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The ground motion in the near-fault region of an earthquake is characterized by exceptional energy levels, powerful velocity impulses, substantial spatial variability, and notable permanent displacement. These unique attributes can dramatically escalate structural damage. Steel truss arch bridges, being critical components of transportation networks, are particularly vulnerable to these phenomena due to their extensive stiffness spans. Such factors are difficult to accurately simulate. In this study, real near-fault ground motions that incorporate spatial variability effects and pulse effects are used to excite the long-span arch bridge, thereby striving to realistically reproduce the structural damage sustained by the bridge under the simultaneous influence of near-fault spatial variability and pulse effects. This study adopts an arch bridge with a span closely approximating the spacing between stations (200 m) of the SMART seismic array as a case study. The near-fault ground motions, characterized by spatial variability and captured by the array, are selected as seismic samples, while the far-field ground motions recorded by the same array serve as a comparative reference. The seismic excitations are then input into the bridge case study, following the spatial correspondence of the stations, using a large-scale finite element program to obtain the structural response. Upon analyzing the seismic response of crucial positions on the bridge, it became evident that the arch foot of the bridge is more susceptible to the spatial variability in near-fault ground motion, whereas the vault experiences a greater impact from the high-energy velocity pulse. Specifically, under nonuniform seismic conditions, the internal force at the base of the bridge arch increased significantly, averaging a rise of 18.69% compared to uniform excitation conditions. Conversely, the displacement and internal force response at the top of the arch exhibited more modest increases of 6.48% and 10.33%, respectively. Under nonuniform excitation, the vault’s response to near-fault earthquakes increased by an average of 20.35% com-pared to far-field earthquakes, while the arch foot’s response rose by 11.55%. In contrast, under uniform excitation, the vault’s response to near-fault earthquakes was notably higher, increasing by 25.04%, while the arch foot’s response showed a minor increase of only 2.28%. The study has revealed significant differences in the sensitivity of different parts of long-span arch bridges to near-fault earthquake characteristics. This finding is of great importance for understanding the behavior of long-span arch bridges under complex earthquake conditions. Specifically, the arch foot of the bridge is more sensitive to the spatial variability of near-fault ground motions, while the arch crown is more significantly affected by high-energy velocity pulses, providing new insights for bridge seismic design. Furthermore, the differences in response between the arch crown and arch foot under different earthquake excitations also reveal the complexity and diversity of bridge structural responses.

The non-Gaussian feature of seismic ground motion has been reported in some works. However, there remains a lack of research on the influence of the ground motion non-Gaussianity on the seismic performance of buildings, which motivates this study. By employing a non-Gaussian non-stationary random process simulation method previously proposed by the authors, 40,000 ground motion acceleration signals are efficiently generated, including 20,000 Gaussian and 20,000 non-Gaussian records. As computational examples, a four-story frame building and a three-tower super-tall building are selected. The generated acceleration signals serve as external excitations for the two buildings, allowing for a comparison of the differences in seismic structural responses caused by the Gaussian and non-Gaussian earthquake groups. Probability analysis is performed using top-layer displacement and maximum inter-story drift ratio as damage indicators. The results show that the structural responses induced by both Gaussian and non-Gaussian earthquake groups have identical first- and second-order moments but different higher-order moments. The responses from non-Gaussian earthquakes display distinct non-Gaussian traits, with their distribution of extreme values exhibiting a longer tail compared to the Gaussian counterparts. This leads to a notably larger value of non-Gaussian responses under high crossing probabilities, with an amplification that can surpass 18%.

