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Singular spectrum analysis is widely used in geodetic time series analysis. However, when extracting time-varying periodic signals from a large number of Global Navigation Satellite System (GNSS) time series, the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient. To improve the efficiency and accuracy of singular spectrum analysis, this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix. The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series, and the extraction efficiency of a single time series is six times that of singular spectrum analysis. The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

One of the key issues in the design of modern ship and offshore structures is the accurate prediction of strength under various load conditions, especially impact, ultimate and fatigue strength [...]

In the design of modern ship and offshore structures, one of the key issues is the accurate prediction of strength with regard to various new materials and structures used in the structural design stage and under extreme sea environment and accidental states [...]

Real-time acquisition of wave-induced hull girder loads of a sailing ship will help the captain make reasonable decisions, which is of great significance for improving the safety of the ship's navigation. This paper investigates the real-time prediction method of hull girder loads based on the Recurrent Neural Network (RNN) model and error correction strategy. Firstly, taking the vertical bending moment, horizontal bending moment, and torsional moment at the mid-ship position of a large container ship as examples, corresponding neural network prediction models are established through parameter influence analysis. Secondly, various sea state conditions are used to verify the feasibility of established network prediction models to predict the hull girder loads in real-time. The VBM prediction model performs better than the TM prediction model and HBM prediction model, and the errors of the TM prediction model and HBM prediction model are slightly larger in some cases. Lastly, an improved prediction model based on an error correction strategy is proposed to improve the prediction accuracy of the neural network prediction model, and the adequate performance of the error correction strategy is discussed.

To fulfill the requirement for deformation measurement within the failure area during rock failure, a newly developed digital image algorithm, known as the corner correlation method, was implemented. A corner correlation measurement system was established by utilizing a Split Hopkinson Pressure Bar (SHPB) and a high-speed camera. The study focused on monitoring the deformation and failure characteristics of sandstone samples under dynamic loading. The results show that the corner correlation method has its unique advantages in rock dynamic mechanics experiments, and can obtain the deformation of the failure region during the rock failure process. Specifically, the initiation, extension, and contraction processes of surface cracks on sandstone were examined. Parameters such as crack width, width propagation rate, and extension shrinkage rate were measured. The entire crack development process was analyzed, including crack width, crack initiation point, crack extension and contraction trajectory, elongation and contraction velocity, width expansion rate, and longitudinal crack penetration, which were obtained at any given time on the surface of the rock sample. Additionally, the attenuation law of stress, characterized by an exponential decay of the stress peak value, was obtained through numerical simulation using a similar model.

Real-time prediction of hull girder loads is of great significance for the safety of ship structures. Some scholars have used neural network technology to investigate hull girder load real-time prediction methods based on motion monitoring data. With the development of deep learning technology, a variety of recurrent neural networks have been proposed; however, there is still a lack of systematic comparative analysis on the prediction performance of different networks. In addition, the real motion monitoring data inevitably contains noise, and the effect of data noise has not been fully considered in previous studies. In this paper, four different recurrent neural network models are comparatively investigated, and the effect of different levels of noise on the prediction accuracy of various load components is systematically analyzed. It is found that the GRU network is suitable for predicting the torsional moment and horizontal bending moment, and the LSTM network is suitable for predicting the vertical bending moment. Although filtering has been applied to the original noise data, the prediction accuracy still decreased as the noise level increased. The prediction accuracy of the vertical bending moment and horizontal bending moment is higher than that of the torsional moment.

ObjectiveIn order to improve the calculation accuracy of the influence of slamming load on ship fatigue damage, and evaluate ship fatigue life more effectively, a direct calculation method of ship fatigue damage which accounts for slamming load is proposed. MethodsFirst, the time domain calculation of nonlinear load is combined with the spectral analysis method of linear frequency domain, and the stress time history of the ship in a short-term sea state is obtained based on beam theory. Second, the damage of the check points is calculated using the rain flow counting method and S-N curve, the contribution and influence coefficients of nonlinear slamming load on fatigue damage are calculated with reference to the CCS guidelines, and the corresponding stress response transfer function is corrected by combining the spectral analysis method. Finally, the stress spectrum is calculated and fatigue damage accounting for nonlinear slamming is obtained. ResultsThe results show that compared with traditional spectral analysis method, it is found that the ship fatigue damage obtained by the direct calculation method accounting for slamming load is about 10%–50%. ConclusionThe proposed method can improve the accuracy of load calculation when evaluating the fatigue strength of ships sailing in harsh sea states.

