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Metonymy resolution (MR) is a challenging task in the field of natural language processing. The task of MR aims to identify the metonymic usage of a word that employs an entity name to refer to another target entity. Recent BERT-based methods yield state-of-the-art performances. However, they neither make full use of the entity information nor explicitly consider syntactic structure. In contrast, in this paper, we argue that the metonymic process should be completed in a collaborative manner, relying on both lexical semantics and syntactic structure (syntax). This paper proposes a novel approach to enhancing BERT-based MR models with hard and soft syntactic constraints by using different types of convolutional neural networks to model dependency parse trees. Experimental results on benchmark datasets (e.g., ReLocaR, SemEval 2007 and WiMCor) confirm that leveraging syntactic information into fine pre-trained language models benefits MR tasks.

Cryo-cooling has been nearly universally adopted to mitigate X-ray damage and facilitate crystal handling in protein X-ray crystallography. However, cryo X-ray crystallographic data provide an incomplete window into the ensemble of conformations that is at the heart of protein function and energetics. Room-temperature (RT) X-ray crystallography provides accurate ensemble information, and recent developments allow conformational heterogeneity (the experimental manifestation of ensembles) to be extracted from single-crystal data. Nevertheless, high sensitivity to X-ray damage at RT raises concerns about data reliability. To systematically address this critical issue, increasingly X-ray-damaged high-resolution data sets (1.02–1.52 Å resolution) were obtained from single proteinase K, thaumatin and lysozyme crystals at RT (277 K). In each case a modest increase in conformational heterogeneity with X-ray damage was observed. Merging data with different extents of damage (as is typically carried out) had negligible effects on conformational heterogeneity until the overall diffraction intensity decayed to ∼70% of its initial value. These effects were compared with X-ray damage effects in cryo-cooled crystals by carrying out an analogous analysis of increasingly damaged proteinase K cryo data sets (0.9–1.16 Å resolution). X-ray damage-associated heterogeneity changes were found that were not observed at RT. This property renders it difficult to distinguish real from artefactual conformations and to determine the conformational response to changes in temperature. The ability to acquire reliable heterogeneity information from single crystals at RT, together with recent advances in RT data collection at accessible synchrotron beamlines, provides a strong motivation for the widespread adoption of RT X-ray crystallography to obtain conformational ensemble information.

The quantitative evaluation for communicable events has been a significant demand in digital society management. This paper takes the 2022 Winter Olympic Games as the object and proposes a quantitative evaluation method for the communication impact of sporting events based on the SIR dynamic diffusion model. Specifically, this study combinesa long short-term memory (LSTM) neural network, wavelet packet decomposition, and other techniques to propose a digital evaluation approach for the quantification of communication impact. Among these, the transfer probability is quantified and calculated by the user node reputation value algorithm. In the experimental simulation, the effects of different mechanisms of joining consensus nodes and blockchain on the propagation probability in the model are discussed, respectively. Some simulation experiments are conducted on the real-world scenes of social networks, and the simulation results show that the number of nodes spreading false information is reduced by 9.89% compared with baseline methods. Finally, a sports event communication effect evaluation index system was constructed, and the data characteristics of indicators at all levels were analyzed to preliminarily predict the communication effect, after which the fuzzy hierarchical comprehensive evaluation method, combined with the expert survey method, was used to empirically evaluate, and test the communication effect of its events.

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

Trochoid milling can improve the quality of machining of difficult materials as well as the efficiency of machining. However, its complex tool trajectory makes it difficult to predict the instantaneous cutting forces during cutting. Therefore, in this paper, the transient cutting thickness model in the cycloidal milling process was established using numerical combined with analytical methods, and the semi-mechanical cutting force model was established. Experiments were designed to compare the differences between the cutting force coefficients extracted from the slot milling experiments and those extracted from the trochoid milling. Finally, experiments were designed to validate the established cutting force model. The results showed that there was an error of 5–23% between the tangential cutting force coefficients extracted from slot milling and the tangential cutting force coefficients extracted from trochoid milling, while there was an error of 21–35% in the radial cutting force coefficients, indicating that the cutting force coefficients extracted from slot milling cannot be used to predict the cutting force in the trochoid milling process. It was verified that the error of the established cutting force model in predicting the cutting force of trochoid milling was 12%, indicating that the established model has a high accuracy, which provides a theoretical basis for the selection of cutting parameters and parameter optimization in the future.

