There are two most widely used transthoracic esophagectomy methods: the McKeown Minimally Invasive esophagectomy (McKeown MIE) and the Sweet Esophagectomy. We evaluated and compared the therapeutic effects of these two methods to determine the appropriate method for the treatment of middle and lower third esophageal cancer patients who received neoadjuvant chemotherapy combined with immunotherapy (NACI). We retrospectively analyzed 43 sweet esophagectomy cases received NACI and 167 cases with McKeown MIE in the fourth hospital of Hebei Medical University from December 2019 to May 2022. This retrospective observational study showed that Sweet esophagectomy and McKeown MIE after NACI therapy for resectable ESCC patients appeared to be safe with low operative mortality and morbidity rate in the current population. In addition, sweet esophagectomy was associated with a lower incidence of severe complications and shorter hospital stay for patients over 70 years of age compared with McKeown MIE. There were no differences were found in length of stay, mortality and complication incidence rate between the two groups. The Sweet approach has advantage in hospital stay for the treatment of the elderly NACI patients with middle or lower third esophageal squamous cell carcinoma. In conclusion, Sweet esophagectomy and McKeown MIE are both safe, effective, and worthwhile approaches for ESCC patients in immunotherapy age.

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments.

With global warming, increasingly severe forest fires have caused serious damage to the Earth’s ecological environment. Remote sensing (RS) technology has become an important means of forest fire monitoring owing to its unique advantages of multiple cycles and wide coverage. In smoke scene detection, the current simple mixed sample data augmentation (MSDA) methods lead to the loss of key objects in images, and training the samples whose images do not match the labels is the main reason for the decline in classifier accuracy. We propose a Class Activation Map-based Mixing method (CAMMix), a new MSDA method. In contrast to previous data augmentation (DA) methods, CAMMix can select the region and degree of mixing throughout the significance map. With the aid of an auxiliary classifier, CAMMix generates a mixed mask with class significance, such that the newly generated mixed sample distribution is close to the original data distribution. We also propose an intervention for the coverage method to further prevent the loss of smoke objects. On a smoke dataset (USTC_SmokeRS), CAMMix achieves the best accuracy of 94.95% and 83% for 64% and 8% training set respectively. It results that CAMMix and variants provides high classification accuracy and preserves key object information, outperforming the Mixup, CutMix, ResizeMix and FMix.

In order to provide a scientific basis for the development of the Yunnan wetland ecosystem, we construct a wetland ecosystem health evaluation system with 15 evaluation indexes selected from three aspects including pressure, status and response based on PSR mathematical model. Analytic Hierarchy Process(AHP) was used to determine the weight of indicators and the Yunnan wetland ecosystem was divided into “the five health status” of “health, sub-health, fragility, illness, and scurviness”, which are used to analyze and evaluate the health status of it through a comprehensive evaluation index. The results show that the comprehensive evaluation index of ecosystem health status of Yunnan wetland is 0.5524 in 2017, locating in the grade of “fragility” and is close to the status of “sub-healthy”; Among the 3 levels of pressure, state, and response, the pressure and state are locating in the grade of “fragility” and the response is close to the status of “health”. Among the 15 evaluation indexes, the rate of change of wetland area, the area of water body, the hydrological regulation and the wetland management level are the most important factors affecting the ecosystem health of Yunnan wetland.

This paper proposes and validates a method based on UAV nap-of-the-object photogrammetry for refined 3D digital modeling to achieve high-precision monitoring during the installation of large steel structures, offering a comparative analysis with traditional oblique photogrammetry methods. Through field tests and data processing on the steel structure of a sports stadium in Zhejiang Province, the results demonstrate that nap-of-the-object photogrammetry can capture high-resolution images, offering significant advantages in detail representation and modeling of complex structures. Experimental data reveal that the model's root mean square error (RMSE) generated by nap-of-the-object photogrammetry is 14.56mm, markedly lower than the 51.99mm achieved by traditional methods, meeting the Level II accuracy standard specified in the "Technical Specifications for Nap-of-the-Object Photogrammetry." The study further shows that, when combined with optimized flight path planning, nap-of-the-object photogrammetry enhances both the efficiency and accuracy of data collection while improving operational safety. This technique is highly applicable and flexible, making it suitable for data acquisition of complex structures and high-rise buildings. In summary, the proposed method exhibits significant advantages in terms of accuracy, efficiency, safety, and applicability, providing a practical solution for high-precision, comprehensive digital modeling of large steel structures.

Impervious Surface Area (ISA) is vital for urban planning, environmental monitoring, and water management. Traditional remote sensing methods struggle with complex urban landscapes, leading to accuracy limitations. To address this, we propose RU-Net++, a deep learning-based ISA extraction model integrating ResNet50 as the encoder with spatial, channel, and dual attention mechanisms. The decoder employs an Atrous Spatial Pyramid Pooling (ASPP) module and multiple refinement modules to enhance feature representation and edge restoration. Trained on GLC_FCS30D and GISA datasets, RU-Net++ outperforms traditional methods in IoU, F1 Score, and Overall Accuracy, offering a reliable tool for sustainable urban development and land-use management.