Considering two eco-label strategies, self-label and certification-label, we construct a duopoly competition model encompasses both green product and ordinary product manufacturing enterprises. By Investigating the optimal eco-label standards, we explore the product pricing, and profits for enterprises facing green-sensitive consumers and price-sensitive consumers. The we analyze the optimal eco-label selection for green enterprises in different preference markets. Research indicates that the green quality standards and product prices under certification labels are invariably higher than those under self-label. However, the choice of eco-label by enterprises is influenced by consumers' individual intrinsic preferences; in price-sensitive markets, enterprises tend to adopt self-label; In green-sensitive markets, when the value of consumers' individual intrinsic preferences is below a certain threshold, enterprises will prioritize certification labels. Additionally, the profits of enterprises in green-sensitive markets are generally higher than those in price-sensitive markets, enterprises should highlight the advantages of green quality and guide consumers to prefer green attributes more when formulating promotional strategies.

This study employs Data Envelopment Analysis (DEA) and the Malmquist productivity index to investigate the influence of infrastructure development on the efficiency and productivity of the agricultural sector in Bangladesh from 2021 to 2023. Focusing specifically on the sectors of irrigation, transportation, and electrical infrastructure, the research highlights how these critical elements underpin the operational and scale efficiencies across Bangladesh's seven main agricultural divisions. Through a detailed examination of efficiency change (Effch), technological change (Techch), and total factor productivity change (Tfpch), significant findings emerge regarding the differential impact of infrastructural advancements on agricultural outputs. The analysis reveals that despite some regions showing marked improvements due to infrastructure enhancements, there remains a notable disparity across divisions, underscoring the need for regionally tailored infrastructure strategies. This paper presents a comprehensive overview of the role that sophisticated infrastructure plays in augmenting agricultural efficiency, offering valuable insights for policymakers, engineers, and stakeholders engaged in infrastructure planning and development.

In the recent 3D object detection methods for point clouds, the combination of point-based methods and voxel-based methods is gradually becoming a trend. Point-based methods retain the accurate position and pose information in the raw points and voxel-based methods get multi-scale structure information through the 3D backbone. However, because of the sparsity and irregularity of point clouds, both representations ignore the context information, which is important for the detection of sparse and small objects. To solve this problem, we propose a multi-stream feature aggregation network to extract features from three representations of the point cloud for object detection. Specifically, we exploit multi-stream features extracted from point, voxel, and perspective view (PV) respectively on a parallel way, where the complementary information between different perspectives can be used to enrich the feature representations, especially for the perspective view containing rich semantic context information. Secondly, to eliminate redundant information and better exploit the correlation between different feature representations, we design an attention-based multi-stream feature fusion module (MSFF) to combine features from three information streams. Besides, we introduce a new voxel RoI pooling with the self-attention in the second refinement stage, which can further strengthen the connection between local features in the proposal to obtain accurate classification and localization predictions. Our method achieves progressive results on the KITTI dataset, especially in the cyclist category, which improves the baseline significantly by 5.56%, 4.73%, 5.16% AP in the test set for easy, moderate, and hard levels respectively. Code will be available at https://github.com/june2678/MR F.

An automatic DPCA technique is presented for SAR Ground Moving Target Indication (GMTI). We note that there exists a shift and a phase difference between the images from two channels. Therefore, SAR-GMTI can be implemented in the following steps: Image registration, phase compensation, image subtraction, and CFAR detection. In our technique, these steps are carried out automatically, and thus no precise information is needed about the length of the baseline and the velocity of the platform. We utilize a set of real data to demonstrate the accuracy and the robustness of our algorithm.