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The wetting behaviors of Al-Si-Cu-Mg-Zn brazing materials on 5083 aluminum alloy substrate were investigated through changing the proportion of Mg from 0 to 2 wt.%. The experimental results showed that the welding process goes through the three following stages: slow spreading, fast spreading, and stabilizing. The wettability of the brazing material was improved effectively, and the porosity of the interfacial layer was reduced, with the addition of Mg. With Mg content at 1 wt.%, the wetting diameter reached a maximum value of 20.46 mm. The reaction mechanism of the wetted interfacial layer between the brazing material and substrate alloy was illustrated with dynamic data, provided through experimentation and simulated thermodynamic calculation, and showed that the wetting behavior of the resultant Al-7.5Si-15Cu-1Mg-5Zn brazing material was dominated primarily by a diffusion reaction from elemental magnesium. [ABSTRACT FROM AUTHOR]

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model's capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results demonstrated significant improvements in precision, recall, accuracy, and mean average precision (mAP). Specifically, precision was 0.92 , recall was 0.89 , accuracy was 0.90 , and mAP was 0.91 , surpassing traditional models such as YOLOv5, YOLOv8, RetinaNet, EfficientDet, and DEtection TRansformer (DETR). Furthermore, through ablation studies, the critical roles of ASLVN and the spatial state attention mechanism in enhancing detection performance were validated. These experiments not only showcased the contributions of each component for improving model performance but also highlighted the method's capability to address the challenges of apricot tree disease detection in complex environments. Eight types of apricot tree diseases were detected, including Powdery Mildew and Brown Rot, representing a technological breakthrough. The findings provide robust technical support for disease management in actual agricultural production and offer broad application prospects. [ABSTRACT FROM AUTHOR]

Nonuniform reaction rate distributions are commonly observed in the microfluidic fuel cell (MFC) systems, which bring a significant limit to the cell output. Herein, systematic analyses are performed to explore the electrochemical reaction rate distributions in the flow‐through electrodes of MFCs, and in‐depth understanding of the distribution mechanisms under various operating conditions is presented via a MFC performance simulation model. The results demonstrate that the high‐reaction‐rate regions locate at the inner parts of the flow‐through electrodes in the high flow rate and high‐reactant concentration cases which are ohmic‐resistance dominated, while moving toward the outer parts of the electrodes with the decreasing of flow rate and/or reactant concentration due to the increased concentration‐related activation resistance. A series of performance enhancement strategies are also proposed and examined. It is found that smaller electrode aspect ratio, reduced electrode distance, excess supporting electrolyte, and larger specific surface area are desirable in the high flow rate and high‐reactant concentration cases, which can boost the cell performances significantly. This work can contribute to the optimal designs of microfluidic fuel cells under various application scenarios in the future. [ABSTRACT FROM AUTHOR]