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For a communication control system in a disaster area where drones (also called unmanned aerial vehicles (UAVs)) are used as aerial base stations (ABSs), the reliability of communication is a key challenge for drones to provide emergency communication services. However, the effective configuration of UAVs remains a major challenge due to limitations in their communication range and energy capacity. In addition, the relatively high cost of drones and the issue of mutual communication interference make it impractical to deploy an unlimited number of drones in a given area. To maximize the communication services provided by a limited number of drones to the ground user equipment (UE) within a certain time frame while minimizing the drone energy consumption, we propose a multi-agent proximal policy optimization (MAPPO) algorithm. Considering the dynamic nature of the environment, we analyze diverse observation data structures and design novel objective functions to enhance the drone performance. We find that, when drone energy consumption is used as a penalty term in the objective function, the drones—acting as agents—can identify the optimal trajectory that maximizes the UE coverage while minimizing the energy consumption. At the same time, the experimental results reveal that, without considering the machine computing power required for training and convergence time, the proposed key algorithm demonstrates better performance in communication coverage and energy saving as compared with other methods. The average coverage performance is 10–45% higher than that of the other three methods, and it can save up to 3% more energy.

Inflammatory breast cancer (IBC) is an aggressive and rare form of breast cancer with a poor prognosis. The occurrence of bilateral IBC in a short period of time is extremely rare. In this case report, a 54-year-old woman diagnosed with invasive ductal carcinoma of the left breast underwent lumpectomy, lymph node dissection, chemotherapy, and radiotherapy but opted against trastuzumab treatment. Four years later, she experienced bilateral breast inflammation, skin changes, edema, and heat (calor). Biopsies confirmed breast cancer metastasis to both breasts. Whole-Exome Sequencing revealed genetic mutations, including PIK3CA and C4orf54, in both primary and recurrent tumors, with significant downregulation in the recurrent tumors. KEGG analysis suggested potential enrichment of axon guidance signal pathways in both tumors. The patient showed a partial response after treatment with liposome paclitaxel, along with targeted therapy using trastuzumab and pertuzumab. This case report sheds light on the rare occurrence of bilateral inflammatory breast cancer post-HER-2 treatment and highlights the importance of genetic profiling in understanding the disease. Further research on clinical targets for breast cancer management is warranted.

Effective forecasting of track irregularity development trend is critical to the railway maintenance and management. In China, track irregularity is commonly evaluated via an index called track quality index (TQI). According to the trend and random of the TQI development, a hybrid forecasting method composed of the improved grey model (GM(1,1)) and a particle swarm optimization support vector machine (PSO-SVM) is proposed in this paper. First, a non-equal interval sequence is transformed into an equal interval sequence, and an improved GM(1,1) based on the compound trapezoid quadrature formula is proposed to estimate the short-term rough trend of the TQI. Then, PSO-SVM is adopted to correct the rough results and obtain an accurate TQI trend. Numerical experiment results show that the proposed forecast method outperforms the track irregularity trend component and the grey nonlinear periodic correction method and the non-equal interval weighted grey model and neural networks in terms of relative error.

The journal retracts the article [...]

The high-speed dynamics of nodes and rapid change of network topology in vehicular ad hoc networks (VANETs) pose significant challenges for the design of routing protocols. Because of the unpredictability of VANETs, selecting the appropriate next-hop relay node, which is related to the performance of the routing protocol, is a difficult task. As an effective solution for VANETs, geographic routing has received extensive attention in recent years. The Greedy Perimeter Coordinator Routing (GPCR) protocol is a widely adopted position-based routing protocol. In this paper, to improve the performance in sparse networks, the local optimum, and the routing loop in the GPCR protocol, the Weighted-GPCR (W-GPCR) protocol is proposed. Firstly, the relationship between vehicle node routing and other parameters, such as the Euclidean distance between node pairs, driving direction, and density, is analyzed. Secondly, the composite parameter weighted model is established and the calculation method is designed for the existing routing problems; the weighted parameter ratio is selected adaptively in different scenarios, so as to obtain the optimal next-hop relay node. In order to verify the performance of the W-GPCR method, the proposed method is compared with existing methods, such as the traditional Geographic Perimeter Stateless Routing (GPSR) protocol and GPCR. Results show that this method is superior in terms of the package delivery ratio, end-to-end delay, and average hop count.