With the continuous development of space and sensor technologies during the recent 40 years, ocean remote sensing has entered into the Big Data era with typical Five-V (volume, variety, value, velocity, and veracity) characteristics. Ocean remote sensing data archives reach several tens of petabytes, and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mining the useful information submerged in such ocean remote sensing data sets is a big challenge. Deep learning, a powerful technology recently emerging in the machine-learning field, has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image information extraction in many industrial-field applications and starts to draw interest in ocean remote sensing applications. In this review paper, we first systematically reviewed two deep learning frameworks that carry out ocean remote sensing image classifications and then presented eight typical applications in ocean internal wave/eddy/oil spill/coastal inundation/sea-ice/green algae/ship/coral reef mapping from different types of ocean remote sensing imagery to show how effective of these deep learning frameworks. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote sensing imagery.

The adverse effects of nickel ions being released into the human body have prompted the development of high-nitrogen nickel-free austenitic stainless steels for medical applications. Nitrogen not only replaces nickel for austenitic structure stability but also much improves steel properties. Here we review the harmful effects associated with nickel in medical stainless steels, the advantages of nitrogen in stainless steels, and emphatically, the development of high-nitrogen nickel-free stainless steels for medical applications. By combining the benefits of stable austenitic structure, high strength and good plasticity, better corrosion and wear resistances, and superior biocompatibility compared to the currently used 316L stainless steel, the newly developed high-nitrogen nickel-free stainless steel is a reliable substitute for the conventional medical stainless steels.

Short‐term traffic flow prediction on a large‐scale road network is challenging due to the complex spatial–temporal dependencies, the directed network topology, and the high computational cost. To address the challenges, this article develops a graph deep learning framework to predict large‐scale network traffic flow with high accuracy and efficiency. Specifically, we model the dynamics of the traffic flow on a road network as an irreducible and aperiodic Markov chain on a directed graph. Based on the representation, a novel spatial–temporal graph inception residual network (STGI‐ResNet) is developed for network‐based traffic prediction. This model integrates multiple spatial–temporal graph convolution (STGC) operators, residual learning, and the inception structure. The proposed STGC operators can adaptively extract spatial–temporal features from multiple traffic periodicities while preserving the topology information of the road network. The proposed STGI‐ResNet inherits the advantages of residual learning and inception structure to improve prediction accuracy, accelerate the model training process, and reduce difficult parameter tuning efforts. The computational complexity is linearly related to the number of road links, which enables citywide short‐term traffic prediction. Experiments using a car‐hailing traffic data set at 10‐, 30‐, and 60‐min intervals for a large road network in a Chinese city shows that the proposed model outperformed various state‐of‐the‐art baselines for short‐term network traffic flow prediction.