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Abstract Corrosion is one of the most common forms of damage to pressure pipes. CIPP liner can improve the mechanical properties of the pipe with corrosion. It is very important to study the mechanical response of corrosion pipes rehabilitated by CIPP liner. In this study, a three-dimensional finite element model of a pressure pipe rehabilitated with CIPP liner under the condition of circular corrosion is established, and the model is evaluated by using the experimental and analytical results. Afterward, the influence of each parameter on the stress and displacement of the liner is analyzed. Finally, the liner stress calculation equation is obtained based on 870 sets of data. The results show that the finite element results are in good agreement with the experimental and analytical results. The maximum stress of the liner first occurs at the center of the corroded circle, and as the corrosion diameter increases, the maximum stress gradually shifts to the edge. The maximum stress of the liner increases first and then tends to be stable with the increase of the corrosion diameter, and the friction coefficient has little effect on the liner stress.

Mental disorders seriously affect people’s health and social stability. This Mendelian randomization (MR) study was designed to investigate the causal relationship between circulating vitamin C (VC) or 25-hydroxyvitamin D (25(OH)D) levels and mental disorders. The data used for the MR analysis were derived from the summary genome-wide association studies (GWAS) database for VC and 25(OH)D and from the FinnGen consortium for fourteen mental disorders. Based on the inverse variance weighted (IVW) method, we found a potential causal association between circulating VC and anxiety disorders (IVW:OR = 1.139, 95% CI: 1.023-1.269, P = 0.018). However, no causal association was found between VC or 25(OH)D and other mental disorders (P > 0.05). In the reverse MR analysis, individuals with Alzheimer’s disease was causally associated with higher concentrations of circulating VC (P = 0.012), while individuals with anxiety disorders had a negative association between the concentrations of 25(OH)D (P = 0.012). However, the current evidence does not support a causal relationship between VC or 25(OH)D and other mental disorders. In addition, there was no causal association between circulating VC and 25(OH)D (P > 0.05). Future studies are needed to confirm these findings and to elucidate the mechanisms of potential causality.

Nursing beds can be widely used in medical institutions and households to provide medical care for patients or disabled individuals. With the increasing demand for nursing, nursing beds are developing towards intelligence and comfort. In this study, the backrest lifting mechanism of nursing beds is selected as the research object. The standards for backrest angle adjustment and the basis for comfort judgment are reviewed, and the research on various adjustment configurations are sorted in order to provide assistance to relevant research institutions in understanding current technologies and assist users in making choices.

Using liner pipe to rehabilitate pressure pipe can effectively improve the mechanical performance of the pipe. To explore the mechanical properties of the liner for repairing pressure pipes under the action of a normal fault, a refined three-dimensional (3D) finite element (FE) model is established and assessed by experiments and FE results. Then, the stress distribution of the liner is analyzed, and the effects of fault slip distance, slip angle, pipe diameter, host thickness, internal pressure, liner elastic modulus, and friction factor on the liner stress and vertical displacement are studied. Finally, the bearing capacity of the pipeline against normal faults is analyzed. The study shows that the distribution morphology of the crown and shoulder is similar and opposite to the distribution morphology of the invert and haunch. The crown and invert stress of the pipe is the highest, while the stress on the springline is the lowest. When the host pipe thicknesses are 3.0 mm, 3.5 mm, 4.0 mm, and 5.0 mm, the slip distance reaches 309 mm, 342 mm, 370 mm, and 449 mm, respectively, and the liner will damage at the crown.

Ground Penetrating Radar (GPR) is an effective non-destructive detection method, that is frequently utilized in the detection of urban underground defects because of its quick speed, convenient and flexible operation, and high resolution. However, there are some limitations to defect detection using GPR, such as less data, poor data quality, and complexity of data interpretation. In this study, an underground defect detection system based on GPR was established. First, a Simple Linear Iterative Clustering (SLIC)-PHash, a Data Augmentation (DA) optimization algorithm, was created to obtain high-quality datasets. Second, the Convolutional Block Attention Module (CBAM)-YOLOv8, a detection model, was produced for the recognition of defects. This model uses GhostConv and CBAM to create a lighter design that better focuses on target detection and increases efficiency. Finally, a one-click detection system was formed by fusing SLIC-Phsh and CBAM-YOLOv8, which were used for one-click GPR dataset optimization and defect detection. The developed system has the best detection mAP and F1 scores of 90.8% and 88.3%, respectively, compared to several well-known Deep Learning (DL)-based techniques. The results demonstrated that the system proposed in this paper can greatly improve detection efficiency and reduce detection time by achieving a good balance between detection speed and accuracy.