Unbalanced fault is the most common fault of high-speed motorized spindle, which is the main factor affecting the machining accuracy of high-speed spindle. Due to the unbalanced magnetic pull produced by the air gap eccentricity of the stator and rotor, the unbalanced vibration of the motorized spindle will be further aggravated. In order to explore the dynamic behavior and motion law of the unbalanced fault motorized spindle under the eccentric state, a dynamic model of the unbalanced fault of the high-speed motorized spindle considering the unbalanced magnetic pull was established. Taking the eccentric motorized spindle customized by the research group as the research object, the dynamic model is established, simulated, and analyzed, and the response change law of motorized spindle under the effect of different speed, unbalance, and air gap is obtained. The simulation results show that the unbalanced magnetic pull caused by static eccentricity will increase the unbalanced vibration of motorized spindle, and the unbalanced vibration will also increase with the increase of static eccentricity. The vibration caused by unbalanced magnetic pull does not increase with the increase of rotating speed. In frequency-domain analysis, when there is unbalanced magnetic pull, the peak appears at 0 Hz, and the amplitude of fundamental frequency vibration will increase with the increase of eccentricity. The experimental results show that the greater the eccentricity is, the greater the unbalance vibration of the motorized spindle is. The experimental results are consistent with the simulation results, which further verify the accuracy of the model. The research results lay a theoretical basis for fault analysis and diagnosis of coupling fault motorized spindle.

Amplitude variation with offset (AVO) inversion is a widely used approach to obtain reliable estimates of elastic parameter in the fields of seismic exploration. However, the AVO inversion is an ill-posed problem because of the band-limited characteristic of seismic data. The regularization constraint plays an important role in improving inversion resolution. Total variation (TV) class regularization based on L $$_1$$ norm has been introduced in seismic inversion. But, these methods may underestimate the high-amplitude components and obtain low-resolution results. To tackle these issues, we propose to combine a smooth nonconvex regularization approach with adaptive individual weight-gain. Compared with the L $$_1$$ norm regularizers, the proposed smoothed nonconvex sparsity-inducing regularizers can lead to more accurate estimation for high-amplitude components. Different from previous regularization methods, the proposed approach also assigns different weight regularization parameters for different strata, which we call adaptive individual weight-gain strategy. To ensure sufficient minimization of the constructed objective function, a spectral Polak–Ribiere–Polyak conjugate gradient method with line search step size is used. Further, we prove that the proposed algorithm converges to a stationary point. The synthetic data tests illustrate that our approach has improved performance compared with the conventional TV class regularization methods. Field data example further verifies the higher resolution of the proposed approach.

We report a genome-wide association study for facial features in > 6,000 Latin Americans. We placed 106 landmarks on 2D frontal photographs using the cloud service platform Face++. After Procrustes superposition, genome-wide association testing was performed for 301 inter-landmark distances. We detected nominally significant association (P-value − 8) for 42 genome regions. Of these, 9 regions have been previously reported in GWAS of facial features. In follow-up analyses, we replicated 26 of the 33 novel regions (in East Asians or Europeans). The replicated regions include 1q32.3, 3q21.1, 8p11.21, 10p11.1, and 22q12.1, all comprising strong candidate genes involved in craniofacial development. Furthermore, the 1q32.3 region shows evidence of introgression from archaic humans. These results provide novel biological insights into facial variation and establish that automatic landmarking of standard 2D photographs is a simple and informative approach for the genetic analysis of facial variation, suitable for the rapid analysis of large population samples.

In order to improve the computational efficiency and alignment accuracy of a microelectromechanical system (MEMS)-based strap-down inertial navigation system (SINS), this article proposes a high-accuracy global positioning system (GPS)-aided coarse alignment method. The attitude matrix between current and initial body frames and the unknown gyro bias, accelerometer bias, and lever arm are jointly estimated based on the proposed closed-loop approach, where the attitude error and unknown parameters are jointly inferred based on the derived linear state-space model using the Kalman filter. Simulation and experimental results illustrate that the proposed GPS-aided coarse alignment method can achieve better accuracy than existing state-of-the-art coarse alignment methods for MEMS-based SINS.

