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The abundance of specific hydrocarbon-oxidizing bacteria (MV value) in surface soil is closely linked to the concentration of thermogenic light hydrocarbons found in the underlying layers. The identification of these bacteria using specific detection techniques offers unique insights into microbiological anomalies associated with oil and gas, underscoring the significance of delineating microbiological geochemical anomalies in the exploration process. Accurate determination of the anomaly range is crucial for forecasting potential oil and gas reservoirs. In this study, a variety of fractal and geological statistical methods were employed to analyze microbiological geochemical data from the Taiwan Strait Basin. The multifractal approach allows for the simultaneous assessment of spatial autocorrelation and singularity in geochemical fields, with the fractal distribution reflecting the localized enrichment and depletion patterns of microbiological geochemical elements in rocks and other substrates. Geological statistical methods, such as the variation function, enable the assessment of spatial autocorrelation in geochemical and geophysical fields. The results indicate that microbiological anomalies in the Jiulongjiang Depression are mainly concentrated in the southwestern part of the study area, with some anomalies also observed in the central region. Conversely, microbiological anomalies in the Jinjiang Depression are predominantly located in the southern portion of the study area. Microbiological anomalies in the study area (Jinjiang Depression and Jiulongjiang Depression) exhibit a northeast-southwest strip distribution, aligning with the trend of the depressions and faults, suggesting that faults could influence the distribution of oil and gas resources in the region. Based on microbiological statistical analyses, the Jiulongjiang Depression shows better potential for oil and gas prospects compared to the Jinjiang Depression, guiding future research efforts in the Jiulongjiang Depression.

In the simulation of foundation pit excavation, the traditional element birth–death method commonly used tends to encounter issues such as uncoordinated deformation and changes in the constitutive model, affecting the accuracy of the prediction results. To address these issues, this study proposes the use of element tracking. By duplicating elements for temporary supports or structures requiring changes in material properties and appropriately activating or deactivating them at the right moments, the simulation of the foundation pit excavation process can be achieved more precisely. Using the construction process of the Tangxi Passenger Transport Station’s comprehensive transportation hub foundation pit as an example, this study applied the proposed simulation method and compared the results with actual measurements, demonstrating its effectiveness. This research offers a more accurate approach for simulating foundation pit excavation and provides a reference for similar numerical simulation problems.

The identification of geochemical anomalies in oil and gas indicators is a fundamental task in oil and gas exploration, as the process of oil and gas accumulation is a low probability event. Machine learning algorithms for anomaly detection are applicable to the identification of oil and gas geochemical anomalies related to oil and gas accumulation. However, when using oil and gas indicators for anomaly detection, the diversity of these indicators often leads to the influence of the indicator redundancy on the identification of such features. Therefore, it is particularly important to select appropriate oil and gas indicators for anomaly detection. In this study, a hybrid model combining unsupervised machine learning methods and singularity analysis methods was used to evaluate oil and gas indicator anomalies using geochemical data from the Taiwan Strait Basin. The models used in this study include the singularity index model, the principal component model combined with the singularity index model, and the cluster analysis combined with the principal component model and the singularity index model. PCA models can reduce the dimensions of the data and retain as much information as possible. CLA divides data samples into different groups, so that samples within the same group are more similar and samples between different groups are less similar. LSP is mainly used for measuring the setting and singular degree of local anomalies in multi-scale geochemistry, geophysics, and other types of local anomalies, and it has a unique advantage in extracting low and weak anomalies and nonlinear characteristics. The results of the study show that the results obtained using the CLA-PCA-LSP hybrid model are very similar to those obtained by performing PCA on the entire index and then calculating the singularity index. This also verifies that, for the study areas of the Jiulongjiang Depression and Jinjiang Depression, we can select oil and gas indicators that are favorable for exploration analysis, without including all indicators in the analysis scope, thereby improving the computational efficiency. The application of a singularity analysis method and generalized self-similarity principle in extracting the geochemical information of oil and gas indicators in the Taiwan Strait Basin highlights key technologies such as the identification of weak anomalies, decomposition of composite anomalies, and integration of spatial information. The combination anomalies delineated by the singularity analysis method and S-A method not only reflect the spatial relationship with known oil and gas reservoir distribution, but also show the multiple combination anomalies in unknown areas, providing favorable guidance for the next exploration direction in the Taiwan Strait Basin.

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.

