R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement.

Digital image correlation (DIC) is an optical metrology method for measuring object deformation and has been widely used in many fields. Recently, the deep learning based DIC methods have achieved good performance, especially for small and complex deformation measurements. However, the existing deep learning based DIC methods with limited measurement range cannot satisfy the needs of real-world scenarios. To tackle this problem, a recursive iterative residual refinement DIC network (R3-DICnet) is proposed in this paper, which mimics the idea of the traditional method of two-step method, where initial value estimation is performed on deep features and then iterative refinement is performed on shallow features based on the initial value, so that both small and large deformations can be accurately measured. R3-DICnet not only has high accuracy and efficiency, but also strong generalization ability. Synthetic image experiments show that the proposed R3-DICnet is suitable for both small and large deformation measurements, and it has absolute advantages in complex deformation measurement. The accuracy and generalization ability of the R3-DICnet for practical measurement experiments were also verified by uniaxial tensile and wedge splitting tests.

Assessing the development potential of non-food biofuel crops under the water-land-biofuel nexus perspective.

Facing water and land scarcity, planting non-food biofuel crops on marginal land depending on natural rainfall has been considered as an attractive means of achieving sustainable biofuel development. However, the complex connection between rainfall and marginal land resources in spatial-temporal distribution affects the optimal planting layout of non-food biofuel crops as well as the assessment of biofuel potential, especially in arid areas. In this study, we constructed a water-land-biofuel nexus centered on non-food biofuel crops, optimized the layout of three non-food biofuel crops, sweet sorghum, Jerusalem artichoke and switchgrass, based on fuzzy mathematics method under the water-land-biofuel nexus perspective, determined yield-rainfall curve to calculate the development potential of non-food biofuel crops. The results showed that sweet sorghum and Jerusalem artichoke are more suitable for planting in Ningxia. Three potential scenarios are set up under different growth conditions and agricultural technologies. The theoretical biofuel production is [9.64× 107, 10.93× 107] GJ, which was verified by the result that the biofuel production per unit area is close to the lower limit of the test production range. It can also be speculated that there may exist irrigation supply and fertilization in the actual crops planting in other studies.