Representation of Spatial Variability of the Water Fluxes over the Congo Basin Region

The Congo Basin, being one of the major basins in the tropics, is important to the global climate, yet its hydrology is perhaps the least understood. Although various reanalysis/analysis datasets have been used to improve our understanding of the basin’s hydroclimate, they have been historically difficult to validate due to sparse in situ measurements. This study analyzes the impact of model resolution on the spatial variability of the Basin’s hydroclimate using the Decorrelation Length Scale (DLCS) technique, as it is not subject to uniform model bias. The spatial variability within the precipitation (P), evaporation/evapotranspiration (E), and precipitation-minus-evaporation (P-E) fields were investigated across four spatial resolutions using reanalysis/analysis datasets from the ECMWF ranging from 9–75 km. Results show that the representation of P and P-E fields over the Basin and the equatorial Atlantic Ocean are sensitive to model resolution, as the spatial patterns of their DCLS results are resolution-dependent. However, the resolution-independent features are predominantly found in the E field. Furthermore, the P field is the dominant source of spatial variability of P-E, occurring over the land and the equatorial Atlantic Ocean, while over the Southern Atlantic, P-E is mainly governed by the E field, with both showing weak spatial variability.

INTRA- AND EXTRA-WORD NATURE OF THE COMPONENT OF THE PHRASEOLOGICAL UNIT

Постановка задачи. В статье приведен анализ взглядов исследователей на определение фразеологического потенциала отдельных лексических единиц с позиций словного и несловного подходов. Цель исследования - рассмотреть семантику компонентов фразеологических единиц на поверхностном и глубинном уровнях. Статья раскрывает содержание следующих понятий: компонент ФЕ, словный подход, внесловный подход. Результаты исследования. Чтобы стать компонентом ФЕ, лексеме необходимо пройти через определенные изменения: деактуализация, метафоризация, лексико-грамматическая трансформация. Эти преобразования проявляются не только в ослаблении связности и проницаемости значений у компонента ФЕ, но и служат основой для возникновения новых значений. Компонентный состав определяет степень идиоматичности всего фразеологического оборота. Рассмотренные в статье противоположные теоретические позиции относительно природы компонента ФЕ (словный и несловный подходы) определяют: ведущая или малозаметная роль в построении значения оборота зависит от словной и внесловной природы самих компонентов ФЕ. Полученные выводы. Понимание природы компонента ФЕ имеет двойственную сущность. С одной стороны, внесловность компонента ФЕ отделяет его от обычного слова, лишая некоторых самостоятельных признаков. С другой стороны, словный характер компонента ФЕ можно выявить даже при высокой степени семантической целостности оборота. Однако анализ словной и несловной природы компонентов ФЕ в большинстве случаев не раскрывает прямого, опосредованного влияния семантики свободного значения лексемы на семантику фразеологизма в целом. Statement of the problem. The article provides an analysis of the views of researchers on determining the phraseological potential of individual lexical units from the standpoint of verbal and non-verbal approaches. The purpose of the study is to consider the semantics of components of phraseological units (PU) at surface and deep levels. The article discloses the content of the following concepts: the PU component, the intra- and extra-word nature of the component. Results. To become a component of PU, lexeme must go through certain changes: deactualization, metaphorization, lexico-grammatical transformation. These transformations appear not only in the weakening of the connectivity and permeability of the values of the PU component, but also serve as the basis for the emergence of new meanings. The component composition determines the degree of idiomaticity of the entire phraseological unit. The opposite theoretical positions considered in the article regarding the nature of the PU component (intra- and extra-word nature approaches) determine: the leading or stealthy role in constructing the value of the phraseologism depends on the intra- and extra-word nature of the PU components. Conclusion. Understanding the nature of the PU component has a dual essence. On the one hand, the extra-word nature of the PU component separates it from the usual word, depriving it of some independent features. On the other hand, the intra-word character of the PU component can be revealed even with a high degree of semantic integrity of the phraseologism. However, the analysis of the intra- and extra-word nature of the components of PU in most cases does not reveal the direct, but indirect effect of the semantics of the free meaning of the lexeme on the semantics of phraseologism as a whole.

Broadband absorption of monolayer MoS2 in visible region using a tetramerized nanorod metasurface

High-efficient broadband absorption of the monolayer MoS2 is achieved by using a tetramerized nanorod metasurface (TNM), and the average absorption of 64.5% for the monolayer MoS2 can be obtained in the visible range of 400-750 nm. The unit cell of the TNM consists four Ag nanorods with different diameters, the incident light can be selectively absorbed by the monolayer MoS2 due to the magnetic resonance associated with the individual Ag nanorod, and broadband absorption of the monolayer MoS2 is realizable due to the cooperative magnetic resonances related to the tetramerized Ag nanorods. In addition, the absorption of the monolayer MoS2 is robust to the variation of the structural parameters, and it exhibits wide-angle and polarization-independent features.

Methods of Estimating the Form of the Probability Distribution Density in Tasks of Processing Measurement Results

Issues associated with methods for estimating the shape of the probability distribution density curve are analyzed in order to classify them when processing measurement results. For example, such nonparametric methods as the method of histograms and frequency polygon, as well as the method of classification of distributions, are considered. It is shown that the values of the anticurtosis and entropy coefficient can be taken as independent features of the form of symmetric distributions. For probability distribution densities that have a one-sided character, such as multiplicative noise, a skewness coefficient should be added to the parameters to consider. Recurrent procedures for obtaining current estimates of numerical characteristics of analyzed random processes are given. The results of processing a random process based on recurrent procedures are presented. It is shown that when the number of samples increases, the estimates obtained by using recurrent and non-recurrent procedures converge. The scattering of estimates of probability distribution density parameters, such as variance, relative mean square error, and entropy error, is determined.

