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A sophisticated characterisation of a porous material structure has been challenging in material science. Three-dimensional (3D) structure analysis allows the evaluation of a material's homogeneity, pore size distribution and pore wall properties. Micro-computed tomography (micro-CT) offers a non-destructive test method for material evaluation. This paper characterises a novel ternary binder's porous structure using micro-CT. Gypsum–cement–pozzolan (GCP) ternary binders are low-carbon footprint binders. Both natural and industrial gypsum were evaluated as a major components of GCP binders. Porous GCP binder was obtained by a foaming admixture, and the bulk density of the material characterised ranged from 387 to 700 kg/m3. Micro-CT results indicate that pores in the range from 0.017 to 3.0 mm can be effectively detected and described for porous GCP binders. The GCP binder structure proved to be dominant by 0.1 to 0.2 mm micropores. For GCP binders produced with natural gypsum, macropores from 2.2 to 2.9 mm are formed, while GCP binders with phosphogypsum possess pores from 0.2 to 0.6 mm. Micro-CT proved to be an effective instrument for characterising the homogeneity and hierarchical pore structure of porous ternary binders. [ABSTRACT FROM AUTHOR]

Background: "Obesity paradox" occurs in type 2 diabetes mellitus (T2DM) patients when body mass index (BMI) is applied to define obesity. We examined the association of visceral fat area (VFA) as an obesity measurement with arterial stiffness in seven ideal cardiovascular health metrics (ICVHMs). Methods: A total of 29 048 patients were included in the analysis from June 2017 to April 2021 in 10 sites of National Metabolic Management Centers. ICVHMs were modified from the recommendations of the American Heart Association. Brachial‐ankle pulse wave velocity (BaPWV) ≥ 1400 cm/s was employed to evaluate increased arterial stiffness. Multivariate regression models were used to compare the different effects of BMI and VFA on arterial stiffness. Results: Lower VFA was more strongly associated with low BaPWV than lower BMI when other ICVHMs were included (adjusted odds ratio [OR], 0.85 [95% confidence interval [CI], 0.80–0.90] vs OR 1.08 [95% CI, 1.00–1.17]). Multivariable‐adjusted ORs for arterial stiffness were highest in patients with the VAT area VFA in the range of 150‐200 cm2 (adjusted OR, 1.26 [95% CI 1.12–1.41]). Compared with participants with VAT VFA < 100 cm2, among participants with higher VAT VFA, the OR for arterial stiffness decreased gradually from 1.89 (95% CI, 1.73–2.07) in patients who had ≤1 ICVHM to 0.39 (95% CI, 0.25–0.62) in patients who had ≥5 ICVHMs. Conclusion: In patients with T2DM, using VAT for anthropometric measures of obesity, VFA was more relevant to cardiovascular risk than BMI in the seven ICVHMs. For anthropometric measures of obesity in the ICVHMs to describe cardiovascular risk VFA would be more optimal than BMI. [ABSTRACT FROM AUTHOR]

Navigation line extraction is critical for precision agriculture and automatic navigation. A novel method for extracting navigation lines based on machine vision is proposed herein using a straight line detected based on a high-ridge crop row. Aiming at the low-level automation of machines in field environments of a high-ridge cultivation mode for broad-leaved plants, a navigation line extraction method suitable for multiple periods and with high timeliness is designed. The method comprises four sequentially linked phases: image segmentation, feature point extraction, navigation line calculation, and dynamic segmentation horizontal strip number feedback. The a* component of the CIE-Lab colour space is extracted to preliminarily extract the crop row features. The OTSU algorithm is combined with morphological processing to completely separate the crop rows and backgrounds. The crop row feature points are extracted using an improved isometric segmented vertical projection method. While calculating the navigation lines, an adaptive clustering method is used to cluster the adjacent feature points. A dynamic segmentation point clustering method is used to determine the final clustering feature point sets, and the feature point sets are optimised using lateral distance and point line distance methods. In the optimisation process, a linear regression method based on the Huber loss function is used to fit the optimised feature point set to obtain the crop row centreline, and the navigation line is calculated according to the two crop lines. Finally, before entering the next frame processing process, a feedback mechanism to calculate a number of horizontal strips for the next frame is introduced to improve the ability of the algorithm to adapt to multiple periods. The experimental results show that the proposed method can meet the efficiency requirements for visual navigation. The average time for the image processing of four samples is 38.53 ms. Compared with the least squares method, the proposed method can adapt to a longer growth period of crops. [ABSTRACT FROM AUTHOR]