In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model’s efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model’s capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO’s adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP_0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP_0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

With the rapid proliferation of the Internet, network security issues that threaten users have become increasingly severe, despite the widespread benefits of Internet access. Most existing intrusion detection systems (IDS) suffer from suboptimal performance due to data imbalance and feature redundancy, while also facing high computational complexity in areas such as feature selection and optimization. To address these challenges, this study proposes a novel network intrusion detection method based on an improved binary simulated annealing algorithm (IBSA) and TPE-FL-LightGBM. First, by integrating Focal Loss into the loss function of the LightGBM classifier, we introduce cost-sensitive learning, which effectively mitigates the impact of class imbalance on model performance and enhances the model’s ability to learn difficult-to-classify samples. Next, significant improvements are made to the simulated annealing algorithm, including adaptive adjustments of the initial temperature and Metropolis criterion, the incorporation of multi-neighborhood search strategies, and the integration of an S-shaped transfer function. These improvements enable the IBSA method to achieve efficient optimal feature selection with fewer iterations. Finally, the Tree-structured Parzen Estimator (TPE) algorithm is employed to optimize the structure of the FL-LightGBM classifier, further enhancing its performance. Through comprehensive visual analysis, ablation studies, and comparative experiments on the NSL-KDD and UNSW-NB15 datasets, the reliability of the proposed network intrusion detection method is validated.

The Carbon Emissions Trading Pilot Policy (CETP) has attracted more scholarly attention. However, most existing studies are only singularly focused on carbon emission reduction or economic development. More research is needed to determine whether it can promote green and sustainable urban development. Therefore, this paper takes the data from 284 prefecture-level cities in China from 2007 to 2016 as the research sample, uses ecological efficiency as the indicator to measure the sustainable green development of cities, and uses the difference method (DID) and the propensity score matching difference method (PSM-DID) to study whether CETP can achieve the sustainable green development of pilot cities. The results show that CETP can improve pilot cities’ ecological efficiency and realize cities’ green and sustainable development by optimizing the industrial structure and promoting technological innovation. In addition, the impact of CETP on different cities is also significantly different. Compared with small and medium-sized cities and non-provincial capital cities, CETP has a greater impact on large cities and provincial capital cities. Compared with central and western cities, CETP has a greater impact on eastern cities. CETP can improve the ecological efficiency of non-resource cities, but it cannot change the ecological efficiency of resource cities. Our models survive numerous robustness checks.

For semantic segmentation tasks, there are problems in using multi-branch structures to enrich feature maps and aggregate different branches of feature maps in a certain network depth, such as the insufficient richness of feature maps and the incomplete aggregation of feature maps. Given multi-branch feature maps and branch aggregation, this study proposes a lightweight method, called multi-branch aggregation atrous spatial pyramidal pooling, by introducing an attention mechanism and CARAFE to enrich the features of the feature maps, giving the feature maps adaptive parameters, periodically adjusting the adaptive parameters, and aggregating the feature maps in both the vertical and horizontal directions. First, the atrous pyramid is retained and the attention mechanism and CARAFE are used to handle the pooling features to obtain 10 different feature maps. Secondly, giving each feature map a cascaded adaptive parameter and periodically adjusting the adaptive parameter to promote or suppress certain feature maps prevents the model from being at a local minimum or saddle point for long periods due to aleatory uncertainty. Finally, the feature maps are vertically aggregated, horizontally aggregated, and weighted for summation. This work demonstrates a competitive performance on the benchmark dataset, with an improvement of 1.88% in MPA, 0.66% in FWIoU, and 1.29% in MIoU compared to atrous spatial pyramid pooling. The benchmark datasets include PASCAL VOC 2012 and CIFAR-100.