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Micro-disturbance grouting is a recovery technique to reduce the excessive deformation of operational shield tunnels in urban areas. The grout mass behaves as a fluid in the ground before hardening to form a grout–soil mixture, which highlights the necessity of using fluid–solid coupling method in the simulation of grouting process. Within a discrete element modeling environment, this paper proposes a novel fluid-solid coupling method based on the pore density flow calculation. To demonstrate the effectiveness of this method, it is applied to numerical simulation of micro-disturbance grouting process for treatment of large transverse deformation of a shield tunnel in Shanghai Metro, China. The simulation results reveal the mechanism of recovering tunnel convergence by micro-disturbance grouting in terms of compaction and fracture of soil, energy analysis during grouting, and mechanical response of soil-tunnel interaction system. Furthermore, the influence of the three main grouting parameters (i.e., grouting pressure, grouting distance, and grouting height) on tunnel deformation recovery efficiency is evaluated through parametric analysis. In order to efficiently recover large transverse deformation of shield tunnel in Shanghai Metro, it is suggested that the grouting pressure should be about 0.55 MPa, the grouting height should be in the range of 6.2–7.0 m, and the grouting distance should be in the range of 3.0–3.6 m. The results provide a valuable reference for grouting treatment projects of over-deformed shield tunnel in soft soil areas.

Abstract Covalent organic frameworks have emerged as a thriving family in the realm of photocatalysis recently, yet with concerns about their high exciton dissociation energy and sluggish charge transfer. Herein, a strategy to enhance the built-in electric field of series β-keto-enamine-based covalent organic frameworks by ionic polarization method is proposed. The ionic polarization is achieved through a distinctive post-synthetic quaternization reaction which can endow the covalent organic frameworks with separated charge centers comprising cationic skeleton and iodide counter-anions. The stronger built-in electric field generated between their cationic framework and iodide anions promotes charge transfer and exciton dissociation efficiency. Moreover, the introduced iodide anions not only serve as reaction centers with lowered H* formation energy barrier, but also act as electron extractant suppressing the recombination of electron-hole pairs. Therefore, the photocatalytic performance of the covalent organic frameworks shows notable improvement, among which the CH3I-TpPa-1 can deliver an high H2 production rate up to 9.21 mmol g−1 h−1 without any co-catalysts, representing a 42-fold increase compared to TpPa-1, being comparable to or possibly exceeding the current covalent organic framework photocatalysts with the addition of Pt co-catalysts.

This study investigates the ground and structural response of adjacent raft foundations induced by large-scale surcharge by ore in soft soil areas through a 130g centrifuge modeling test with an innovative layered loading device. The prototype of the test is a coastal iron ore yard with a natural foundation of deep soft soil. Therefore, it is necessary to adopt some measures to reduce the influence of the large-scale surcharge on the adjacent raft foundation, such as installing stone columns for foundation treatment. Under an acceleration of 130 g, the model conducts similar simulations of iron ore, stone columns, and raft foundation structures. The tested soil mass has dimensions of 900 mm × 700 mm × 300 mm (length × width × depth), which is remodeled from the soil extracted from the drilling holes. The test conditions are consistent with the actual engineering conditions and the effects of four-level loading conditions on the composite foundation of stone columns, unreinforced zone, and raft foundations are studied. An automatic layer-by-layer loading device was innovatively developed to simulate the loading process of actual engineering more realistically. The composite foundation of stone columns had a large settlement after the loading, forming an obvious settlement trough and causing the surface of the unreinforced zone to rise. The 12 m surcharge loading causes a horizontal displacement of 13.19 cm and a vertical settlement of 1.37 m in the raft foundation. The stone columns located on both sides of the unreinforced zone suffered significant shear damage at the sand-mud interface. Due to the reinforcement effect of stone columns, the sand layer below the top of the stone columns moves less. Meanwhile, the horizontal earth pressure in the raft foundation zone increases slowly. The stone columns will form new drainage channels and accelerate the dissipation of excess pore pressure.

Face stability is an essential issue in tunnel design and construction. Layered rock masses are typical and ubiquitous; uncertainties in rock properties always exist. In view of this, a comprehensive method, which combines the Upper bound Limit analysis of Tunnel face stability, the Polynomial Chaos Kriging, the Monte-Carlo Simulation and Analysis of Covariance method (ULT-PCK-MA), is proposed to investigate the seismic stability of tunnel faces. A two-dimensional analytical model of ULT is developed to evaluate the virtual support force based on the upper bound limit analysis. An efficient probabilistic analysis method PCK-MA based on the adaptive Polynomial Chaos Kriging metamodel is then implemented to investigate the parameter uncertainty effects. Ten input parameters, including geological strength indices, uniaxial compressive strengths and constants for three rock formations, and the horizontal seismic coefficients, are treated as random variables. The effects of these parameter uncertainties on the failure probability and sensitivity indices are discussed. In addition, the effects of weak layer position, the middle layer thickness and quality, the tunnel diameter, the parameters correlation, and the seismic loadings are investigated, respectively. The results show that the layer distributions significantly influence the tunnel face probabilistic stability, particularly when the weak rock is present in the bottom layer. The efficiency of the proposed ULT-PCK-MA is validated, which is expected to facilitate the engineering design and construction.

Abstract Osteoporosis (OP) is a bone metabolism disease that is associated with inflammatory pathological mechanism. Nonetheless, rare studies have investigated the diagnostic effectiveness of immune-inflammation index in the male population. Therefore, it is interesting to achieve early diagnosis of OP in male population based on the inflammatory makers from blood routine examination. We developed a prediction model based on a training dataset of 826 Chinese male patients through a retrospective study, and the data was collected from January 2022 to May 2023. All participants underwent the dual-energy X-ray absorptiometry (DXEA) and blood routine examination. Inflammatory markers such as systemic immune-inflammation index (SII) and platelet-to-lymphocyte ratio (PLR) was calculated and recorded. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection. Multivariable logistic regression analysis was applied to construct a predicting model incorporating the feature selected in the LASSO model. This predictive model was displayed as a nomogram. Receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance. Internal validation was test by the bootstrapping method. This study was approved by the Ethic Committee of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (Ethic No. JY2023012) and conducted in accordance with the relevant guidelines and regulations. The predictive factors included in the prediction model were age, BMI, cardiovascular diseases, cerebrovascular diseases, neuropathy, thyroid diseases, fracture history, SII, PLR, C-reactive protein (CRP). The model displayed well discrimination with a C-index of 0.822 (95% confidence interval: 0.798–0.846) and good calibration. Internal validation showed a high C-index value of 0.805. Decision curve analysis (DCA) showed that when the threshold probability was between 3 and 76%, the nomogram had a good clinical value. This nomogram can effectively predict the incidence of OP in male population based on SII and PLR, which would help clinicians rapidly and conveniently diagnose OP with men in the future.