The drainage pipeline network constitutes a critical component of municipal infrastructure. Effective inspection of drainage pipelines is crucial for maintaining urban functions. However, manual inspection methods are inefficient and prone to error. To address this, an intelligent system for detecting, tracking and managing defects in drainage pipelines using YOLO-DeepSort (YOLO-DS) is proposed for automated defect information collection. Initially, YOLOv7 is employed to train the defect detection model, achieving a mean Average Presion (mAP) of 91.1% and processing at 172 frames per second (FPS). Subsequently, the YOLO-DS detection-based tracking algorithm is utilized to track defects in the pipeline video. Moreover, within the tracking framework, an innovative fusion module combining detection and ReID features enhances cross-frame matching robustness. Under conditions of lens rotation, jitter, and blur, the system achieves confidence levels exceeding 87% for potholes, 78% for misalignments, and 80% for obstructions, respectively, demonstrating strong robustness. Lastly, the detection and tracking algorithms are integrated into the information management platform. The management platform facilitates intelligent identification and counting of pipeline defects, and includes a professional communication module for automatic generation drainage pipeline health assessment reports, thereby streamlining manual inspection processes and saving assessment time.

Polyurethane grouting trenchless technology has been widely applied to the rehabilitation of concealed defects in engineering structures. The interfacial properties between polyurethane and engineering structures are key factors determining the stability of the composite structure. In practical applications, the interface shapes of different engineering structures vary significantly, and the influence of the interface shape on interfacial properties should not be overlooked. This study focuses on engineering structures with curved interfaces, such as pile foundations, pipelines, and tunnels. Direct shear tests were conducted on polyurethane and concrete composite specimens with curved interfaces. A comparative analysis of the shear behavior between curved and planar composite specimens was performed, and the influence of arc diameter and polyurethane density on the shear behavior of curved and planar composite specimens was investigated. Additionally, SEM (Scanning Electron Microscopy) was used to conduct a microscopic examination of the interfaces with different polyurethane densities after failure, and the microscopic shear mechanisms between polyurethane and concrete materials were explored. The results revealed that the shear behavior of curved specimens was significantly higher than that of planar specimens. The shear strengths of curved specimens with diameters of 400 mm, 500 mm, and 700 mm were approximately 1.50, 1.39, and 1.10 times those of planar specimens, respectively. With increasing polyurethane density, the variation trend of shear strength in curved specimens was similar to that of planar specimens. However, significant differences were observed in the shear modulus, peak displacement, and shear residual strength between curved and planar specimens as the polyurethane density varied. Different diameter curved interface specimens exhibited a similar trend of shear strength variation with polyurethane density, gradually decreasing as the curvature diameter increased, and ultimately approaching that of planar specimens.

In the above article [1], inaccuracies are present in the naming and corresponding explanation of a concept. Upon careful review and analysis, we have identified inaccuracies in the definition and naming of a specific concept within the article. During the training process, we explored various module optimizations. Unfortunately, due to the similar effects of the two modules, the definition and naming of the concept in the article were inaccurately represented. Specifically, the GhostConv module was incorrectly identified as the AKConv module that had been incorporated. To ensure the accuracy and integrity of our research, all authors unanimously request the necessary corrections to rectify the inaccuracies in the naming and explanation. This correction is crucial for maintaining the precision and completeness of our study.

