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Abstract The accuracy of a fuzzy system’s approximation is closely tied to the performance of fuzzy control systems design, while this system’s interpretability depends on the description of a mechanical model using human language. This research introduces a quasi-Gaussian membership function characterized by a pair of parameters to achieve the sensitivity of a triangular membership function along with the interpretability of Gaussian membership functions. Consequently, a two-dimensional (2-D) quasi-Gaussian membership function is derived, and a method for establishing quasi-Gaussian fuzzy systems (QGFS) using a rectangular grid is proposed. After validating the approximation properties using the sine function for the one-dimensional (1-D) and 2-D QGFS, the systems are applied to approximate the depyrogenation tunnel, a significant piece of equipment in the pharmaceutical industry with various mechanical designs. Validation results indicate that the 1-D and 2-D QGFS can achieve an approximation error varying within a ± 5% range. Meanwhile, the 1-D and 2-D QGFSs are applied to mechanical models of the depyrogenation tunnel with satisfactory final approximation results. Lastly, the 2-D QGFS is capable of demonstrating an excellent description of models with coupled parameters.

High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads. To address these challenges, this study introduces the RC-MSFNet model, based on the U-Net architecture, to enhance rural road extraction performance. The RC-MSFNet model mitigates the vanishing gradient problem in deep networks by incorporating residual neural networks in the downsampling stage. In the upsampling stage, a connectivity attention mechanism is added after dual convolution layers to improve the model’s ability to capture road completeness and connectivity. Additionally, the bottleneck section replaces the traditional dual convolution layers with a multi-scale fusion atrous convolution module to capture features at various scales. The study focuses on rural roads in the Xiong’an New Area, China, using high-resolution imagery from China’s Gaofen-2 satellite to construct the XARoads rural road dataset. Roads were extracted from the XARoads dataset and DeepGlobe public dataset using the RC-MSFNet model and compared with some models such as U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. Experimental results showed that: (1) The proposed method achieved precision (P), intersection over union (IOU), and completeness (COM) scores of 0.8350, 0.6523, and 0.7489, respectively, for rural road extraction in Xiong’an New Area, representing precision improvements of 3.8%, 6.78%, 7.85%, 2.14%, 0.58%, and 2.53% over U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. (2) The method excelled at extracting narrow roads and muddy roads with unclear boundaries, with fewer instances of omission or false extraction, demonstrating advantages in complex rural terrain and areas with indistinct road boundaries. Accurate rural road extraction can provide valuable reference data for urban development and planning in the Xiong’an New Area.

Breeding in large-scale marine aquafarms faces many challenges in terms of precise feeding, including real-time decisions as to the precise feeding amount, along with disturbances caused by the feeding speed and the moving speed of feeding equipment. Involving many spatiotemporal distributed parameters and variables, an effective predictive model for environment and growth stage perception is yet to obtained, further preventing the development of precise feeding strategies and feeding equipment. Therefore, in this paper, a hierarchical type-2 fuzzy system based on a quasi-Gaussian membership function for fast, precise, on-site feeding decisions is proposed and validated. The designed system consists of two layers of decision subsystems, taking in different sources of data and expert experience in feeding but avoiding the rule explosion issue. Meanwhile, the water quality evaluation is considered as the secondary membership function for type-2 fuzzy sets and used to adjust the parameters of the quasi-Gaussian membership function, decreasing the calculation load in type reduction. The proposed system is validated, and the results indicate that the shape of the primary fuzzy sets is altered with the secondary membership, which influences the defuzzification results accordingly. Meanwhile, the hardware of feeding bins for UAVs with variable-speed coupling control systems with disturbance compensation is improved and validated. The results indicate that the feeding speed can follow the disturbance in the level flying speed.

