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2D materials and their heterostructures are prominent for fabricating next-generation optical and photonic devices. The optical, electrical, and mechanical properties of 2D materials largely depend on atomic layer numbers. Although machine learning techniques are implemented to identify large-area thickness distribution using microscopic images, the existing work mainly focuses on rough identification of thicknesses with in-house datasets which limits fair and comprehensive comparisons of new machine learning approaches. Here, first a microscopic dataset is collected and released for three fundamental image processing tasks including multilabel classification, segmentation, and detection. Then three deep-learning architectures DenseNet, U-Net, and Mask-region convolutional neural network (RCNN) are benchmarked on three tasks and their robustness is evaluated on the augmented 2D microscopic images with different optical contrast variations. Deep learning models are trained and evaluated to identify mono-, bi-, tri-, multilayer and bulk flakes using microscopic images of MoS 2 fabricated on the SiO 2 /Si substrate by chemical vapor deposition. The relation between model performances and statistics of datasets is studied based on the international commission on illumination (CIE) 1931 color space and red, green, blue (RGB) histograms of optical contrast differences. Finally, the robust pretrained models are integrated into a graphic user interface for the on-site use of full field-of-view images captured by bright-field microscopes.

Rivets are used to assemble layers in the air intakes, fuselages, and wings of an aircraft. After a long time of working under extreme conditions, pitting corrosion could appear in the rivets of the aircraft. The rivets could be broken down and thread the safety of the aircraft. In this paper, we proposed an ultrasonic testing method integrated with convolutional neural network (CNN) for the detection of corrosion in the rivets. The CNN model was designed to be lightweight enough to be able to run on edge devices. The CNN model was trained with a very limited sample of rivets, from 3 to 9 artificial pitting corrosive rivets. The results show that the proposed approach could detect up to 95.2% of pitting corrosion using experimental data with three training rivets. The detection accuracy could be improved to 99% by nine training rivets. The CNN model was implemented and ran on an edge device (Jetson Nano) in real-time with a small latency of 1.65 ms.

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This paper examines challenges of agricultural SMEs in export by investigating cases studies of small and medium-sized enterprises (SMEs) in Da Lat and seeking perceptions of authorities in this city by conducting a series of individual in-depth interviews. The issue related to management capacity of Da Lat SMEs was also explored as it is considered as the key factor leading to the constraints of SMEs in agricultural export. The evaluations on the limitations and causes are then provided, based on which the solutions with regard to management capacity enhancement are offered. The paper is expected to make theoretical contribution in the development of knowledge related to human resource and supply chain management.