BackgroundDepression can result in changes in eating behavior and decrease the quality of eating. It has been shown that maternal depression during pregnancy can result in malnutrition, which can have adverse effects on the pregnancy and the offspring. There is currently no clear association between depression and diet.MethodsFive hundred and forty-nine pregnant women recruited from Danyang Maternal and Child Health Hospital in Jiangsu Province participated in this study and were administered the Intuitive Eating Scale-2 (IES-2), Edinburgh Post-natal Depression Scale (EPDS), Pregnancy Stress Scale (PPS), Self-rating Anxiety Scale (SAS), and Dietary Guidelines Adherence Index for Pregnant Women during Pregnancy (CDGCI-PW). The nutritional software collected dietary records for three consecutive days in mid-pregnancy to calculate dietary intake and nutrients that support energy production. The mediation analyses were conducted using SPSS 24.0 macro PROCESS.ResultsThe relationship between depressive symptoms during pregnancy and diet quality was moderated primarily by two aspects of eating behavior, “Reliance on Hunger and Satiety Cues” (RHS) and “Body-Food Choice Congruence” (BFC). Depressive symptoms (EPDS scores) showed a negative correlation with RHS, BFC, and RHS, and BFC showed a positive correlation with diet quality, yielding a significant specific indirect effect. The multiple mediation model explained 14.7% of the variance in the diet quality.ConclusionThis study highlights the important role of eating behaviors during pregnancy in the relationship between depressive symptoms (EPDS scores) and diet quality, and provides preliminary evidence for feasible ways pregnant women with depressive symptoms can improve diet quality, promote maternal and child health, and reduce depression.

Abstract Objective We explored the dietary effects of replacing normal dietary staple foods with supplementary nutritional protein powder, dietary fiber, and fish oil on several metabolic parameters. We examined weight loss, glucose and lipid metabolism, and intestinal flora in obese individuals when compared with individuals on a reduced staple food low carbohydrate diet. Methods From inclusion and exclusion criteria, 99 participants (28 kg/m2 ≤ body mass index (BMI) ≤ 35 kg/m2) were recruited and randomly assigned to control and intervention 1 and 2 groups. Physical examinations and biochemical indices were performed/gathered before the intervention and at 4 and 13 weeks post intervention. After 13 weeks, feces was collected and 16s rDNA sequenced. Results After 13 weeks, when compared with controls, body weight, BMI, waist circumference, hip circumference, systolic blood pressure, and diastolic blood pressure values in intervention group 1 were significantly reduced. In intervention group 2, body weight, BMI, waist circumference, and hip circumference were significantly reduced. Triglyceride (TG) levels in both intervention groups were significantly reduced. Fasting blood glucose, glycosylated hemoglobin, glycosylated albumin, total cholesterol, and apolipoprotein B levels in intervention group 1 were decreased, while high density lipoprotein cholesterol (HDL-c) decreased slightly. Glycosylated albumin, TG, and total cholesterol levels in intervention group 2 decreased, while HDL-c decreased slightly, High sensitive C-reactive protein, MPO, Ox-LDL, LEP, TGF-β1, IL-6, GPLD1, pro NT, GPC-4, and LPS levels in both intervention groups were lower when compared with controls. Adiponectin (ADPN) levels in intervention groups were higher when compared with controls. Tumor necrosis factor-α (TNF-α) levels in intervention group 1 were lower when compared with controls. There is no obvious difference in α diversity and β diversity between intestinal flora of 3 groups. Among the first 10 species of Phylum, only the control group and the intervention group 2 had significantly higher Patescibacteria than the intervention group 1. Among the first 10 species of Genus, only the number of Agathobacter in intervention group 2 was significantly higher than that in control group and intervention group 1. Conclusions We showed that an LCD, where nutritional protein powder replaced some staple foods and dietary fiber and fish oil were simultaneously supplemented, significantly reduced weight and improved carbohydrate and lipid metabolism in obese individuals when compared with an LCD which reduced staple food intake.

Corrosion is one of the most common types of damage inburied oil and gas pipelines. Corrosion leaks can cause serious accidents and can be harmful to pipelinesduring service. The maximum corrosion depth of an oil and gas pipeline is an important indicator for assessing the remaining strength of the pipeline. An accurate prediction of the maximum corrosion depth is important for the safe operation of pipelines. Machine learning has been shown to perform well in predictive assessment efforts. However, previous studies have rarely considered the effects of corrosion characterization and parameter optimization simultaneously. In this study, a multi-parameter maximum corrosion depth prediction model for pipelines based on GSCV-XGBoost is proposed, which can be applied to real projects. The model performs feature extraction on the pipeline dataset through Pearson correlation analysis, identifies the parameters that contribute more to the maximum corrosion depth, and predicts the maximum corrosion depth of the pipeline using an optimized machine learning model. The machine learning model used in this study was obtained by optimizing the XGBoost model usingthe GirdSearchCV method. That is, the optimal hyperparameter combination of the model was obtained by 10-fold cross-validation and grid searching. The prediction results werecompared with those of five common machine learning models. The conclusions show that the GSCV-XGBoost model performs the best in predicting the maximum corrosion depth of the pipeline with the smallest error. The R2 and Root Mean Square Error(RMSE)scores forthe test set were 0.9886 and 0.2057, respectively. The prediction accuracy was improved by 34.59% over that of the conventional XGBoost model.