Objective The Beikang Basin, located in the southern South China Sea, is a significant area for offshore oil and gas exploration in China. Previous studies in this region have primarily focused on the Palaeogene source rock and the Middle Miocene carbonate reservoirs, neglecting the investigation of deep-water sediments since the Late Miocene. Methods Therefore, this study aims to explore the spatiotemporal distribution, characteristics, and controlling factors of deep-water sediments in the Beikang Basin since the Late Miocene, utilizing 2D seismic data. Results Three types of sedimentary deposits have been identified in the Beikang Basin since the Late Miocene: draping strates, mass-transport deposits (MTDs), and turbidites. Turbidites can be further classified into confined turbidites and delta-front ones. The study reveals that the draping state is predominantly developed in the forebulge tectonic regions of the Beikang Basin, with a decreasing thickness from South to North. MTDs, on the other hand, are mainly distributed in the Backbulge zone, with a thickness that decreases from Southwest to Northeast. The findings indicate that the development and distribution of sediments in the Beikang Basin are influenced by various factors, including sedimentary supply, geomorphic features, tectonic activity, and eustatic sea level changes. The location and thickness of deep-water sediments are primarily controlled by the supply of materials. Additionally, the distribution range of gravity flow deposits and draping states is influenced by the topography of the area. Conclusion These results provide a theoretical foundation for understanding the development and controlling factors of deep-water sediments in the Beikang Basin.

Abstract Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers from issues of missing the optimal solution or collapsing to local optimum when facing complicated problems. To conquer these drawbacks, we consider adding a novel multi-chaotic local operator to the bio-shock feedback mechanism of SMA to compensate for the lack of exploration of the local solution space with the help of the perturbation nature of the chaotic operator. Based on this, we propose an improved algorithm, namely MCSMA, by investigating how to improve the probabilistic selection of chaotic operators based on the maximum Lyapunov exponent (MLE), an inherent property of chaotic maps. We implement the comparison between MCSMA with other state-of-the-art methods on IEEE Congress on Evolution Computation (CEC) i.e., CEC2017 benchmark test suits and CEC2011 practical problems to demonstrate its potency and perform dendritic neuron model training to test the robustness of MCSMA on classification problems. Finally, the parameters’ sensitivities of MCSMA, the utilization of the solution space, and the effectiveness of the MLE are adequately discussed.

We study the propagation of bending waves along the free edge of a semi-infinite piezoelectric plate within the framework of two-variable refined plate theory (TVPT, a high-order plate theory), Reissner-Mindlin refined plate theory (RMPT, a first-order plate theory), and the classical plate theory (CPT). The piezoelectric plate has macroscopic symmetry of orthogonal mm2 The governing equations are derived using Hamilton principle. The dispersion relations for electrically open and shorted boundary conditions at the free edge are obtained analytically. The difference in dispersion property between the three plate theories is analyzed. The numerical results show that the dispersion curves predicted by TVPT and RMPT are similar and have small difference over the complete frequency range, which means both the two theories are valid for the analysis of edge waves in a piezoelectric plate. But the wave velocity calculated by CPT is much larger than the two theories above and is no longer valid for high frequency and thick plate. The electrical boundary condition at the free edge has an insignificant effect on phase velocity and group velocity which can be ignored for the analysis of edge waves in a piezoelectric plate governed by bending deformation. The velocity of bending edge waves in a semi-infinite piezoelectric plate is positively related to that of Rayleigh surface wave in a traction-free piezoelectric half-space. The edge wave velocity can be enhanced when the piezoelectric plate is considered as one with weaker anisotropy.

Contourite drifts are significant sedimentary features and provide clues for the reconstruction of paleoceanography and paleoenvironment. Although they have been increasingly identified in the world’s ocean, shallow-water contourite drifts (< 300 m depth) remain poorly understood and the examples are rare. This study documents a Middle Miocene shallow-water contourite depositional system in the southwest South China Sea by interpreting seismic reflection data and calibrating results with the previous chronological framework. The depositional system consisted of six mounded drifts and six moats. The contourite features were generated in seismic unit III (16-10.5 Ma) and distributed adjacent to carbonate reefs. They were formed on the proto-continental shelf (50-200 m depth) and shaped by the wind-driven currents. Changes in the sedimentary stacking patterns suggest three evolutionary stages of the contourite features. Stage I represents the growth of the Middle Miocene contourite depositional system between 16 and 10.5 Ma. Stage II marks the termination of carbonate drifts and the burial of the Late Miocene sedimentation during 10.5-5.3 Ma. Stage III started with the development of modern deep-water sedimentary systems since 5.3 Ma. The contourite features are compared with the examples on other South China Sea margins. Significant changes in the paleoceanography occurred at 10.5 Ma and 6.5-5.3 Ma when the dominated bottom currents shifted from the monsoonal wind-driven currents to the North Pacific waters, and then the modern circulation system. The Middle Miocene mounded drifts were likely sourced by the coarse-grained carbonate sands. Fluid flow escaped from the coarse-grained contourite layers and natural gas leakage occurs on the seafloor. Shallow-water carbonate contourite drifts can be served as a good gas reservoir and have great economic potential.

