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In this paper, multifunctional, multilevel phase plates of quartz substrate were efficiently prepared by using a newly developed polygon scanner-based femtosecond laser photolithography system combined with inductively coupled discharge plasma reactive-ion etching (ICP-RIE) technology. The femtosecond laser photolithography system can achieve a scanning speed of 5 m/s and a preparation efficiency of 15 cm2/h while ensuring an overlay alignment accuracy of less than 100 nm and a writing resolution of 500 nm. The ICP-RIE technology can control the etching depth error within ±5 nm and the mask-to-mask edge error is less than 1 μm. An 8-level Fresnel lens phase plate with a focal length of 20 mm and an 8-level Fresnel axicon phase plate with a cone angle of 5° were demonstrated. The diffraction efficiency was greater than 93%, and their performance was tested for focusing and glass cutting, respectively. Combined with the high-speed femtosecond laser photolithography system's infinite field-of-view (IFOV) processing capability, the one-time direct writing preparation of phase plate masks of different sizes was realized on a 6-inch wafer. This is expected to reduce the production cost of quartz substrate diffractive optical elements and promote their customized mass production. [ABSTRACT FROM AUTHOR]

Signal detection and classification tasks, especially in the realm of audio, suffer from difficulties in capturing long short-term dependencies and effectively utilizing samples. Firstly, audio signal detection and classification need to classify audio signals and detect their onset and offset times; therefore, obtaining long short-term dependencies is necessary. The methods based on RNNs have high time complexity and dilated convolution-based methods suffer from the "gridding issue" challenge; thus, the HDC-Inception module is proposed to efficiently extract long short-term dependencies. Combining the advantages of the Inception module and a hybrid dilated convolution (HDC) framework, the HDC-Inception module can both alleviate the "gridding issue" and obtain long short-term dependencies. Secondly, datasets have large numbers of silent segments and too many samples for some signal types, which are redundant and less difficult to detect, and, therefore, should not be overly prioritized. Thus, selecting effective samples and guiding the training based on them is of great importance. Inspired by support vector machine (SVM), combining soft margin SVM and cross-entropy loss (CE loss), the soft margin CE loss is proposed. Soft margin CE loss can adaptively select support vectors (effective samples) in datasets and guide training based on the selected samples. To utilize datasets more sufficiently, a hybrid CE loss is proposed. Using the benefits of soft margin CE loss and CE loss, hybrid CE loss guides the training with all samples and gives weight to support vectors. Soft margin CE loss and hybrid CE loss can be extended to most classification tasks and offer a wide range of applications and great potential. [ABSTRACT FROM AUTHOR]

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion. [ABSTRACT FROM AUTHOR]

This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to quantify the degree of waterlogging in wheat. Remote sensing data on soil moisture and wheat distribution are utilized to establish a monitoring system for waterlogging disasters specific to winter wheat. The analysis focused on affected areas in Bengbu and Jingzhou. Experimental results from 2017 to 2022 indicate that the predominant levels of waterlogging disasters in Bengbu and Jingzhou were moderate and mild, with the proportion of mild waterlogging ranging from 30.1% to 39.3% and moderate waterlogging from 14.8% to 25.6%. A combined analysis of multi-source remote sensing data reveals the key roles of precipitation, evapotranspiration, and altitude in waterlogging disasters. This study highlights regional disparities in the distribution of waterlogging disaster risks, providing new strategies and tools for precise assessment of waterlogging disasters. [ABSTRACT FROM AUTHOR]