ObjectiveBrain-derived neurotrophic factor (BDNF), a member of the neurotrophic family that plays a vital role in regulating neuronal activity and synaptic plasticity in the brain, affects attention. However, studies investigating the association between BDNF and attention in long-term high-altitude (HA) migrants are limited in the literature. As HA affects both BDNF and attention, the relationship between these factors becomes more complex. Therefore, this study aimed to evaluate the relationship between peripheral blood concentrations of BDNF and the three attentional networks in both behavioral and electrical aspects of the brain in long-term HA migrants.Materials and methodsNinety-eight Han adults (mean age: 34.74 ± 3.48 years, 51 females and 47 males, all have lived at Lhasa for 11.30 ± 3.82 years) were recruited in this study. For all participants, the serum BDNF levels were assessed using enzyme-linked immunosorbent assay; event-related potentials (N1, P1, and P3) were recorded during the Attentional Networks Test, which was used as the measure of three attentional networks.ResultsExecutive control scores were negatively correlated with P3 amplitude (r = −0.20, p = 0.044), and serum BDNF levels were positively correlated with executive control scores (r = 0.24, p = 0.019) and negatively correlated with P3 amplitude (r = −0.22, p = 0.027). Through grouping of BDNF levels and three attentional networks, executive control was found to be significantly higher in the high BDNF group than in the low BDNF group (p = 0.010). Different BDNF levels were associated with both orienting scores (χ2 = 6.99, p = 0.030) and executive control scores (χ2 = 9.03, p = 0.011). The higher the BDNF level, the worse was the executive function and the lower was the average P3 amplitude and vice versa. Females were found to have higher alerting scores than males (p = 0.023).ConclusionThis study presented the relationship between BDNF and attention under HA. The higher the BDNF level, the worse was the executive control, suggesting that after long-term exposure to HA, hypoxia injury of the brain may occur in individuals with relatively higher BDNF levels, and this higher BDNF level may be the result of self-rehabilitation tackling the adverse effects brought by the HA environment.

Deep learning algorithms have achieved encouraging results for pipeline defect segmentation. However, existing defect segmentation methods may encounter challenges in accurately segmenting the complex features of pipeline defects and suffer from low processing speeds. Therefore, in this study, we propose Pipe-Sparse-Net, a pipeline defect segmentation system that combines StyleGAN3 to segment the complex forms of underground drainage pipe defects. First, we introduce a data augmentation algorithm based on StyleGAN3 to enlarge the dataset. Next, we propose Pipe-Sparse-Net, a pipeline segmentation model based on SparseInst, to accurately predict the defect regions in drainage pipes. Experimental results demonstrate that the segmentation accuracy of this model can reach 91.4% with a processing speed of 56.7 frames per second (FPS). To validate the superiority of this method, comparative experiments were conducted against Yolact, Condinst, and Mask R-CNN, and the model achieved a speed improvement of 45% while increasing the accuracy by more than 4%.

To solve the problems of low accuracy and false counts of existing models in road damage object detection and tracking, in this paper, we propose Road-TransTrack, a tracking model based on transformer optimization. First, using the classification network based on YOLOv5, the collected road damage images are classified into two categories, potholes and cracks, and made into a road damage dataset. Then, the proposed tracking model is improved with a transformer and a self-attention mechanism. Finally, the trained model is used to detect actual road videos to verify its effectiveness. The proposed tracking network shows a good detection performance with an accuracy of 91.60% and 98.59% for road cracks and potholes, respectively, and an F1 score of 0.9417 and 0.9847. The experimental results show that Road-TransTrack outperforms current conventional convolutional neural networks in terms of the detection accuracy and counting accuracy in road damage object detection and tracking tasks.

