After fracturing, the solid particles carried by the high speed liquid will cause serious erosion to the oil nozzle, and it is difficult to ensure the stable operation of the oil nozzle. To address the serious erosion problem of the nozzle, numerical simulation was employed to study the erosion wear of the nozzle, and the influence patterns of sand content, sand grain diameter, sand grain density, pump displacement, and liquid viscosity on the erosion wear of the nozzle were analyzed. The research indicates that: when the sand content and liquid viscosity increase, the maximum erosion rate exhibits linear growth; when the sand grain density and pump displacement increase, the maximum erosion rate exhibits exponential growth; and when the sand grain diameter increases, the maximum erosion rate shows exponential decrease. The orthogonal test method is used to judge the significance of each factor. The factors affecting the erosion wear of the nozzle are as follows: sand content ratio > pump displacement > sand density > sand diameter > liquid viscosity.Based on the results of numerical simulation, the machine learning method is used to compare and analyze SVR(support vector regression), CNN(convolutional neural network), BP(back propagation) neural network and RFR(random forest regression) algorithm to predict the erosion wear results of oil nozzle respectively. By preferring the SVR algorithm and adopting the particle swarm optimization algorithm to optimize the prediction model, a better nozzle erosion prediction model is obtained.

In view of the high cost of controlling all the nodes in the traffic network, a pinning control framework for urban traffic network analysis and control network in the case of limited resources were constructed by this paper, and a new pinning control algorithm for urban traffic network was proposed.By using the mutual coupling and containment relationship between nodes and controlling some key nodes in the road network, the expected behavior of the whole network was guaranteed, and the limitation that the system consumes too much computing and control resources were effectively solved.The control input was set as the variation of the duration of the green light, a new pinning controller was designed, and the conditions for effectively ensuring the stability of the urban road traffic network are proposed.Through the simulation analysis, the signal control method proposed can make the urban road traffic network achieve the desired state, and effectively improve the utilization of road resources in the case of limited infrastructure and control costs.

In order to investigate the role of rheumatoid arthritis (RA)-related pathways in lung squamous cell carcinoma (LSCC). By obtaining gene expression data for RA and LSCC from the GEO and TCGA database, differentially expressed genes were screened using GEO2R tool and Rstudio software. GO/KEGG functional enrichment analysis identified key genes in the RA signaling pathway. Combining SNP data from the IEUopenGWAS database, Mendelian randomization analysis was used to assess the causal relationship between the RA signaling pathway and LSCC. The constructed gene-drug and ceRNA networks, along with immune cell infiltration analysis, revealed 188 co-expressed differential genes, mainly enriched in the RA signaling pathway. Mendelian randomization analysis showed that increased activity of the RA signaling pathway is associated with a reduced risk of LSCC. This study provides new insights into the pathogenesis and potential research directions for the treatment of LSCC.

Corn is one of the important grain reserve crops in China, and its yield directly impacts national food security. The chlorophyll content of corn is closely related to its photosynthetic capacity and significantly affects the photosynthetic rate of the leaves and vegetation productivity. It is an important crop parameter for monitoring crop growth, pest and disease surveillance, and maturity prediction. Real-time and accurate monitoring is of great significance for corn parameters and yield prediction. This study was conducted in the typical black soil area of Lishu County, Siping City, Jilin Province. To solve the problem of missing effective images that may occur during the revisit period of Sentinel-2 satellites, a method for retrieving corn leaf chlorophyll based on the fusion data of Sentinel-2 and MODIS images was proposed. Using fused imagery, three machine learning algorithms were employed: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBOOST) to construct a model for estimating corn leaf chlorophyll content, and the accuracy of the model was verified. The conclusions obtained were as follows. The data simulated using the ESTARFM data fusion algorithm maintained a high correlation with the real imagery. Among the leaf chlorophyll inversion models for missing image dates, where input variables included fused image band reflectance and vegetation index, the XGBOOST model showed good fitting accuracy The research demonstrates that accurate estimation of leaf chlorophyll content can be achieved even on days with missing imagery, when fusion image feature bands are integrated with machine learning algorithms. This notably improves the temporal precision of corn chlorophyll content measurement, presenting a novel method for daily or large-scale inversion studies of leaf chlorophyll content, particularly in scenarios involving image gaps. Furthermore, it illuminates the potential for refined monitoring of physiological and biochemical parameters across a wider range of crops, with shortened time intervals.

The high-precision fault diagnosis of cross modal high-dimensional fault data under unsupervised conditions is a challenging problem. To address this issue, a rotating machinery fault diagnosis method based on unsupervised cross-modal Euler discriminant space (UCEDS) was proposed. In this method, cross-modal fault data samples were mapped to Euler representations through cosine metrics to enhance the differences and separability between different types of fault samples. Then, an unsupervised cross modal Euler discriminant space learning model was constructed in this space, and the analytical solution of the model was theoretically derived. This model not only considered the local neighborhood structure of fault samples, but also effectively discovered the local structural information of complex and nonlinear fault feature samples. At the same time, on the basis of cross modal consistent discriminative fusion, it further improved the complementarity between low dimensional discriminative feature subsets. Targeted experiments on the Paderborn fault bearing dataseht showed that the proposed UCEDS method had superior fault diagnosis and classification performance.