Accurate assessment and prediction of water shortage risk are essential prerequisites for the rational allocation and risk management of water resources. However, previous water shortage risk assessment models based on copulas have strict requirements for data distribution, making them unsuitable for extreme conditions such as insufficient data volume and indeterminate distribution shapes. These limitations restrict the applicability of the models and result in lower evaluation accuracy. To address these issues, this paper proposes a water shortage risk assessment model based on kernel density estimation (KDE) and copula functions. This approach not only enhances the robustness and stability of the model but also improves its prediction accuracy. The methodology involves initially utilizing kernel density estimation to quantify the random uncertainties in water supply and demand based on historical statistical data, thereby calculating their respective marginal probability distributions. Subsequently, copula functions are employed to quantify the coupled interdependence between water supply and demand based on these marginal probability distributions, thereby computing the joint probability distribution. Ultimately, the water shortage risk is evaluated based on potential loss rates and occurrence probabilities. This proposed model is applied to assess the water shortage risk of the Yuxi water receiving area in the Central Yunnan Water Diversion Project, and compared with existing models through experimental contrasts. The experimental results demonstrate that the model exhibits evident advantages in terms of robustness, stability, and evaluation accuracy, with a rejection rate of 0 for the null hypothesis of edge probability fitting and a smaller deviation in joint probability fitting compared to the most outstanding model in the field. These findings indicate that the model presented in this paper is capable of adapting to non-ideal scenarios and extreme climatic conditions for water shortage risk assessment, providing reliable prediction outcomes even under extreme circumstances. Therefore, it can serve as a valuable reference and source of inspiration for related engineering applications and technical research. [ABSTRACT FROM AUTHOR]

Water, soil, and heat are strategic supporting elements for human survival and social development. The degree of matching between human-land-water-heat elements directly influences the sustainable development of a region. However, the current evaluation of the matching of human-land-water-heat elements overlooks the influence of elevation factors on the matching results, especially evident in mountainous areas. Taking the Yunnan Plateau with distinctive mountainous features as the research subject, divided into 11 elevation ranges, the Lorenz Gini coefficient, asymmetry coefficient, matching distance, and imbalance index are used to assess the spatial matching and balance of human-land-water-heat elements. A projection tracing model is employed to analyze its water resource carrying capacity. Analyses revealed that the Gini coefficient of monthly precipitation from the 1950s to 2022 on the Yunnan Plateau increases with increasing latitude, whereas the correlation with elevation is notably lower. The asymmetry coefficient increases gradually from west to east with change in longitude. The mismatch of the human–land–water–heat system in regions at different elevations is in the order 1800–2000 m > 2000–2200 m > 1400–1600 m > 800 m > other areas. The matching of the human–land–water–heat system in different wet–dry years and seasons also fluctuates with elevation, resulting in serious seasonal drought and water shortage problems in mountainous areas with elevations of 1200–1600, 1800–2000 m, and >2600 m. The spatial equilibrium of temperature and precipitation in regions of different elevations is best, followed by that of cultivated land, while that of the population is the worst. The Gini coefficients for different water cycle processes of precipitation, surface runoff, and regulating storage capacity for water supply continue to increase. Specifically, the Gini coefficient of industrial water supply is the highest, reaching 0.576, and that of agricultural irrigation is the lowest (0.424). Through artificial regulation of lake and reservoir water, seasonal changes in the demand for agricultural irrigation water are offset to achieve a demand–supply balance and matching of land and water resources. The water resource capacity of different elevation ranges is evenly underloaded. However, the potential of the water resource capacity varies obviously with elevation in the order 2000–2200 m < 1800–2000 m < 1600–8000 m < 1400–1600 m < other areas. It appears that the greater the human–land–water–heat system mismatch, the smaller the regional potential of the water resource capacity. [ABSTRACT FROM AUTHOR]

Enhancing the assessment of compressive strength and the efficiency of rebound hammers in non-destructive testing for high-strength concrete is an urgent issue in construction engineering. This study involved C50 to C90 high-strength concrete specimens, utilizing rebound hammers with nominal energies of 4.5 J and 5.5 J, along with a compression machine. A regression analysis was performed on the compressive strength and rebound values, resulting in linear, polynomial, power, exponential, and logarithmic equations for two different types of rebound hammers. Additionally, the precision of rebound hammers with different nominal energies and the representativeness of various rebound representative values in the measurement area were investigated. The experimental results indicate that the precision of the regionally representative strength curve in Xinjiang meets national specifications. The 4.5 J nominal energy rebound hammer exhibited a higher testing accuracy. When reducing the high-strength concrete measurement area's rebound representative values from 16 to 14, 12, and 10, the coefficients of variation for the different rebound representative values were mostly below 10%. Within high-strength concrete structures, the strength curve formula derived from rebound representative value 16 is equally applicable to 14, 12, and 10. In practical engineering applications, prioritizing 10 ensures testing accuracy while reducing on-site testing efforts. The outcomes of this experiment establish a foundation for the development and promotion of rebound method-testing technology for high-strength concrete in Xinjiang. [ABSTRACT FROM AUTHOR]

