In order to solve the problems of inaccurate dense target recognition and difficult detection of small targets in bird recognition, a bird recognition algorithm based on improved YOLOv8 was proposed. Firstly, in order to solve the problem of difficult dense object recognition, the multi-scale linear attention mechanism EfficientViT was used to replace the backbone network to realize the global receptive field and multi-scale learning, improve the performance and efficiency of the model, and improve the dense object recognition effect. Then, in order to solve the problem that it is difficult to detect small target birds and is prone to missed detection, an efficient multi-scale attention EMA (efficient multi-scale attention) mechanism was introduced to realize cross-dimensional aggregation features through channel recombination, so as to better capture global information, realize multi-scale feature fusion, and reduce the probability of missed detection. The experimental results show that the mAP50 of the improved model on the benchmark dataset CUB-200-2011 and birds28 reaches 77.1% and 88.4%, respectively, which is 4.5 and 5.4 percentage points higher than the original YOLOv8 model, respectively, which verifies the effectiveness of the improved model.

In order to explore the strengthening effect of reinforced concrete beams strengthened by carbon fiber reinforced polymer reinforced geopolymer matrix (FRGM), the mechanical properties of reinforced concrete beams reinforced with flexural reinforcement reinforced concrete in FRGM system were studied by numerical simulation. Three-dimensional finite element models were established to simulate the failure mode, characteristic load and load-mid-span deflection curve of reinforced concrete beams reinforced by FRGM system, and the influence of the thickness and length of the FRGM layer and the pre-damage degree of the original specimen on the reinforcement effect was discussed. The results show that the thickness of the FRGM layer has no obvious effect on the ultimate bearing capacity of reinforced concrete beam, and the increase range is 72.29%~79.38%, but the stiffness of the FRGM layer is improved to a certain extent, up to 38%. The ultimate bearing capacity of reinforced concrete beams can be increased by increasing the length of the FRGM layer, but with the increase of the length of the FRGM layer, the increase of the ultimate bearing capacity gradually weakens, indicating that there is no linear increase relationship between the length of the FRGM layer and the ultimate bearing capacity. Compared with the original components, the bearing capacity of reinforced concrete beam members is improved to a certain extent after pre-damage reinforcement(36.5%~73.66%), which shows the effectiveness of FRGM reinforcement method.

It is of great significance for accurate forecasting of multi-load to be carried out to improve the consumption of new energy, realize energy saving and emission reduction, and ensure the safe and reliable operation of the power grid. To enhance the accuracy of simultaneous multi-load forecasting,a model which singular spectrum analysis and bi-directional long short-term memory networks SSA-BiLSTM (singular spectrum analysis-bidirectional long short-term memory) was proposed. First, A approach Pearson correlation coefficients for coupled feature extraction was proposed to identify correlations and dependencies within multivariate load data. Then, SSA was employed for feature extraction to capture dynamic characteristics and reduced forecasting complexity. Finally, a multi-ask learning framework was introduced to leverage shared information among multiple forecasting tasks, improving prediction accuracy. Experimental using datasets from multi-area electricity, heat, cold multivariate loads, flexible and wind-solar power generation, the effectiveness of the model. The results show that the proposed model average improves in mean absolute percentage error (MAPE) for the prediction of electrical, heating, and cooling loads in multiple regions is 0.41%, with an average root mean square error (RMSE) increase of 0.02 MW.

CLT(cross-laminated timber) shear wall structure has emerged as one of the rapidly advancing mid-to-high-rise timber structural systems in recent years. Extensive research on the lateral resistance performance of CLT shear walls has been conducted by both domestic and international scholars. A comprehensive synthesis of findings concerning lateral resistance capabilities was conducted for CLT shear walls, including single-panel, multi-panel, and CLT shear walls with openings. Failure modes, load-bearing capacities, and stiffness characteristics were systematically examined across these structural variations. Comparative evaluations of multiple calculation methods for lateral bearing capacity and stiffness determination were performed, alongside a compilation of standardized methodologies from domestic and international specifications for timber shear wall analysis. Specialized recommendations were formulated specifically for CLT structural applications. Current research advancements were consolidated, and strategic directions were proposed to guide subsequent investigations into CLT shear wall performance under lateral loading conditions, establishing critical references for ongoing research development in this specialized engineering field.

In order to analyze the design parameters and technology adaptability of multi-stage temporary plugging and fracturing in the Triassic Chang-6 reservoir of a block in Suijing Oilfield. Based on the geological and engineering design data of the block, the influencing factors of multi-stage temporary plugging and fracturing were quantitatively analyzed in terms of reservoir characteristics, technology parameters, and construction effect. The combined weighting method was used to determine the weights of each parameter, and the ridge-type membership function was used to determine the membership degree of each factor. Based on the principles of fuzzy transformation and maximum membership degree, a fuzzy comprehensive evaluation model for multi-stage temporary plugging and fracturing was established. This model transforms the traditional single-index qualitative development effect evaluation into multi-factor quantitative evaluation. The model was applied to evaluate and analyze the Triassic Chang-6 reservoir in a block of Suijing Oilfield. The results show that this evaluation model results have an adaptation rate of 89.13% when compared to the actual effect data in the field, which can effectively evaluate the effectiveness of the implementation of temporary plugging and fracturing measures in the oil wells of the study area. The evaluation system can provide valuable references for the next implementation of temporary plugging and fracturing measures in the study area, and even optimize parameters for multi-stage temporary plugging and fracturing transformation in similar reservoirs.