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces. In tunneling practice, the rock mass quality is often assessed via a combination of qualitative and quantitative parameters. However, due to the harsh on-site construction conditions, it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction. In this study, a novel improved Swin Transformer is proposed to detect, segment, and quantify rock mass characteristic parameters such as water leakage, fractures, weak interlayers. The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%, 81%, and 86% for water leakage, fractures, and weak interlayers, respectively. A multi-source rock tunnel face characteristic (RTFC) dataset includes 11 parameters for predicting rock mass quality is established. Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset, a novel tree-augmented naive Bayesian network (BN) is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%. In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset. By utilizing the established BN, a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters, results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Abstract Single-atom catalysts show excellent catalytic performance because of their coordination environments and electronic configurations. However, controllable regulation of single-atom permutations still faces challenges. Herein, we demonstrate that a polarization electric field regulates single atom permutations and forms periodic one-dimensional Au single-atom arrays on ferroelectric Bi4Ti3O12 nanosheets. The Au single-atom arrays greatly lower the Gibbs free energy for CO2 conversion via Au-O=C=O-Au dual-site adsorption compared to that for Au-O=C=O single-site adsorption on Au isolated single atoms. Additionally, the Au single-atom arrays suppress the depolarization of Bi4Ti3O12, so it maintains a stronger driving force for separation and transfer of photogenerated charges. Thus, Bi4Ti3O12 with Au single-atom arrays exhibit an efficient CO production rate of 34.15 µmol·g−1·h−1, ∼18 times higher than that of pristine Bi4Ti3O12. More importantly, the polarization electric field proves to be a general tactic for the syntheses of one-dimensional Pt, Ag, Fe, Co and Ni single-atom arrays on the Bi4Ti3O12 surface.

Abstract Background Kidney clear cell carcinoma (KIRC) is the most common subtype of renal cell carcinoma. Peroxisomes play a role in the regulation of tumorigenesis and cancer progression, yet the prognostic significance of peroxisome-related genes (PRGs) remains rarely studied. The study aimed to establish a novel prognostic risk model and identify potential biomarkers in KIRC. Methods The significant prognostic PRGs were screened through differential and Cox regression analyses, and LASSO Cox regression analysis was performed to establish a prognostic risk model in the training cohort, which was validated internally in the testing and entire cohorts, and further assessed in the GSE22541 cohort. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function and pathway differences between the high-risk and low-risk groups. The relationship between risk score and immune cell infiltration levels was evaluated in the CIBERSORT, ESTIMATE and TIMER databases. Finally, potential biomarkers were identified and validated from model genes, using immunohistochemistry. Results Fourteen significant prognostic PRGs were identified using multiple analyses, and 9 genes (ABCD1, ACAD11, ACAT1, AGXT, DAO, EPHX2, FNDC5, HAO1, and HNGCLL1) were obtained to establish a prognostic model via LASSO Cox regression analysis. Combining the risk score with clinical factors to construct a nomogram, which provided support for personalized treatment protocols for KIRC patients. GO and KEGG analyses highlighted associations with substance metabolism, transport, and the PPAR signaling pathways. Tumor immune infiltration indicated immune suppression in the high-risk group, accompanied by higher tumor purity and the expression of 9 model genes was positively correlated with the level of immune cell infiltration. ACAT1 has superior prognostic capabilities in predicting the outcomes of KIRC patients. Conclusions The peroxisome-related prognostic risk model could better predict prognosis in KIRC patients.

Powered by optical energy, photocatalytic reduction for fuel production promises to be an ideal long-term solution to a number of key energy challenges. Photocatalysts with enhanced light absorption, fast electron/hole separation rates, and exposed activity sites are essential to improve photocatalytic efficiency. Semiconductors are constrained by their own intrinsic properties and have limited performance in photocatalysis, but defect engineering provides an opportunity to modulate the physical and chemical properties of semiconductors. Defect engineering has been shown to be effective in regulating electron distribution and accelerating photocatalytic kinetics during photocatalysis. This review introduces the definition and categorization of defects, then explains the main effects of defect engineering on photoabsorption, carrier separation/migration, and surface reduction reactions. We then review the milestones in the design of defect-engineered photocatalysts for key chemical reactions, including hydrogen evolution, CO2 reduction, and N2 reduction, and tabulate their respective effects on catalytic performance. Finally, we provide insights and perspectives on the challenges and potential of defect engineering for photoreduction reactions.

This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.

Abstract The recent outbreaks of mpox have raised concerns over the need for effective vaccines. However, the current approved vaccines have either been associated with safety concerns or are in limited supply. mRNA vaccines, which have shown high efficacy and safety against SARS-CoV-2 infection, are a promising alternative. In this study, three mRNA vaccines are developed that encode monkeypox virus (MPXV) proteins A35R and M1R, including A35R extracellular domain -M1R fusions (VGPox 1 and VGPox 2) and a mixture of encapsulated full-length mRNAs for A35R and M1R (VGPox 3). All three vaccines induce early anti-A35R antibodies in female Balb/c mice, but only VGPox 1 and 2 generate detectable levels of anti-M1R antibodies at day 7 after vaccination. However, all three mRNA vaccine groups completely protect mice from a lethal dose of vaccinia virus (VACV) challenge. A single dose of VGPox 1, 2, and 3 provide protection against the lethal viral challenge within 7 days post-vaccination. Long-term immunity and protection were also observed in all three candidates. Additionally, VGPox 2 provided better passive protection. These results suggest that the VGPox series vaccines enhance immunogenicity and can be a viable alternative to current whole-virus vaccines to defend against mpox.

Abstract The city development is closely related to the performance of the transportation network system. Bridges and roads are important parts of the transportation system, and are also inseparable components of the transportation network. However, the effect of the correlation between bridges and roads on the network system has not been studies thoroughly in the literature. Therefore, it is necessary to analyse the vulnerability of the road network when both bridges and roads are involved. In this paper, the urban road network is modeled into the form of network connection and node, based on the analysis of the related research results of road network vulnerability in the literature. Taking the urban roads at all levels as the connection and the transportation hubs (including bridges) as the nodes, the paper puts forward the corresponding measurement indexes and calculation methods, and establishes the importance and correlation analysis model of roads and bridges in the urban road network. At last, the model is applied to the road network which is 5 × 3 km2 besides Yangpu Bridge of Shanghai for verification, the importance and correlation of specific roads and bridges in the analyzed urban road network are calculated, which provides a certain basis for dealing with various emergencies leading to the decline of urban road network vulnerability. In this paper, the importance analysis of urban road network is extended to the bridge correlation analysis, so that the proposed model of the vulnerability assessment of the urban road network system is more suitable for the increasingly demand of road and bridge construction in China, and provides a certain basis for dealing with the decline of road network vulnerability caused by various emergencies.

