Oil transfer station plays a crucial role in the oil and gas gathering and transportation system of an oilfield, ensuring stable production and continuous supply of oil and gas. However, given the complexity of its process system and the ambiguous uncertainty surrounding fault modes and relationships, a systematic reliability assessment method integrating T-S fuzzy fault trees with BNs(Bayesian networks) was proposed. Firstly, a T-S fuzzy fault tree was established based on T-S gates and their descriptive rules, which is subsequently converted into a Bayesian network model. Secondly, leveraging limited fault samples and general data sources, Bayesian updating estimation was employed to determine the failure rates of basic events, addressing the uncertainty inherent in fault sample data. Lastly, the T-S fault tree and BN model were synergistically utilized for forward reasoning to predict the reliability of the process system and the contribution of basic events, while reverse diagnosis is conducted to pinpoint the key factors causing different fault states of the system. Research conducted on typical oil transfer station process systems has demonstrated that the proposed method can effectively predict system failure rates and diagnose weak links even under conditions of uncertainty in basic data and event relationships. This provides crucial decision support for the optimal design and reliability maintenance of complex oil and gas process systems.

Underground coal mining faces hazards like gas explosions, coal dust explosions, and fires, underscoring the need for safety robotics based on 3D reconstruction. While essential for precise navigation and detection in complex environments, traditional methods fall short in data quality, accuracy, and cost. To address this, a 3D reconstruction method called 2DGS-DbTrans for underground coal mine tunnels was proposed, which is based on pure vision-based 3D reconstruction technology. To improve image resolution, a Transformer module was designed to enhance the input images, consisting of two core components: the multi-head depth convolutional axis attention mechanism and the deep convolutional gated network. In the processing workflow, sparse point clouds were first generated using Colmap, and the underground mine tunnel environment was represented by 2D Gaussian surfaces, where each Gaussian surface contains the coordinates, color, size, and orientation information of the objects. In addition, two loss functions were defined: the color loss function and the road smoothness loss function. Experimental results show that the 2DGS-DbTrans method outperforms other methods in 3D reconstruction of underground coal mine tunnels.

With the rapid development of urban rail transit in China, a large amount of muck is generated during shield tunneling, and its transportation and landfill will cause serious environmental issues. Based on the Suzhou Metro Line 8 section 6, full life cycle assessment method was used to analyze and compare the carbon emissions of conventional disposal and resource utilization of shield muck. Research results indicate that the total amount of muck produced during shield tunneling is 2.64×10⁵ m³ in this project. When traditional burial treatment is carried out for shield muck, the carbon emissions throughout its entire life cycle will reach 3.00×10⁶ carbon emission equivalents. In this case, 5.28×10⁴ m² of land with a 5-meter landfill depth will be occupied by shield muck. If shield muck and industry-byproducts are used for preparing synchronous grouting materials, the 28-day strength of the grouting material can reach 2.5 MPa. Grouting raw material cost by 467 thousand yuan/km will be reduced, and about 4.6×10⁵ carbon emission equivalents per kilometer will be reduced. The findings provide reference for optimizing the resource utilization of shield muck.

The deep-sea is rich in polymetallic nodules resources that are promising for mining, but the process of mining poses a number of environmental problems, in particular the migratory diffusion of plumes formed by sediments disturbance. To evaluate the environmental impact of sediment setting behavior during polymetallic nodule mining, seafloor surface sediments and seawater from the Beijing Pioneer polymetallic nodule mining area in the northwestern Pacific Ocean were employed. Sedimentation experiments were conducted to investigate the deposition characteristics of the suspended sediments. The results indicate that the deep-sea sediments are primarily composed of viscous particles, with median particles sizes ranging from 2.57 to 4.47 μm, and maximum particle size of 66.9 μm. The relationship between settling velocity and particle size was analyzed, showing that the settling velocity of viscous particles increased due to flocculation effects. After 5 hours of sedimentation, the median particle size of suspended sediments decreased to below 0.1 μm, and the maximum optical density has observed at 14 minutes after the onset of sedimentation. A positive correlation is observed between the mass concentration and optical density of the suspension. By fitting the temporal variation of sediment mass concentration, the settling pattern is determined. The mass concentration reached its peak 14 minutes after the onset of settling, and decreased to 2.46% of the initial value after 5 hours, indicating that 97.54% of the total mass has settled. These findings provide supporting data for environmental impact assessments related to plume behavior during deep-sea polymetallic nodule mining.

In order to improve the accuracy of subtle fault identification, an artificial intelligence subtle fault prediction method based on the seismic data was developed. By making sample labels based on subtle fault interpretation results, a sample label library based on interpretation results was built. The subtle fault modeling and forward methods for subtle faults were developed, and a label library based on model forward was built. The special neural network for identifying subtle faults was developed, which can directly generate attribute data for subtle faults. In addition, the seismic preprocessing approaches such as removing strong seismic events and structure oriented smoothing filtering were added to improve the original seismic data. Multi-attribute fusion based on principal component was used to reflect multi-scale faults. Finally, the prediction results were verified through three steps, forming the subtle fault prediction workflow with artificial intelligence. This study demonstrates good application in the Daniudi gas field in the Ordos Basin. Compared with conventional attributes, the number of subtle fault identification has increased by 30%, and the resolution and continuity of the subtle faults have significantly improved. The prediction results are consistent with seismic data, well data, and conventional seismic attributes. Based on the subtle faults identification, the new understandings of regional structure are revealed: the dominant orientation of subtle faults is northwest, the most subtle faults are relatively concentrated in the western and northeastern parts of the survey, and the fractures between faults are also very dense. Some subtle faults are distributed along the boundary of the high-quality reservoir, perhaps related to the development of reservoirs. The prediction results contribute to evaluating high-quality reservoirs in the Lower Paleozoic and well deployment, and have application prospects for similar areas.

