Micro-nano pores are developed in unconventional oil and gas reservoirs such as shale and tight sandstone, and the study of oil-water two-phase seepage law and relative permeability in micro-nano pores is the theoretical basis for the effective development of such reservoirs. Addressing the two-phase flow of oil and water within nanopores, a mathematical model was established for nanopore-scale two-phase oil-water flow, grounded on the Hagen-Poiseuille (HP) equation and taking into account microscale seepage mechanisms such as oil-water distribution, viscous coupling, multi-layer adsorption, and slippage. Based on this model, a calculation method for relative permeability was developed. The validity of the model was verified by fitting the results from Lattice Boltzmann method (LBM) simulations. Furthermore, through a parametric sensitivity analysis, the characteristics of two-phase oil-water flow and the influence patterns of relative permeability within nanopores were investigated. The results showed that the slip length significantly impacted the velocity distribution of oil and water. An increase in the wetting contact angle led to varying degrees of augmentation in both oil and water relative permeabilities. As the viscosity ratio rose, the oil-phase relative permeability experienced a notable increase, while the water-phase relative permeability remained relatively unchanged. The presence of positive slip could result in relative permeabilities exceeding unity. As the pore radius enlarged, the pore area available for fluid flow expanded, thereby enhancing both oil and water relative permeabilities. This study holds guiding significance for elucidating the fluid flow mechanisms in micro-porous media and facilitating the exploitation of shale oil.

Conventional diagnostic methods that require a large amount of data support in practical engineering are difficult to effectively perform centrifugal pump fault diagnosis under small sample conditions. Therefore, the residual network (ResNet) in deep learning was combined with dilated convolution and extended into a siamese network to construct a dilated residual siamese network (DRSN). The dilated residual network was used as the feature extraction module of the siamese network, which enhanced the feature extraction ability of the model. Positive and negative sample pairs were constructed to extract more information from each sample, and make more effective use of limited data.The two sub-networks share parameters, the number of free parameters and lowering the risk of overfitting was reduced when the sample was insufficient. The proposed network model alleviated the problem of insufficient training samples, improved the efficiency of data utilization, and realized the fault classification of centrifugal pump under the condition of small samples. The research results show that even in the most sample-scarce situation, the accuracy of the model on the centrifugal pump test dataset can still reach 82.20%, which is at least 8.8 percentage points higher than other models.

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 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.

An open world object detection method based on shape perception and class balance optimization was proposed to address the issue of poor prediction performance of unknown class objects in open world object detection. Unknown classes referred to classes that were not labeled during the training phase. Due to the lack of guidance from labels, detecting unknown class objects was a challenging task. An unknown class enhanced detector has been constructed as an unknown class detection branch. During training, this detector was supervised using only known class labels, allowing it to learn the similarities in features of known class objects and generalize to unknown class objects. To improve the detector's sensitivity to unknown classes, the region proposal network (RPN) module's ability to distinguish between foreground and background was utilized. A specific filtering method was employed to select results with “unknown class potential” from the RPN output, which were then used as pseudo labels in the training process. Due to the absence of confidence scores, traditional non-maximum suppression (NMS) methods were difficult to apply for post-processing unknown objects. Therefore, a redundant unknown object suppression mechanism was designed, consisting of a center point-based grouping strategy and a redundancy score matrix based on shape perception. The center point-based grouping strategy included three methods based on the unknown class center points to determine the suppression range. Subsequently, a redundancy score matrix was constructed based on the redundancy scores of each prediction box within the group to suppress highly redundant predictions. Experimental results on open world object detection datasets demonstrated that the open world object detection based on shape perception and class balance optimization maintained high recall rates for unknown classes while achieving high prediction accuracy. This method effectively addressed the challenges of open world scenarios and avoided generating a large number of useless predictions.

To address the issue that traditional fault diagnosis methods struggle to accurately diagnose faults in the nuclear reactor coolant system (RCS) of nuclear power plants under uncertain conditions, a dynamic fuzzy radial basis function neural network (DFRBFNN) model was established for RCS fault diagnosis following these steps. First, based on the fault types and sample data of the RCS, the initial structure of the DFRBFNN model was determined. Then, using the radial basis function neural network method, the initial DFRBFNN model for RCS fault diagnosis was constructed, and a random initialization method was applied to initialize the connection weights from the defuzzification layer to the output layer of the initial DFRBFNN model. Finally, the error reduction rate method was used to adjust the structure and parameters of the initial DFRBFNN model, resulting in the final DFRBFNN model for RCS fault diagnosis. The established model was applied to diagnose loss of coolant, flow loss, and steam generator tube rupture accidents, and its performance was compared with traditional fault diagnosis models to verify its effectiveness. The research shows that the constructed DFRBFNN model can accurately diagnose RCS faults under uncertain conditions.