Regular damage detection plays an important role in timely pavement maintenance. However, the existing detection methods struggle to efficiently and accurately identify the category and contour of the damage. Therefore, this paper proposes a Road-Mask R-CNN mobile damage detection model to automatically segment and measure multiple pavement damages. First, the optimized k-means clustering algorithm is used to intelligently determine the size and ratio of the anchor. Subsequently, the traditional nonmaximum suppression (NMS) algorithm is replaced by the distance intersection over union nonmaximum suppression (DIoU-NMS) algorithm, which improves the detection accuracy of multiple damages in the same image with a mean average precision (mAP) value of 0.934. Then, a comparative experiment with U-Net, the unimproved Mask R-CNN, MSNet and the unsupervised domain adaptation network (UDA) is carried out to verify the effectiveness of the proposed model. And combined with the segmentation and measurement results, the damage is quantitatively evaluated. Moreover, a webcam damage detection system combined with a workstation and an automatic damage detection system for smartphones is developed to quickly detect multiple types of pavement damage. In addition, on- site experiments are carried out on real pavements to verify the feasibility and effectiveness of the proposed method.

Guar gum is recognized as a soluble dietary fiber but its effects on blood lipid levels remain to be established. The purpose of the study was to focus on assessing randomized controlled trials (RCTs) and investigate the effect of guar gum on lipid levels. Seventeen studies were systematically selected and included in the meta-analysis. Decreases in total cholesterol (TC) (standard mean difference (SMD) [95 %CI] = −1.38 [−2.01,−0.74]) and low density lipoprotein cholesterol (LDL-C) (SMD [95 %CI] = −1.67 [−2.52, −0.82]) were significantly more pronounced with guar gum consumption, whereas no difference was observed on high density lipoprotein cholesterol (HDL-C) (SMD [95 %CI] = −0.07 [−0.34, 0.19]) or triglyceride (TG) (SMD [95 %CI] = 0.06 [−0.50, 0.61]), which suggests that guar gum has the potential to promote lipid health and prevent cardiovascular disease (CVD).

Considerable road mileage puts tremendous pressure on pavement crack detection and maintenance. In practice, using a small parameter model for fast and accurate image-based crack segmentation is a challenge. However, current mainstream convolutional neural networks allocate computing resources to the same type of operators, which ignores the impact of different levels of feature extractors on the model performance. In this research, an end-to-end real-time pavement crack segmentation network (RIIAnet) is designed to improve performance by deploying different types of operators in separate layers of the network structure. Based on the extraction characteristics of cracks by convolution, involution, and asymmetric convolution, in the shallow layers the crack segmentation task is matched to extract rich low-level features by the designed asymmetric convolution enhancement module (ACE). Meanwhile, in the deep layers, the designed residual expanded involution module (REI) is used to enhance the high-level semantic features. Furthermore, the existing involution operator that fails to converge during training is improved. The ablation experiment demonstrates that the optimal ratio of the convolution and REI is 1/3 to obtain the optimal resource allocation and ACE improves the performance of the model. Especially compared with seven classical deep learning models of different structures, the results show that the proposed model reaches the highest MIOU, MPA, Recall, and F1 score of 0.7705, 0.9868, 0.8047, and 0.8485, respectively. More importantly, the parameter size of the proposed model is dramatically reduced, which is 0.04 times that of U-Net. In practice, the proposed model can be implemented in images with a high resolution of 2048 × 1024 in real time.

The Internet of Things (IoT) is a promising technology for the collection data among the seamless connectivity with massive physical devices for future 5G networks. While device-to-device (D2D) communication is an emerging technique, which potentially tends to promote the machine-type communications developments of the IoT. In this paper, we consider a wireless distributed storage network where the IoT devices cache decentralized data in each memory and use D2D links to share the whole content, and to save the bandwidth and energy consumptions compared to traditional base station cellular network. We analyze and compare the energy costs of regenerating codes and maximum distance separable (MDS) codes for data downloading and repairing processes with the different parameter configuration. We also define a target named D2D link availability to measure the download and repair efficiency, and obtain the optimal repair intervals of various schemes for the lowest energy cost. Furthermore, we propose a novel caching scheme, termed double replication MDS (DR-MDS), which can modify the repair bandwidth by utilizing more storage occupation, and improve the D2D link availability in the system. The simulation results have shown that the DR-MDS caching significantly outperformed the MDS caching in the energy saving, that also verifies the practicability and the feasibility of our D2D distributed caching system for the low-power IoT scenarios.