The Boltzmann–Gibbs–Shannon (BGS) entropy is the only entropy form satisfying four conditions known as Khinchin’s axioms. The uniqueness theorem of the BGS entropy, plus the fact that Shannon’s mutual information completely characterizes independence between the two underlying random elements, puts the BGS entropy in a special place in many fields of study. In this article, the fourth axiom is replaced by a slightly weakened condition: an entropy whose associated mutual information is zero if and only if the two underlying random elements are independent. Under the weaker fourth axiom, other forms of entropy are sought by way of escort transformations. Two main results are reported in this article. First, there are many entropies other than the BGS entropy satisfying the weaker condition, yet retaining all the desirable utilities of the BGS entropy. Second, by way of escort transformations, the newly identified entropies are the only ones satisfying the weaker axioms.

This study aimed to evaluate the therapeutic efficacy and effect of blood purification (BP) therapy on severe acute pancreatitis (SAP). Information on 305 patients (BP group 68, control group 237) diagnosed with SAP was retrieved from the Medical Information Mart for Intensive Care IV (MIMIC IV) database. Firstly, the influence of BP treatment was preliminarily evaluated by comparing the outcome indicators of the two groups. Secondly, multiple regression analysis was used to screen the mortality risk factors to verify the impact of BP on the survival outcome of patients. Then, the effect of BP treatment was re-validated with baseline data. Finally, cox regression was used to make the survival curve after matching to confirm whether BP could affect the death outcome. The results indicated that the BP group had a lower incidence of shock (p = 0.012), but a higher incidence of acute kidney injury (AKI) (p < 0.001), with no differences observed in other outcome indicators when compared to the control group. It was also found that the 28-day survival curve of patients between the two groups was significantly overlapped (p = 0.133), indicating that BP treatment had no significant effect on the survival outcome of patients with SAP. Although BP is beneficial in stabilizing hemodynamics, it has no effect on short- and long-term mortality of patients. The application of this technology in the treatment of SAP should be done with caution until appropriate BP treatment methods are developed, particularly for patients who are not able to adapt to renal replacement therapy.

It is essential to cast light on the construction risks in tunnel excavations through the fault-fracture zone (FFZ). This study adopts the material point method (MPM) to simulate the failure responses of a rock tunnel face during excavation through the FFZ. A numerical study was conducted to compare a physical model test and validate the feasibility of using the MPM in simulating tunnel face failure. One hundred ninety numerical simulation cases were constructed to represent a rock tunnel excavation project with different site configurations. The simulation results suggest that the cohesion and the friction angle significantly influence failure responses. The tunnel cover depth can magnify the failure responses, and the FFZ thickness significantly affects the mobilized rock mass volume when the FFZ consists of a weak rock mass. The numerical simulation results suggest three deformation patterns: face bulge, partial failure, and slide collapse. The failure responses can be characterized by stress arch, slip surface, angle of reposing, and influence range. The insights suggested by the face failure responses during excavation through the FFZ can aid field engineers in determining the scope of possible damage, and in establishing emergency measures to minimize losses if such failure occurs.

Tunnel horizontal convergence monitoring is essential to ensure the operation safety. However, only a few representative tunnel sections are chosen for monitoring due to the cost limitation. It is difficult to capture the horizontal convergence of each tunnel ring with limited measurements. Confronted with this difficulty, the paper proposes a horizontal convergence reconstruction method based on the measurements of deployed sensors. The tunnel horizontal convergence along the longitudinal direction is seen as a one-dimensional stationary and ergodic random field. The reconstruction problem is then transformed into the generation of conditional random fields. Monte Carlo simulation is adopted to generate possible realizations and the mean of realizations is considered as the maximum likelihood reconstruction. Error analysis proves the effectiveness of the proposed reconstruction method. The proposed method is proved to be applicable in reconstructing the time-variant horizontal convergence and is verified by the monitoring results of the shield tunnel of Shanghai Metro Line 2. The effect of sensor numbers is parametrically studied, and an optimal sensor placement scheme is decided. Additional sensors placed at the deformation drastically changed location can significantly improve the performance of the proposed method.

Highlights It is the first attempt to combine covalent organic frameworks with hexagonal boron nitride (h-BN) to construct efficient metal-free photocatalyst. The composite displays superior photocatalytic hydrogen production performance in metal-free systems. The integrated porous h-BN can suppress electron backflow to optimize the composite photocatalytic activity.

Abstract Background Sexual violence is far more prevalent in most societies than is usually suspected in daily life. However, no study has systematically summarized the global prevalence rate and the major outcomes of sexual violence against women. Methods We directed a wide-raging search in the PubMed, Embase, and Web of Science, catalogs since the beginning to December 2022 for relevant reports about the incidence of sexual fighting touching females. The occurrence frequency was assessed with a random-effects model. The heterogeneity was estimated with I 2 values. Differences by research features were assessed over subgroup evaluation and meta-regression. Results A total of 32 cross-sectional studies were included (a total of 19,125 participants). The pooled sexual violence rate was 0.29 (95% CI = 0.25–0.34). Subgroup analyses found that there was a higher rate of sexual violence against women in 2010–2019 period (0.33, 95% CI = 0.27–0.37), developing countries (0.32, 95% CI = 0.28–0.37), and interview (0.39, 95% CI = 0.29–0.49). The analysis found that more than half of women (0.56, 95% CI = 0.37–0.75) had post-traumatic stress disorder (PTSD) after experiencing sexual violence, and only a third of women considered seeking support (0.34, 95% CI = 0.13–0.55). Conclusions Nearly one out of every three (29%) women around the world has been a victim of sexual violence in their life. This current study investigated the status and characteristics of sexual violence against women, which could provide an important reference for police and emergency health services management.