The rapid advancement of information technologies, such as cloud computing, internet of things (IoT), and big data, has significantly enhanced the intelligence level in dam construction. Intelligent monitoring systems for dam filling have garnered considerable attention from water conservancy and hydropower engineering units, owing to their capability for refined management and quality control of layered and compartmentalized construction processes. The construction of earth–rock dams has undergone a transformation, shifting from manual management of dam compaction equipment, layer management, and quality control to a comprehensive intelligent approach. This new approach encompasses fine-grained control of unit engineering, dam material identification, unmanned driving technology, real-time assessment and monitoring of compaction quality, and early warning forecasting. This article presents a systematic overview of key technologies employed in the intelligent construction process of earth–rock dams. These technologies include the fine decomposition of building information modeling (BIM) models on web platforms, rapid identification of dam material particle size, the application of autonomous driving technology for intelligent dam filling, and real-time monitoring and evaluation of dam compaction characteristics. Additionally, the paper discusses the latest research results in the field, building upon existing technology progress, and concludes by exploring the future development trends of intelligent dam construction. The construction of intelligent earth–rock dams serve as a crucial reference and valuable lessons for the development and construction of high earth–rock dam projects.

Lead (Pb) is a widespread environmental contaminant, associated with a higher risk of functional impairment that can lead to frailty in older adults. However, few studies focused on the association of Pb exposure with frailty among the oldest old (aged ≥ 80 years). In this study, we aimed to assess the associations of Pb with frailty and its components in the oldest old. The included individuals were the oldest old aged ≥ 80 years who participated in a 2017 cross-sectional survey of the Healthy Aging and Biomarkers Cohort Study. Frailty was ascertained by the frailty index, which was created based on health deficits. We used logistic regression models to estimate the association of blood Pb with frailty and its components. The geometric mean and median of blood Pb were 38.51 μg/L and 36.27 μg/L among the oldest old, respectively. Compared with the first quartile of blood Pb, participants in the fourth quartile had higher risk of frailty and its components, the ORs are 1.71 (1.22–2.41), 1.99 (1.35–2.94), 1.91 (1.25–2.93), 1.57 (1.13–2.17) and 1.43 (1.05–1.96), for frailty, ADL disability, IADL disability, functional limitations, and hearing loss in the oldest old, respectively. There was a significant interaction between blood Pb and frailty in different age groups. In conclusion, our findings provide preliminary evidence that higher blood Pb may increase the risk of frailty among the oldest old by increasing the risk of disability in four physical functions: disability in ADL, disability in IADL, functional limitations, and hearing loss.

The purpose of this paper is to investigate and evaluate the ship engine room fire spreading characteristics based on the effect of firefighting interventions. The large eddy simulation (LES) and theoretical models are employed to simulate a fire scene in the engine room. The fire spreading characteristics and the mechanism of the use of a fine water mist are obtained by varying the ventilation conditions and parameters of the water sprinkler. The results show that the reason why a pool fire can be more easily extinguished as the spray speed increases is because the water mist with a higher spray speed has a larger spray area. Meanwhile, the temperature in the engine room would drop more rapidly with the increasing spray area due to the higher evaporation rate. Furthermore, the arrangement of the ventilation conditions had an obvious effect on the fire spreading characteristics, as the depletion of the combustion medium affects the combustion situation.

Deformation monitoring and dynamic characteristic analysis of bridge structures are the vital and basic requirements for the safe operation of bridges. In recent years, Global Navigation Satellite System (GNSS) has become increasingly widely used in bridge structural health monitoring with the development of the GNSS technology, especially the continuous improvement and development of China’s Beidou navigation satellite system (BDS). This article summarizes the application process of GNSS dynamic deformation monitoring and the development of GNSS deformation measurement technology of bridge structural health monitoring, the dynamic characteristic identification method and its application in bridge GNSS monitoring. The positioning solution methods for GNSS monitoring, the high sampling rate GNSS receiver for monitoring, multi-frequency and multi-system GNSS monitoring and the weakening of multipath effect of GNSS monitoring are summarized in detail. Then, the conclusions and prospects are posed for future research and related application.