Relation classification is one of the most fundamental upstream tasks in natural language processing and information extraction. State-of-the-art approaches make use of various deep neural networks (DNNs) to extract higher-level features directly. They can easily access to accurate classification results by taking advantage of both local entity features and global sentential features. Recent works on relation classification devote efforts to modify these neural networks, but less attention has been paid to the feature design concerning syntax. However, from a linguistic perspective, syntactic features are essential for relation classification. In this article, we present a novel linguistically motivated approach that enhances relation classification by imposing additional syntactic constraints. We investigate to leverage syntactic skeletons along with the sentential contexts to identify hidden relation types. The syntactic skeletons are extracted under the guidance of prior syntax knowledge. During extraction, the input sentences are recursively decomposed into syntactically shorter and simpler chunks. Experimental results on the SemEval-2010 Task 8 benchmark show that incorporating syntactic skeletons into current DNN models enhances the task of relation classification. Our systems significantly surpass two strong baseline systems. One of the substantial advantages of our proposal is that this framework is extensible for most current DNN models.

Amplitude variation with offset (AVO) inversion is a widely used approach to obtain reliable estimates of elastic parameter. Tikhonov and total variation regularization are commonly used methods to address ill-posed problem of AVO inversion. However, these model-driven methods are only for special geological structure such as smoothness or blockiness. In this letter, a robust data-driven-based regularization method with logarithm absolute error loss function (DDI-Log) for AVO inversion is proposed. In DDI-Log, the information of well-log data and the complex geology are considered in a sparse representation framework. In pre-stack seismic data, outlier noise can negatively influence inversion results. Thus, different from the previous data-driven inversion based on $$L _2$$ norm loss function, we extend the logarithm absolute error function as the loss function. In the iteration, a new spectral PRP conjugate gradient method is used to solve the large-scale optimization problem. The synthetic data and field data tests illustrate that the proposed approach is robust against outlier noise and that the resolution and accuracy of the solutions are improved.

The purpose is to apply the fractal statistical model to analyze tourist flow feature of scenic area, and promote the effective performance of scenic area. Zhangjiajie scenic area is taken as an example. First, its natural resources characteristics, ecological environment and problems in the development of scenic area are analyzed in detail; moreover, fractal theory and fractal dimension calculation method are elaborated; finally, wavelet transform is proposed to analyze the nonlinear transformation characteristics of tourist flow. The results show that the changes of the high-frequency layer [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] all have cyclical fluctuations in the change of tourist flow. The change range of tourist flow in spring and autumn is relatively large, and the tourist flow in winter is low. Inbound tourism tourist flow has a certain seasonal regularity, with obvious characteristics of low and peak seasons. Wulingyuan scenic spot and Tianmen Mountain scenic spot of Zhangjiajie have three non-scale ranges, which are related to the scale and change of tourist flow. The concentration of inbound tourist flow in the peak season still exists in both scenic spots, and it is more and more serious. Tourist flow of inbound tourists from April to May and from September to October is large, accounting for more than half of the annual tourist flow, while most of the other periods are in the off-season.

Identification and characterization of methylation quantitative trait loci (mQTLs) can help elucidate the role of DNA methylation changes as a potential mediator of genetic risk loci. However, mQTLs remain poorly characterized: they have not yet been mapped in the largest ethnic populations, their cell-type specific nature has not been resolved, and the proportion of mQTLs attributed to different molecular mechanisms is unknown. Here we perform the first mQTL-mapping study in a large Han Chinese population, demonstrating that over 80% of mQTLs are shared with those identified in White Caucasians. We further estimate that over 90% of mQTLs are shared between different blood cell-lineages. mQTLs demonstrate a strong enrichment for variants influencing chromatin accessibility. We identify a number of GWAS-linked transcription factor trans-mQTL hotspots associated with eosinophilia, ulcerative colitis and body mass index, and a subset of trans-mQTLs within the NFKB-pathway that may mediate the risk of obesity. In summary, this study significantly expands our understanding of mQTLs and their potential role in mediating disease risk, whilst also contributing the first mQTL-database in an Asian population.