A novel, highly efficient terahertz fully polarized transmission line is designed by two-dimensional tellurium photonic crystals consisting of square lattice rod arrays with a complete photonic bandgap. The TE and TM photonic bandgaps of the tellurium photonic crystals, which are computed by plane wave expansion, happen to coincide, and the complete photonic bandgap covers from 2.894 to 3.025 THz. The function of the designed waveguide is simulated by the finite element method, and the transmission characteristics are optimized by accurately adjusting its structural parameters. The transmission efficiency of the waveguide for TE mode achieves a peak value of −0.34 dB at a central frequency of 2.950 THz and keeps above −3 dB from 2.82 THz to 3.02 THz, obtaining a broad relative bandwidth of about 6.84 percent. The operating bandwidth of the tellurium photonic crystals’ waveguide for TM mode is narrower than that of TE mode, whose relative bandwidth is about 4.39 percent or around 2.936 THz above −5 dB. The designed terahertz photonic crystals’ waveguide can transmit both TE and TM waves, and not only can it be used as a high-efficiency transmission line, but it also provides a promising approach for implementing fully polarized THz devices for future 6G communication systems.

Study region: The Yulin Region in the Northern China. Study focus: There is an urgent need to balance supply and demand in respond to water shortages in arid and semi-arid areas. A new water-adaptable allocation framework is used to simulate different allocation scenarios. Firstly, the supply-demand was analyzed to determine whether an imbalance existed. Then, the structure of grain and energy of Yulin was optimized. In order to produce new water-adaptable boundary of industry demand. Finally, the allocation of water resources was optimized based on the target of minimizing the water shortage rate. New hydrological insights for the region: The new scheme results highlight that by 2025, it is predicted that in a BAU (business as usual) scenario, there will be an imbalance between the water supply and water demand in Yulin Region. Following the optimization of the grain and energy industrial structure (GES), and the WAS (water allocation and simulation) model used to create a water resources allocation scheme and tested its applicability using two rainfall guarantee rates scenarios, it was determined that the total water demand established for the both scenarios decreased, and the economic benefit increased by 12.43 %. Also, 87.41∼99.93 % of potable water is satisfied to the initial water needs.

Abstract Owing to the need for regenerant and self‐reduction problem, the hydrogen performance of sub‐nano‐sized trinuclear iron‐oxo complexes is still far from satisfied with affordability and practicality. Herein, two binary photocatalytic systems based on trinuclear metal‐oxo complexes have been first constructed and experimentally confirmed to be competent for seawater hydrogen evolution (715.4 and 271.9 μmol of hydrogen can be found, respectively, after 48 h). Notably, chloride ions act as the hole catcher and move into the gas phase in the stable form of chlorine. Similar to heterogeneous structures, homogeneous systems not only enhance the hydrogen performance while ensuring the stability of metal‐oxo complexes, but also shorten the consumption of photogenerated carriers by dissolved impurities in the seawater. This new attempt of building pluralistic sub‐nanometric systems may offer novel design strategies with noble‐metal‐free catalysts and low‐cost candidates for traditional semiconductor materials in enhancing photocatalytic efficiency and performing chlorine evolution from seawater splitting.

An analysis of tunnel face stability generally assumes a single homogeneous rock mass. However, most rock tunnel projects are excavated in stratified rock masses. This paper presents a two-dimensional (2D) analytical model for estimating the face stability of a rock tunnel in the presence of rock mass stratification. The model uses the kinematical limit analysis approach combined with the block calculation technique. A virtual support force is applied to the tunnel face, and then solved using an optimization method based on the upper limit theorem of limit analysis and the nonlinear Hoek–Brown yield criterion. Several design charts are provided to analyze the effects of rock layer thickness on tunnel face stability, tunnel diameter, the arrangement sequence of weak and strong rock layers, and the variation in rock layer parameters at different positions. The results indicate that the thickness of the rock layer, tunnel diameter, and arrangement sequence of weak and strong rock layers significantly affect the tunnel face stability. Variations in the parameters of the lower layer of the tunnel face have a greater effect on tunnel stability than those of the upper layer.

Bonding steel plate has been used as a strengthening approach to repair disrupted segmental lining of operational tunnels. This paper introduces numerical investigation into the composite behaviour of the initially deformed segmental tunnel linings strengthened by bonding steel plates using finite element modelling. Cohesive zone modelling was used to simulate the interface bonding behaviour between the segmental linings and steel plates. The full history of the tunnel behaviour before and after strengthening were simulated, where the segmental tunnel lining is initially loaded to create some deformation, then after bonding steel plate, the strengthened tunnel is reloaded until failure occurs. By comparing the results with experimental data from the literature, the proposed model was proved to be capable of simulating the strengthened lining behaviour and able to capture the strengthening failure process in terms of the interface debonding. Subsequently, the segmental lining response and interface shear stress distribution and propagation were analysed to interpret the interaction and failure mechanism of the steel plate strengthened segmental linings. The influence of the initial deformation and the steel plate thickness were investigated and discussed in terms of the strengthened stiffness and capacity. It has been found that the interface shear stress concentration occurred at the positions of the segment joints, where bond damage first initiated. The ultimate failure of the steel plate strengthening happened suddenly once a local debonding zone close to the segmental joint was formed. In addition, the predicted results indicate that a delay in strengthening would result in an increase in strengthened capacity but a decrease in strengthened stiffness. By using thicker steel plates, the strengthened stiffness was improved, while the strengthened capacity could be improved only if the thickness was relatively thin.

This paper presents a novel integrated method for interactive characterization of fracture spacing in rock tunnel sections. The main procedure includes four steps: (1) Automatic extraction of fracture traces, (2) digitization of trace maps, (3) disconnection and grouping of traces, and (4) interactive measurement of fracture set spacing, total spacing, and surface rock quality designation (S-RQD) value. To evaluate the performance of the proposed method, sample images were obtained by employing a photogrammetry-based scheme in tunnel faces. Experiments were then conducted to determine the optimal parameter values (i.e. distance threshold, angle threshold, and number of fracture trace grouping) for characterizing rock fracture spacing. By applying the identified optimal parameters involved in the model, the proposed method could lead to excellent qualitative results to a new tunnel face. To perform a quantitative analysis, three methods (i.e. field, straightening, and the proposed method) were employed in the same study and comparisons were made. The proposed method agrees well with the field measurement in terms of the maximum and average values of measured spacing distribution. Overall, the proposed method has reasonably good accuracy and interactive advantage for estimating the ultimate fracture spacing and S-RQD. It can be a possible extension of existing methods for fracture spacing characterization for two-dimensional (2D) rock tunnel faces.

For a tunnel driven by a shield machine, the posture of the driving machine is essential to the construction quality and environmental impact. However, the machine posture is controlled by the experienced driver of shield machine by setting hundreds of tunneling parameters empirically. Machine learning (ML) algorithm is an alternative method that can let the computer to learn from the driver's operation and try to model the relationship between parameters automatically. Thus, in this paper, three ML algorithms, i.e. multi-layer perception (MLP), support vector machine (SVM) and gradient boosting regression (GBR), are improved by genetic algorithm (GA) and principal component analysis (PCA) to predict the tunneling posture of the shield machine. A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro. In total, 53,785 pairwise data points are collected for about 373 d and the ratio between training set, validation set and test set is 3:1:1. Each pairwise data point includes 83 types of parameters covering the shield posture, construction parameters, and soil stratum properties at the same time. The test results show that the averaged R2 of MLP, SVM and GBR based models are 0.942, 0.935 and 0.6, respectively. Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models. The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.