Background Microwaves (MWs) deliver relatively high temperatures into biological tissue and cover a large ablation zone. This study aims to evaluate the efficacy and effectiveness of water-cooled double-needle MW ablation arrays in assisting the hepatic transection of an in vivo pig model. Methods Our research program comprised computer modeling, tissue-mimicking phantom experiments, and in vivo pig liver experiments. Computer modeling was based on the finite element method (FEM) to evaluate ablation temperature distributions. In tissue-mimicking phantom and in vivo pig liver ablation experiments, the performances of the water-cooled MW ablation array and conventional clamp crushing liver resection were compared. Results FEM showed that the maximum lateral ablation diameter at 100 W output and a duration of 60 s was 3 cm (assessed at 50 °C isotherm). In the phantom, the maximum transverse ablation diameter of the double-needle MW ablation increased rapidly to 3 cm in 60 s at 50 W. The blood loss and blood loss per transection area in Group A were significantly lower than those in Group B (18 (7–26) ml vs. 34 (19–57) ml, and 2.4 (2–3.1) ml/cm2 vs. 6.9 (3.2–8.3) ml/cm2, respectively) (p

Gas hydrates were newly discovered in the Dongsha Waters of the South China Sea by drilling in 2013 and 2019. The drilling results show that the hydrates consist of microbial gas and thermogenic gas, but the source layers of the thermogenic gas are not clear. The Chaoshan depression is the largest relict Mesozoic basin under the Dongsha Waters where thick T3—K strata, up to 5000 m, have developed under thin Cenozoic sediments. The T3—J1 and J3 bear the two main layers of hydrocarbon source rocks rich in II2-III type organic matter, mainly in the western sag, the middle low bulge, and the eastern sag. The T3—J1 developed bathyal - neritic mud rocks, generally greater than 400 m in thickness. The J3 mainly developed closed and semi-closed bay type mud, also II2-III type. Nowadays these two layers are in their mature - over mature stages, thus potentially high-producing. There have developed deep faults cutting through the Mesozoic strata and distributing fractures within the Cenozoic sediments. Analysis of geochemical and geological samples dredged from the seabed indicates a significant oil and gas leakage activity well correlated spatially with the Mesozoic-associated faults. The association of fault-fractures can act as good channels for the thermogenic gas to move upward and diffuse into the temperature pressure stabilization zone of the hydrate. Thus, sufficient gas from the Mesozoic production layers is deemed the main source of thermogenic gas. The area where the Mesozoic faults are activated is the main leakage area of thermogenic gas, thus the main target for future hydrate exploration.

In order to improve the spatial resolution of a one-dimensional aperture synthesis (1-D AS) radiometer without increasing the size of the antenna array, the method of visibility extension (VE) is proposed in this article. In the VE method, prior information about the visibility distribution of various scenes is learnt by a residual convolutional neural network (ResCNN). Specifically, the relationship between the distribution of low-frequency visibility and that of high-frequency visibility is learnt. Then, the ResCNN is used to estimate the high-frequency visibility samples from the low-frequency visibility samples obtained by the AS system. Furthermore, the low- and high-frequency visibility samples are combined to reconstruct the brightness temperature image of the scene, to enhance the spatial resolution of AS. The simulation and experiment both demonstrate that the VE method can enhance the spatial resolution of 1-D AS.

In mirrored aperture synthesis (MAS), the antenna array determines the rank of the transformation matrix connecting the cross-correlations to the cosine visibilities. However, the transformation matrix is rank-deficient, resulting in errors in the reconstructed brightness temperature (BT) image. In this paper, the signal propagations for the vertically polarized wave and horizontally polarized wave are analyzed. Then, the optimization model of the antenna array based on dual-polarization is established. The optimal array configurations are presented, with the corresponding transformation matrices being almost column full ranks. Simulation results demonstrate the validity of the proposed optimization model.