Soil salinization is a major environmental stressor hindering global crop production. Hydropriming has emerged as a promising approach to reduce salt stress and enhance crop yields on salinized land. However, a better mechanisitic understanding is required to improve salt stress tolerance. We used a biochemical and metabolomics approach to study the effect of salt stress of hydroprimed maize to identify the types and variation of differentially accumulated metabolites. Here we show that hydropriming significantly increased catalase (CAT) activity, soluble sugar and proline content, decreased superoxide dismutase (SOD) activity and peroxide (H2O2) content. Conversely, hydropriming had no significant effect on POD activity, soluble protein and MDA content under salt stress. The Metabolite analysis indicated that salt stress significantly increased the content of 1278 metabolites and decreased the content of 1044 metabolites. Ethisterone (progesterone) was the most important metabolite produced in the roots of unprimed samples in response to salt s tress. Pathway enrichment analysis indicated that flavone and flavonol biosynthesis, which relate to scavenging reactive oxygen species (ROS), was the most significant metabolic pathway related to salt stress. Hydropriming significantly increased the content of 873 metabolites and significantly decreased the content of 1313 metabolites. 5-Methyltetrahydrofolate, a methyl donor for methionine, was the most important metabolite produced in the roots of hydroprimed samples in response to salt stress. Plant growth regulator, such as melatonin, gibberellin A8, estrone, abscisic acid and brassinolide involved in both treatment. Our results not only verify the roles of key metabolites in resisting salt stress, but also further evidence that flavone and flavonol biosynthesis and plant growth regulator relate to salt tolerance.

Abstract The illusion device developed from the scattering cancellation employs very simple homogeneous and isotropic materials, but this device is only valid for electrically small objects. In this paper, we prove that the illusion device optimized by genetic algorithm can be applied to large-scale occasions. For an electrically small target, an optimized core–shell illusion device can achieve better illusion effect than the analytical design based on the scattering cancellation. With the increase of the device size, the ability of the single-layered shell to manipulate the scattering is very limited. For a moderate-size target, two optimized multi-layered examples are presented: one is to make a dielectric cylinder appear as another dielectric target, and the other is to make a conducting cylinder behave like a double-negative-material target. The full-wave simulations are carried out to visualize the similar field distributions of the target and the optimized multi-layered design. This optimized design greatly widens the size application range of the illusion device and can also improve the illusion performance with simple material parameters.

Fruit branch angle (FBA), a pivotal component of cotton plant architecture, is vital for field and mechanical harvesting. However, the molecular mechanism of FBA formation is poorly understood in cotton. To uncover the genetic basis for FBA formation in cotton, we performed a genome-wide association study (GWAS) of 163 cotton accessions with re-sequencing data. A total of 55 SNPs and 18 candidate genes were significantly associated with FBA trait. By combining GWAS and transcriptome analysis, four genes underlying FBA were identified. An FBA-associated candidate gene Ghi_A09G08736, which is homologous to SAUR46 in Arabidopsis thaliana, was detected in our study. In addition, transcriptomic evidence was provided to show that gravity and light were implicated in the FBA formation. This study provides new insights into the genetic architecture of FBA that informs architecture breeding in cotton.

Ice cloud optical thickness (IOT) is an important parameter to characterize ice cloud properties and in the determination of cloud–radiation parameterization schemes, and the variation trend of ice clouds is more concerned with the study of weather and climate. In this paper, we analyzed the spatial and temporal distributions of IOT over the region between ±60° latitude. Cloud product data from March 2000 to February 2021 acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Aqua satellite were used in this study. Theil–Sen median trend analysis and EOF analysis methods were used to study the variation trend of IOT. The research results indicate that the monthly average IOT shows a “W” distribution from January to December, with a maximum reached in July (12.15) and a double bottom reached in March (10.7) and October (10.99), respectively. The average global IOT reaches the maximum in June–August, it tends to decrease with time, and its slope is −0.01 year−1. The statistical analysis results show that the area with an increase accounted for 49.4% of the total ice cloud coverage area; the area with a trend of significant increased and decreased is both 2.2%. The probability distribution of IOT reaches the maximum, around 3.25%, when the IOT is larger than 1.5 and less than or equal to 2.

Ozone is a very important trace gas in the atmosphere, it is like a “double-edged sword”. Because the ozone in the stratosphere can effectively help the earth’s organisms to avoid the sun’s ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA’s Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a “-” distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit.