Background: Ossification of ligamentum flavum (OLF) is a hidden, indolent disease condition with variable unexplained etiology and pathology. Growing evidences show a correlation between senile osteoporosis (SOP) and OLF, but the fundamental relationship between SOP and OLF remains unclear. Therefore, the purpose of this work is to investigate unique SOP-related genes and their potential functions in OLF. Methods: Gene Expression Omnibus (GEO) database was utilized to gather the mRNA expression data (GSE106253) and then analyzed by R software. A variety of methods, including ssGSEA, machine learning (LASSO and SVM-RFE), GO and KEGG enrichment, PPI network, transcription factor enrichment analysis (TFEA), GSEA and xCells were employed to verified the critical genes and signaling pathways. Furthermore, ligamentum flavum cells were cultured and used in vitro to identify the expression of the core genes. Results: The preliminary identification of 236 SODEGs revealed their involvement in BP pathways associated with ossification, inflammation, and immune response, including the TNF signaling pathway, PI3K/AKT signaling pathway and osteoclast differentiation. Four down-regulated genes (SERPINE1, SOCS3, AKT1, CCL2) and one up-regulated gene (IFNB1) were among the five hub SODEGs that were validated. Additionally, they were performed by ssGSEA and xCell to show the relationship of immune cells infiltrating in OLF. The most fundamental gene, IFNB1, which was only found in the classical ossification- and inflammation-related pathways, suggested that it may affect OLF via regulating the inflammatory response. In vitro experiment, we found that IFNB1 expression was dramatically higher in cells cocultured with osteogenic induction than in controls. Conclusion: As far as we are concerned, this is the first observation using transcriptome data mining to reveal distinct SOP-related gene profiles between OLF and normal controls. Five hub SODEGs were ultimately found using bioinformatics algorithms and experimental verification. These genes may mediate intricate inflammatory/immune responses or signaling pathways in the pathogenesis of OLF, according to the thorough functional annotations. Since IFNB1 was discovered to be a key gene and was connected to numerous immune infiltrates in OLF, it is possible that IFNB1 expression has a substantial impact on the pathogenesis of OLF. Our research will give rise to new possibilities for potential therapeutics that target SOP reverent genes and immune-associated pathways in OLF.

Various structural defects deteriorate tunnel operation status and threaten public safety. Current tunnel inspection methods face problems of low efficiency, high equipment expense, and difficult data management. Combining the deep learning model and the 3D reconstruction method based on structure from motion (SfM), this paper proposes a novel SfM-Deep learning method for tunnel inspection. The high-quality 3D tunnel model is constructed by using images taken every 1 m along the longitudinal direction. The instance segmentation of leakage in longitudinal images is realized using the mask region-based convolutional neural network deep learning model. The SfM-Deep learning method projects the texture of the images after defect recognition to the 3D model and realizes the visualization of leakage defects. By projecting the model to the design cylindrical surface and expanding it, the tunnel leakage area is quantified. Through its practical application in a Shanghai metro shield tunnel, the reliability of the proposed method was verified. The novel SfM-Deep learning method can help engineers efficiently carry out intelligent tunnel detection.

Underground infrastructure (UI) plays a great important role in the urbanization and modernization of megacities in the world. However, the intensive development of the UI during the past decades has posed great risks to the safety of city infrastructures under the impact of multi-hazards, especially with the condition of global climate change. In this paper, a general conceptualized framework to assess the resilience of UI in cities under multi-hazards impact is proposed. The urban tunnel system, e.g., metro tunnel, road tunnel etc., is selected as the typical underground infrastructure discussed with the emphasis both on the structural level in terms of mechanical behaviors and system level in terms of network efficiency. The hazards discussed in this paper include the natural hazards and human-related ones, with emphasis on earthquake, flood, and aggressive disturbances caused by human activities. After the general framework proposed for resilience of the structural and network behavior of the UI, two application examples are illustrated. The structural resilience of the shield tunnel under earthquake impact is analyzed by using the proposed resilience model, and the network resilience of the road tunnel system under the flood impact due to climate change is analyzed, respectively. The resilience enhancement by using the adaptive design strategy of real-time observational method is mathematically presented in this case. Some other practical engineering recovery measures are briefly discussed at the end of this application example. The findings in the application examples should be helpful to enhance the resilience-based design of the structural and network of tunnels from the component to the system level.

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique (SMOTE), random search (RS) hyper-parameters optimization algorithm and gradient boosting tree (GBT) to achieve efficient and accurate rock trace identification. A thirteen-dimensional database consisting of basic, vector, and discontinuity features is established from image samples. All data points are classified as either “trace” or “non-trace” to divide the ultimate results into candidate trace samples. It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4. Then, sixteen classifiers generated from four basic machine learning (ML) models are applied for performance comparison. The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and non-trace classifications. Finally, discussions on feature importance, generalization ability and classification error are conducted for the proposed classifier. The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features. Besides, cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance. The proposed method provides a new alternative approach for the identification of 3D rock trace.

Continuous consumption of fossil energy and excessive CO2 emission severely restrict human society. Sustainable carbon cycle is a promising technology to simultaneously relieve greenhouse effect and energy crisis based on electrocatalysis and photocatalysis. However, the energy conversion efficiency is confined by the poor carriers utilization and insufficient reactive sites. Single‐atom catalysts (SACs) display outstanding performance in effectively overcoming the aforementioned problems. Herein, recent advances of SACs for enhancing the efficiency, selectivity, and long‐range stability of CO2 reduction are provided. First, the characteristics of SACs have been introduced in detail to provide rational design for SACs based on the relationship between structure and performance, including type, structure, and synthesis of SACs. Then, the high performance of SACs in electrocatalytic, photocatalytic, and thermocatalytic CO2 reduction has been discussed for disclosing reaction mechanism, such as charge transfer, activation barriers, and reaction pathway. In particular, the strategies of enhancing CO2 reduction performance have been summarized to provide deep insight into designing and developing more efficient SACs. Finally, an outlook on the current challenges and perspectives of SACs for electrocatalytic, photocatalytic, and thermocatalytic CO2 reduction is proposed. This review aims to provide a systematic reference for developing SACs in advanced CO2 catalytic conversion.

