Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot’s proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution.

With the development of deep fusion intelligent control technology and the application of low-carbon energy, the number of renewable energy sources connected to the distribution grid has been increasing year by year, gradually replacing traditional distribution grids with active distribution grids. In addition, as an important component of the distribution grid, substations have a complex internal environment and numerous devices. The problems of untimely defect detection and slow response during intelligent inspections are particularly prominent, posing risks and challenges to the safe and stable operation of active distribution grids. To address these issues, this paper proposes a high-performance and lightweight substation defect detection model called YOLO-Substation-large (YOLO-SS-large) based on YOLOv5m. The model improves lightweight performance based upon the FasterNet network structure and obtains the F-YOLOv5m model. Furthermore, in order to enhance the detection performance of the model for small object defects in substations, the normalized Wasserstein distance (NWD) and complete intersection over union (CIoU) loss functions are weighted and fused to design a novel loss function called NWD-CIoU. Lastly, based on the improved model mentioned above, the dynamic head module is introduced to unify the scale-aware, spatial-aware, and task-aware attention of the object detection heads of the model. Compared to the YOLOv5m model, the YOLO-SS-Large model achieves an average precision improvement of 0.3%, FPS enhancement of 43.5%, and parameter reduction of 41.0%. This improved model demonstrates significantly enhanced comprehensive performance, better meeting the requirements of the speed and precision for substation defect detection, and plays an important role in promoting the informatization and intelligent construction of active distribution grids.

With the increasing demand for safe operation in harsh environments, polymer dielectric capacitors with high energy density (U_e) and charge-discharge efficiency (η) operating at high temperatures are urgently needed. In this work, the sulfone group-containing polyimide (SO₂PI) film with optimal imidization degree is prepared by regulating the kinetics of imidization reaction. The functional groups obtained by partial imidization enable polyimide to achieve excellent energy storage performance, and it is found that the presence of sulfone groups is beneficial for the further improvement of η. The optimal SO₂PI film achieves a U_e of 2.40 J cm⁻³ at 400 MV m⁻¹ and 150 °C, while it maintains a high η of 92%. Moreover, its preparation process without any additional modification steps matches commercial production equipment, indicating a simple and effective strategy for fabrication of a high-performance dielectric film. Thus, this work exhibits great potential in the field of high temperature energy storage.

So far, the inspection of power line corridors is mainly carried out by manual or helicopters. However, manual inspection is very labour-intensive and inefficient. On the other hand, the cost of the inspection using helicopters is very high. Also the safety of helicopters cannot be guaranteed. In recent years, some researchers have started to use unmanned helicopters to inspect power lines. Nevertheless, their focuses are the development of autonomous control systems. In this paper, we present an applied inspection robotic system based on an unmanned autonomous helicopter (UAH) for power line corridor inspection. Our interest is on the combination of the autonomous helicopter and the inspection system for the practical application in the field. This paper describes the whole architecture of the robotic system and presents some design issues. The experimental results are presented as well.

Hypoxia is a common feature of solid tumors and plays a critical role in cancer progression. A thorough understanding of tumor hypoxia is essential for gaining deeper insights into various aspects of cancer biology. This review examines the key factors contributing to tumor hypoxia, such as inadequate blood supply and oxygen delivery resulting from rapid tumor growth. We present a detailed classification of hypoxic regions and provide an overview of current methods used to identify these areas—from molecular techniques to imaging approaches—offering a comprehensive and multifaceted perspective. Additionally, we explore the mechanisms by which hypoxia drives tumor progression. Under low-oxygen conditions, tumor cells can alter their biological behavior, influencing processes such as cell proliferation, immune evasion, and the maintenance of tumor stem cells. By addressing these dimensions, we aim to enhance understanding of how hypoxia contributes to cancer development. Through this in-depth exploration, we hope this review will offer valuable insights to guide future research and clinical applications.