Strategies to Reduce Air Pollution in Shipping Industry

AbstractPollution emissions from international ocean-going vessels have a significant impact on public health and global climate changes. The purpose of this paper is to review the status of pollution mitigation measures implemented to date in shipping sector. Emissions control options for ocean going vessels can be classified in three broad categories: technological improvement, operational changes and market-based strategies. In addition, shipping companies have also emphasized environmental policy for the purpose of achieving corporate social responsibility and eco-efficiency. The policy implications of this paper are as follows. First, public awareness of the importance and emergency of environment in shipping industry should be required. Second, it need to investigate the actual conditions of environmental pollution from ship and port area and develop environmental evaluation scheme. Third, integrated approach is more useful method to mitigate air pollution in shipping sector. Finally, stakeholders' collaboration is a key factor for the successful environmental prevention in shipping industry

k-Space Deep Learning for Reference-free EPI Ghost Correction

Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k-space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non-accelerated EPI acquisitions. To take advantage of the even and odd-phase directional redundancy, the k-space data is divided into two channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi-coil k-space data into additional input channels. Then, our k-space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k-space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k-space data from both ghost and subsampling. Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster.The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.Comment: To appear in Magnetic Resonance in Medicin