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Throughfall makes up the major portion of understory rainfall, and thereby plays a vital role in regulating the hydrological and biogeochemical processes in forest ecosystems. The aim of this study was to explore the alterations in throughfall and the associated chemical composition (Ca2+, Na2+, K+, Mg2+, H+, SO42−, NO3−, Cl−, and F−) under Quercus acutissima Carruth. and Broussonetia papyrifera (L.) L’Her. ex Vent. trees, in relation to trunk distance and rainfall characteristics. Event-based measurements were carried out from April to December 2018 in a subtropical forest of eastern China. The throughfall amount (TF) and throughfall ratio (TF%) of Q. acutissima (35.7 mm, 83.0%) were higher than those of B. papyrifera (32.1 mm, 74.7%). Although no significant species differences in the ion concentrations of throughfall were detected, almost all ions (Ca2+, Na2+, K+, Mg2+, H+, SO42−, NO3−, and Cl−) were enriched after passing through the canopies of the two tree species. TF and TF% increased with increasing distance from the trunk, while the concentrations of most ions in throughfall increased, since the trunk was approximated. Regression analysis and redundancy analysis revealed that rainfall amount, duration and intensity exerted significant impacts on throughfall generation and ion concentrations, and the antecedent dry period had a notable influence only on ion concentrations. Our findings indicated that forest canopy and rainfall characteristics play an important role in the alterations in throughfall and the associated chemical compositions.

Surface defect detection for printed circuit board (PCB) is indispensable for managing PCB production quality. However, automatic detection of PCB surface defects is still a challenging task because, even within the same category of surface defect, defects present great differences in morphology and pattern. Although many computer vision-based detectors have been established to handle these problems, current detectors struggle to achieve high detection accuracy, fast detection speed and low memory consumption simultaneously. To address those issues, we propose a cost-effective deep learning (DL)-based detector based on the cutting-edge YOLOv4 to detect PCB surface defect quickly and efficiently. The YOLOv4 is improved upon with respect to its backbone network and the activation function in its neck/prediction network. The improved YOLOv4 is evaluated with a customized dataset, collected from a PCB factory. The experimental results show that the improved detector achieved a high performance, scoring 98.64% on mean average precision (mAP) at 56.98 frames per second (FPS), outperforming the other compared SOTA detectors. Furthermore, the improved YOLOv4 reduced the parameter space of YOLOv4 from 63.96 M to 39.59 M and the number of multiply-accumulate operations (Madds) from 59.75 G to 26.15 G.