In order to study the development law of pile top cumulative displacement of ring wing single pile foundation under horizontal cyclic load, a three-dimensional numerical model of the interaction between ring wing single pile and saturated clay was established through secondary development using ABAQUS, and also simulated the process of soil stiffness attenuation. Numerical results indicate that installing gravity type ring wings at the mud surface position of traditional single pile foundations can enhance the overall horizontal resistance of the ring wing single pile foundation, thereby reducing the cumulative displacement of the ring wing single pile under cyclic loads. The displacement of the pile top will decrease with the increase of the height and diameter of the gravity type ring wing, but increasing the diameter of the gravity type ring wing has a more significant effect on reducing the horizontal displacement of the pile top. Increasing the depth of the pile into the soil can significantly reduce the cumulative displacement at the top of a single circular wing pile under cyclic loading.

Accurately and in real-time understanding the changes in the scope and species communities of intertidal wetlands is an important foundation for achieving sustainable development and management of wetland intertidal zones. In recent years, global warming, rising sea levels, and human activities such as coastal development, reclamation, and aquaculture have caused serious damage to the intertidal zone. At present, there is a lack of systematic research on the classification of mangrove tidal flats in the intertidal zone of Guangxi. In order to achieve large-scale and high-precision extraction of intertidal resources in Guangxi, this article was based on GEE (Google Earth Engine) platform, using Landsat series image data of Guangxi coastal zone from 2012 to 2022, and threshold segmentation processing of the images. The various remote sensing features under the influence of tidal dynamic inundation were analyzed, and the intertidal zone range of coastal wetlands in Guangxi was extracted. Achieved the classification of tidal flats and water bodies, mangrove vegetation, and non-mangrove vegetation in the study area, with areas of 5 641.67 hm², 1 625.29 hm², and 2 156.04 hm² respectively. The overall classification accuracy reached 93.3%, with a Kappa coefficient of 0.9.

With the improvement of intelligence, the drilling industry ’s demand for real-time identification of lithology while drilling was becoming more and more urgent. An intelligent inversion method of lithology while drilling is proposed based on the acoustic signal and vibration signal ( acoustic vibration signal ) of broken rock during drilling. Firstly, the original signal samples were obtained by drilling seven different types of rocks through indoor micro-drilling experiments. During the acquisition process, the drilling parameters ( drilling speed, rotation speed, bit size ) were changed and the corresponding signal data were obtained. According to the characteristics of the collected acoustic vibration signal, the time-frequency image with signal characteristics was obtained by short-time Fourier transform. On this basis, an improved VGG16 convolutional neural network model was constructed to realize the intelligent identification of lithology, and the training, evaluation and tuning of the model are realized by hyperparameter optimization. Then, the transfer learning training strategy is introduced, and different drilling parameters were used as data labels. According to the parameter values, the source domain and the target domain were divided to realize the rapid identification of the small sample target domain. The experimental results show that the transfer learning results of the model are different with the change of drilling parameters. The lithology inversion model based on acoustic-vibration signal training has high prediction accuracy and strong generalization ability. The accuracy of the acoustic signal test set is up to 99%, and the accuracy of the vibration signal test set is up to 100%. Under the change of penetration rate, the acoustic and vibration signals are least affected, which can achieve more excellent results when used as data labels for lithology inversion, and the accuracy of lithology inversion is the highest when the penetration rate is small as the target domain. In the process of lithology inversion, different signal types are suitable for different rocks. Among them, the sound signal has the highest applicability to coarse yellow sandstone, and the vibration signal is more suitable for granite. The research results have certain reference value for improving the intelligent degree of working face drilling.

Due to the complex reservoir conditions and multi-scale pore structure of shale gas, the production shows significant nonlinear characteristics over time. Traditional production prediction methods, which rely on statistical analysis of geological and engineering data, find it difficult to adapt to the complexity of geological conditions and thus cannot achieve high accuracy. A method that combines the hyperbolic decline model with a composite function having time attributes was proposed. The improved A-PSO (adaptive particle swarm optimization algorithm) was used to find the optimal model parameters, establishing a composite time hyperbolic decline model. The research results show as follows. The A-PSO optimization algorithm can automatically adjust parameters and model structure according to the complexity of production data and data changes, finding the optimal parameter combination more quickly and accurately, thereby improving prediction accuracy. The production fluctuates greatly over time, making it difficult for conventional decline models to reflect its characteristics. The composite time decline model, with its strong flexibility, can consider the complexity and variability of oil and gas reservoirs, more accurately describe the production changes of shale gas wells at different stages, and provide higher fitting accuracy, making the production prediction closer to the actual value.

To determine the presence of sulfhydryl groups on natural aquatic biofilms and their adsorption characteristics for typical heavy metals, a method for sulfhydryl masking in the biofilms based on a specific masking agent was established in this study. Based on this method, surface concentration of sulfhydryl group in natural biofilms and their adsorption characteristics for typical heavy metals, including Cu, Pb, and Cd, at different pH were investigated. The results indicate that the established masking method can effectively mask sulfhydryl groups and has little effect on microorganisms in biofilms. There are relatively low concentrations of sulfhydryl groups on the surface of natural biofilms, with a concentration of (5.8 ± 0.6) μmol/g, accounting for 5.7% of the total site concentration on the biofilms. Despite the low concentration of sulfhydryl groups, their stronger metal binding capacity makes them significantly contribute to metal adsorption when the metal concentration is low (the theoretical loading of heavy metals by the biofilm is less than 1.0 μmol/g). This pattern is essentially unaffected by the pH of the adsorption system and the type of heavy metal. This proves that sulfhydryl groups in biofilms also have an important impact on the behavior and risk of heavy metals in natural aquatic environments with low metal content, further highlighting the environmental significance of natural biofilms.