Bridge dynamic monitoring based on GNSS has become an important means of monitoring bridge structure. GNSS dynamic monitoring signals are often overwhelmed by strong noises and multipath errors. Thus the conventional data processing methods such as Fourier transform, wavelet analysis, and others have poor denoising effect or obtain unconspicuous dynamic characteristics from weak vibration signals. To solve this problem, the present study proposes a new adaptive stochastic resonance method based on quantum genetic algorithm with known frequency as optimal parameter. Analyzing the simulation signals not only verifies the validity and scientificity of the method, but also analyzes its frequency extraction effect in the approximate error range of target frequency with different noise intensity. A notable bridge vibration frequency is obtained when the new method is applied to analyze the bridge dynamic monitoring data based on GNSS.

For ultra-long tunnels, the effective means of alignment and breakthrough accuracy optimization is to measure the gyro azimuths or transfer the coordinates of surface known points or the directions to the inside of the tunnel through vertical shafts or inclined shafts. Compared with bored tunnels, immersed tunnels have different engineering characteristics and construction environments, and have higher alignment control accuracy requirements. However, the immersed tunnel has no vertical shafts or inclined shafts that can be used for coordinate transfer to improve the measurement accuracy. In order to provide high-accuracy alignment control and breakthrough for long immersed tunnels, we put forward a new strategy after network stability analysis, and consistency analysis between the results of the element positioning system and the results of the control network. The results of the element positioning system are taken as constraints to improve the accuracy of the control network. The analysis on 100,000 simulations of a 20 km-long immersed tunnel shows that the accuracy of the last point pair of the traverse network with angle and distance observations is 60.8 mm, while the accuracy of the same point pair after adopting the new strategy in this paper is 11.8 mm. The results of a 7 km-long experimental traverse network show that the lateral breakthrough accuracy is improved by 73%. All of these indicate that the new strategy can effectively improve the measurement accuracy and the lateral errors can be kept at a small level. Following the new strategy, the high-accuracy alignment and breakthrough can be achieved for long immersed tunnels.

This paper presents a risk assessment method for a ship’s power system under the conditions of an engine room fire based on the expert comprehensive evaluation (ECE) method combined with the fuzzy fault tree analysis (FFTA) method. The composition of the main engine system in the engine room and the failure logic of each subsystem were analyzed, and the fuzzy fault tree of a ship engine room fire was constructed. The probability of system failure and the importance of basic events were calculated. The fire safety risk assessment model was established using the safety risk matrix. The risk assessment of a ship engine room fire was implemented. The results demonstrated that the fire frequency of the ship engine room was 5.232 × 10−6 h−1. The fire risk of the main engine fuel system was the highest. Fuel leakages from diesel supply tanks and heavy fuel oil tanks are the main cause of accidents. The proposed method eliminated the influence of incomplete statistics in the risk assessment process and improved the accuracy and credibility of the reassessment results.

This paper proposes a dynamic evolutionary model to quantify the domino effect of ship engine room fires. Based on the spatial and temporal characteristics of fire accidents, the dynamic probability of the domino effect of multiple accident units is calculated using matrix calculation and Monte Carlo simulation. The uncertainties of shipboard personnel, automatic detection systems, sprinkler systems, and the synergistic effects of multiple escalation vectors from different units are addressed. The dynamic probability of the domino effect of multiple accident units is calculated, and a risk assessment of complex fire scenarios in ship engine rooms is implemented. This study also presents the model feasibility in terms of fire risk assessment in cabins with numerous pieces of equipment. The results indicate that 2 min and 4 min are vital time nodes for the development and spread of fires. The extinguishing work on key equipment in the path of the fire's spread can effectively restrain its further expansion. The results can provide critical references for ship fire prevention, fire suppression, and fire protection design.

