It is strongly desirable to accurately detect the clouds in hyperspectral images onboard before compression. However, conventional onboard cloud detection methods are not appropriate to all situation such as shadowed cloud or darken snow covered surfaces which are not identified properly in the NDSI test. In this paper, we propose a new spectral–spatial classification strategy to enhance the orbiting cloud screen performances obtained on hyperspectral images by integrating threshold exponential spectral angle map (TESAM), adaptive Markov random field (aMRF) and dynamic stochastic resonance (DSR). TESAM is performed to classify the cloud pixels coarsely based on spectral information. Then aMRF is performed to do optimal process by using spatial information, which improved the classification performance significantly. Some misclassification points still exist after aMRF processing because of the noisy data in the onboard environment. DSR is used to eliminate misclassification points in binary labeling image after aMRF. Taking level 0.5 data from hyperion as dataset, the average overall accuracy of the proposed algorithm is 96.28% after test. The method can provide cloud mask for the on-going EO-1 images and related satellites with the same spectral settings without manual intervention. The experiment indicate that the proposed method reveals better performance than the classical onboard cloud detection or current state-of-the-art hyperspectral classification methods.

There have been many researches on the surface acoustic wave (SAW) sensors for detecting sulfur-containing toxic or harmful gases. This paper aims to give an overview of the current state of the polymer films used in SAW sensors for detecting deleterious gases. By covering most of the important polymer materials, the structures and types of polymers are summarized, and a variety of devices with different frequency, such as delay lines and array sensors for detecting mustard gas, hydrogen sulfide and sulfur dioxide are introduced. The preparation method of polymer films, the sensitivity of the SAW gas sensor, the limit of detection, the influence of temperature and humidity, and the anti-interference ability are discussed in detail. The advantages and disadvantages of the films are also analyzed, and the potential application of polymer films in the future is also forecasted.

Background: Genetic factors are considered important risk factors for the occurrence of osteoarthritis (OA), which has received extensive attention. This study examined the research on the relationship between genetic factors and the pathogenesis of OA from a bibliometric perspective. Methods: We searched Web of Science Core Collection for original and review articles on genetic factors and OA. We used R package “bibliometrix” to perform statistical and visualization analysis of countries, institutions, authors, journals, and keywords. Results: A total of 1,127 articles met the inclusion criteria, with an average citation of 20.41 per article. From 2012 to 2022, the average annual growth rate of research in this field was 7.83%. The corresponding authors of the published articles were mainly from China, the USA, and the UK. Boston University emerged as the most active and important institution, while the Osteoarthritis and Cartilage published the most articles in this field. The keywords “polymorphism”, “genetic” and “mendelian randomization” indicate the future research directions. Conclusion: This bibliometric study provides an overview of the key topics and trends in genetic factors and OA pathogenesis research. The high-frequency keywords suggest the current and potential future focus of this field, involving methods, mechanisms, and populations.