The awareness, treatment, and control rates of hypertension for young adults are much lower than average. It is urgently needed to explore the variances of metabolic profiles for early diagnosis and treatment of hypertension. In current study, we applied a GC–MS based metabolomics platform coupled with a network approach to analyze plasma samples from young hypertensive men and age-matched healthy controls. Our findings confirmed distinct metabolic footprints of young hypertensive men. The significantly altered metabolites between two groups were enriched for the biological module of amino acids biosynthesis. The correlations of GC–MS metabolomics data were then visualized as networks based on Pearson correlation coefficient (threshold = 0.6). The plasma metabolites identified by GC–MS and the significantly altered metabolites (P < 0.05) between patients and controls were respectively included as nodes of a network. Statistical and topological characteristics of the networks were studied in detail. A few amino acids, glycine, lysine, and cystine, were screened as hub metabolites with higher values of degree (k), and also obtained highest scores of three centrality indices. The short average path lengths and high clustering coefficients of the networks revealed a small-world property, indicating that variances of these amino acids have a major impact on the metabolic change in young hypertensive men. These results suggested that disorders of amino acid metabolism might play an important role in predisposing young men to developing hypertension. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism underlying complex diseases.

Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°.

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Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications.

The effect of glass powder (GP) on the microstructure of cement pastes was studied by using mercury intrusion porosimetry, thermogravimetric and differential thermal analysis, scanning electron microscopy, energy dispersive spectrum analysis and X-ray diffraction techniques. The compressive strength and alkali-silica reaction (ASR) expansion was also tested. The test results indicated that GP can increase the compressive strength of cement pastes and control the ASR expansion of mortars. With the increase in GP content, ASR expansion decreases sharply, although compressive strength decreases a little with a large amount of GP. The characteristic pore sizes decreased with some capillary pores transforming to interstitial pores or gel pores as GP content increased. GP could diminish pore size in cement pastes, which has important significance in improving durability. Incorporating GP in cementbased materials had little effect on the kinds of hydration products, but it significantly affected the amount of hydrates, especially calcium hydroxide. The amount of calcium hydroxide reduces with the increase in curing age and GP content. The microstructure of hardened pastes containing GP was relatively dense with an abundance of calcium silicate hydrate, whose morphology was different from the gel in pure cement systems. This kind of calcium silicate hydrate gel with a low calcium/silicon ratio had a network structure formed by irregular stumpy and lamelliform gel granules. GP has high pozzolanic activity.

Since the data generation rate of high-resolution satellites is increasing rapidly, to relieve the stress of data downloading and processing systems while enhancing the time efficiency of information acquisition, it is important to deploy on-board edge computing on satellites. However, the volume, weight, and computability of on-board systems are strictly limited by the harsh space environment. Therefore, it is very difficult to match the computability and the requirements of diversified intelligent applications. Currently, this problem has become the first challenge of the practical deployment of on-board edge computing. To match the actual requirements of the Luojia3 satellite of Wuhan University, this manuscript proposes a three-level edge computing architecture based on a System-on-Chip (SoC) for low power consumption and expandable on-board processing. First, a transfer level is designed to focus on hardware communications and Input/Output (I/O) works while maintaining a buffer to store image data for upper levels temporarily. Second, a processing framework that contains a series of libraries and Application Programming Interfaces (APIs) is designed for the algorithms to easily build parallel processing applications. Finally, an expandable level contains multiple intelligent remote sensing applications that perform data processing efficiently using base functions, such as instant geographic locating and data picking, stream computing balance model, and heterogeneous parallel processing strategy that are provided by the architecture. It is validated by the performance improvement experiment that following this architecture, using these base functions can help the Region of Interest (ROI) system geometric correction fusion algorithm to be 257.6 times faster than the traditional method that processes scene by scene. In the stream computing balance experiment, relying on this architecture, the time-consuming algorithm ROI stabilization production can maintain stream computing balance under the condition of insufficient computability. We predict that based on this architecture, with the continuous development of device computability, the future requirements of on-board computing could be better matched.

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