This letter presents a novel graph-driven synthetic aperture radar (SAR) image change detection approach. A hierarchical heterogeneous graph is proposed, combining two distinct graphs: a weighted graph based on adjacency of superpixels of an initial over-segmentation, and the dual-weighted heterogeneous graph. The superpixel-based regional affinities are coupled with pixel-based heterogeneous affinities, being embedded into the structure of hierarchical heterogeneous graph. The difference image generation relies on the matching of the bitemporal graphs, as well as the multiscale features of vertex domain. Finally, traditional graph cuts algorithm is applied to separate the difference image into changed and unchanged areas. Experiments on three real SAR datasets show that the proposed approach outperforms other experimental approaches and is a good candidate for SAR image change detection tasks.

The Curriculum Sequencing (CS) problem is a challenging task to tackle in the field of online teaching and learning system development. The current methods of education management might still possess certain drawbacks that would cause ineffectiveness and incompatibility of the whole system. A solution for achieving better user satisfaction would be to treat users individually and to offer educational materials in a customized way. Adaptive Curriculum Sequencing (ACS) plays an important role in education management system, for it helps finding the optimal sequence of a curriculum among various possible solutions, which is a typical NP-hard combinatorial optimization problem. Therefore, this paper proposes a novel metaheuristic algorithm named Group-Theoretic Particle Swarm Optimization (GT-PSO) to tackle the ACS problem. GT-PSO would rebuild the search paradigm adaptively based on the solid mathematical foundation of symmetric group through encoding the solution candidates, decomposing the search space, guiding neighborhood movements, and updating the swarm topology. The objective function is the learning goal, with additional intrinsic and extrinsic information from those users. Experimental results show that GT-PSO has outperformed most other methods in real-life scenarios, and the insights provided by our proposed method further indicate the theoretical and practical value of an effective and robust education management system.

Traditional product design evaluation processes are resource-intensive and time-consuming, resulting in unsustainably higher costs and longer lead times. Therefore, sustainable product design evaluation has become an increasingly crucial aspect of product design, focusing on creating a high-efficiency, high-reliability, and low-carbon-emission approach. In this study, we proposed an integrated approach that combines manual design evaluation based on the analytic hierarchy process (AHP) with an automatic design evaluation based on a ResNet-50 network in order to develop a sustainable design evaluation method. First, the evaluation level and indicators for the shape design of a tail-light were defined using the AHP. We followed this by establishing a determination matrix and weight coefficients for the design indicators to create a manual design evaluation model. Second, tail-light shape image datasets were manually annotated based on the evaluation indicators, and design datasets were constructed. The ResNet-50 algorithm was introduced to train the datasets, and the automatic evaluation model for product design was constructed through training and tuning. Finally, we validated the feasibility and effectiveness of the product design evaluation method, which was based on AHP and ResNet-50, by comparing the results obtained using both manual design and automatic design evaluations. The results showed that the proposed sustainable product design evaluation model provides an efficient and reliable method for evaluating product design, improves the decision-making process, and empowers the design and development process. The model enhances resource efficiency and economic sustainability.