LEACS: a learnable and efficient active contour model with space-frequency pooling for medical image segmentation.

Diseases can be diagnosed and monitored by extracting regions of interest (ROIs) from medical images. However, accurate and efficient delineation and segmentation of ROIs in medical images remain challenging due to unrefined boundaries, inhomogeneous intensity and limited image acquisition. To overcome these problems, we propose an end-to-end learnable and efficient active contour segmentation model, which integrates a global convex segmentation (GCS) module into a light-weighted encoder-decoder convolutional segmentation network with a multiscale attention module (ED-MSA). The GCS automatically obtains the initialization and corresponding parameters of the curve deformation according to the prediction map generated by the ED-MSA, while provides the refined object boundary prediction for ED-MSA optimization. To provide precise and reliable initial contour for the GCS, we design the space-frequency pooling operation layers in the encoder stage of ED-MSA, which can effectively reduce the number of iterations of the GCS. Beside, we construct ED-MSA using the depth-wise separable convolutional residual module to mitigate the overfitting of the model. The effectiveness of our method is validated on four challenging medical image datasets. Code is here:https://github.com/Yang-fashion/ED-MSA_GCS.

Surface Crack Detection in Precasted Slab Track in High-Speed Rail via Infrared Thermography.

The surface crack of ballastless track slab can seriously reduce the serviceability and durability of high-speed railway (HSR). Aiming at accurately and efficiently detecting the slab cracks, this research proposes an infrared thermography (IRT)-based method for the surface crack, which is the most serious and common crack type in track slab. A three dimensional finite element (FE) model of IRT detection of concrete slab with surface cracks is established. The relation between the width of detectable cracks and the ambient temperature can be thereby obtained by inputting the measured thermodynamic parameters in the model. Parametric study shows that with ambient temperature higher than 15 °C, cracks with a width of no less than 0.2 mm can be well detected. Scale model test and field test are conducted, IRT method can effectively locate the slab surface cracks with width as small as 0.14 mm when ambient temperature is no less than 20 °C.

Output-Only Damage Detection of Shear Building Structures Using an Autoregressive Model-Enhanced Optimal Subpattern Assignment Metric.

This paper proposes a novel output-only structural damage indicator by incorporating the pole-based optimal subpattern assignment distance with autoregressive models to localize and relatively assess the severity of damages for sheared structures. Autoregressive models can model dynamic systems well, while their model poles can represent the state of the dynamic systems. Structural damage generally causes changes in the dynamic characteristics (especially the natural frequency, mode shapes and damping ratio) of structures. Since the poles of the autoregressive models can solve the modal parameters of the structure, the poles have a close relationship with the modal parameters so that the changes in the poles of its autoregressive model reflect structural damages. Therefore, we can identify the damage by tracking the shifts in the dynamic system poles. The optimal subpattern assignment distance, which is the performance evaluator in multi-target tracking algorithms to measure the metric between true and estimated tracks, enables the construction of damage sensitive indicator from system poles using the Hungarian algorithm. The proposed approach has been validated with a five-story shear-building using numerical simulations and experimental verifications, which are subjected to excitations of white noise, El Centro earthquake and sinusoidal wave with frequencies sweeping, respectively; the results indicate that this approach can localize and quantify structural damages effectively in an output-only and data-driven way.

Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings.

The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies.

Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China: A nested case-control study.

OBJECTIVE: We aimed to prospectively evaluate the association of oral microbiome with malignant esophageal lesions and its predictive potential as a biomarker of risk. METHODS: We conducted a case-control study nested within a population-based cohort with up to 8 visits of oral swab collection for each subject over an 11-year period in a high-risk area for esophageal cancer in China. The oral microbiome was evaluated with 16S ribosomal RNA (rRNA) gene sequencing in 428 pre-diagnostic oral specimens from 84 cases with esophageal lesions of severe squamous dysplasia and above (SDA) and 168 matched healthy controls. DESeq analysis was performed to identify taxa of differential abundance. Differential oral species together with subject characteristics were evaluated for their potential in predicting SDA risk by constructing conditional logistic regression models. RESULTS: A total of 125 taxa including 37 named species showed significantly different abundance between SDA cases and controls (all P<0.05 & false discovery rate-adjusted Q<0.10). A multivariate logistic model including 11 SDA lesion-related species and family history of esophageal cancer provided an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI, 0.84-0.93). Cross-validation and sensitivity analysis, excluding cases diagnosed within 1 year of collection of the baseline specimen and their matched controls, or restriction to screen-endoscopic-detected or clinically diagnosed case-control triads, or using only bacterial data measured at the baseline, yielded AUCs>0.84. CONCLUSIONS: The oral microbiome may play an etiological and predictive role in esophageal cancer, and it holds promise as a non-invasive early warning biomarker for risk stratification for esophageal cancer screening programs.

Mono-Component Feature Extraction for Condition Assessment in Civil Structures Using Empirical Wavelet Transform.

This paper proposes a methodology to process and interpret the complex signals acquired from the health monitoring of civil structures via scale-space empirical wavelet transform (EWT). The FREEVIB method, a widely used instantaneous modal parameters identification method, determines the structural characteristics from the individual components separated by EWT first. The scale-space EWT turns the detecting of the frequency boundaries into the scale-space representation of the Fourier spectrum. As well, to find meaningful modes becomes a clustering problem on the length of minima scale-space curves. The Otsu's algorithm is employed to determine the threshold for the clustering analysis. To retain the time-varying features, the EWT-extracted mono-components are analyzed by the FREEVIB method to obtain the instantaneous modal parameters and the linearity characteristics of the structures. Both simulated and real SHM signals from civil structures are used to validate the effectiveness of the present method. The results demonstrate that the proposed methodology is capable of separating the signal components, even those closely spaced ones in frequency domain, with high accuracy, and extracting the structural features reliably.