The random finite difference method (RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels. However, the high computational cost is an ongoing challenge for its application in complex scenarios. To address this limitation, a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed. The proposed method uses one-dimensional convolutional neural network (CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output. The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96, respectively. It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples (about 40 samples for each case in this study). It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given. This calls for an approach to gauge the model's confidence interval. It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference. The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.

BackgroundThe impact of screen time on mental health, including depression, has attracted increasing attention from not only children and adolescents but also the elderly. Thus, we conducted a meta-analysis of cohort studies to evaluate the association between screen time and depression risk.MethodsThe PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases were searched for cohort studies up to May 2022, and the reference lists of the included studies were also retrieved. A random-effect model was used to estimate the combined effect size. Heterogeneity was assessed with the I2 statistic. Potential publication bias was evaluated using a funnel plot and Begg’s and Egger’s tests.ResultsThe final analysis included 18 cohort studies with a combined total of 241,398 participants. The pooled risk ratio (RR) was 1.10 (95% confidence interval: 1.05–1.14), with significant heterogeneity (I2 = 82.7%, P < 0.001). The results of subgroup analyses showed that the pooled RRs varied according to geographic locations, gender, age group, screen time in the control group, depression at the baseline, and whether the study was conducted during the COVID-19 pandemic. No obvious evidence of publication bias was found.ConclusionThis study indicates that screen time is a predictor of depressive symptoms. The effects of screen time on depression risk may vary based on the participant’s age, gender, location, and screen time duration. The findings could have important implications for the prevention of depression.

BackgroundThe relationship between daytime napping and depression remains debatable. Thus, a meta-analysis in this study was conducted to evaluate the relationship between daytime napping and depression.MethodsThe PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases were searched up to February 2022, and the reference lists of the included studies were also retrieved. A random-effects model was used to estimate the combined effect size.ResultsNine studies with 649,111 participants were included in the final analysis. The pooled odds ratio (OR) was 1.15 (95% confidence interval: 1.01–1.31) with a significant heterogeneity (I2 = 91.3%, P for heterogeneity

Solar-driven CO2 reduction into value-added chemicals and fuels is attracting worldwide attention. Here, substantially enhanced photocatalytic CO2 reduction activity is achieved via the synergy of surface oxygen vacancies and ferroelectric polarization over Bi3TiNbO9 photocatalyst.

To better understand the physiological events involving death after spawning in S. japonica (Japanese spineless cuttlefish), we have presently generated a proteomic data set to properly examine this phenomenon. As such, a proteomic-based approach was employed to identify differentially expressed proteins (DEPs) in the optic glands of S. japonica, at three distinct growth stages: pre-spawning after sexual maturity (group A); spawning (group B) and postspawning before death (group C). About 955, 1000, and 1024 DEPs were identified for each comparative group analysis (i.e., group B vs A, group B vs C, and group C vs A). We further discovered that the function of these newly identified DEPs was mostly related to molecular events such as gene translation and signal transduction. According to the enriched GO terms obtained by Gene Ontology analysis, the function of most DEPs was correlated with structural molecule activity, ribosome function and gene expression. The majority of DEPs were known to be involved in signal transduction and energy metabolism, interestingly, some aging-related DEPs were also identified. Putting together, our study provides new insights, at the protein level, in the phenomenon of death after spawning in S. japonica, by referring to anti-aging effects conserved in other cephalopoda species.

Future 6G communication systems will require wideband nonreciprocal devices in the terahertz frequency domain. A novel ultra-wideband terahertz circulator is implemented by inserting a NixZn1−xFe2O4 ferrite sphere into the Al2O3 dielectric rod-array. The operating bandwidth of the circulator is broadened to 40 GHz via the external matching method through two hexagonal aluminum sheets. The three-dimensional numerical simulation suggests that the designed circulator also has an excellent insertion loss and isolation of 49.37 dB and 0.56 dB, respectively, via the finite element method. The low loss, high isolation and ultra-wideband show that the proposed scheme provides an effective path for realizing high-performance THz devices.

This paper presents a three-dimensional fully hydro-mechanical coupled distinct element study on fault reactivation and induced seismicity due to hydraulic fracturing injection and subsequent backflow process, based on the geological data in Horn River Basin, Northeast British Columbia, Canada. The modeling results indicate that the maximum magnitude of seismic events appears at the fracturing stage. The increment of fluid volume in the fault determines the cumulative moment and maximum fault slippage, both of which are essentially proportional to the fluid volume. After backflow starts, the fluid near the joint intersection keeps flowing into the critically stressed fault, rather than backflows to the wellbore. Although fault slippage is affected by the changes of both pore pressure and ambient rock stress, their contributions are different at fracturing and backflow stages. At fracturing stage, pore pressure change shows a dominant effect on induced fault slippage. While at backflow stage, because the fault plane is under a critical stress state, any minor disturbance would trigger a fault slippage. The energy analysis indicates that aseismic deformation takes up a majority of the total deformation energy during hydraulic fracturing. A common regularity is found in both fracturing- and backflow-induced seismicity that the cumulative moment and maximum fault slippage are nearly proportional to the injected fluid volume. This study shows some novel insights into interpreting fracturing- and backflow-induced seismicity, and provides useful information for controlling and mitigating seismic hazards due to hydraulic fracturing.

Semiconductor photocatalytic hydrogen production technology is a pollution‐free and low‐cost technology, which is principal to alleviate the energy crisis. The metal–organic frameworks (MOFs)‐based materials have been widely used in this field because of their regular and controllable pore structure and high specific surface area. Accordingly, the recent progress of MOFs‐based materials in the production of hydrogen through water splitting is reviewed. First, the mechanism of photocatalytic hydrogen production and the existing problems are proposed and then their advantages in photocatalytic hydrogen production and various photocatalytic hydrogen production catalysts based on MOFs are summarized. Subsequently, several methods to improve their photocatalytic hydrogen production property are summarized. Finally, some views on the prospects and challenges of the use of MOFs‐based catalysts for photocatalytic hydrolysis are expounded. Although some reviews associated with MOFs for solar‐driven H2 production have been published, most of them focus on the classification of MOFs photocatalysts. This is the first comprehensive review concentrating on the strategies for optimizing the photocatalytic activity of MOFs‐based materials for H2 production. It is believed that the suggestions provided here can help to study MOFs‐based photocatalytic reactions and to further promote the development of this research direction in the future.