The Yingxiongling shale oil reservoirs in the Qaidam Basin are notably characterized by the development of the laminated shale and thin-layered shale. In order to identify the superior sweet spots, an experimental study on the physical and mechanical properties of laminated and thin-layered shales was conducted, and the sweet spots of the Yingxiongling shale oil reservoirs were systematically evaluated in conjunction with the analysis of oilfield production data. The results show that laminated shale exhibits higher total organic carbon (TOC) content, stronger hydrocarbon generation capacity, and higher initial oil saturation compared to thin-layered shale. Although laminated shale has lower porosity, it exhibits stronger anisotropy, higher stress sensitivity coefficient, and more developed initial natural microcracks. Additionally, the laminated shale demonstrates high horizontal permeability, strong fluid absorption capacity, and effective imbibition oil displacement ability. Its lower compressive strength facilitates the formation of complex fracture networks. The experimental research results are consistent with the in site fluid production analysis. The fracture morphology generated by the laminated shale is more complex with strong oil displacement ability and high oil displacement efficiency, indicating that the laminated shales produce fluids first; therefore, it is concluded that the laminated shales are the preferred sweet spots. The findings of this study have important theoretical significance for the exploration and development of shale oil in Yingxiongling.

Compressed air energy storage, as a new energy storage technology, plays an important role in peak shaving and valley filling. Based on the compressed air energy storage with abandoned oil wellbores, a pipeline-wellbore gas storage chamber, that is, the storage space was composed of above ground pipelines and underground wellbores, was proposed. Its inflation process was simulated, with a focus on analyzing the thermodynamics and flow characteristics of the internal gas. The results showed that with compressed gas flowed into the pipeline wellbore gas storage chamber, the gas temperature rapidly increased under the heating effect of the high-temperature wellbore wall. Subsequently, the temperature of the gas became slightly higher than that of the wall, at this point, a heat release of the gas to the wall. The gas temperature and heat dissipation tended to remain stable until the gas storage pressure rose to about 3 MPa. Due to the presence of geothermal gradient, there were significant differences in gas characteristics in different areas of the underground wellbore during the inflation process. As the depth of the wellbore increased, gas flow rate, density, and frictional resistance decreased. With the increase of the gas storage pressure, the differences in the gas flow rate and frictional resistance in different areas diminished. The results of this study provide significant theoretical insights that can effectively inform the practical application of compressed air energy storage systems, particularly those that employ underground wellbores as the repository for gas storage.

The oscillating water column buoy utilizes wave energy by channelling waves into an air chamber, inducing oscillations within the water column. However, wave impact also causes buoy oscillation and rocking, which reduces the relative water column displacement. To address this, a double-layer concave damping plate with a weight-enhancing ring was implemented to stabilize buoy movement. A double-concave damping plate with a weight-enhancing ring was used to inhibit the movement of the floating buoy and increase the pressure of the air chamber. Based on the small amplitude wave theory and Newton's second law, a theoretical model was developed to analyze wave energy capture by the damping plate-enhanced oscillating water column buoy, calculating buoy oscillations and air chamber pressure characteristics. The finite element simulations, conducted using AQWA software, replicated the wave-induced hydrodynamic effects on the buoy. Air chamber pressure was simulated via Fluent software's fluid volume method and open channel wave-making method, and the model's accuracy was validated against theoretical calculations. The simulation results show how effective the damping plate is in limiting buoy motion, raising mass and buoy inertia, and improving water column stability in the air chamber. The theoretical calculation of the air chamber air pressure parameters provides a basis for the design of the damping plate oscillating water column buoy wave energy harvesting system and the green low-carbon energy conversion structure.

To address the issues of incomplete feature extraction, poor stability, and limited generalization in traditional fault diagnosis models, a model based on a multi-scale convolutional neural networks (MCNN), bidirectional gated recurrent units (BiGRU), and multi-head self-attention mechanism (MSA) was proposed. The model was designed to achieve comprehensive feature extraction from both spatial and temporal perspectives. It took raw vibration signals as input, and multi-scale features were extracted through convolution kernels of different sizes. A multi-head self-attention mechanism was used to dynamically adjust output weights, disregarding redundant information and weighting the extracted features for fusion. Then the fused features were input into a BiGRU network, which utilized a bidirectional information fusion mechanism to explore information from both past and future directions, capturing dependencies between different parts of the input sequence. Finally, Softmax was employed for classification. Experimental validation was conducted using three bearing fault datasets, and the results show that the proposed model has excellent performance metrics on different datasets and showcases good generalization and feasibility.

The estimation of the state of health (SOH) for lithium-ion batteries is considered crucial for ensuring the safe and stable operation of battery management system. However, the accurate estimation of SOH has been a challenge due to the capacity regeneration phenomenon during the discharge process of lithium-ion batteries. To improve estimation accuracy, a hybrid model based on variational mode decomposition (VMD) and bidirectional long short-term memory network with attention mechanism (BiLSTM-ATT) was proposed. First, the battery capacity was decomposed using the VMD algorithm, producing a set of stable sub-sequences. Then, permutation entropy was introduced to reconstruct the sub-sequences to reduce computational complexity. The reconstructed sequences were input into the BiLSTM-ATT model, and feature weights were assigned by the attention mechanism. The SOH values were trained and estimated by the BiLSTM model. Finally, the complete SOH estimation result was obtained by summing all estimated values. Validation was performed using the CS2_36, CS2_38, and CX2_35 datasets from the CALCE lithium battery dataset. The results show that the proposed algorithm maintains a root mean square error within 0.6% and a mean absolute error within 0.4%, which demonstrates higher accuracy and performance compared to other estimation models.