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Inversion of surface‐wave free fields is of great importance in providing seismic excitations for the finite element (FE) analysis of soil–structure interaction (SSI) systems. In the case of layered media, the propagating surface waves are characterized by their unique dispersion property, resulting in dispersive multi‐mode surface wavefields. Available studies primarily focus on only the fundamental mode and neglect the influence of higher modes, which fail to identify the real structural response characteristics. To enhance the precision of seismic excitations for SSI systems situated in layered media, a multi‐mode inversion method for dispersive surface‐wave free fields is proposed in this study. The proposed method employs the energy flow density to characterize the participation volume of dispersive modes, and further calculates corresponding modal participation factors by combining with the dispersion curve and dynamic‐stiffness matrix (DSM) method. The modal participation factors are then utilized to assign the ground surface components of each mode and invert respective single‐mode surface‐wave free fields. Based on the mode superposition theory, all the single‐mode surface‐wave free fields are finally superimposed to construct the multi‐mode wavefields. The accuracy of the proposed method is thoroughly verified and validated across various types of layered media. Furthermore, the engineering application of the proposed method is illustrated by performing the FE analysis on an SSI example. The analysis results highlight the significant participation of surface waves' higher modes in structural seismic responses, particularly in the weak‐interbed type media. [ABSTRACT FROM AUTHOR]

Robustness is one of the most critical characteristics of complex networks, determining their stability and resilience in the face of various disturbances. With the research on complex networks, people have observed that higher-order networks can also affect the structure and function of the network. Therefore, when studying network robustness, we also need to consider the interaction between higher-order networks and low-order networks. To delve deeper into the investigation and comprehension of robustness in complex networks, this paper introduces a model of higher-low order networks with adjustable coupling strength. In this model, network nodes are categorized into autonomous and nonautonomous nodes, with coupling strength adjusted using three methods: random coupling, degree-related coupling, and local coupling. Validation is performed on ER networks, BA networks, SW networks, as well as several real-world networks. The results indicate that as coupling strength increases, network robustness correspondingly diminishes, and the network exhibits intricate percolation behavior. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non‐local (NL) block, which has the ability to efficiently capture long‐range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi‐scale context integration and long‐range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U‐Net, an effective crack segmentation model called CrackResU‐Net was developed. The test results on the existing CrackForest dataset showed that CrackResU‐Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state‐of‐the‐art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high‐precision recognition of pavement cracks for engineering purposes. [ABSTRACT FROM AUTHOR]

Stationary symmetrical fish keratocyte cells break symmetry and become motile spontaneously but slowly. We found that applying electric field (EF) accelerates the polarization by an order of magnitude. While spontaneously polarized cells move persistently for hours, the EF-induced polarity is lost in a majority of cells when the EF is switched off. However, if the EF is applied for a long time and then switched off, the majority of cell move stably. Myosin inhibition abolishes spontaneous polarization, but does not slow down EF-induced polarization, and after the EF is turned off, motility does not stop; however, the cell movements are erratic. Our results suggest that the EF rapidly polarizes the cells, but that resulting polarization becomes stable slowly, and that the EF bypasses the requirement for myosin action in motility initiation.

Objectives To explore factors affecting the efficacy of Bernese periacetabular osteotomy for the treatment of hip dysplasia. Methods A retrospective study was conducted on 44 patients with hip dysplasia who underwent Bernese periacetabular osteotomy with a modified Smith‐Peterson approach between January 2017 and November 2019. Among them, 40 were women and four were men. The average age was 31.2 ± 9.4. Preoperative and postoperative imaging parameters were measured. The acetabular top tilt angle, lateral central edge angle, acetabular abduction angle, femoral head extrusion index, sphericity index of femoral head, Shenton line, Tonnis grade of osteoarthritis, joint congruency, p/a ratio, acetabular anteversion angle, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale scores, and modified Harris hip score (MHHS) were observed. MHHS were divided into three clinically relevant categories: poor (

Proteomics can aid in the identification of molecular subtypes in cancers. Here, the authors perform proteomic profiling of 124 paired oesophageal cancer and adjacent non-tumour tissues and identify two subtypes that are associated with patient survival for therapeutic targeting.

Introduction: With rapid increase in the aging population, falls injuries have become an important public health problem. However, limited data have been reported on the associations between meteorological factors and falls injuries in the elderly. This study assessed the epidemiology of falls injuries and explored this association in the elderly in Guangzhou, China. Methods: Data on elderly falls injury cases and meteorological variables from 2014 to 2018 in Guangzhou were collected from the Guangzhou Injury Monitoring System and Guangzhou Meteorological Bureau, respectively. The monthly average data on falls injuries and meteorological factors were applied to the data analysis. These correlations were conducted using Pearson correlation analysis. A multiple linear regression model was used to estimate the effects of meteorological factors on falls injuries in the elderly in Guangzhou, China. Results: Accounting for 49.41% of causes of elderly injury were falls in the Guangzhou Injury Monitoring System from 2014 to 2018, which occupied first place for five consecutive years. The monthly number of elderly falls injury cases was lowest in April and highest in December, and had a positive correlation with monthly mean wind speed (r = 0.187, P < 0.01) and a negative correlation with monthly atmospheric pressure (r = -0.142, P < 0.05). A multiple linear regression model was constructed (F = 10.176, P < 0.01), which explained 23.7% of the variances (R2 = 0.237). Monthly mean wind speed (β = 76.85, P < 0.01) and monthly mean atmospheric pressure (β = −3.162, P < 0.01) were independent factors affecting monthly elderly falls injuries. Conclusions: Falls are the primary cause of injury among elderly people in Guangzhou, China. Meteorological factors are related to falls injuries in the elderly population. Decreasing activity during high wind and low atmospheric pressure weather may help reduce the number of elderly falls injury cases.