Facial and cranial variation represent a multidimensional set of highly correlated and heritable phenotypes. Little is known about the genetic basis explaining this correlation. We develop a software package ALoSFL for simultaneous localization of facial and cranial landmarks from head computed tomography (CT) images, apply it in the analysis of head CT images of 777 Han Chinese women, and obtain a set of phenotypes representing variation in face, skull and facial soft tissue thickness (FSTT). Association analysis of 301 single nucleotide polymorphisms (SNPs) from 191 distinct genomic loci previously associated with facial variation reveals an unexpected larger number of loci showing significant associations (P 1e-3) with cranial phenotypes than expected under the null (O/E = 3.39), suggesting facial and cranial phenotypes share a substantial proportion of genetic components. Adding FSTT to a SNP-only model shows a large impact in explaining facial variance. A gene ontology analysis reveals that bone morphogenesis and osteoblast differentiation likely underlie our cranial-significant findings. Overall, this study simultaneously investigates the genetic effects on both facial and cranial variation of the same sample, supporting that facial variation is a composite phenotype of cranial variation and FSTT.

In the actual operation of landing gear oil-pneumatic shock absorber, there is a complex gas-liquid two-phase flow problem inside the shock absorber. In this paper, a simplified model of a metering pin variable damping orifice shock absorber is established, and the compression process of the shock absorber is simulated numerically based on CFD method combined with dynamic mesh technology to study the gas-liquid two-phase flow characteristics and the change of oil damping force. The results show that the oil flows out of the damping orifice into the nitrogen cavity in the form of turbulent jet, and the oil and gas phases are mixed and entrained, and the presence of the metering pin significantly affects the flow pattern and flow field structure of the two phases; the damping force increases continuously during the time period of 0-0.02s, and decreases slightly in the late compression period, and the different initial inflation pressures of the nitrogen cavity have little effect on the change of damping force.

With the progress of science and technology industrial development on the use of parts performance and requirements continue to improve, difficult-to-machine materials are more and more widely used, in order to better predict the difficult-to-machine materials milling process cutting force and other parameters, the traditional equivalent chip thickness can no longer meet the calculation accuracy, so it is necessary to describe the chip thickness according to the real trajectory of the cutting edge, using the numerical solution method to find out the intersection between the tool track and the tool track. Thus, the chip geometry during milling is obtained, the instantaneous chip thickness is obtained by solving the transcendental equation, and the chip thickness model is numerically simulated, and finally the chip thickness model in this paper is compared with the traditional chip thickness model, and it is found that this model can better reflect the chip thickness in the machining process.

Facial morphology, the most conspicuous feature of human appearance, is highly heritable. Previous studies on the genetic basis of facial morphology were mainly performed in European populations. Applying a proven data-driven phenotyping and multivariate genome-wide scanning protocol to the largest collection of 3D facial images of an East Asian population to date, we identified 244 leading variants associated with normal-range facial variation, of which 130 are novel. A newly proposed polygenic shape analysis indicates that the effects of the variants on East Asian facial shape can be generalized into the European population. Based on this analysis, we further identified 13 variants mainly related to differences between European and East Asian facial shape. Natural selection analyses suggest that the difference in European and East Asian nose shape is caused by a directional selection, mainly due to a local adaptation in Europeans. Our results expand the knowledge of human facial genetics and illustrates for the first time the underlying genetic basis for facial differences across populations.

Two series of novel 4-phenoxypyridine derivatives containing imidazole-4-carboxamide and 4-methyl-5-oxo-4,5-dihydro-1,2,4-triazole-3-carboxamide moieties were synthesized and evaluated for their in vitro inhibitory activities against c-Met kinase and antiproliferative activities against MKN-45, A549 and H460 cancer cell lines. The results indicated that most of the compounds showed moderate to good antitumor activities. The most promising compound T14 (with c-Met IC

Travel route planning is an important part of traveler’s tourism planning to make the travel more healthier and to improve the tourists’ satisfying and wellbeing.With the continuous accumulation of network resources, tourists are faced with the problem of information overload when they are planning the travel route. In this work, a new method of travel path mining is proposed, which takes the topic hierarchy of scenic spots and the features of scenic spots into consideration. First of all, the travel text data is captured by the network crawler software, and after the preprocessing of the data, such as word segmenting and denoising, the standardized travel data set is formed. Then, according to the location information of hot scenic spots in the travel notes, carry out the topic stratification of the scenic spots and construct the travel path data set. Finally, the travel route is excavated by taking into account the topic hierarchy and the features of a scenic spot. The experimental results show that the proposed method can effectively extract the travel route from the mass texts of travel notes.

