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Objective To explore the effect on children's occlusion and temporomandibular joint (TMJ) after placement of preformed metal crowns and provide a reference for the restoration of defective primary molars. Methods A total of 39 children who underwent all primary molar metal crown restorations under general anesthesia were enrolled. The participants underwent occlusal contact area, percentage of total occlusal force, asymmetric ratio of occlusal force and occlusal contact time examinations with the T-scan computerized occlusal analysis system. Vertical dimension of occlusion (VDO) was measured using a dental vertical dimension gauge. TMJ dysfunction signs were recorded using the modified clinical dysfunction index (Di), and TMJ dysfunction symptoms were recorded using questionnaires. All data were collected at pretreatment, 1-week, 1-month, 3-month, and 6-month follow-up visits. Results The occlusal contact area at the 3-month and 6-month follow-up visits returned to the occlusal contact area of pretreatment, and the difference was statistically significant (P>0.05). Compared with pretreatment, the percentage of total occlusal force at the 3-month and 6-month follow-up visits increased significantly, and the difference was statistically significant (P0.05). The final statistical analysis of the TMJ assessment showed that there was no significant difference in the clinical disorder index and TMJ dysfunction symptoms before and after treatment (P>0.05). Conclusions After undergoing all primary molar metal crown restorations, occlusal re-equilibrations were attained after approximately one month. The percentage of total occlusal force and occlusal stability were significantly increased after six months of treatment, without significant temporomandibular joint dysfunction.

Berberine is a natural isoquinoline alkaloid extracted from Rhizoma Coptidis and other Chinese herbal medicines. Since berberine has a variety of biological effects, such as bacteriostasis, anti-inflammatory, anti-bone resorption and blood sugar-lowering effects, and is associated with few side effects, studies are increasingly discovering its potential application in the prevention and treatment of periodontitis. This review summarizes the relevant research progress of berberine in the prevention and treatment of periodontitis in recent years, aiming to provide a new strategy for the clinical prevention and treatment of periodontitis. The results of the literature review showed that berberine could prevent the occurrence and development of periodontitis by inhibiting periodontal pathogens, reducing periodontal tissue inflammation and inhibiting alveolar bone resorption. However, the mechanism of periodontitis is complicated, and current research remains limited. In the future, more in vivo and in vitro studies are needed to further explore the mechanism of berberine in inhibiting the occurrence and development of periodontitis, and more large-sample prospective clinical studies should be conducted to confirm the effect of berberine on the prevention and treatment of periodontitis.

The research on the significant toxic weed Oxytropis glabra, which adversely affects the grazing industry and the ecological integrity of natural grasslands in the arid and semi-arid regions of northern China, aims to delineate its potential distribution amidst changing climate conditions. This analysis involves both current conditions (1970–2000) and future projections (2050s and 2070s) under four climate scenarios using an R-optimized MaxEnt model. The results indicate that the distribution of O. glabra was primarily influenced by the temperature of the coldest quarter (bio11, ranging from −12.04 to −0.07 °C), precipitation of the coldest quarter (bio19, 0 to 15.17 mm), and precipitation of the warmest quarter (bio18, 0 to 269.50 mm). Currently, the weed predominantly occupies parts of Xinjiang, Inner Mongolia, Gansu, Qinghai, Ningxia, and Tibet. Projections indicate that, across four future climate scenarios, the area of suitable habitats for O. glabra is expected to expand and shift toward higher latitudes and elevations. The research provides valuable information and a theoretical foundation for the management of O. glabra, alongside advancing grassland ecological research and grazing practices. [ABSTRACT FROM AUTHOR]

The efficient development of oil reservoirs mainly depends on the comprehensive optimization of the subsurface fluid flow process. As an intelligent analysis technique, artificial intelligence provides a novel solution to multi-objective optimization (MOO) problems. In this study, an intelligent agent model based on the Transformer framework with the assistance of the multi-objective particle swarm optimization (MOPSO) algorithm has been utilized to optimize the gas flooding injection–production parameters in a well pattern in the Middle East. Firstly, 10 types of surveillance data covering 12 years from the target reservoir were gathered to provide a data foundation for model training and analysis. The prediction performance of the Transformer model reflected its higher accuracy compared to traditional reservoir numerical simulation (RNS) and other intelligent methods. The production prediction results based on the Transformer model were 21, 12, and 4 percentage points higher than those of RNS, bagging, and the bi-directional gated recurrent unit (Bi-GRU) in terms of accuracy, and it showed similar trends in the gas–oil ratio (GOR) prediction results. Secondly, the Pareto-based MOPSO algorithm was utilized to fulfil the two contradictory objectives of maximizing oil production and minimizing GOR simultaneously. After 10,000 iterations, the optimal injection–production parameters were proposed based on the generated Pareto frontier. To validate the feasibility and superiority of the developed approach, the development effects of three injection–production schemes were predicted in the intelligent agent model. In the next 400 days of production, the cumulative oil production increased by 25.3% compared to the average distribution method and 12.7% compared to the reservoir engineering method, while GOR was reduced by 27.1% and 15.3%, respectively. The results show that MOPSO results in a strategy that more appropriately optimizes oil production and GOR compared to some previous efforts published in the literature. The injection–production parameter optimization method based on the intelligent agent model and MOPSO algorithm can help decision makers to update the conservative development strategy and improve the development effect. [ABSTRACT FROM AUTHOR]

Production forecasting plays an important role in development plans during the entire period of petroleum exploration and development. Artificial intelligence has been extensively investigated in recent years because of its capacity to extensively analyze and interpret complex data. With the emergence of spatiotemporal models that can integrate graph convolutional networks (GCN) and recurrent neural networks (RNN), it is now possible to achieve multi-well production prediction by considering the impact of interactions between producers and historical production data simultaneously. Moreover, an accurate prediction not only depends on historical production data but also on the influence of neighboring injectors' historical gas injection rate (GIR). Therefore, based on the assumption that introducing GIR can enhance prediction accuracy, this paper proposes a deep learning-based hybrid production forecasting model that is aimed at considering both the spatiotemporal characteristics of producers and the GIR of neighboring injectors. Specifically, we integrated spatiotemporal characteristics and GIR into an attribute-augmented spatiotemporal graph convolutional network (AST-GCN) and gated recurrent units (GRU) neural network to extract intricate temporal correlations from historical data. The method proposed in this paper has been successfully applied in a well pattern (including five producers and seven gas injectors) in a low-permeability carbonate reservoir in the Middle East. In single well production forecasting, the error of AST-GCN is 63.2%, 37.3%, and 16.1% lower in MedAE, MAE, and RMSE compared with GRU and 62.9%, 44.6%, and 28.9% lower compared with RNS. Similarly, the accuracy of AST-GCN is 15.9% and 35.8% higher than GRU and RNS in single well prediction. In well-pattern production forecasting, the error of AST-GCN is 41.2%, 64.2%, and 75.2% lower in RMSE, MAE, and MedAE compared with RNS, while the accuracy of AST-GCN is 29.3% higher. After different degrees of Gaussian noise are added to the actual data, the average change in AST-GCN is 3.3%, 0.4%, and 1.2% in MedAE, MAE, and RMSE, which indicates the robustness of the proposed model. The results show that the proposed model can consider the production data, gas injection data, and spatial correlation at the same time, which performs well in oil production forecasts. [ABSTRACT FROM AUTHOR]