The growing sophistication of deepfake speech poses significant security threats to ASV(automatic speaker verification) systems. Current anti-spoofing models based on CNNs(convolutional neural networks) are constrained by inadequate global feature extraction and limited generalization capability against unseen spoofing attacks. To address these challenges, a novel network architecture integrating CT-DSCNet(channel-temporal attention mechanisms with depthwise separable convolutions) was proposed. Building upon the RawNet2 framework, the developed model incorporates dual-domain attention modules to enhance discriminative feature representation while suppressing irrelevant acoustic artifacts. Furthermore, depthwise separable convolutional residual blocks were strategically implemented to optimize computational efficiency and real-time processing capabilities. Comprehensive evaluations were conducted across three benchmark datasets: ASVspoof2019 LA, ASVspoof2021 DF, and FMFCC-A. Experimental results demonstrate state-of-the-art performance with EER(equal error rate) of 1.53% on ASVspoof2019 LA, representing a 70.58% relative improvement over baseline systems. Notably, the proposed architecture exhibits superior cross-dataset generalization, achieving a 25.35% lower EER on the FMFCC-A evaluation set compared with conventional approaches. These findings validate the effectiveness of the hybrid attention-convolution design in advancing spoofing detection robustness and domain adaptability.

To address the issues of slow convergence speed and susceptibility to local optima in ELM (electromagnetism-like mechanism) algorithm for fault location problems in distribution networks, BELM (binary electromagnetism-like mechanism) algorithm was proposed. First, the Sobol sequence was introduced to initialize the population to ensure the quality of the initial population. Second, based on the fitness value, the population was divided into a high-quality population and an ordinary population, and an optimal particle guidance strategy and a local search strategy based on the XOR operation were adopted for these two sub-populations, respectively, the former guides the high-quality particles to the potentially optimal location to accelerate the convergence speed, the latter performs global exploration and enriches the diversity of the population by exchanging information with the elite particles. Finally, the search efficiency of the algorithm was further improved by the improvement of the combined force calculation and particle movement rules. The simulation results show that compared with other algorithms, the proposed algorithm demonstrates superior accuracy and rapid convergence in locating faults within distribution networks.

Low level wind shear is an important factor affecting aircraft flight safety. Based on the observations from a three-dimensional scanning wind lidar at Baiyun Airport, Guangzhou during March 2023, the measurements from the wind lidar were preprocessed firstly. Then, TSSI (two-step identification method for wind shear) was proposed, which combined TDSI (two-dimensional synthetic wind shear identification) method with an adaptive window and the temporal wind shear identification method. The wind shear results recognized by the TSSI and TDSI methods were compared, and the evolutions of wind shear were analyzed. The main conclusions are as follows. Data preprocessing effectively removes isolated points and radial fluctuations observed by wind lidar, and fills in the missing data. The TSSI method is conducive to early warning of wind shear. During the observation period at Baiyun Airport, Guangzhou in March 2023, a total of 25 wind shear processes are identified by the TSSI. Among them, 21 cases are warned ahead of the TDSI, with an average warning time of 3~5 minutes, and TSSI also has a good alarm recognition function for both time and space dimension wind shear. Most of the identified wind shear processes occur around noon (e.g. 11:00-15:00) and last for about 15 minutes. The wind shear position is greatly influenced by the background wind field. The TSSI method proposed in this study can identify low-level wind shear earlier and more comprehensively, which is helpful to improve the accuracy of wind shear warning and provide guarantees for aircraft flight safety.

Dual-mode communication based on wireless and PLC (power line communication) can complement each other, and is widely used in smart metering and power Internet of Things. For the application of OFDM (orthogonal frequency division multiplexing) dual-mode communication system in new energy low-latency service access and other applications, an OFDM subcarrier diversity combination and power adaptive allocation algorithm considering the non-ideal channel estimation was proposed. Firstly, an adaptive optimal power allocation model was established with the constraints of service data volume and transmission power and the goal of minimizing the total delay. Then, on the basis of diversity grouping, whale optimization algorithm and threshold recovery were used to optimize power allocation, to achieve the compromise between algorithm complexity and performance. The simulation results show that the proposed algorithm can reduce the average transmission delay and stability while satisfying constraints such as rate, providing enhanced performance guarantees for real-time data acquisition in applications such as distribution equipment status monitoring.

The stability of double-limb high pier cannot be ignored in the process of construction. In order to ensure the safety of construction, finite element models were established for three construction stages: bare high pier, large cantilever, and completed bridge to study the stability of double-limb high pier, based on the Miaoluhe bridge of Zhengzhou-Luoyang Expressway. The buckling modes and stability coefficient of double-limb high pier were obtained, and the nonlinear stability analysis of the most unfavorable construction stage was carried out. Finally, the influencing factors on stability such as the alignment deviation, the hole defects, the pier height and the number of crossbeams were analyzed. The results show that under the three construction stages, the double-limb high pier are longitudinal instability, and the stability coefficient of the most unfavorable conditions are 70.092, 33.513, and 55.034.After considering geometric nonlinearity and geometric and material double nonlinearity, the stability of the large cantilever stage has decreased by 15% and 57%, compared to linear analysis. The alignment deviation and the hole defects occurring in the climbing formwork construction of double-limb high piers have an impact on the stability. Therefore, it is necessary to control alignment deviations and repair holes in time. As the height of the pier increases, stability decreases continuously. When one crossbeam is installed between the two limbs, stability improves by 140%. However, further increasing the number of crossbeams does not significantly improve stability. Therefore setting one crossbeam within the range of 50~70 m pier height is the best.