Ground Penetrating Radar (GPR) is a shallow geophysical method for detecting and locating subsurface targets. The GPR image echo characteristics of complex underground structures can be obtained by carrying out GPR forward modeling research. The traditional finite-difference time-domain (FDTD) method has low efficiency and accuracy. The alternating direction implicit FDTD (ADI-FDTD) algorithm surmounts the stability limitations of the traditional FDTD method, making it possible to select a larger time step for higher computational efficiency. For circular underground structures, a pseudowave produced by the ladder approximation method can be corrected using the surface conformal technique. This paper proposes a high-efficiency and high-accuracy GPR forward modeling method that combines the ADI-FDTD algorithm and surface conformal technology. The performance of the conformal ADI-FDTD algorithm is verified by a simple two-layer model. Based on the proposed algorithm, the GPR image features of three complex underground structure models are obtained. Finally, a field experiment is used to support the accuracy and usefulness of the conformal ADI-FDTD algorithm. The numerical simulation results and experimental results show that the conformal ADI-FDTD algorithm reduces the pseudodiffraction wave caused by the ladder approximation method and can significantly improve the computing efficiency for complex underground structure models.

The urban drainage system is an important part of the urban water cycle. However, with the aging of drainage pipelines and other external reasons, damages such as cracks, corrosion, and deformation of underground pipelines can cause serious consequences such as urban waterlogging and road collapse. At present, the detection of underground drainage pipelines mostly focuses on the qualitative identification of pipeline damage, and it is impossible to quantitatively analyze pipeline damage. Therefore, a method to quantify the damage volume of concrete pipes that combines surface segmentation and reconstruction is proposed. An RGB-D sensor is used to collect the damage information of the drainage pipeline, and the collected depth frame is registered to generate the pipeline’s surface point cloud. Voxel sampling and Gaussian filtering are used to improve data processing efficiency and reduce noise, respectively, and the RANSAC algorithm is used to remove the pipeline’s surface information. The ball-pivoting algorithm is used to reconstruct the surface of the segmented damage data and pipe’s surface information, and finally to obtain the damage volume. In order to evaluate, we conducted our research on real-world materials. The measurement results show that the method proposed in this paper measures an average relative error of 7.17% for the external damage volume of concrete pipes and an average relative error of 5.22% for the internal damage measurements of concrete pipes.

With the age of pipeline and increase in the volume of urban sewage, the pipeline has different degrees of defects, which can cause safety problems such as road collapse and urban flooding. The service life of drainage pipes is closely related to daily maintenance and inspection, so it is very important to inspect the defects and monitor the operation of drainage pipes regularly. However, the existing research lacks quantitative detection and intelligent management of pipeline defect information. Therefore, the depth camera is used as the sensor to quantitatively detect the volume and area of the pit on the concrete pipe, and a defect information management platform is constructed in this paper. Firstly, combined BIM model with 3D point cloud, this paper proposes a 3D defect information management platform of drainage pipeline. Then, the depth camera is used to collect the damage data and preprocess the data, and a method for calculating the damage volume and surface area of drainage pipeline based on 3D mesh reconstruction of the defect point cloud is proposed. The verification experiment results show that the error between the quantized volume and the real volume is mostly within 10%, and the maximum error is 17.54%, indicating high accuracy. The drainage pipeline information model is created. Finally, the data is uploaded to the information management platform to realize the visualization and informatization of pipeline defects and the later operation and maintenance requirements of the pipeline.