Brain storm optimization (BSO) is a swarm intelligence optimization algorithm which is proven to have practical values in various fields. During these years, many modifications have been facilitated to effectively improve BSO's search performance. So far, these modifications focus on improving the solution quality by applying different clustering methods and learning strategies, in which the population diversity is often neglected. However, in recent studies, population diversity plays a more significant role in designing optimization algorithm. A population that maintains its diversity in a high level can easily obtain better solutions than the one with low level of diversity. Therefore, this paper proposes a control method that evaluates the population diversity of BSO to improve its performance. Two diversity measures, which are known as distance-based diversity and fitness-based diversity, are implemented to realize the adaptation of algorithm parameters. The new algorithm is called multiple diversity-driven BSO (MDBSO). Its performance is verified by CEC2017 benchmark function suit and a neuron model training task. The results demonstrate the effectiveness and efficiency of MDBSO.

By analyzing the measured data of rail corrugation in a small radius curve track with Cologne fasteners, the typical passing frequencies of corrugation were obtained. Then, according to actual line conditions, the vehicle-track coupled model and rail material friction and wear model were established and simulated, and rail corrugation characteristics of the curved section were studied. The results show that the variation amplitude of creep force on the inside of a guiding wheelset is large, and it coincides with that of the saturated creep force partially and the coincident part appears periodically. The variation amplitude of creep force on the outside of the guiding wheelset is small, and it is approximately equal to that of the saturated creep force. In addition, the variation range of the wear depth curve on inner rail is mainly changing from 0 μm to 0.0014 μm, showing periodic wavy wear, and when the creep force is equal to the saturated creep force, the wear depth reaches the peak. The variation range of the wear depth curve on outer rail is mainly changing from 0.0005 μm to 0.0008 μm, showing uniform wear. Combined with the predicted profile of rail wear, it can be seen that the inner rail mainly suffers from corrugation on the top of rail and the outer rail mainly suffers from uniform wear on the side, which results in serious corrugation on inner rail and slight corrugation on outer rail. The frequency characteristics analysis of wear shows that the inner rail wear has characteristic frequencies similar to the passing frequencies. Modal analysis results show that the vibration of inner rail at the characteristic frequencies is greater than that of outer rail, which lead to the corrugation of the corresponding frequencies more easily. The wear growth rates at the characteristic frequencies are relatively large, which indicates that the wear at the corresponding frequencies will continue to develop and eventually form corrugation.

Field investigation shows that most corrugations occur on the inner rail surface of curved tracks with a radius of less than 700 m. In order to explore the formation mechanism of metro corrugation, the relationship between wheel–rail stick–slip characteristics and rail corrugation is studied by combining single wheelset curving and a rigid–flexible coupling model. The numerical results illustrate that the lateral torsional stick–slip vibration of inner rail–inner wheel of the guiding wheelset on the small radius curve eventually leads to the generation of inner rail corrugation, and the lateral torsional stick–slip vibration of outer rail–outer wheel of the driven wheelset may also occur, but the intensity is weak and the probability is low. The lateral torsional stick–slip vibration of inner rail–inner wheel of the guiding wheelset on the large radius curve is also easy to cause inner rail corrugation, but the degree of inner rail corrugation is lower than that on the small radius curve; the outer rail on the large radius curve is still not easy to produce corrugation. The formation mechanism of rail corrugation on the straight track is different from that on the curve track, which is mainly related to the wheel–rail longitudinal torsional stick–slip vibration.

In order to explain the phenomenon of corrugation on measured line sections of the modern tram, a numerical model of modern tram-track coupling has been established by utilizing the multi-body dynamics method, and the formation mechanism of corrugation has been studied from the perspective of wheel-rail contact creep. The results show that the smaller the curve radius is, the greater the probability of corrugation occurs. The corrugation wavelength range is 40 mm~50 mm, the wave depth range is 0.05 mm~0.1 mm, and the wave depth is relatively small compared with the corrugation wave depth of metro lines, which is in line with the characteristics of the light-load operation of the tram. The formation mechanism of corrugation on modern tram sharp curves can be described as wheel-rail contact creep saturation leads to the generation of corrugation, and the source of creep is related to the guiding mechanism of the longitudinally coupled independent wheelset bogie. The formation of corrugation on the straight line of the modern tram is related to the difference in wheel diameters between the front and rear wheelsets on the same side, which can lead to different longitudinal velocities, thus generating longitudinal creep forces and inducing rail corrugation. [ABSTRACT FROM AUTHOR]