In recent years, land–air collaborative logistics delivery is a promising distribution method. It combines the flexibility of unmanned aerial vehicles (UAVs) with the high payload capacity of vehicles, expanding the service range of UAVs while reducing carbon emissions. However, most existing research has focused on path planning and resource allocation for either UAVs or vehicles alone. Therefore, to address the shortcomings of the current research, this paper proposes an intelligent land–air collaboration delivery algorithm for trajectory optimization and resource scheduling. Subsequently, this paper develops a land–air collaboration reliable delivery logistics distribution system, showcasing the driving routes of vehicles and UAVs. Meanwhile, the mode of UAV–vehicle collaboration not only saves operating costs compared to traditional logistics delivery but also achieves energy conservation and emission reduction goals. During the specific design and implementation process of this platform, blockchain technology is integrated into the logistics delivery service to ensure data security and prevent tampering, making the system more efficient and reliable. Finally, through testing and verification of the system’s functionalities, its completeness is demonstrated.

Wind energy, as a clean energy source, has been experiencing rapid development in recent years. However, there is often a significant difference between the designed electricity generation capacity and the actual electricity generation capacity during the construction of wind farms, making it difficult to assess the economic benefits of wind farms. Therefore, the development post-evaluation technology is required to support the renovation of old wind farms. In addition, traditional data analysis techniques are unable to handle and analyze massive data in a timely manner. Therefore, big data technology must be developed to improve processing efficiency. To address these issues and meet actual business needs, this paper designs an intelligent grid electricity generation post-evaluation platform for wind farms based on a microservice framework and big data analysis technology. The platform evaluates the operating status of wind farms by analyzing their operational and design data and visualizes relevant big data information. It provides technical support and improvement solutions for wind farm renovation and new wind farm construction. The platform has been tested and proven to meet the requirements for processing and analyzing massive data, post-evaluating electricity generation, and visualization.

Recently, for indoor personal drones, there have been a number of challenges in localizing users, such as how to accurately identify their location and identification. This article proposes a distance measurement method for indoor unmanned aerial vehicle (UAV) localization combining fine-time measurement (FTM) ranging equipment and the deep neural network (DNN) model. Specifically, we can calculate the distance between the UAV and user by measuring the signal round-trip time. However, the indoor ranging accuracy of the FTM protocol is inevitably affected by the multipath and nonline-of-sight (NLOS). Besides, we prove that the FTM ranging under multipath is related to either the distance between the transceiver pair or the length of the reflected path relative to the direct one. Moreover, we also study the relationship between the FTM ranging error and the response of the multipath channel. Furthermore, we design an FTM error calibration model based on physical-layer (PHY) information by using the DNN model, termed DeepF, which can not only automatically distinguish environmental characteristics but also estimate the length of propagation paths of a signal. The designed DeepF can adopt the DNN model to extract the time domain information of channel state information (CSI) and learn the nonlinear mapping between delay, power, and mean error from signal features. Finally, we use a trained model to calibrate the FTM error and predict the user location. Experimental results show DeepF significantly improves the ability of indoor UAVs to localize users.

Background: Endoscopic dissection (ED) shows relatively high clinical value in early esophageal cancer (cT1N0) such as lower incidence of postoperative complications and hospitalization costs and enhanced recovery. However, whether ED still has certain advantages over esophagectomy in terms of long-term survival remains unclear.Purpose: The aim of this meta-analysis was to compare the long-term outcomes of ED and surgery in the treatment of cT1N0 esophageal cancer.Methods: Several electronic databases including the PubMed, EMBASE, Web of Science and Cochrane Library databases were searched up to April 7, 2022 for studies which compared the overall survival (OS) and disease-specific survival (DSS) of cT1N0 esophageal cancer patients receiving the ED or esophagectomy. The hazard ratios (HRs) and 95% confidence intervals (CIs) were combined and all statistical analysis was conducted through STATA 15.0 software.Results: A total of 12 studies involving 3,732 patients were enrolled. No significant difference in the OS between ED and surgery groups was observed (HR = 0.78, 95% CI, 0.59–1.04, p = 0.089). However, the DSS of the ED group was significantly longer than that of the surgery group (HR = 0.56, 95% CI, 0.39–0.82, p = 0.003).Conclusion: In overall, the current evidence manifested that the long-term survival of cT1N0 esophageal cancer patients undergoing ED was not worse than that of patients undergoing esophagectomy. ED may be considered as the primary treatment for cT1N0 esophageal carcinoma patients.

The rapid advancement of Wise Information Technology of med (WITMED) has made the integration of traditional Chinese medicine tongue diagnosis and computer technology an increasingly significant area of research. The doctor obtains patient’s tongue images to make a further diagnosis. However, the tongue image may be broken during the process of collecting the tongue image. Due to the extremely complex texture of the tongue and significant individual differences, existing methods fail to fully obtain sufficient feature information, which result in inaccurate inpainted tongue images. To address this problem, we propose a recurrent tongue image inpainting algorithm based on multi-scale feature fusion called Multi-Scale Fusion Module and Recurrent Attention Mechanism Network (MSFM-RAM-Net). We first propose Multi-Scale Fusion Module (MSFM), which preserves the feature information of tongue images at different scales and enhances the consistency between structures. To simultaneously accelerate the inpainting process and enhance the quality of the inpainted results, Recurrent Attention Mechanism (RAM) is proposed. RAM focuses the network’s attention on important areas and uses known information to gradually inpaint image, which can avoid redundant feature information and the problem of texture confusion caused by large missing areas. Finally, we establish a tongue image dataset and use this dataset to qualitatively and quantitatively evaluate the MSFM-RAM-Net. The results shows that the MSFM-RAM-Net has a better effect on tongue image inpainting, with PSNR and SSIM increasing by 2.1% and 3.3%, respectively.

In intelligent transportation systems, efficient traffic management and population monitoring is attributed to the real-time scheduling of multiple Transportation Big Data (TBD) flows, which strongly supported risk assessment and control during the COVID-19 pandemic. However, as a complex and heterogeneous network, it is difficult to meet the priority and real-time requirements of multi-modal TBD flow scheduling. To improve the real-time performance of TBD flow scheduling, a scheme for Time-Triggered (TT) flow scheduling and Audio-Video Bridging (AVB) flow scheduling is proposed. First, an online scheduling method for TT flows (RFSD) is proposed, which uses Lion Swarm Optimization (LSO) algorithm for priority assignment and dynamic queues to adjust the scheduling order in real time. It ensures fairness in scheduling and effectively improves the utilization of time slot resources. Furthermore, an online scheduling method for AVB flows (RFSU) is proposed, which uses the Imperialist Competitive Algorithm (ICA) to construct the utility function for evaluating the scheduling value of AVB flows, effectively increasing the throughput of AVB flows. Finally, extensive experiments show that RFSD increases successful scheduling by 22% over the PAS algorithm. Compared to the TTA algorithm, RFSU achieves a 24% reduction in average delay and a 27% reduction in jitter.