Since July 2022, the Yangtze River basin has experienced the most severe hydro-meteorological drought since record collection started in 1961, which has greatly affected the ecological environment of the Dongting Lake (DTL) basin. To investigate the effects of drought events on the eutrophication and phytoplankton community structure of DTL, the lake was sampled twice in August and September 2022 based on the water level fluctuations resulting in 47 samples. Furthermore, we combined the comprehensive trophic level index (TLI) and phytoplankton Shannon–Wiener diversity index (H) to characterize and evaluate the eutrophication status. The key influencing factors of the phytoplankton community were identified using redundancy analysis (RDA), hierarchical partitioning, and the Jaccard similarity index (J). Our results showed that the TLI of DTL changed from light–moderate eutrophication status (August) to mesotrophic status (September), whereas the H changed from light or no pollution to medium pollution. The phytoplankton abundance in August (122.06 × 104 cells/L) was less than that in September (351.18 × 104 cells/L) in DTL. A trend in phytoplankton community succession from Bacillariophyta to Chlorophyta and Cyanophyta was shown. The combination of physiochemical and ecological assessment more accurately characterized the true eutrophic status of the aquatic ecosystem. The RDA showed that the key influencing factors in the phytoplankton community were water temperature (WT), pH, nitrogen and phosphorus nutrients, and the permanganate index (CODMn) in August, while dissolved oxygen (DO) and redox potential (ORP) were the key factors in September. Hierarchical partitioning further indicated that temporal and spatial variations had a greater impact on the phytoplankton community. And the J of each region was slightly similar and very dissimilar, from August to September, which indicated a decreased hydrological connectivity of DTL during drought. These analyses indicated that the risk to the water ecology of DTL intensified during the summer–autumn drought in 2022. Safeguarding hydrological connectivity in the DTL region is a prerequisite for promoting energy flow, material cycle, and water ecosystem health.

Asexual reproduction through seeds in plants (i.e., apomixis) is a heritable trait, and apomixis- linked loci have been identified in multiple species. However, direct identification of genomic elements is typically hindered as apomixis-linked loci and are commonly found in recombination-suppressed and repetitive regions. Heterochromatinized elements, such as B chromosomes and other supernumerary chromosomal DNA fragments have long been known to be associated with asexuality in both plants and animals and are prime candidate regions for the evolution of multiple apomixis factors controlling the individual elements of apomixis. Here, we examined molecular evolution, gene regulation, and chromosomal location of a male apomeiosis factor (UPG2), a long noncoding RNA gene, in sexual and apomictic Boechera with and without male apomeiosis (i.e., balanced and unbalanced apomicts). We revealed the origin of the gene in the apomixis genome on an apomixis-specific, supernumerary heterochromatic Boechera chromosome (Boe1). The UPG2 is active in the tapetum at male meiosis. We found allele classes specific to apomictic and sexual Boechera accessions and a third class that shares the features of both and points to a convergent transition state. Sex alleles are found only in some of the sexual accessions and have higher nucleotide divergence and lower transcriptional activity compared to apo alleles. These data demonstrate selective pressure to maintain the function of UPG2 for unreduced pollen formation in apomicts as the occasional transmission of the allele from unbalanced apomicts into sexual organisms that lead to pseudogenization and functional decay of copies in sexual organisms.

Flax (Linum usitatissimum L.) is an important multipurpose crop widely grown for oil and fiber. Despite recent advances in genomics, detailed gene activities during the important reproductive phase of its development are not well defined. In this study, we employed high-throughput RNA-sequencing methods to generate in-depth transcriptome profiles of flax tissues with emphasis on the reproductive phases of five key stages of embryogenesis (globular embryo, heart embryo, torpedo embryo, cotyledon embryo, and mature embryo), mature seed, and vegetative tissues viz. ovary, anther, and root. These datasets were used to establish the co-expression networks covering 36 gene modules based on the expression patterns for each gene through weighted gene co-expression network analysis (WGCNA). Functional interrogation with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) of dominantly expressed genetic modules in tissues revealed pathways involved in the development of different tissues. Moreover, the essential genes in embryo development and synthesis of storage reserves were identified based on their dynamic expression patterns. Together, this comprehensive dataset for developing embryos, mature seeds and vegetative tissues provides new insights into molecular mechanisms of seed development with potential for flax crop improvement.