Background:The existing pipeline siltation analysis lacks the analysis of the influence of pipeline siltation intensity on the water level depth of the manhole.Objective:To address these issues, in this study, the high-resolution numerical simulation model of ‘silted pipeline-manhole' was established.Methods:First, a spatial and temporal hydraulic response model of the ‘silted pipeline-manhole' system is constructed. The vibrational differential equation of the ‘silted pipeline-manhole' system is then adopted to quantify the physical boundary constraints on the fluid–solid coupling relationship. The local and global feature search capabilities were enhanced by an embedded self-attention network, which in turn established an intelligent early warning model for manhole water levels in silted pipelines.Results:The results of the test analysis showed that, owing to the blocking effect of siltation, the water level of the manhole at the first end of the silted pipe was high, leading to a decrease in the water level of the manhole at the tail end. The pipe with a transverse siltation intensity of 10% showed an 8.07% reduction in the maximum overflow flow rate, whereas the pipe with a longitudinal siltation intensity of 5.2% exhibited a 15.09% reduction, suggesting that longitudinal siltation intensity has a more significant impact on pipeline failure.Conclusion:The research results provide technical support for the diagnosis of pipeline siltation and flood risk assessment considering the change of water level in the manhole and the inlet and outlet flow velocity of the pipe section.

Currently, due to the rapid development and popularization of smartphones, the usage of ubiquitous smartphones has attracted growing interest in the field of structural health monitoring (SHM). The portable and rapid cable force measurement for cable- supported structures, such as a cable-stayed bridge and a suspension bridge, has an important and practical significance in the evaluation of initial damage and the recovery of transportation networks. The extraction of dynamic characteristics (natural frequencies) of cable is considered as an essential issue in the cable force estimation. Therefore, in this study, a vision-based approach is proposed for identifying the natural frequencies of cable using handheld shooting of smartphone camera. The boundary of cable is selected as a target to be tracked in the region of interest (ROI) of video image sequence captured by smartphone camera, and the dynamic characteristics of cable are identified according to its dynamic displacement responses in frequency domain. The moving average is adopted to eliminate the noise associated with the shaking of smartphone camera during measurement. A laboratory scale cable model test and a pedestrian cable-stayed bridge test are carried out to evaluate the proposed approach. The results demonstrate the feasibility of using smartphone camera for cable force estimation. [ABSTRACT FROM AUTHOR]

Recently, exploring the cognitive functions of the brain by establishing a network model to understand the working mechanism of the brain has become a popular research topic in the field of neuroscience. In this study, electroencephalography (EEG) was used to collect data from subjects given four different mathematical cognitive tasks: recite numbers clockwise and counter-clockwise, and letters clockwise and counter-clockwise to build a complex brain function network (BFN). By studying the connectivity features and parameters of those brain functional networks, it was found that the average clustering coefficient is much larger than its corresponding random network and the average shortest path length is similar to the corresponding random networks, which clearly shows the characteristics of the small-world network. The brain regions stimulated during the experiment are consistent with traditional cognitive science regarding learning, memory, comprehension, and other rational judgment results. The new method of complex networking involves studying the mathematical cognitive process of reciting, providing an effective research foundation for exploring the relationship between brain cognition and human learning skills and memory. This could help detect memory deficits early in young and mentally handicapped children, and help scientists understand the causes of cognitive brain disorders. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the seismic performance of the prestressed steel strips retrofitted RC beam-column joints. Two series of joint specimens were conducted under compression load and reversed cyclic loading through quasi-static tests. Based on the test results, the seismic behavior of the strengthened joints specimens in terms of the failure modes, hysteresis response, bearing capacity, ductility, stiffness degradation, energy dissipation performance and damage level were focused. Moreover, the effects of the amount of the prestressed steel strips and the axial compression ratio on seismic performance of retrofitted specimens were analyzed. It was shown that the prestressed steel strips retrofitting method could significantly improve the seismic behavior of the RC joint because of the large confinement provided by prestressed steel strips in beamcolumn joints. The decrease of the spacing and the increase of the layer number of the prestressed steel strips could result in a better seismic performance of the retrofitted joint specimens. Moreover, increasing the axial compression ration could enhance the peak load, stiffness and the energy performance of the joint specimens. Furthermore, by comparison with the specimens reinforced with CFRP sheets, the specimens reinforced with prestressed steel strips was slightly better in seismic performance and cost-saving in material and labor. Therefore, this prestressed steel strips retrofitting method is quite helpful to enhance the seismic behavior of the RC beam-column joints with reducing the cost and engineering time. [ABSTRACT FROM AUTHOR]