When an entity contains one or more entities, these particular entities are referred to as nested entities. The Layered BiLSTM-CRF model can use multiple BiLSTM layers to identify nested entities. However, as the number of layers increases, the number of labels that the model can learn decreases, and it may not even predict any entities, thereby causing the model to stop stacking. Furthermore, the model will be constrained by the one-way propagation of information from the lower layer to the higher layer. The incorrect entities extracted by the outer layer will affect the performance of the inner layer. We propose a novel neural network for nested named entity recognition (NER) that dynamically stacks flat NER layers to address these issues. Each flat NER layer captures contextual information based on a pretrained model with more robust feature extraction capabilities. The model parameters of a flat NER layer and its input are entirely independent. The input of each layer is all of the word representations generated by the input sequence through the embedding layer. The independent input ensures that different flat NER layers will not be interfered with by other flat NER layers during model training and testing to reduce error propagation. Experiments show that our model obtains F1 scores of 76.9%, 78.1%, and 78.0% on the ACE2004, ACE2005, and GENIA datasets, respectively.

The multi-station agricultural machinery scheduling process mainly involves two key stages: order allocation and path planning. Order allocation methods based solely on spatial distance cannot ensure the continuity of agricultural operations. Multi-objective evolutionary algorithms are sensitive to the initial population quality and local search strategies for path planning, where unreasonable initial solutions or improper local search strategies can affect the diversity of solutions. Therefore, we propose a spatiotemporal allocation algorithm that constructs a spatiotemporal distance function to describe the feasibility of continuous operations and evaluates the spatiotemporal proximity of operation points and stations for clustering allocation. In terms of path planning, we design a learnable multi-objective evolutionary algorithm (LMOEA). First, a hybrid initialization strategy is used to enhance the initial population quality; second, a Q-learning-based local search method is constructed to adaptively adjust the search strategy to reduce ineffective iterations; finally, a dynamically adjusted crowding distance mechanism is introduced to improve the distribution of the solution set. Experimental results show that the spatiotemporal allocation algorithm improves the average cost and satisfaction by 4.09% and 3.28% compared to the spatial method. Compared with INSGA-II, HTSMOGA, and NNITSA algorithms, the LMOEA can obtain solutions of higher quality and greater diversity.

With the increasing adoption of cooperative multi-UAV systems in applications such as cargo delivery and ground reconnaissance, the demand for scalable and efficient path planning methods has grown substantially. However, traditional heuristic algorithms are frequently trapped in local optima, require task-specific manual tuning, and exhibit limited generalization capabilities. Furthermore, their dependence on iterative optimization renders them unsuitable for large-scale real-time applications. To address these challenges, this paper introduces an end-to-end deep reinforcement learning framework that bypasses the reliance on handcrafted heuristic rules. The proposed method leverages an encoder–decoder architecture with multi-head attention (MHA), where the encoder generates embeddings for UAVs and task parameters, while the decoder dynamically selects actions based on contextual embeddings and enforces feasibility through a masking mechanism. The MHA module effectively models global spatial-task dependencies among nodes, enhancing solution quality. Additionally, we integrate a Multi-Start Greedy Rollout Baseline to evaluate diverse trajectories via parallelized greedy searches, thereby reducing policy gradient variance and improving training stability. Experiments demonstrated significant improvements in scalability, particularly in 100-node scenarios, where our method drastically reduced inference time compared to conventional methods, while maintaining a competitive path cost efficiency. A further validation on simulated mission environments and real-world geospatial data (sourced from Google Earth) underscored the robust generalization of the framework. This work advances large-scale UAV mission planning by offering a scalable, adaptive, and computationally efficient solution.

In recent years, many scholars have chosen to use word lexicons to incorporate word information into a model based on character input to improve the performance of Chinese relation extraction (RE). For example, Li et al. proposed the MG-Lattice model in 2019 and achieved state-of-the-art (SOTA) results. However, MG-Lattice still has the problem of information loss due to its model structure, which affects the performance of Chinese RE. This paper proposes an adaptive method to include word information at the embedding layer using a word lexicon to merge all words that match each character into a character input-based model to solve the information loss problem of MG-Lattice. The method can be combined with other general neural system networks and has transferability. Experimental studies on two benchmark Chinese RE datasets show that our method achieves an inference speed up to 12.9 times faster than the SOTA model, along with a better performance. The experimental results also show that this method combined with the BERT pretrained model can effectively supplement the information obtained from the pretrained model, further improving the performance of Chinese RE.

The nonholonomic constraints of fixed-wing UAVs, characterized by coupled heading-curvature feasibility and asymmetric costs, fundamentally deviate from classical Euclidean routing assumptions. While standard neural combinatorial optimization (NCO) architectures could theoretically incorporate Dubins costs via reward signals, such naive adaptations lack the capacity to explicitly model the intrinsic SE(2) geometric invariance and directional asymmetry of fixed-wing motion, leading to suboptimal generalization. To bridge this gap, we propose a Dubins-Aware NCO framework. We design a dual-channel embedding to decouple asymmetric physical distances from rotation-stable geometric features. Furthermore, we introduce a Rotary Phase Encoding (RoPhE) mechanism that theoretically guarantees strict SO(2) equivariance within the attention layer. Extensive sensitivity, ablation, and cross-distribution generalization experiments are conducted on tasks spanning varying turning radii and problem variants with instance scales of 10, 20, 36, and 52 nodes. The results consistently validate the superior optimality and stability of our approach compared with state-of-the-art DRL and NCO baselines, while maintaining significant computational efficiency advantages over classical heuristics. Our results highlight the importance of explicitly embedding geometry-physics consistency, rather than relying on scalar reward signals, for real-world fixed-wing autonomous scheduling.