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This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images.

This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images.

Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images.

This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.

In this paper, the authors aim to consider the manufacturer's battery research and development (R&D) decision under subsidy. The supply chain includes two manufacturers, which produce substitutable electric vehicles, and a battery supplier. One of the manufacturers can choose to develop batteries or buy batteries. The authors assume consumers do not have enough trust in the manufacturer-made battery.

Stackelberg game is made use of to study the battery R&D strategy of the manufacturer under the incentive of government subsidies. This paper makes a comparative analysis on six situations, then the authors get some conclusions and give some managerial insights.

The results show that subsidy strategies do not necessarily reduce actual payments when the manufacturer does not research and develop batteries. The retail prices and actual payments are closely related to the substitutability and total cost advantage of product. The authors also find consumer trust positively affects the demand of the electric vehicles using the manufacturer-made batteries and then affects the manufacturer's battery R&D decision. When consumers have low trust in manufacturer-made battery, subsidy can bring greater sales and make R&D more profitable than procurement, so that the manufacturer chooses R&D. This study's findings also suggest consumer subsidy is always better for the government.

Distinguished from previous studies, the authors discuss the decision-making of component research, and introduce various government subsidy strategies and consumer trust to study their roles in the manufacturer's battery R&D choice.

This study aims to reveal the differential concentration corrosion (DCC) mechanism, which has been ignored by researchers for a long time.

The ionic conductive layer near the pipe wall was extracted and discretized. In the case of DCC, the equations of corrosion potential after polarization in units are derived according to Kirchhoff’s Law. By solving these equations, the corrosion potential and current on situation of DCC are calculated.

DCC can change origin distribution of (nature) potential and current greatly; it will cause polarization. The positions with original lower corrosion potential will produce anodic polarization; meanwhile, the speed of corrosion also increases; the position with original higher corrosion potential will produce cathodic polarization, and the corrosion current is also decreased. Generally speaking, the potential will be homogenized by DCC mechanism.

This model makes an in-depth analysis of the traditional FAC theory, greatly supplements it and enriches the theory.

This paper aims to study flow‐induced corrosion mechanisms for carbon steel in high‐velocity flowing seawater and to explain corrosive phenomena.

An overall mathematical model for flow‐induced corrosion of carbon steel in high‐velocity flow seawater was established in a rotating disk apparatus using both numerical simulation and test methods. By studying the impact of turbulent flow using the kinetic energy of a turbulent approach and the effects of the computational near‐wall hydrodynamic parameters on corrosion rates, corrosion behavior and mechanism are discussed here. It is applicable in order to understand in depth the synergistic effect mechanism of flow‐induced corrosion.

It was found that it is scientific and reasonable to investigate carbon steel corrosion through correlation of the near‐wall hydrodynamic parameters, which can accurately describe the influence of fluid flow on corrosion. The computational corrosion rates obtained by this model are in good agreement with measured corrosion data. It is shown that serious flow‐induced corrosion is caused by the synergistic effect between the corrosion electrochemical factor and the hydrodynamic factor, while the corrosion electrochemical factor plays a dominant role in flow‐induced corrosion.

The corrosion kinetics and mechanism of metals in a high‐velocity flowing medium is discussed here. These results will help those interested in flow‐induced corrosion to understand in depth the type of issue.