The selection of the right suppliers is crucial to ensure supply chain resilience. This study proposes a two-stage robust optimization model that combines box uncertainty sets with a supplier delivery outcome that is described by a portfolio state represented as a binary vector indicating for every supplier whether it delivers or fails. The model aims to maximize the total profit by considering the uncertainty of the market selling price and the risk of supply failures, while also incorporating flexible production capacity constraints. The experimental results demonstrate that enhancing supplier capacity flexibility and prioritizing low-risk suppliers can significantly increase total profit and reduce unmet demand losses. Maintaining price fluctuations within a reasonable range also contributes to higher overall profit.

Characterizing the coal particle size distribution is an effective means to reduce potential safety hazards in smart mine construction for disaster prevention and security management. Aiming at the existing approach, pixelwise manual labeling on low-contrast particle images that have irregular contours is challenging and time consuming. Thus, a novel deep learning model based on BlendMask instance segmentation is proposed for investigating the detail characteristic of complex coal particles. First, a nonparametric attention mechanism is merged into the backbone network to enhance the detail feature attention of coal dust particles. Second, a bottom-up path is added within the feature fusion structure to reduce the loss of semantic feature information for small target particles. Meanwhile, a compact dual-attention structure is built in the bottom module to improve the edge feature discrimination of coal particles and enhance the segmentation accuracy of particle adhesion regions. Finally, depthwise separable convolutions are applied to balance the computational burden brought by the improved modules. Experimental results show that the proposed model performs better than other segmentation models in terms of pixel accuracy, intersection ratio, precision, and recall for different particle sizes. In the particle size characterization evaluation, the average relative error of the particle size distribution is less than 5% for three particle size ranges, which achieves desirable tradeoffs between computational speed and segmentation accuracy.

Computer vision software is complex, involving many tens of thousands of lines of code. Coding mistakes are not uncommon. When the vision algorithms are run on controlled data which meet all the algorithm assumptions, the results are often statistically predictable. This renders it possible to statistically validate the computer vision software and its associated theoretical derivations. In this paper, we review the general theory for some relevant kinds of statistical testing and then illustrate this experimental methodology to validate our building parameter estimation software. This software estimates the 3D positions of buildings vertices based on the input data obtained from multi-image photogrammetric resection calculations and 3D geometric information relating some of the points, lines and planes of the buildings to each other.