The technical research on determining the drought limit water level can be used as an important basis for starting the emergency response of drought resistance in the basin and guiding the drought resistance scheduling of water conservancy projects. When the concept of drought limit water level was first proposed, the main research object was reservoirs, and the method for determining the lake drought limit water level was not established. Referring to the calculation method of reservoir drought limit water level, the drought limit water level is used as a single warning indicator throughout the year, which lacks graded and staged standards, and also lacks rationality and effectiveness in practical application. Therefore, this article has improved the concept of lake drought limit water level (flow). Under different degrees of drought and water use patterns during the drought period, combined with the characteristics of lake water inflow, considering the factors such as ecology, water supply, and demand, lake inflow, evapotranspiration loss, a graded and staged standard of lake drought limit water level has been developed. For different types of lakes, a general method for determining the lake's graded and staged drought limit water level has been established. The SCSSA-Elman neural network is used to construct the medium and long-term water inflow prediction model for lakes, and the calculation results of this model are used for the warning and dynamic control analysis of the lake drought limit water level. The application of this method has the characteristics of strong applicability and high reliability. Finally, the determination method and dynamic control method of the lake's graded and staged drought limit water level have been successfully applied at Dianchi Lake in Yunnan. [ABSTRACT FROM AUTHOR]

Distributed radar systems promise to significantly enhance target localization by virtue of the superiority of multi-view observations from widely separated radars, compared to their monostatic counterparts. Nevertheless, when the radar number is limited, performing target localization bears the brunt of the parameter identifiability requirement that the parameter number must be no less than the number of independent measurements. In this way, the canonical two-stage target localization method, as well as its developments, is no longer appropriate for direct application. Hence, in this paper, we propose a novel target localization method using time-difference-of-arrival (TDOA) measurements with the minimum number of radars under platform position uncertainties. The referred distributed system is a bistatic multi-receiver system, where the primary signal is transmitted by a geostationary Earth orbit (GEO) satellite while receivers are equipped on several unmanned aerial vehicles (UAVs). In the first stage, the reference range from the reference radar to the target is estimated by a quadratic function, and then the weighted least squares (WLS) solution of the target location is updated by substituting the range estimate back into it. In the second stage, we invoke the Taylor series approximation to further refine the target localization obtained by the first stage. It can be foreseen that the developed method is beneficial for scenarios with a limited number of radars, including engineering projects such as fire control, surveillance, and guidance, to support high-accuracy target localization. The simulation results show the superiority of the localization performance of the proposed method over other existing methods. [ABSTRACT FROM AUTHOR]

N6-methyladenine (6mA) DNA methylation has emerged as an important epigenetic modification in eukaryotes. Nevertheless, the evolution of the 6mA methylation of homologous genes after species and after gene duplications remains unclear in plants. To understand the evolution of 6mA methylation, we detected the genome-wide 6mA methylation patterns of four lotus plants (Nelumbo nucifera) from different geographic origins by nanopore sequencing and compared them to patterns in Arabidopsis and rice. Within lotus, the genomic distributions of 6mA sites are different from the widely studied 5mC methylation sites. Consistently, in lotus, Arabidopsis and rice, 6mA sites are enriched around transcriptional start sites, positively correlated with gene expression levels, and preferentially retained in highly and broadly expressed orthologs with longer gene lengths and more exons. Among different duplicate genes, 6mA methylation is significantly more enriched and conserved in whole-genome duplicates than in local duplicates. Overall, our study reveals the convergent patterns of 6mA methylation evolution based on both lineage and duplicate gene divergence, which underpin their potential role in gene regulatory evolution in plants. [ABSTRACT FROM AUTHOR]

