avian breeds) were randomly assigned to a control (C) group fed a basal diet, an unfermented (U) group fed a basal diet containing 0.5% UA, or a fermented (F) group fed a basal diet containing 0.5% FA, for 42 days. The F/G ratio was lower in F and U groups than in C group from 22 to 42 days (P

Abstract The gut microbiota play important roles in the degradation of chemical compounds of herbal medicines (HMs). However, little information regarding the interplay between HMs and the gut microbiota is available. Thus, the aim of this study was to investigate the composition of the fecal microbiota of young (age, 11 weeks) hens fed a conventional diet containing a crude Astragalus (0.5%) additive for 21 days (group A) vs. controls (group B) that were fed only conventional feed. The fecal contents of 14-week-old hens were collected for DNA extraction, and then the V3 and V4 hyper-variable regions of the 16S rRNA gene were amplified and analyzed using high-throughput sequencing technology. A distinctive difference in microbial diversity was observed between the two groups. The microbial composition of hens fed a diet supplemented with Astragalus was greater than that of the control group. At the genus level, Lactobacillus was more abundant in group A than group B (p

Background: In this study, we tested the hypothesis that miR-181a levels increase during acute myocardial infarction. We investigated circulating miR-181a as a potential novel biomarker for early diagnosis of acute myocardial infarction (AMI). Methods: From June 2014 to June 2016, 120 consecutive eligible patients with AMI (n = 60) or unstable angina (UA; n = 60) and 60 control subjects were enrolled. Plasma miR-181a levels were determined by quantitative reverse transcriptase-polymerase chain reaction. Results: Circulating miR-181a expression levels detected immediately after admission were higher in the AMI group than in the UA and control groups. Relative miR-181a levels in AMI patients were positively correlated with the concentrations of the creatine kinase-MB fraction and cardiac troponin I. Correlation analysis showed that plasma miR-181a was positively correlated with coronary Gensini score (r = 0.573, P P P Conclusion: Circulating miR-181a levels in patients with AMI were significantly changed in a time-dependent manner, indicating the value of plasma miR-181a as a novel biomarker for diagnosing AMI.

Background: Here, we determined miR-499 involvement in the protective effect of ischemic postconditioning (IPC) against myocardial ischemia/reperfusion (I/R) injury and identified the underlying mechanisms. Methods: To investigate the cardioprotective effect of IPC-induced miR-499, rats were divided into the following five groups: sham, I/R, IPC, IPC + scramble, and IPC + antagomiR-499. Hemodynamic indexes were measured by carotid-artery intubation to assess left ventricular function . Ischemia and infarction areas of rat hearts were determined by Evans blue and triphenyltetrazolium chloride staining, and cardiomyocyte apoptosis was detected by terminal deoxynucleotidyl transferase dUTP nick-end-labeling assay. Results: IPC attenuated I/R-induced infarct size of the left ventricle (45.28 ± 5.40% vs. 23.56 ± 6.20%, P vs. 990.21 ± 172.39%, P vs. 1289.11 ± 347.28%, P vs. 4.85 ± 1.52%, P in vivo and in vitro by knockdown of cardiac miR-499, suggesting that miR-499 may participate in the protective function of IPC against I/R injury through targeting programmed cell death 4 (PDCD4). Conclusion: Our data revealed that IPC-regulated miR-499 plays an important role in IPC-mediated cardiac protection against I/R injury by targeting PDCD4.

Microgrid (MG) is generally developed at utility terminal which contains lots of unbalanced loads and distributed generations (DGs). The interaction between MG and the unbalance loads or DGs will degrades the control performance of interfaced inverter in MG and dramatically leads to MG voltage unbalance. In this paper, a negative-sequence compensation based three-phase voltage unified correction strategy is proposed. While MG operates in islanded mode, a positive virtual impedance control is used to eliminate the negative voltage resulted from the negative-sequence current, and then a positive-sequence voltage control loop and negative-sequence control loop are used to improve the inverter control performance. While MG operates in grid-tied mode, the inverter operates as a negative-sequence current source to compensate the negative-sequence currents of loads to guarantee the point of common coupling (PCC) voltage balance. By using the proposed strategy, the voltage control performance of inverter can be improved; the MG power quality can be enhanced significantly. Simulation and experimental results verify the effectiveness of the proposed method.

Background: Atherosclerosis is widely recognized as a complex inflammatory disease involving pathogenic immune response of T cells and antigen-presenting cells (APCs) such as dendritic cells (DCs) and macrophages. Accumulating evidence has revealed that mature DCs play critical roles in the differentiation of effector T cells into CD4+ T cells, which effectively participate in the onset of acute coronary syndrome (ACS). IFN regulatory factor (IRF)-1 has been shown to be involved in various immune processes. The role of IRF-1 in DCs in the pathogenesis of ACS has not been investigated. Methods and Results: We examined the relative mRNA and protein expression of IRF-1 in human monocyte-derived DCs in patients with coronary artery disease (CAD). The overexpression or silencing of IRF-1 expression in DCs in patients with ACS was performed to explore the possible role of IRF-1 in the maturation and function of DCs involved in ACS. The results showed that the relative expression of IRF-1 in DCs is obviously increased in patients with ACS. The overexpression or silencing of IRF-1 expression could effectively promote or attenuate the maturation and function of DCs. In addition, we revealed that the MAPK pathway (phosphorylation of JNK, p38 and ERK1/2) might be downstream of IRF-1 signalling pathway in activation of circulating DCs in ACS patients. Conclusion: The present data demonstrate that IRF-1 could effectively promote the immune maturation and function of DCs in ACS patients.

Most of the traditional supervised classification methods using full-polarimetric synthetic aperture radar (PolSAR) imagery are dependent on sufficient training samples, whereas the results of pixel-based supervised classification methods show a high false alarm rate due to the influence of speckle noise. In this paper, to solve these problems, an object-based supervised classification method with an active learning (AL) method and random forest (RF) classifier is presented, which can enhance the classification performance for PolSAR imagery. The first step of the proposed method is used to reduce the influence of speckle noise through the generalized statistical region merging (GSRM) algorithm. A reliable training set is then selected from the different polarimetric features of the PolSAR imagery by the AL method. Finally, the RF classifier is applied to identify the different types of land cover in the three PolSAR images acquired by different sensors. The experimental results demonstrate that the proposed method can not only better suppress the influence of speckle noise, but can also significantly improve the overall accuracy and Kappa coefficient of the classification results, when compared with the traditional supervised classification methods.

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.