Two new combined technologies, reverse osmosis (RO) + electrodeionization (EDI) and reverse osmosis (RO) + ion exchange (IE), were studied for simultaneous desalination and boron removal of seawater. The results show that the RO system has excellent desalination performance under the experimental conditions, and can provide stable desalination rate for the subsequent processes of the two combined schemes. The boron concentration in water has little effect on the desalination rate of RO system, and the boron removal rate of RO system is between 47% and 50%, which provides a good early treatment for the subsequent process. The blank experiment shows that the salinity has little effect on the boron removal rate of RO-EDI equipment, and the boron removal rate is more than 93%, which has excellent and stable boron removal performance, but the water yield of RO-EDI equipment is low (less than 60%). The RO-IE system has a high water yield (> 70%), but the equipment has poor adaptability to influent boron concentration and salinity. The results of effluent water show that the boron concentration of raw water by RO-EDI combined process can be adapted to a wider range, and the influence of salinity is less. Considering comprehensively, RO-EDI process has more advantages and broad application prospects.

Multi-walled carbon nanotubes (MWNTs) were used to adsorb low concentration erythromycin from natural water. The kinetic curves and adsorption isotherms were measured and the thermodynamic parameters were calculated. The effects of pH value, ionic strength and humic acid on the adsorption process were investigated. The results showed that the adsorption of carbon nanotubes to erythromycin was rapid in the first 40 min, and reached equilibrium within 200 min. The dynamics curve conforms to the quasi-second-order dynamics model. The Freundlich model can better fit the adsorption test data. Thermodynamic parameters show that the adsorption of Erythromycin by MWNTs is a spontaneous endothermic process. The adsorption activation energy Ea shows that the adsorption between MWNTs and erythromycin is a chemisorption process, and the content of oxygen-containing functional groups on the surface of CNTs determines the equilibrium adsorption amount. Ionic strength has obvious effect on adsorption. Increasing the pH value of the solution from 5 to 9 was beneficial to increasing the adsorption capacity of erythromycin. The adsorption capacity of carbon nanotubes to erythromycin was significantly increased with appropriate humic acid.

The installation accuracy of the vertical launcher will have varying degrees of influence on the initial accuracy of the test missile launching. Therefore, it is of great practical significance to study the assembly error characteristics of the launcher. This article will perform ANSYS simulation of the vertical launcher based on the marine force environment, use the maximum entropy method to perform statistical analysis on the extracted simulation error data, and calculate and solve the probability density function and distribution curve of the error data. The analysis results provide guidance for the next step in the accuracy control research of the launcher.

With the development of tourism, the number of tourists is rapidly increasing. However, too many concentrated tourists at peak-tour have resulted in the distribution imbalance of tourists in the scenic area. Now the imbalance diminishes tourist satisfaction and destructs the ecological environment. Thus the contradictions between economic development and ecological environment protection of the scenic area are becoming increasingly acute. Tourists shunt and scheduling, which is a hot topic of tourism management, will help realize the load balance of the scenic area and ease the contradictions between economic development and environmental protection. In this paper, we build a simulation model of tourist shunt in real-time for scenic area, and search an optimal shunt scheme of various time-periods through genetic algorithm. Finally we take the scenic area of Jiuzhai Valley as an example and verify the effectiveness of the proposed scheme in this paper.

During the peak travel period, the high concentration of tourists in a short time results in great negative influence for the scenic areas. Therefore, increasing attention from both scholars and practitioners of tourism has been paid to the tourists shunt management model during peak travel period, however, the traditional shunt strategies may cause the Matthew Effect, a complex system effect. This paper introduces the Logit model on the basis of the preliminary study, to evaluate the performance of the proposed method, two evaluation indexes of tourists balanced distribution including the variance model and the Gini–Simpson are introduced. Furthermore, the paper takes Jiuzhaigou as an example and constructs the Jiuzhaigou tourist shunt simulation model based on multi-agent simulation platform of NetLogo. Numerical simulations show that the effects based on Logit model is superior to the preliminary study.