For distributed satellite constellations, detection performance can be equivalently regarded as a single large satellite by the cooperative operation of multiple small satellites, which is a promising research topic of the Next-Generation Radar (NGR) system. However, dense grating lobes inevitably occur in the synthetic transmit pattern due to its distributed configuration, as a result of which the detection performance of dynamic coherent radar is seriously weakened. In this paper, a novel transmit beampattern optimization method for dynamic coherent radar based on a distributed satellite constellation is presented. Firstly, the effective coherent detection range interval is determined by several influence factors, i.e., coherent detection, far-field, and system link constraints. Then, we discuss the quantitative evaluation method for coherent integration in terms of synchronization error, beam pointing error, and high-speed motion characteristics and we allocate the corresponding terms in a reasonable way from the perspective of engineering. Finally, the space–time–frequency degrees of freedom (DOFs), which can be collected from satellite spacing, carrier frequencies, and platform motion characteristics, are utilized to realize a robust transmit beampattern with low sidelobe by invoking a genetic algorithm (GA). Simulation results validate the effectiveness of our theoretic analysis, and unambiguous coherent transmit beamforming with a satellite constellation of limited scale is accomplished. [ABSTRACT FROM AUTHOR]

In this work, the parameter identifiability of sensor position perturbations in a distributed network is analyzed through establishing the link between rank of the Jacobian matrix and parameter identifiability under Gaussian noise. Here, the calibration is classified as either external or internal, dependent on whether auxiliary sources are exploited. It states that, in the case of internal calibration, sensor position perturbations can be precisely calibrated when the position of a sensor and orientation to a second sensor along with the coordinate of a third sensor along some axis, are known. In the case of external calibration where auxiliary sources are introduced to support the process, the identifiability condition for configuration calibration is to have at least three noncollinear auxiliary sources with the distributed sensor network avoiding the collinear and coplanar geometries. As the assumption of small perturbations is considered, the parameter identifiability is capable of being measured by virtue of the Bayesian Cramer–Rao lower bound (BCRLB), after asymptotical tightness of the BCRLB is verified. Simulations corroborate well with the theoretical development. [ABSTRACT FROM AUTHOR]

Surface albedo is an essential parameter in many solar radiation applications. Although several models are available, it remains debatable whether they are applicable to other locations. Using long-term daily measurements of radiation acquired by observational networks in China, this study examined the applicability of six existing albedo models: Ineichen model (IeM), Gueymard model (GM), Dong model (DeM), Iziomon-Mayer model (IMM), Morton model (MM), and Zhou model (ZeM). The evaluation results of model performance through statistical analysis showed that among the available ground albedo models, ZeM had the best overall performance at 12 selected stations, IeM was shown to provide acceptable estimations for locations where albedo records are readily available. The statistical results of individual station have shownthat the number of input parameters is not the only key factor for determining the predictive performance of ground albedo models. In other words, a simple model has potential for accurate estimation of ground albedo with appropriate model parameters. Therefore, the simple two-parameter DeM was selected to re-calibrate with in-situ radiometric measurements, which can be adopted as a surrogate for ZeM to predict surface albedo in China. [ABSTRACT FROM AUTHOR]

In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The order Nymphaeales, consisting of three families with a record of eight genera, has gained significant interest from botanists, probably due to its position as a basal angiosperm. The phylogenetic relationships within the order have been well studied; however, a few controversial nodes still remain in the Nymphaeaceae. The position of the Nuphar genus and the monophyly of the Nymphaeaceae family remain uncertain. This study adds to the increasing number of the completely sequenced plastid genomes of the Nymphaeales and applies a large chloroplast gene data set in reconstructing the intergeneric relationships within the Nymphaeaceae. Five complete chloroplast genomes were newly generated, including a first for the monotypic Euryale genus. Using a set of 66 protein-coding genes from the chloroplast genomes of 17 taxa, the phylogenetic position of Nuphar was determined and a monophyletic Nymphaeaceae family was obtained with convincing statistical support from both partitioned and unpartitioned data schemes. Although genomic comparative analyses revealed a high degree of synteny among the chloroplast genomes of the ancient angiosperms, key minor variations were evident, particularly in the contraction/expansion of the inverted-repeat regions and in RNA-editing events. Genome structure, and gene content and arrangement were highly conserved among the chloroplast genomes. The intergeneric relationships defined in this study are congruent with those inferred using morphological data. [ABSTRACT FROM AUTHOR]

In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method.