The existing forward collision warning (FCW) systems that adopt kinematic or perceptual parameters have some drawbacks in the warning performance because of poor adaptability to the users or ineffectiveness of the warnings. To solve the problems of adaptability, several FCW models have been proposed based on algorithms (machine learning, deep learning). However, there is a lack of consideration for the multi-staged warning to avoid an abrupt warning that may startle or distract the driver. In this study, a light gradient boosting machine (LGBM) was adopted to develop a multi-staged FCW. The proposed model was trained and evaluated on a platform based on a driving simulator by twenty drivers. Through Shapley Additive Explanations (SHAPs), the output of the proposed model was explained. Specifically, the front vehicle acceleration, time-to-collision (TTC), and relative speed were found to strongly affect the warning stages from the proposed model. To evaluate the utility and acceptability of the developed model, it was compared with three existing FCW models in terms of subjective and objective indicators. As a result, a trade-off was found between the utility and user acceptance. Additionally, the comparison study also indicated that the developed model outperformed other previous models due to not only the high accuracy but also the suitable trigger timing for each participant.

Abstract Background To describe the pathological distribution, imaging manifestations, and surgical managements and prognosis of large adrenal tumors (LATs) ≥ 5 cm Methods A total of 251 patients with LATs were analyzed on the basis of pathological or clinical diagnosis. Regarding surgery, open adrenalectomy was performed on 89 patients, and laparoscopic adrenalectomy was performed on 89 patients. Thirty-two patients with bilateral tumors were analyzed in terms of clinical characteristics. The survival rate was determined for 43 patients with adrenal metastases and 29 patients with primary adrenal malignancies. The CT characteristics including tumor diameter, shape, edge, heterogeneity, necrosis, calcification, pre-contrast attenuation, and contrast attenuation were analyzed for 117 patients. Results The majority of LATs were still benign, but they had a higher probability to be malignant. Benign LATs made up 68.13% of all cases, mainly adrenal cysts (19.52%), pheochromocytoma (18.73%), benign adenoma (16.73%), and myelolipoma (7.17%). Malignant LATs accounted for 28.69% of cases, mainly including adrenocortical carcinoma (8.76%) and metastases (17.13%). Laparoscopic surgery was found to involve less trauma than open surgery. It was also safer and postoperative recovery was faster, but it had drawbacks and could not completely replace open surgery. CT features had obvious specificity for the diagnosis of benign and malignant tumors. For example, benign adenomas had a smaller pre-contrast (

A simplified analytical solution for steel plate strengthened circular tunnel concrete linings is presented, considering various interface slip modes. The full derivation for the analytical solution is introduced in detail, from which explicit expressions for the stress and displacement fields throughout the system are obtained. The distribution of lining deformation, internal force, and the interaction stress between tunnel lining and steel plate were investigated by considering various tunnel-steel interface scenarios, namely no slip, full slip, and elastic slip. The result demonstrates the importance of having a well-bonded interface to maximize the benefits of the steel plate strengthening approach. The maximum interface shear stress locates at the position with minimum interface normal stress. Subsequently, the influence of interface shear stiffness and steel plate thickness on the composited tunnel lining performance was investigated. It is founded that the improvement of the interface shear stiffness can enhance the overall structural stiffness of the strengthened tunnel lining, which results in increases in the interaction stresses and lining forces simultaneously. With a ratio of the steel thickness to the tunnel lining thickness less than 0.1, greater achievement in both strengthening effectiveness and cost efficiency is obtained.

Train-induced vibration is increasingly attracting people’s concern nowadays. In the coastal areas, many metro tunnels are built in the soft deposits, so the train-induced vibration effect is more serious. Nevertheless, the existing research studies mainly focus on the dynamic responses in the tunnel or on the ground surface while vibration characteristics in the ground are seldom studied. In this paper, a comprehensive field measurement was performed by installing accelerometers in the tunnel and soil layers and on the ground surface to capture the response characteristics of the track-tunnel-ground system. Elastoplastic numerical simulation considering the soil-water coupling was conducted to reveal the responses of acceleration, dynamic displacement, and excess pore water pressure using FE code DBLEAVES. Measurement results indicate that high-frequency contents (>500 Hz) attenuate rapidly in the propagation process; the dominant frequency of the rail concentrates in the middle- and high-frequency bands, about 25–400 Hz and 1000–1500 Hz, while the frequencies of the track bed, soil layers, and ground surface drop to below 400, 200, and 100 Hz, respectively. The vertical ground acceleration decreases like an arc in the transverse direction while there is transverse acceleration amplification phenomenon at a distance from the upper haunch and lower haunch of the tunnel. Overall, the area affected by train vibration in the soft deposits is about 30 m away from the metro tunnel. Numerical simulation based on soil-water coupled analysis is feasible to model the vibration characteristics in the soft deposits and confirms that there is acceleration amplification in the ground. Moreover, numerical results indicate that excess pore water pressure can be accumulated during each train vibration, so the train-induced settlement will be a potential problem in the long term for the metro tunnel.

Abstract Photocatalytic water splitting has attracted a lot of attention in recent years, and O2 evolution is the decisive step owing to the complex four‐electrons reaction process. Though many studies have been conducted, it is necessary to systematically summarize and introduce the research on photocatalytic O2 evolution, and thus a systematic review is needed. First, the corresponding principles about O2 evolution and some urgently encountered issues based on the fundamentals of photocatalytic water splitting are introduced. Then, several types of classical water oxidation photocatalysts, including TiO2, BiVO4, WO3, α‐Fe2O3, and some newly developed ones, such as Sillén–Aurivillius perovskites, porphyrins, metal–organic frameworks, etc., are highlighted in detail, in terms of their crystal structures, synthetic approaches, and morphologies. Third, diverse strategies for O2 evolution activity improvement via enhancing photoabsorption and charge separation are presented, including the cocatalysts loading, heterojunction construction, doping and vacancy formation, and other strategies. Finally, the key challenges and future prospects with regard to photocatalytic O2 evolution are proposed. The purpose of this review is to provide a timely summary and guideline for the future research works for O2 evolution.

Abstract Supercapacitors with the advantages of high power density and fast discharging rate have full applications in energy storage. However, the low energy density restricts their development. Conventional methods for improving energy density are mainly confined to doping atoms and hybridizing with other active materials. Herein, a Co3O4/g‐C3N4 p–n junction with excellent capacity is developed and its application in an all‐solid‐state flexible device is demonstrated, whose capacity and energy density are considerably enhanced by simulated solar light irradiation. Under photoirradiation, the capacity is increased by 70.6% at the maximum current density of 26.6 mA cm−2 and a power density of 16.0 kW kg−1. The energy density is enhanced from 7.5 to 12.9 Wh kg−1 with photoirradiation. The maximum energy density reaches 16.4 Wh kg−1 at a power density of 6.4 kW kg−1. It is uncovered that the lattice distortion of Co3O4, reduces defects of g‐C3N4, and the facilitated photo‐generated charge separation by the Co3O4/g‐C3N4 p–n junction all make contributions to the promoted electrochemical storage performance. This work may provide a new strategy to enhance the energy density of supercapacitors and expand the application range of photocatalytic materials.