Forest fires have the characteristics of strong suddenness, great destructiveness, many uncertain factors and high risk of fighting. In order to study effective strategies of firefighting, firstly, based on the theory of cellular automata, the forest fire system was analyzed, and a forest fire model considering external factors such as wind and flame retardant was established. Then, on this basis, the fire-fighting agent was modeled and correlated with the forest fire model, so as to build the fire-fighting model. Finally, the simulation algorithm based on cellular automata was designed to simulate the effect of fire-fighting strategies under the influence of different environmental factors, and the effect of different fire force allocation strategies. The results show that the method can combine fire-fighting simulation with actual decision making, and provide visualized and quantified strategy scheme for relevant departments to make fire-fighting decision, which is helpful to reduce forest fire loss and rescue cost.

Defect detection is regarded as an indispensable step in the industrial production process. At present, manual detection is faced with the problems of low efficiency and high cost. A ceramic small target defect detection algorithm based on deep learning was proposed. For small target defects, a slice pre-training layer was first added to reduce the loss of graphics memory resources by large-size images. Secondly, a small target detection layer was added for the detection of small target defects, and a large target detection layer was removed to reduce the number of parameters. In addition, a feature selection fusion module based on MLCA (mixed local channel attention) was proposed to improve the perception of small target defects. Finally, a detection head with shared parameters was designed to further reduce the number of learnable parameters of the algorithm. By comparing with the baseline model, taking the ceramic cup as an example, the detection accuracy of this algorithm has been improved by 20.9%. Combined with the developed detection software and experimental platform, the detection efficiency of the ceramic cup has been enhanced by about 46.9%.

To design and prepare high-quality one-dimensional hexagonal quasicrystal nano-composites, the interface and interface phase models were applied to study the infinite one-dimensional hexagonal quasicrystal anti-plane fracture problem with cylindrical inclusions containing nano coatings by using the complex function method and Gurtin-Murdoch's surface/interface elasticity theory. Under two different models, the series form expressions of phonon and phason field stress fields in matrix, coating and inclusion were obtained, respectively. Numerical examples were used to analyze the effects of interface elastic constants and size effects on the stress field around inclusions. The results showed that the positive or negative values of interface elastic constants would affect the stress field around nano-inclusions. As the size of nano-inclusions increased, the stress field exhibited significant size dependence, and surface effects had significant differences in their effects on the stress fields of dimensionless phonon and phason fields. The relevant results provide a certain theoretical reference for studying the mechanical behavior of quasicrystalline nano-inclusions.

To effectively address the contradiction between the uncertainty of the internal and external environment in the construction industry and the complexity and vulnerability of the construction supply chain, as well as to promote the overall security and stability of the construction supply chain network, and to prevent and mitigate the risk of disruption among node enterprises, a construction supply chain network invulnerability analysis method was proposed based on the complex network theory and cascade failure model. Firstly, from the perspectives of business, resources and information flow, the TOPSIS(technique for order preference by similarity to an ideal solution ) method was used to assess the importance of node enterprises based on multiple complex network centrality indicators. Secondly, combined with the operational characteristics of the construction supply chain, an improved load-capacity-elasticity cascade failure model was established to measure the impact of enterprise disruption from the perspective of network loss under intentional attack, and to analyze and explore the network invulnerability improvement strategy from the perspectives of node capacity, load, and resilience. After numerical simulation and analysis, the results show these as follows. When the upstream node enterprises of the construction supply chain network suffer from the impact of disruption risk, the supply chain network can show strong network invulnerability, but it should focus on the downstream supplier enterprises, so as to avoid the network as a whole suffering from more losses due to the shortage of the supply of construction materials or basic services. To keep the small difference in the business capacity among node enterprises, the large difference in the business load and an appropriate high level of risk remediation cost investment can effectively reduce the loss of the supply chain network when the node enterprises are interrupted, thus improving the level of network invulnerability. Among the multiple types of strategies, the node capacity strategy is better than the node resilience strategy and the node load strategy in order to improve the network invulnerability. The results of the study can provide scientific references for improving the security level of construction supply chain and proposing disruption risk management strategies.