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Objective To investigate the effect of denture stomatitis of selective laser melting (SLM) titanium alloy for removable partial denture frameworks. Methods Twenty patients with dentition defects in our hospital were divided into two groups according to the different methods of creating a removable partial denture framework: the SLM group and casting group. The success rate, placement rate, masticatory efficiency and incidence of denture stomatitis were compared. Experimental data were analyzed with SPSS20.0. Results The success rate of the SLM framework group was 100.00%, which was higher than that of the casting group (90.00%) (P ＜ 0.01). The rate of framework placement in the SLM group was slightly lower than that in the casting group (P ＜ 0.05). The masticatory efficiency of the SLM group was higher than that of the casting group (0.783 ± 0.030 vs. 0.699 ± 0.037, P ＜ 0.001). The incidence of denture stomatitis (10.00%) in the SLM group was significantly lower than that in the casting group (30.00%) (P ＜ 0.001). Conclusion SLM is superior to the traditional casting method in mastication efficiency and reducing the incidence of denture stomatitis. This method can meet the clinical requirements, but the accuracy of the long-term stent needs to be improved.

In order to test the precipitation forecast of the European central numerical model (hereinafter referred to as EC model) in the Beipan River Basin,this paper tests the forecast effect of different starting times and the overall forecast effect of 2020 flood season for an extreme precipitation process in the Beipan River Basin.The results are as follows:①For the prediction of 500 hPa circulation field,rainfall area,correlation coefficient between EC rainfall prediction and actual rainfall,and TS score of rainstorm,the shortened the prediction time is,the better the prediction effect will be.The effect is the most obvious for the maximum rainfall forecast adjustment,and the rainfall after adjustment is closer to the actual rainfall.The EC model is also applicable to forecast heavy rain.②In 2020,the forecast effect of no rain,light rain and moderate rain is better than that of heavy rain and rainstorm.EC model is almost not available to forecast severe rainstorm.③The rainstorm forecast effect differs with different basins,and the rainstorm forecast effect of Dongqing Basin is better than that of other basins.Therefore,it can be concluded that the correlation coefficient of EC model in the whole Beipan River Basin is about 0.9,and the ratio of the standard deviation of prediction to that of observation is 0.77,which indicates that EC model has good prediction effect for the trend of precipitation change,but there are some deviations in the area and magnitude.So,the forecast results can be applied after being corrected.

In table tennis, there are various movements involved in hitting a ball, which are called strokes, and these are an important factor in determining the contents of a game. Therefore, research has been conducted to classify these types of strokes using video gameplay data or inertial sensor information. However, the classification of strokes from actual videos of table tennis is more difficult than general action recognition tasks because many strokes display strong similarity. Therefore, this study proposes a multi-label stroke classification method, assigning multiple classes per stroke. Specifically, multi-labeling is performed by assigning two types of labels—namely the player's posture and the rotation and velocity of the ball—to one stroke. By changing the head of the action recognition model to adopt multiple outputs for stroke classification, the difficulty in each classification task is reduced and the accuracy is improved. As a result, when performing multi-labeling classification with a conventional action recognition model, the accuracy of the validation data was improved by up to 8.6%, and the accuracy of the test data was improved by up to 18.1%. In addition, when two types of input—namely video and 3D joint coordinates—were used, the accuracy of the validation and test data was higher by 17.1 and 5.4% for 3D joint coordinates, respectively, confirming that 3D joint coordinates are effective. [ABSTRACT FROM AUTHOR]

BACKGROUND: Poplar in China has long been plagued by the fall webworm Hyphantria cunea. Enhancing plant immunity using chemical elicitors is an environmentally friendly approach to pest control. The phytohormone methyl jasmonate (MeJA) can stimulate the chemical defenses of poplars against herbivores but has been shown to have limited efficacy in practice. Here, we studied the effects of a MeJA and mesoporous silica nanoparticle (MSN) complex (MeJA@MSN) regarding the induction of poplar resistance to H. cunea, which may provide strategies for the effective use of MeJA. RESULTS: The silicon‐based phytohormone complex (MeJA@MSNs) exhibited excellent biological and physiochemical properties, such as excellent biocompatibility and plant tissue transportability. The changes in metabolites in poplar leaves induced by MeJA, MSNs, and MeJA@MSNs were investigated by metabolic analysis. MeJA@MSNs led to highly potent induced resistance along with elevated salicylaldehyde content, which increased with the dose administered. The salicylaldehyde metabolite showed a strong antifeedant effect on H. cunea larvae at a dosage of 1 μg, with the 50% lethal dose being 20.4 μg/mg. Furthermore, transcriptional analysis showed that MeJA@MSNs upregulated key genes in biosynthetic pathways more than MeJA and MSNs. CONCLUSION: Our results show that MeJA and MSNs interact positively in poplar, leading to salicylaldehyde accumulation and increased induced resistance to H. cunea, providing new insights into the underlying resistance mechanisms induced by MeJA@MSNs. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]