Given the increasing impact of extreme rainfall and flooding on human life, studying and predicting changes in atmospheric water vapor (AWV) becomes particularly important. This paper analyzes the moderate-resolution imaging spectroradiometer (MODIS) data of the East Asian region from January 2003 to February 2023. The AWV data are examined in the time and frequency domain using methods such as empirical orthogonal function (EOF), Mann–Kendall (MK) analysis, and others. Additionally, four prediction models are applied to forecast the monthly average AWV data for the next year. The accuracy of these models is evaluated using metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings reveal several key insights: (1) The East Asian region exhibits highly variable seasonal variability in AWV, with identified mutation points after the MK test. (2) Spatial analysis shows high AWV data in the southern coastal areas of China, Thailand, Myanmar, Nansha Islands, and other regions during winter, while the Qinghai-Tibet Plateau region experiences low AWV during summer. (3) The first mode obtained through EOF decomposition contributes over 60% of the variance. Analysis of this mode reveals an increasing trend in AWV data for regions such as the Indian peninsula, Mongolia, and central and northeastern China over the past nine years. Conversely, the Bay of Bengal, Spratly Islands, eastern coast, and certain areas display a decreasing trend. (4) Employing the ensemble empirical mode decomposition (EEMD), the study identifies AWV data as a non-stationary series with an overall decreasing trend from 2003 to 2022. The filtered AWV series undergoes fast Fourier transform (FFT), uncovering periodicities of 2.6 years, 5 years, and 19 years. (5) Among the four forecasting models compared, the seasonal autoregressive integrated moving average model (SARIMA) demonstrates superior performance with the smallest MSE of 0.00782, MAE of 0.06977, RMSE of 0.08843, and the largest R2 value of 0.98454. These results clearly indicate that the SARIMA model provides the best fit. Therefore, the SARIMA forecasting model can be effectively utilized for forecasting AWV data, offering valuable insights for studying weather variability.

Outgoing longwave radiation (OLR) data are one of the key factors in studying the radiation balance of the earth–atmosphere system in East Asia. It is of great significance to explore the influence factors on OLR. This paper processes the data of nearly 19 years, from September 2002 to February 2022, and conducts in-depth research using the exponential smoothing method, empirical orthogonal decomposition (EOF), correlation analysis, and other methods. We found that the spatial distribution of OLR is zonal symmetry and gradually decreases with the increase of latitude. Using EOF analysis, it is found that the total variance contribution of the first four decomposed spatial features exceeds 70%, and the overall change trend of the four-time coefficients in the past 19 years all show a downward trend. OLR is positively correlated with total column water vapor (TCWV), air temperature (AT), and cloud top temperature (CTT), but negatively correlated with cloud top pressure (CTP). OLR has a similar spatial correlation distribution with TCWV and AT, while the spatial correlation between OLR and CTP is opposite to the first two parameters. In most parts of East Asia, the spatial correlation with CTT exceeds 0.8. The change in OLR value is affected by various meteorological parameters. In East Asia, the positive correlation between 30° N and 60° N is significantly affected by TCWV, AT, and CTT; and the negative correlation is more significantly affected by CTP. At 0–25° N, the positive correlation is significantly affected by CTP and CTT, while the negative correlation is significantly affected by TCWV and AT.

In order to alleviate global warming and the energy crisis, it is of great significance to develop and utilize solar energy resources. Sunshine duration (SD) is considered to be the best substitute for solar radiation and a key factor in evaluating solar energy resources. Therefore, the spatial and temporal characteristics of SD and the reasons for its changes have received extensive attention and discussion. Based on the data of 415 meteorological stations from 1970 to 2019, this paper uses linear trend analysis, Mann–Kendall mutation analysis, the Hurst index, empirical orthogonal decomposition, correlation analysis and partial correlation analysis to analyze the spatiotemporal characteristics of SD and its relationship with influencing factors. The results show that the annual SD in China shows a downward trend, with a climate trend rate of −37.93 h/10a, and a significant decline from 1982 to 2019. The seasonal SD shows a downward trend, and the downward trend is most obvious in summer. The annual and seasonal SD will still show a downward trend in the future. The spatial distribution of SD not only has an overall consistent distribution but also takes the Yellow River from Ningxia to Shandong as the boundary, showing a north–south opposite distribution. Annual SD has a significant positive correlation, a significant negative correlation, a positive correlation and a negative correlation with wind speed, precipitation, temperature and relative humidity, respectively, and it is most closely related to wind speed and precipitation. In addition, the change in SD may also be related to human activities.