Cloning of coding sequence (CDS) is an important step for gene function research. Here, we reported a simple and efficient strategy for assembling multiple-exon into an intron-free CDS from genomic DNA (gDNA) by an isothermal recombination reaction-based PCR (IRR-PCR) method. As an example, a 2067-bp full-length CDS of the anther-specific expression gene OsABCG15, which is composed of seven exons and six introns, was generated by IRR-PCR using genomic DNA of rice leaf as the template. Actually, this approach can be wildly applied to any DNA sequences assembly to achieve CDS cloning, gene fusion and multiple site-directed mutagenesis in functional genomics studies in vitro.

Named entity recognition (NER) is a crucial task in natural language processing, particularly challenging in the legal domain due to the intricate and lengthy nature of legal entities. Existing methods often struggle with accurately identifying entity boundaries and types in legal texts. To address these challenges, we propose a novel sequence-to-sequence framework designed specifically for the legal domain. This framework features an entity-type-aware module that leverages contrastive learning to enhance the prediction of entity types. Additionally, we incorporate a decoder with a copy mechanism that accurately identifies complex legal entities without the need for explicit tagging schemas. Our extensive experiments on two legal datasets show that our framework significantly outperforms state-of-the-art methods, achieving notable improvements in precision, recall, and F1 score. This demonstrates the effectiveness of our approach in improving entity recognition in legal texts, offering a promising direction for future research in legal NER.

A hybrid data and simulation--based (HDSB) approach that incorporates simulated data based on an autoregression (AR) model and an inverse Johnson transformation for estimating wind pressure extremes is introduced. With the target statistical characteristics, i.e., probability density function (PDF) and power spectra density (PSD) in hand, massive simulations of non-Gaussian wind pressure can be carried out, which provides the peak factors or any desired fractile levels for design applications. Existing methods concerning the peak factors of wind pressure are briefly reviewed, and the fundamental basis and the simulation procedure of the HDSB approach are presented. The invocation of the inverse Johnson transformation in the HDSB approach enhances its applicability to the entire Pearson diagram, i.e., for all the softening and hardening stationary non-Gaussian processes with any combination of skewness and kurtosis. The efficacy of the HDSB approach in comparison with existing methods is demonstrated and verified by comparing the numerically simulated results with the directly observed data in long-duration wind tunnel tests, and with results from previous methods. The computational demand of the HDSB approach is favorable when compared to long-duration wind tunnel tests or model driven approaches. The proposed HDSB approach can serve as a building block for the development of a data-driven framework for efficiently simulating stationary non-Gaussian processes and estimating the extremes accurately. [ABSTRACT FROM AUTHOR]

One decisive problem of short text classification is the serious dimensional disaster when utilizing a statistics-based approach to construct vector spaces. Here, a feature reduction method is proposed that is based on two-stage feature clustering (TSFC), which is applied to short text classification. Features are semi-loosely clustered by combining spectral clustering with a graph traversal algorithm. Next, intra-cluster feature screening rules are designed to remove outlier feature words, which improves the effect of similar feature clusters. We classify short texts with corresponding similar feature clusters instead of original feature words. Similar feature clusters replace feature words, and the dimension of vector space is significantly reduced. Several classifiers are utilized to evaluate the effectiveness of this method. The results show that the method largely resolves the dimensional disaster and it can significantly improve the accuracy of short text classification.

Word embeddings are effective intermediate representations for capturing semantic regularities between words in natural language processing (NLP) tasks. We propose sentiment-aware word embedding for emotional classification, which consists of integrating sentiment evidence within the emotional embedding component of a term vector. We take advantage of the multiple types of emotional knowledge, just as the existing emotional lexicon, to build emotional word vectors to represent emotional information. Then the emotional word vector is combined with the traditional word embedding to construct the hybrid representation, which contains semantic and emotional information as the inputs of the emotion classification experiments. Our method maintains the interpretability of word embeddings, and leverages external emotional information in addition to input text sequences. Extensive results on several machine learning models show that the proposed methods can improve the accuracy of emotion classification tasks.