Interferon-chi (IFN-χ) is a type of function-unknown IFN. IFN-χ in bovines (BoIFN-χ) has evolved as a multigene family. This family comprises four IFN-χ subtypes, two of which are functional genes, which we demonstrated to (i) have antiviral and antiproliferative activities, (ii) be highly sensitive to trypsin, and (iii) remain stable with changes in pH and temperature. BoIFN-χ is a key intermediate in antiviral response, PAbs against BoIFN-χs could downregulate the transcriptional activation of ISGs induced by poly(I:C), and BoIFN-χs could be induced upon virus infection at the early and late phase. Additionally, BoIFN-χs bind with type-I IFN receptors, induce transcription of interferon regulatory factor 7 (IRF7), interferon-stimulated genes (ISGs), and type-I IFNs as well as myxovirus resistance protein 1 (Mx1) expression. Expression of ISGs and activation of IFN-stimulated response element (ISRE) induced with BoIFN-χs could be downregulated significantly by the Janus kinase (JAK) 1 and signal transducers and activators of transcription (STAT) 1 inhibitor. The promoters of BoIFN-β, nuclear factor-kappa B, and ISRE could be activated with BoIFN-χs, and the BoIFN-χ promoter could be activated by other type-I IFNs. Overall, BoIFN-χ could be induced with virus infection and signal through the JAK-STAT pathway to form a positive-feedback regulation of IFN production. These findings may facilitate further research on the role of IFN-χ in innate immune responses.

The limit state function is important for the assessment of the longitudinal strength of damaged ships under combined bending moments in severe waves. As the limit state function cannot be obtained directly, the common approach is to calculate the results for the residual strength and approximate the limit state function by fitting, for which various methods have been proposed. In this study, four commonly used fitting methods are investigated: namely, the least-squares method, the moving least-squares method, the radial basis function neural network method, and the weighted piecewise fitting method. These fitting methods are adopted to fit the limit state functions of four typically sample distribution models as well as a damaged tanker and damaged bulk carrier. The residual strength of a damaged ship is obtained by an improved Smith method that accounts for the rotation of the neutral axis. Analysis of the results shows the accuracy of the linear least-squares method and nonlinear least-squares method, which are most commonly used by researchers, is relatively poor, while the weighted piecewise fitting method is the better choice for all investigated combined-bending conditions.

When the dynamic characteristics of a bridge structure are analyzed though Hilbert–Huang transform (HHT), the noise contained in the bridge dynamic monitoring data may seriously affect the performance of the first natural frequency identification. A time-frequency analysis method that integrates wavelet threshold denoising and HHT is proposed to overcome this deficiency. The denoising effect of the experimental analysis on the simulated noisy signals proves the effectiveness of the proposed method. This method is used to perform denoising pre-processing on the dynamic monitoring data of Sutong Bridge, and the denoised results of different methods are compared and analyzed. Then, the best denoising data are selected as the input data of Hilbert spectrum analysis to identify the structural first natural frequency of the bridge. The results indicate that the wavelet-empirical mode decomposition (EMD) method effectively reduces the interference of random noise and eliminates useless intrinsic modal function (IMF) components, and the excellent properties of the signal evaluation index after denoising make the method suitable for processing non-stationary signals with noise. When Hilbert spectrum analysis is applied to the denoised data, the first natural frequency of the bridge structure can be identified clearly and is consistent with the theoretical calculation. The proposed method can effectively determine the natural vibration characteristics of the bridge structure.

The ultimate limit state function is one of the premises for the assessment of structure strength and the safety of ships under severe conditions. In order to study the residual strength of damaged ships under the combined load of vertical and horizontal bending moments acting on the hull girder, the ultimate limit state function of a damaged ship under combined load, and its fitting methods are investigated in this paper. An improved Smith Method is adopted to calculate the residual load carrying capacity of damage ships, where the rotation and translation of the neutral axis of the damaged cross-section are obtained using a particle swarm optimisation method. Because the distribution curve of the residual load carrying capacity of a damaged ship under combined load is asymmetric, the application of traditional explicit polynomial fitting methods results in poor accuracy. In this study, a piecewise weighted least square fitting method is adopted so as to guarantee the continuity in the transitions, and a method is proposed for fitting the ultimate limit state function of a damaged ship under combined load. Calculations of the residual strength show that the improved Smith Method is more accurate than the original Smith Method for the accurate position of the neutral axis. The error analysis of the fitting methods shows that the ultimate limit state function that is fitted using a piecewise weight least square method is more accurate.