The cracked zones caused by tunnel excavations are among the major reasons for structural instability in mechanized tunnel projects. Thus, various methods have been proposed to evaluate such cracks in terms of size and depth. However, deterministic estimations have been associated with a margin of error due to the inherent uncertainties of the respective problems as well as the approximation nature of the existing methods. Therefore, in this study, a probabilistic approach was adopted to assess the depth of a cracked zone induced around a tunnel. For this purpose, the problem was formulated as a reliability model, and the Monte Carlo method was utilized to calculate the exceedance probability for the crack depth around the tunnel. The exceedance probability was then calculated for any different depths of crack and presented as probability contours around the excavation zone. It was demonstrated that the exceedance probability is reduced sharply with an increase in the crack depth such that it fell below 5% for the cracks deeper than 2.28 times the tunnel radius. Effects of the maximum tangential stress at excavation boundary and crack initiation threshold on the exceedance probability were further calculated using the reliability sensitivity analysis. Eventually, the influence of in-plain stress ratio on the failure probability was investigated, and the results were reported for three rock types, including limestone, granite, and mudstone. Keywords: Excavation-induced damage (EDZ), Uncertainty modelling, Monte Carlo sampling method, Failure probability, Reliability sensitivity analysis

V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.

In this study, a two-dimensional (2D) soil–water coupling dynamic finite element (FE) analysis is conducted to investigate the effect of repeated train vibrations on the long-term settlement of a metro tunnel in saturated clay. Particular attention is paid to the leakage problem of the metro tunnel by assuming different permeability conditions, namely fully permeable, fully impermeable, and partially permeable, on the periphery of the tunnel for simplicity. The train vibration load is first evaluated using a rail–fastener–tunnel–subgrade model and averaged over a characteristic length for 2D numerical analysis. Cyclic Mobility model is used to simulate the mechanical behaviors of saturated soft clay in the FE analysis. Excess pore water pressure (EPWP) and associated tunnel settlement in trial operation and normal operation are calculated using the FE code DBLEAVES for different permeability conditions. It is found that a very low EPWP is generated in the trial operation, which then increases rapidly to peak values at the early days of normal operation. Afterward, the EPWP diminishes gradually as the train vibration continues. The permeability of the tunnel lining plays a significant role in the distribution of EPWP around the tunnel but produces a minor influence on the development of tunnel settlement. The train-induced tunnel settlement is mainly caused by the static settlement resulting from the EPWP dissipation during train interval, while the dynamic settlement arising from dynamic consolidation in each train vibration only accounts for a small portion. According to the 2D dynamic FE analysis, the final train-induced settlement of the metro tunnel in saturated clay is estimated to reach 160 mm while the peak EPWP value can reach 26.55 kPa. The settlement discrepancies between the numerical method and empirical method are discussed in detail. Keywords: Dynamic finite element analysis, Cyclic Mobility model, Saturated clay, Excess pore water pressure, Settlement of metro tunnel, Permeability

The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. In this study, an integrated approach based on laser intensity and depth features is proposed for the automated detection and quantification of concrete spalling. The Railway Tunnel Spalling Defects (RTSD) database, containing intensity images and depth images of the tunnel linings, is established via mobile laser scanning (MLS), and the Spalling Intensity Depurator Network (SIDNet) model is proposed for automatic extraction of the concrete spalling features. The proposed model is trained, validated and tested on the established RSTD dataset with impressive results. Comparison with several other spalling detection models shows that the proposed model performs better in terms of various indicators such as MPA (0.985) and MIoU (0.925). The extra depth information obtained from MLS allows for the accurate evaluation of the volume of detected spalling defects, which is beyond the reach of traditional methods. In addition, a triangulation mesh method is implemented to reconstruct the 3D tunnel lining model and visualize the 3D inspection results. As a result, a 3D inspection report can be outputted automatically containing quantified spalling defect information along with relevant spatial coordinates. The proposed approach has been conducted on several railway tunnels in Yunnan province, China and the experimental results have proved its validity and feasibility.

Osteoporosis is a common bone disorder where the declined bone mass is far more than normal physiological status and usually associated with enhanced fracture risk, reduced bone strength and even deteriorated quality of life. Recent studies showed that statins could exert beneficial effects on bones via promoting osteoblastic activity mediated by increased expression of bone morphogenetic protein 2 and also by suppressing osteoclast proliferation. In this study, we developed atorvastatin-loaded tetracycline-poly (ethylene glycol)-poly(lactic-co-glycolic acid) (TC-PEG-PLGA/ATO) micelles for the targeted treatment of osteoporosis. The TC-PEG-PLGA was synthesized under the action of coupling reagents and then ATO was encapsulated through solvent diffusion method with encapsulation efficiency and drug loading of 89.32 ± 2.48% and 8.20 ± 0.53%, respectively. The release of ATO from micelles could be maintained for more than 48 h in pH 7.4 PBS. Pharmacokinetic results further demonstrated that TC-PEG-PLGA micelles could effectively shield ATO leakage from micelles and prolong their circulation time. Benefiting from TC specifically binding to hydroxyapatite (HAp), TC-PEG-PLGA/ATO micelles exerted good bone-targeted ability, as demonstrated by in vitro HAp affinity assay and biodistribution. Pharmacodynamic studies showed that TC-PEG-PLGA/ATO micelles could effectively improve bone mineral density and bone mechanical strength in osteoporotic rats. These results suggest that TC-PEG-PLGA/ATO micelles hold significant promise for the targeted treatment of osteoporosis.

An important segment of the reliability-based optimization problems is to get access to the sensitivity derivatives. However, since the failure probability is not a closed-form function of the input variables, the derivatives are not explicitly computable and rather require a full reliability analysis which is computationally expensive. In this paper, a step-by-step algorithm has been presented to calculate the derivatives of the probability of failure and safety index with respect to the input parameters based on the advanced first-order second-moment (AFOSM) reliability method. The proposed algorithm is then implemented in a spreadsheet using Visual Basic for Application (VBA) programming language. Two geotechnical and structural examples are then presented to examine the program and describe the modeling procedure. The robustness of the proposed method is examined using a Gaussian random perturbation. The capability of the proposed method in the calculation of the sensitivity derivatives of the model uncertainty is explained in a separate section. Finally, the proposed model has been compared to the forward finite difference (FFD) method and the results are validated.