Lysine malonylation is a kind of post-translational modifications (PTMs) discovered in recent years, which plays an important regulatory role in plants. Maize (Zea mays L.) is a major global cereal crop. Immunoblotting revealed that maize was rich in malonylated proteins. We therefore performed a qualitative malonylome analysis to globally identify malonylated proteins in maize. In total, 1,722 uniquely malonylated lysine residues were obtained in 810 proteins. The modified proteins were involved in various biological processes such as photosynthesis, ribosome and oxidative phosphorylation. Notably, a large proportion of the modified proteins (45%) were located in chloroplast. Further functional analysis revealed that 30 proteins in photosynthesis and 15 key enzymes in the Calvin cycle were malonylated, suggesting an indispensable regulatory role of malonylation in photosynthesis and carbon fixation. This work represents the first comprehensive survey of malonylome in maize and provides an important resource for exploring the function of lysine malonylation in physiological regulation of maize.

Objective This study aimed to investigate whether differential expression of the retinoic acid receptor-related orphan receptor A ( RORA ) gene is related to occurrence of acute myocardial infarction (AMI). Methods This was a retrospective study. White blood cells of 93 patients with acute myocardial infarction and 74 patients with stable coronary artery disease were collected. Reverse transcription quantitative polymerase chain reaction and western blotting were used to measure RORA mRNA and protein expression, respectively. Results RORA mRNA expression levels in peripheral blood leukocytes in patients with AMI were 1.57 times higher than those in patients with stable coronary artery disease. Protein RORA levels in peripheral blood of patients with AMI were increased. Binary logistic regression analysis showed that high expression of RORA was an independent risk factor for AMI, and it increased the risk of AMI by 2.990 times. Conclusion RORA expression levels in patients with AMI is significantly higher than that in patients with stable coronary artery disease. High expression of RORA is related to AMI and it may be an independent risk factor for AMI.

To comprehensively explore the characteristics of global SST anomalies, a novel time–frequency combination method, based on the COBE data and NCEP/NCAR reanalysis products in the past 100 years, was developed. From the view of the time domain, the global SST generally showed an upward trend from 1920 to 2019, the upward trend was significant after 1988, and the growth mutation occurred in 1930, according to the Mann–Kendall (MK) mutation test. Moreover, we extracted spatiotemporal modes of SST anomalies’ variability by empirical orthogonal function (EOF) analysis and obtained global spatial EOFs that closely correspond to regionally defined climate modes. Our results demonstrated that El Niño–Southern Oscillation (ENSO) is the typical character for the first mode of SST anomaly EOF, and Atlantic multidecadal oscillation (AMO) for the second. From the view of the frequency domain, our data suggested that there is a multi-period nesting phenomenon in global SST variations, in which the first main cycle with the most obvious oscillation was a 30-year cycle and changed in 20-year cycles, and the second cycle was a 15-year cycle and changed in 10-year cycles through wavelet analysis. As for the perspective of time–frequency characteristics, the dominant period of ENSO in the first mode of EOF is 4 years, obtained through filtering and cross wavelet transform. In addition, SST anomalies will maintain an upward trend for the next 60 months, according to the seasonal autoregressive integrated moving average (SARIMA) model, which has the potential value for predicting ENSO.