Mass redistribution within the Earth system deforms the surface elastically. Loading theory allows us to predict loading induced displacement anywhere on the Earth’s surface using environmental loading models, e.g., Global Land Data Assimilation System. In addition, different publicly available loading products are available. However, there are differences among those products and the differences among the combinations of loading models cannot be ignored when precisions of better than 1 cm are required. Many scholars have applied these loading corrections to Global Navigation Satellite System (GNSS) time series from mainland China without considering or discussing the differences between the available models. Evaluating the effects of different loading products over this region is of paramount importance for accurately removing the loading signal. In this study, we investigate the performance of these different publicly available loading products on the scatter of GNSS time series from the Crustal Movement Observation Network of China. We concentrate on five different continental water storage loading models, six different non-tidal atmospheric loading models, and five different non-tidal oceanic loading models. We also investigate all the different combinations of loading products. The results show that the difference in RMS reduction can reach 20% in the vertical component depending on the loading correction applied. We then discuss the performance of different loading combinations and their effects on the noise characteristics of GNSS height time series and horizontal velocities. The results show that the loading products from NASA may be the best choice for corrections in mainland China. This conclusion could serve as an important reference for loading products users in this region.

The welded stiffened plate is widely used in naval architecture and offshore engineering as a basic structural member. The aim of this study is to investigate the effect of welding residual stress and steel grade on the ultimate strength of stiffened plates under uniaxial compressive load by non-linear finite element analysis. Nineteen stiffened plates built with three types of stiffeners with various column slenderness ratios provided in the ISSC’2000 VI.2 benchmark calculations are employed in the present study. The commercial finite element code ABAQUS is applied to simulate the collapse behavior of the stiffened plates and verified against the benchmark calculations. Fabrication-related imperfections, such as initial deflections and residual stresses, are accounted for in the simulations. The ultimate strength of stiffened plates built in common shipbuilding steels, namely S235, S315, S355 and S390, are investigated by varying the yield strength of materials in the simulation. Analysis of the numerical results shows that the welding residual stress reduces the ultimate strength of stiffened plates, and increase in yield strength of the material can effectively improve the ultimate strength of common ship stiffened plates; and quantitative analyses of their influences have also been performed.

BACKGROUND: Evidence suggested that abiotic airborne exposures may be associated with changes in body composition. However, more evidence is needed to identify key pollutants linked to adverse health effects and their underlying biomolecular mechanisms, particularly in sensitive older adults. OBJECTIVES: Our research aimed to systematically assess the relationship between abiotic airborne exposures and changes in body composition among healthy older adults, as well as the potential mediating mechanisms through the serum lipidome. METHODS: From September 2018 to January 2019, we conducted a monthly survey among 76 healthy adults (60–69 years old) in the China Biomarkers of Air Pollutant Exposure (BAPE) study, measuring their personal exposures to 632 abiotic airborne pollutions using MicroPEM and the Fresh Air wristband, 18 body composition indicators from the InBody 770 device, and lipidomics from venous blood samples. We used an exposome-wide association study (ExWAS) and deletion/substitution/addition (DSA) model to unravel complex associations between exposure to contaminant mixtures and body composition, a Bayesian kernel machine regression (BKMR) model to assess the overall effect of key exposures on body composition, and mediation analysis to identify lipid intermediators. RESULTS: The ExWAS and DSA model identified that 2,4,5-T methyl ester (2,4,5-TME), 9,10-Anthracenedione (ATQ), 4b,8-dimethyl-2-isopropylphenanthrene, and 4b,5,6,7,8,8a,9,10-octahydro-(DMIP) were associated with increased body fat mass (BFM), fat mass indicators (FMI), percent body fat (PBF), and visceral fat area (VFA) in healthy older adults [Bonferroni-Hochberg false discovery rate (FDRBH)<0.05]. The BKMR model demonstrated a positive correlation between contaminants (anthracene, ATQ, copaene, di-epi-a-cedrene, and DMIP) with VFA. Mediation analysis revealed that phosphatidylcholine [PC, PC(16:1e/18:1), PC(16:2e/18:0)] and sphingolipid [SM, SM(d18:2/24:1)] mediated a significant portion, ranging from 12.27% to 26.03% (푝-value <0.05), of the observed increase in VFA. DISCUSSION: Based on the evidence from multiple model results, ATQ and DMIP were statistically significantly associated with the increased VFA levels of healthy older adults, potentially regulated through lipid intermediators. These findings may have important implications for identifying potentially harmful environmental chemicals and developing targeted strategies for the control and prevention of chronic diseases in the future, particularly as the global population is rapidly aging. [ABSTRACT FROM AUTHOR]