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The lens for monitoring the rubber conveyor belt is easy to adhere to a large number of water droplets, which seriously affects the image quality and then affects the effect of fault monitoring. In this paper, a new method for detecting and removing water droplets on rubber conveyor belts based on the attentive generative adversarial network is proposed to solve this problem. First, the water droplet image of the rubber conveyor belt is input into the generative network composed of a cyclic visual attentive network and an autoencoder with skip connections, and an image of removing water droplets and an attention map for detecting the position of the water droplet are generated. Then, the generated image of removing water droplets is evaluated by the attentive discriminant network to assess the local consistency of the water droplet recovery area. In order to better learn the water droplet regions and the surrounding structures during the training, the image morphology is added to the precise water droplet regions. A dewatered rubber conveyor belt image is generated by increasing the number of circular visual attention network layers and the number of skip connection layers of the autoencoder. Finally, a large number of comparative experiments prove the effectiveness of the water droplet image removal algorithm proposed in this paper, which outperforms of Convolutional Neural Network (CNN), Discriminative Sparse Coding (DSC), Layer Prior (LP), and Attention Generative Adversarial Network (ATTGAN).

Real-time quantitative PCR (RT-qPCR) has become a widely used method for exploring plant gene expression level. The method requires using some stably expressed genes as a reference to accurately normalize the RT-qPCR data. However, under various stresses and hormone treatments, the levels of most reference genes vary. Environmental variations also influence their expression levels. The lack of validated, stably expressed reference genes can mislead the study of gene function in pears. “Huangguan” pears have recently become the focus of research on stress resistance mechanisms, such as high resistance. Therefore, the aim of this study was to select the optimal reference genes in Huangguan pears, and we analyzed the expression of the genes EF1α, ACT, SKD1, YLS8, UBQ, GAPDH, TUB, and WDP in a series of pear leaf sets under various stresses and hormone treatments. Using different statistical algorithms, we found that under various treatments, the WDP gene had more stable expression, ACT was the most stable under MeJA treatment, YLS8 was the most valuable reference gene under ABA hormone and heat stress conditions, and GAPDH showed worst results compared to other housekeeping genes, except under heat stress. These results will supply valuable and updated information for the selection of housekeeping genes in pears under biotic and abiotic stresses in the future.

Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.

With the advancement and implementation of the “One Belt, One Road” strategy, Chinese companies are paying more attention to the pursuit of mutual benefit and win-win development concepts overseas. The “integration of investment, construction, and operation” model that integrates project investment, planning, design, construction, and operation and maintenance has become an important transformation direction for power companies’overseas investment and a strategic choice for Chinese companies to deeply integrate into the local market and achieve sustainable development.

Greece’s superior location advantages make it a key gateway for global energy enterprises to open the European market. At present, the consolidation of energy security in Greece provides investment opportunities for Chinese energy and power companies. However, it also faces risks such as the strengthening of the EU’s foreign investment review. This paper proposes that policy should be further strengthened at the national level, in order to guide domestic energy. At the same time, the enterprises must improve investment strategies and strengthen the construction of transnational operation capacity.

Based on literature review, this paper studies the empirical research on total factor productivity (TFP) of power industry and its determinants. We found the TFP estimations were generally conducted on the industry level, less research on the micro firm level, and most studies were focused on growth factors including power reform, environmental constraints and technological progress. With insufficient decompose of firm TFP growth, it is difficult to provide practical advice for companies to increase their TFP.

In response to the call of the 19th CPC National Congress, China’s large-scale power grid companies have proposed in recent years to improve total factor productivity. Based on the analysis of the problems existing in the traditional efficiency evaluation methods of power grid enterprises and the importance of the evaluation of total factor productivity, combined with literature research, this paper focuses on the input-output factors with characteristics of power grid companies.

A maximum likelihood (ML) estimator of carrier frequency offset (CFO) based on virtual carriers is firstly proposed in [1]. Its main disadvantage is large computational amount. By IFFT and piecewise searching techniques, its complexity is significantly reduced. Simulation results in AWGN and Typical Urban (TU) channels show that the simplified ML estimator has low complexity and achieves the same performance as the original one.

Channel impulse response (CIR) is supposed to be second-order polynomial (SOP) variation within two neighboring blocks with each consisting of several OFDM symbols for NDS