Accurately predicting the critical differential pressure (CDP) of sand production contributes to improving the peak-shaving capacity and ensuring safe operation of underground gas storage (UGS). The CDP of sanding production in the target wells of the UGS was predicted coupling laboratory tests, inversed analysis with well logging data and numerical simulations. The in-situ mechanical properties of rock were estimated by coupling the laboratory test results and well-logging data. The in-situ stress field of the target formation was then deduced through inversed analysis coupled finite element method (FEM) and genetic algorithm (GA), based on the existing known stress data and the seismic data of the measured points. Using the critical strain limit (CSL) of 5‰ as the sanding criterion of the wellbore, the CDPs of the gas production in the UGS were predicted, which was 5.59 MPa, 3.98 MPa, and 4.01 MPa for well #1, well #2 and well #3, when the pressure of the gas storage was 30 MPa, respectively. The simulation results showed good agreements with the field-measured benchmark data of well #2 and well #3. The effects of moisture contents (ranging from 10 to ~40%), and cycling times of gas injection and withdrawal (ranging from 40 to ~200 cycling times) on the critical differential pressure were simulated and analyzed. The results indicated that the CDP decreased with an increase of the moisture content and the cycling times. This study provides a reliable tool for the sanding prediction of the wellbore in the UGS.

With the promotion and popularization of machine cotton-picking, more and more attention has been paid to the selection of early-maturity varieties with compact plant architecture. The type of fruit branch is one of the most important factors affecting plant architecture and early maturity of cotton. Heredity analysis of the cotton fruit branch is beneficial to the breeding of machine-picked cotton. Phenotype analysis showed that the types of fruit branches in cotton are controlled by a single recessive gene. Using an F2 population crossed with Huaxin102 (normal branch) and 04N-11 (nulliplex branch), BSA (Bulked Segregant Analysis) resequencing analysis and GhNB gene cloning in 04N-11, and allelic testing, showed that fruit branch type was controlled by the GhNB gene, located on chromosome D07. Ghnb5, a new recessive genotype of GhNB, was found in 04N-11. Through candidate gene association analysis, SNP 20_15811516_SNV was found to be associated with plant architecture and early maturity in the Xinjiang natural population. The GhNB gene, which is related to early maturity and the plant architecture of cotton, is a branch-type gene of cotton. The 20_15811516_SNV marker, obtained from the Xinjiang natural population, was used for the assisted breeding of machine-picked cotton varieties.

Objective. To find molecular markers for the diagnosis of acute myocardial infarction (AMI), this research further verified the relationship between the expression level of FFAR2 gene and AMI by expanding the sample size based on the previous gene chip results. Methods. Peripheral venous leukocytes were collected from 113 patients with AMI and 94 patients with noncoronary artery disease as the experimental group and the control group, respectively. Real-time fluorescence quantitative polymerase chain reaction was used to detect the expression of the FFAR2 gene. Western blot analysis was applied to detect the relative expression of the FFAR2 gene at the level of protein. Furthermore, the relationship between gene expression and clinical data was also analyzed and compared. Results. The level of expression of FFAR2 gene in peripheral blood of patients with AMI was significantly lower than that of the control group (0.33 [0.04–1.08], 0.62 [0.07–1.86], respectively; p

Outgoing longwave radiation (OLR) is a key factor to study the radiation balance of the earth–atmosphere system. It is of great significance to explore the temporal and spatial variation characteristics over the OLR value in China region and to predict its future variation trend. We investigate the characteristic distribution of OLR value over China and predict its results in time series using the seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) methods based on the OLR data by the Atmospheric Infrared Sounder (AIRS). The Mann–Kendall (MK) mutation test was used to analyze the annual average of OLR values in China and the mutation points in the four seasons. The empirical orthogonal function (EOF) is used to decompose the spatial characteristics and temporal variation of OLR values in China. The MK mutation test is used to obtain the mutation points in the three seasons of spring, summer and autumn. The cumulative variance contribution of the four modes obtained by EOF decomposition exceeds 70%, and the variance contribution of the first mode exceeds 50%. The prediction accuracy with SARIMA model is 99% and LSTM algorithm is 97%. The results of spatiotemporal analysis show that the OLR value near the equator is significantly higher than that of the north and south poles and decreases with the increase of latitude; the OLR value in spring, summer and autumn is higher than that in winter. The results of the MK test show that there are many mutation points in autumn, and the location of the mutation points cannot be determined. The mutation points in spring and summer meet the confidence interval; the first mode of EOF decomposition has a meridional structure, and the OLR value is dropped within 18 years as a whole. The spatial characteristics of modes 1 and 3 have obvious changes in the Qinghai-Tibet Plateau and Northeast China. The prediction results show that the prediction accuracy of SARIMA is higher than that of LSTM. Therefore, the results predicted by SARIMA may provide a reference for the study of the radiation balance of the earth–atmosphere system in China.