Coding sequence-independent homology search identifies highly divergent homopteran putative effector gene family

Many genomes contain rapidly evolving and highly divergent genes whose homology to genes of known function often cannot be determined from sequence similarity alone. However, coding sequence-independent features of genes, such as intron-exon boundaries, often evolve more slowly than coding sequences and can provide complementary evidence for homology. We found that a linear logistic regression classifier using only structural features of rapidly evolving bicycle aphid effector genes identified many putative bicycle homologs in aphids, phylloxerids, and scale insects, whereas sequence similarity search methods yielded few homologs in most aphids and no homologs in phylloxerids and scale insects. Subsequent examination of sequence features and intron locations supported homology assignments. Differential expression studies of newly-identified bicycle homologs, together with prior proteomic studies, support the hypothesis that BICYCLE proteins act as plant effector proteins in many aphid species and perhaps also in phylloxerids and scale insects.

Image classification method of cashmere and wool based on the multi-feature selection and random forest method

Cashmere and wool play an important role in the wool industry and textile industry, and suitable features are the key to identifying them. To obtain effective features and improve the accuracy of cashmere and wool classification, the multi-feature selection and random forest method is used to express in this article. Firstly, the gray-gradient co-occurrence matrix model is used for texture feature extraction to construct the original high-dimensional feature data set; secondly, considering that the original feature data set contains a large number of invalid and redundant features, the feature selection algorithm combining correlation analysis and principal component analysis–weight coefficient evaluation is used to obtain important features, independent features, and principal component sensitive features to complement each other; last but not least, the optimized random forest model analyzes the results. The results show that the combination of multi-feature selection subsets and random forest makes the classification accuracy of cashmere and wool more reliable, and the accuracy fluctuates around 90%.

A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis

Liver cirrhosis poses a major risk for the development of hepatocellular carcinoma (HCC). This retrospective study investigated to what extent radiomic features allow the prediction of emerging HCC in patients with cirrhosis in contrast-enhanced computed tomography (CECT). A total of 51 patients with liver cirrhosis and newly detected HCC lesions (n = 82) during follow-up (FU-CT) after local tumor therapy were included. These lesions were not to have been detected by the radiologist in the chronologically prior CECT (PRE-CT). For training purposes, segmentations of 22 patients with liver cirrhosis but without HCC-recurrence were added. A total of 186 areas (82 HCCs and 104 cirrhotic liver areas without HCC) were analyzed. Using univariate analysis, four independent features were identified, and a multivariate logistic regression model was trained to classify the outlined regions as “HCC probable” or “HCC improbable”. In total, 60/82 (73%) of segmentations with later detected HCC and 84/104 (81%) segmentations without HCC were classified correctly (AUC of 81%, 95% CI 74–87%), yielding a sensitivity of 72% (95% CI 57–83%) and a specificity of 86% (95% CI 76–96%). In conclusion, the model predicted the occurrence of new HCCs within segmented areas with an acceptable sensitivity and specificity in cirrhotic liver tissue in CECT.

Statistical prediction of biogas and methane yields during anaerobic digestion based on the composition of lignocellulosic biomass

In the described study, the relationships between the percentage and structure of selected lignocellulosic components and the efficiency of their anaerobic digestion and the quality of the produced biogas were analyzed. This research included various lignocellulosic raw materials. The biogas efficiency and quality tests were carried out according to DIN standard 38 414-8 (1985) and VDI standard 4630 (2016). Multiple TAPPI standards and the Seifert method were used to determine the chemical composition of the lignocellulose materials. Lignin structure analysis was performed using Fourier transform infrared spectroscopy. Wide-angle X-Ray scattering analysis was used to determine the degree of crystallinity of cellulose. The biogas was positively correlated with C=O and the syringyl to guaiacyl ratio, and negatively correlated with the crystalline structure of cellulose, lignin, cellulose, and extractives. In addition, methane was positively correlated with holocellulose and extractives and negatively correlated with the crystalline structure of cellulose, cellulose, substances soluble in NaOH, and the OH groups. The found independent features accounted for 86.0% of the biogas variability and 68.0% of the methane variability.

Effective Point Cloud Analysis Using Multi-Scale Features

Fully exploring the correlation of local features and their spatial distribution in point clouds is essential for feature modeling. This paper, inspired by convolutional neural networks (CNNs), explores the relationship between local patterns and point coordinates from a novel perspective and proposes a lightweight structure based on multi-scale features and a two-step fusion strategy. Specifically, local features of multi-scales and their spatial distribution can be regarded as independent features corresponding to different levels of geometric significance, which are extracted by multiple parallel branches and then merged on multiple levels. In this way, the proposed model generates a shape-level representation that contains rich local characteristics and the spatial relationship between them. Moreover, with the shared multi-layer perceptrons (MLPs) as basic operators, the proposed structure is so concise that it converges rapidly, and so we introduce the snapshot ensemble to improve performance further. The model is evaluated on classification and part segmentation tasks. The experiments prove that our model achieves on-par or better performance than previous state-of-the-art (SOTA) methods.