Experimental and Numerical Investigation on the Ultimate Vertical Bearing Capacity of U-Shaped Girder with Damaged Web.

U-shaped girder has been extensively used for its excellent adaptability in the urban railway transit system. As an open thin-walled structure, significant difference of working mechanism exists between U-shaped girder and conventional section girder (e.g., T section or box section). The thin-walled web plays significant role in the flexural performance of U type girder particularly. Moreover, severe collision may occur between the moving train and the girder, and subsequently results in the decrease of the structural bearing capacity. In this paper, a full-scale test was carried out to examine the ultimate bearing capacity and the failure mechanism of the U-shaped girder, and a refined numerical model was developed to simulate the damage evolution and the failure process. It was shown that the flexural failure occurred on the U-shaped girder under vertical loads. In addition, the ultimate bearing capacity of the structure under different web damage conditions (e.g., web damaged region or damaged range) was studied by applying the displacement based lateral load on the flange of the U-shaped girder to simulate the damage caused by accidental train collision. The numerical results have shown that the damaged web greatly affects the ultimate bearing capacity of U-shaped girder, more severe bearing capacity descending occurs around the middle span rather than the beam ends. The damaged range (length) of the web has less influence on the falling amplitude of bearing capacity. It can be concluded that the major reason accounting for the bearing capacity decrease is that the original section is weakened by the web damage, and consequently results in the buckling of the damaged web and lead to the total failure of the structure. It is recommended that the lateral resistant design for the web should be taken into consideration to ensure the operation safety of the urban railway transportation.

Vibration Mitigation Effect Investigation of a New Slab Track Plate Design.

This study proposed a novel vibration mitigation slab track plate design to mitigate the vibration induced in urban rail transit operations. The optimal recipe for the newly designed slab track plate is obtained by a series of laboratory tests, and both newly designed vibration mitigation slab track plates and normal slab track plates are fabricated and hereinafter tested. The newly designed slab track plate was examined with a series of laboratory tests in comparison with the normal slab track plate. The PolyMAX method is then adopted for extracting the modal properties, including resonant frequencies, and damping ratios are also determined for both the designed slab and normal slab track plates. A vibration mitigation level (Ls) is defined to address the vibration mitigation performance taking the acceleration response of the normal slab track plate as reference. The newly designed slab track plate demonstrated better dynamic and damping characteristics in comparison with the normal slab track plate. Under the same excitation force, the newly designed slab track plate can mitigate 8.9 dB on average in the frequency range [20, 400] Hz, expressing the feasibility of effective vibration mitigation capacity.

Degradation of the In-plane Shear Modulus of Structural BFRP Laminates Due to High Temperature.

The behavior of fiber reinforced polymer (FRP) composites at high temperature is a critical issue that needs to be clearly understood for their structural uses in civil engineering. However, due to technical difficulties during testing at high temperature, limited experimental investigations have been conducted regarding the thermal behavior of basalt fiber reinforced polymer (BFRP) composites, especially for the in-plane shear modulus of BFRP laminates. To this end, both an analytical derivation and an experimental program were carried out in this work to study the in-plane shear modulus of BFRP laminates. After the analytical derivation, the in-plane shear modulus was investigated as a function of the elastic modulus in different directions (0°, 45° and 90° of the load-to-fiber angle) and Poisson's ratio in the fiber direction. To obtain the in-plane shear modulus, the four parameters were tested at different temperatures from 20 to 250 °C. A novel non-contacting digital image correlation (DIC) sensing system was adopted in the high-temperature tests to measure the local strain field on the FRP samples. Based on the test results, it was found that the elastic moduli in different directions were reduced to a very low level (less than 20%) from 20 to 250 °C. Furthermore, the in-plane shear modulus of BFRP at 250 °C was only 3% of that at 20 °C.

Dynamic Model Updating for Bridge Structures Using the Kriging Model and PSO Algorithm Ensemble with Higher Vibration Modes.

This study applied the kriging model and particle swarm optimization (PSO) algorithm for the dynamic model updating of bridge structures using the higher vibration modes under large-amplitude initial conditions. After addressing the higher mode identification theory using time-domain operational modal analysis, the kriging model is then established based on Latin hypercube sampling and regression analysis. The kriging model performs as a surrogate model for a complex finite element model in order to predict analytical responses. An objective function is established to express the relative difference between analytically predicted responses and experimentally measured ones, and the initial finite element (FE) model is hereinafter updated using the PSO algorithm. The Jalón viaduct—a concrete continuous railway bridge—is applied to verify the proposed approach. The results show that the kriging model can accurately predict the responses and reduce computational time as well.

Output-Based Structural Damage Detection by Using Correlation Analysis Together with Transmissibility.

Output-based structural damage detection is becoming increasingly appealing due to its potential in real engineering applications without any restriction regarding excitation measurements. A new transmissibility-based damage detection approach is presented in this study by combining transmissibility with correlation analysis in order to strengthen its performance in discriminating damaged from undamaged scenarios. From this perspective, damage detection strategies are hereafter established by constructing damage-sensitive indicators from a derived transmissibility. A cantilever beam is numerically analyzed to verify the feasibility of the proposed damage detection procedure, and an ASCE (American Society of Civil Engineers) benchmark is henceforth used in the validation for its application in engineering structures. The results of both studies reveal a good performance of the proposed methodology in identifying damaged states from intact states. The comparison between the proposed indicator and the existing indicator also affirms its applicability in damage detection, which might be adopted in further structural health monitoring systems as a discrimination criterion. This study contributed an alternative criterion for transmissibility-based damage detection in addition to the conventional ones.