High-precision automatic identification and mapping of forest tree species composition is an important content of forest resource survey and monitoring. The airborne hyperspectral image contains rich spectral and spatial information, which provides the possibility of high-precision classification and mapping of forest tree species. Few-shot learning, as an application of deep learning, has become an effective method of image classification. Prototypical networks (P-Net) is a simple and practical deep learning network, which has significant advantages in solving few-shot classification problems. Considering the high band correlation and large data volume associated with airborne hyperspectral images, how to fully extract effective features, filter or reduce redundant features is the key to improving the classification accuracy of P-Net, in order to extract effective features in hyperspectral images and obtain a high-precision forest tree species classification model with limited samples. In this research, we embedded the convolutional block attention module (CBAM) between the convolution blocks of P-Net, the CBAM-P-Net was constructed, and a method to improve the feature extraction efficiency of the P-Net was proposed, although this method makes the network more complex and increases the computational cost to a certain extent. The results show that the combination strategy using Channel First for CBAM greatly improves the feature extraction efficiency of the model. In different sample windows, CBAM-P-Net has an average increase of 1.17% and 0.0129 in testing overall accuracy (OA) and kappa coefficient (Kappa). The optimal classification window is 17 × 17, the OA reaches 97.28%, and Kappa reaches 0.97, which is an increase of 1.95% and 0.0214 along with just 49 s of training time expended, respectively, compared with P-Net. Therefore, using a suitable sample window and applying the proposed CBAM-P-Net to classify airborne hyperspectral images can achieve high-precision classification and mapping of forest tree species.

Deep learning has become an effective method for hyperspectral image classification. However, the high band correlation and data volume associated with airborne hyperspectral images, and the insufficiency of training samples, present challenges to the application of deep learning in airborne image classification. Prototypical networks are practical deep learning networks that have demonstrated effectiveness in handling small-sample classification. In this study, an improved prototypical network is proposed (by adding L2 regularization to the convolutional layer and dropout to the maximum pooling layer) to address the problem of overfitting in small-sample classification. The proposed network has an optimal sample window for classification, and the window size is related to the area and distribution of the study area. After performing dimensionality reduction using principal component analysis, the time required for training using hyperspectral images shortened significantly, and the test accuracy increased drastically. Furthermore, when the size of the sample window was 27 × 27 after dimensionality reduction, the overall accuracy of forest species classification was 98.53%, and the Kappa coefficient was 0.9838. Therefore, by using an improved prototypical network with a sample window of an appropriate size, the network yielded desirable classification results, thereby demonstrating its suitability for the fine classification and mapping of tree species.

In this paper, a new backstepping-based adaptive stabilization of a fractional-order system with unknown parameters is investigated. We assume that the controlled system is perturbed by external disturbance, the bound of external disturbance to be unknown in advance. Moreover, the effects of sector and dead-zone nonlinear inputs both are taken into account. A fractional-order auxiliary system is established to generate the necessary signals for compensation the nonlinear inputs. Meantime, in order to deal with these unknown parameters, some fractional-order adaption laws are given. The frequency-distributed model is used so that the indirect Lyapunov theory is available in designing controllers. Finally, simulation results are presented to verify the effectiveness and robustness of the proposed control strategy.

In this paper, the problem of stabilizing a class of fractional-order chaotic systems with sector and dead-zone nonlinear inputs is investigated. The effects of model uncertainties and external disturbances are fully taken into account. Moreover, the bounds of both model uncertainties and external disturbances are assumed to be unknown in advance. To deal with the system’s nonlinear items and unknown bounded uncertainties, an adaptive fractional-order sliding mode (AFSM) controller is designed. Then, Lyapunov’s stability theory is used to prove the stability of the designed control scheme. Finally, two simulation examples are given to verify the effectiveness and robustness of the proposed control approach.

