This paper reports a numerical research on MEMS (microelectromechanical system) micronozzles through multiphysics coupling simulation along with design optimization based on simulation results. The micronozzle, which is a core component of the electrothermal microthruster, features a micron-scale geometry, a 2-dimensional (2D) Laval configuration, a rectangular cross section, and a highly thermal conductive silicon wall due to MEMS fabrication. As a result, viscous loss in the flow field and heat transfer to the nozzle wall can strongly influence nozzle performance, namely, thrust force and specific impulse. To accurately understand the flow field inside the micronozzle and how the highly thermal conductive silicon wall interacts with gas flow, a numerical simulation that couples fluid dynamics field and solid heat transfer field is employed in the research. The influence of different structural parameters on micronozzle performance is then investigated to set a basis for design optimization. The optimum design of the linear expander micronozzle is obtained through constrained optimization by linear approximation. To further improve micronozzle performance, the bell-shaped expander is adapted. The optimization result shows that the bell-shaped expander is not suitable for micronozzle featuring 2D Laval configuration, and the reason behind the phenomenon is thoroughly discussed.
As a microbial tryptophan metabolite, indole-3-carboxaldehyde (ICA) has been suggested to confer benefits to host, such as regulation of intestinal barrier function. This study aimed to elucidate the role of ICA in modulating intestinal homeostasis via using a weaned pig model. Twenty-four weaned piglets were randomly allocated into three groups: the control group (a basal diet), ICA100 group (the basal diet supplemented with 100 mg/kg ICA), and ICA200 group (the basal diet supplemented with 200 mg/kg ICA). The experiment lasted 14 d, and pigs from the control and ICA100 groups were slaughtered. The results showed no significant differences in the average daily gain (ADG) and average daily feed intake (ADFI) among the three groups (P > 0.05). However, the ICA100 group had a lower feed to gain ratio (F:G) (P < 0.05). Dietary ICA supplementation did not alter the villus height, crypt depth, and villus height/crypt depth ratio in the small intestine, and did not change the intestinal permeability and antioxidant parameters (P > 0.05). Intriguingly, ICA treatment significantly increased the jejunal, ileal and colonic indexes in piglets (P < 0.05). Besides, the expression of proliferating cell nuclear antigen (PCNA) in the intestine was up-regulated by ICA treatment. Moreover, in vitro experiments demonstrated that 15 μM ICA significantly accelerated the proliferation activity of IPEC-J2 cells, and increased the expression of the ICA receptor aryl hydrocarbon receptor (AHR) and the proliferation markers PCNA and Cyclin D1 (P < 0.05). In addition, dietary ICA supplementation modulated the intestinal flora, increasing the richness estimators and diversity index, decreasing the abundances of phylum Fibrobacterota and genera Alloprevotella, Prevotella, and Parabacteroides, and enriching the abundance of genera Butyrivibrio. These data reveal a beneficial role for the microbial metabolite ICA on intestinal epithelial proliferation, rather than intestinal barrier function, in weaned piglets.
Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.
Succinate is a vital signaling metabolite produced by the host and gut microbiota. Succinate has been shown to regulate host metabolic homeostasis and inhibit obesity-associated inflammation in macrophages by engaging its cognate receptor, SUCNR1. However, the contribution of the succinate-SUCNR1 axis to intestinal barrier dysfunction in obesity remains unclear. In the present study, we explored the effects of succinate-SUCNR1 signaling on high-fat diet (HFD)-induced intestinal barrier dysfunction. Using a SUCNR1-deficient mouse model under HFD feeding conditions, we identified the effects of succinate-SUCNR1 axis on obesity-associated intestinal barrier impairment. Our results showed that HFD administration decreased goblet cell numbers and mucus production, promoted intestinal pro-inflammatory responses, induced gut microbiota composition imbalance, increased intestinal permeability, and caused mucosal barrier dysfunction. Dietary succinate supplementation was sufficient to activate a type 2 immune response, trigger the differentiation of barrier-promoting goblet cells, suppress intestinal inflammation, restore HFD-induced mucosal barrier impairment and intestinal dysbiosis, and eventually exert anti-obesity effects. However, SUNNR1-deficient mice failed to improve the intestinal barrier function and metabolic phenotype in HFD mice. Our data indicate the protective role of the succinate-SUCNR1 axis in HFD-induced intestinal barrier dysfunction.
The investigation of novel ternary metal oxide based breath acetone chemiresistors has garnered interest in a facile diagnosis of diabetes via breath analysis. In this work, sodium dodecyl sulfate (SDS)...
Weaning stress-induced diarrhea is widely recognized as being associated with gut microbiota dysbiosis. However, it has been challenging to clarify which specific intestinal microbiota and their metabolites play a crucial role in the antidiarrhea process of weaned piglets.
Tomatoes are a crop of significant economic importance, and disease during growth poses a substantial threat to yield and quality. In this paper, we propose IBSA_Net, a tomato leaf disease recognition network that employs transfer learning and small sample data, while introducing the Shuffle Attention mechanism to enhance feature representation. The model is optimized by employing the IBMax module to increase the receptive field and adding the HardSwish function to the ConvBN layer to improve stability and speed. To address the challenge of poor generalization of models trained on public datasets to real environment datasets, we developed an improved PlantDoc++ dataset and utilized transfer learning to pre-train the model on PDDA and PlantVillage datasets. The results indicate that after pre-training on the PDDA dataset, IBSA_Net achieved a test accuracy of 0.946 on a real environment dataset, with an average precision, recall, and F1-score of 0.942, 0.944, and 0.943, respectively. Additionally, the effectiveness of IBSA_Net in other crops is verified. This study provides a dependable and effective method for recognizing tomato leaf diseases in real agricultural production environments, with the potential for application in other crops.
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