Through the observation of ground-based LIDAR and satellite sensors, the weather conditions of continuous foggy-hazy alternations in the Huainan region from 26 December 2016 to 5 January 2017 were analyzed and observed. In this study, the formation and influence of this event were discussed by analyzing pollutant concentrations, meteorological factors and aerosol optical characteristics. The concentrations of PM10 and PM2.5 increased significantly. The maximum value of PM10 was 412 μg/m3, and the maximum value of PM2.5 was 258 μg/m3. The transportation of pollutants and the production of man-made pollutants promote the accumulation of pollutants. In this weather process, meteorological factors such as the surface wind speed, humidity, surface temperature, and inversion also promote the accumulation of pollutants, which is the main reason for the formation of this weather process. Furthermore, the near surface air mass mainly came from the cities near the Huainan region and the heavily polluted areas in the north, while the upper air mass came from Inner Mongolia. In this paper, piecewise inversion was adopted to achieve accurate all-weather extinction coefficient profile inversion by reasonably selecting a cloud LIDAR ratio through a backscatter ratio, and the LIDAR ratio of cloud in this period was 22.57−34.14 Sr. By means of extinction coefficient inversion and correlation analysis, the correlation index of PM2.5 and the aerosol optical depth (AOD) was 0.7368, indicating that there was a positive correlation between PM2.5 and AOD, and AOD can also reflect the pollution condition of this region. The formation process of foggy-hazy weather in the Huainan region studied in this paper can provide a research basis for foggy-hazy pollution in this region.

The complex modified projective synchronization (CMPS) between fractional-order chaotic real and complex systems is investigated for the first time. The parameters of both master and slave systems are assumed to be unknown in advance; moreover, the slave system is perturbed by unknown but bounded external disturbances. The master and slave systems that achieved CMPS can be synchronized up to a complex constant matrix. On the basis of frequency distributed model of fractional integrator and Lyapunov stability theory, a robust adaptive control law is designed to realize the CMPS for two different types of fractional-order chaotic systems. Meanwhile, to deal with these unknown parameters, some fractional-order type parametric update laws are provided. An example is given to demonstrate the effectiveness and feasibility of the proposed synchronization scheme.

A new definition of fractional derivatives called Caputo-Fabrizio definition is proposed in this paper, and used to develop mathematical models for the finance system. For the purpose of realizing the robust stabilization of fractional-order finance system, the influence of double constraints in input is taken into account. Meanwhile, the details about numerical computation method of Caputo-Fabrizio fractional differential equation are shown. Furthermore, in order to improve the feasibility of fractional-order financial system based on new definition, we also consider the effects of unknown parameters and external perturbations, and their upper bounds are previously unknown. In the final step, simulation results are given to verify that the proposed control strategy is effectively in overcome constraints of inputs, and the controlled system has a good robustness against the external disturbances and system uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time stabilization of a class of engineering system is investigated, to realize this control objective, the fractional-order backstepping strategy is applied. In the process of design the finite-time controller, the controlled system is assumed to be affected by disturbance, and the upper bound of the disturbance is unknown in advance. Furthermore, the effect of saturated nonlinear input is taken into consideration. Meantime, some special forms of adaption laws are proposed so as to recognize these unknown parameters. The Lyapunov theory in fractional-order form is available to verify that every subsystem is finite-time stable. In the end, simulation example is provided to confirm the efficiency of the given control method. [ABSTRACT FROM AUTHOR]

A new backstepping-based sliding mode control strategy to realize the stabilization of a class of fractional-order system is introduced in this paper. We assume that the system is fluctuated by unmodeled dynamics and external disturbances, meanwhile, the bounds of these uncertainties and the system parameters are unknown in advance. The effect of nonsymmetrical dead-zone input is taken into account in this paper. To deal with these unknown parameters, a sliding mode surface is proposed and some adaptive update laws are assigned. Then the indirect Lyapunov theory is given to analyze the stability of every subsystem. Simulation results are presented to prove the correctness and feasibility of the given control strategy. [ABSTRACT FROM AUTHOR]