To address the challenges in solar greenhouses where cucumber canopy point clouds are highly dense, leaves exhibit severe overlap, and inter-plant adhesion is pronounced, resulting in insufficient accuracy and poor robustness of traditional point cloud segmentation algorithms, this study proposes a comprehensive cucumber hierarchical segmentation framework (Cuc-HiSeg) for terrestrial laser scanning (TLS) point clouds, enabling high-precision segmentation from population level to individual plants and further to leaves. High-resolution three-dimensional (3D) point cloud data of cucumber canopies at the seedling, early flowering, and fruiting stages were collected using TLS. For segmentation from population to individual plants, an improved hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm integrating k-dimensional tree (KD-tree) optimization and secondary clustering with mean shift was developed, improving the F1-score by 0.7%, 5.0%, and 8.0% across the three growth stages compared with the original algorithm. For leaf segmentation, a leaf point cloud data augmentation algorithm named CuLeafExpander was proposed to enhance the generalization capability of deep learning models. Experimental results demonstrate that the improved HDBSCAN and region-growing (RG) algorithms attained F1-scores above 91.9% and 93.9%, respectively. The proposed Cuc-HiSeg provides technical support for efficient processing of crop point clouds and advances digital crop phenotyping and intelligent greenhouse management.

This paper investigates the optimal control of discrete-time nonlinear systems with two strategically coupled control inputs arising from a Nash-type interaction. By augmenting the coupled inputs into a unified control vector and embedding the equilibrium conditions into a single performance index, the original game-based problem is reformulated as anaugmented optimal control problem. Under standard regularity assumptions, we establish that the Nash stationarity conditions are locally equivalent to the first-order optimality conditions characterized by the Pontryagin maximum principle (PMP). Based on this reformulation, two PMP-based iterative algorithms are developed to solve the resulting forward-backward difference equations (FBDEs). The first algorithm exploits exact second-order information through the inversion of a regularized Hessian matrix, while the second constructs a Hessian-free recursive update that avoids explicit Hessian inversion. For both methods, a rigorous local convergence analysis is provided, showing that the effective linear contraction factor vanishes as the iteration horizon increases, leading to a superlinear-type local convergence behavior. Numerical experiments on representative nonlinear systems validate the theoretical findings and demonstrate improved convergence efficiency compared with classical gradient-based and Newton-type methods. The proposed theoretical framework and algorithms are particularly relevant to cooperative control problems in unmanned aerial vehicle (UAV) swarms, where multiple decision-makers interact through coupled dynamics and performance objectives.

The vehicle to everything (V2X) communication of intelligent and connected vehicles faces uncertainties such as random latency and packet loss. These issues pose safety challenges. This paper proposes a closed-loop integrated strategy that combines perception, decision-making, and control. A communication uncertainty metric is established based on recent packet loss rate and average delay. A three-state machine (cooperative cruise mode, perception uncertainty mode, and local safety mode) is designed to adjust the control strategy based on a preset uncertainty threshold. A proportion integration differentiation (PID) controller ensures safe car following. MATLAB simulations under high uncertainty show that the proposed method guarantees that the following distance always satisfies the dynamic safety criterion (a time headway of 1.5 s plus a standstill gap), whereas the fixed-strategy baseline fails under high uncertainty, significantly outperforming a fixed-strategy baseline. This method is computationally efficient and can be deployed on embedded devices. Simulation results validate the effectiveness of this approach in handling V2X communication uncertainties.

Human-agent interaction (HAI) is an increasingly important research area that investigates how humans interact with intelligent agents. Effective HAI relies heavily on social signal understanding, particularly visual social signals that convey human attention, intention, and affect, enabling agents to perceive, interpret, and respond appropriately to human behaviour. Recent advances in artificial intelligence (AI) have substantially improved visual perception and representation learning, allowing machines to detect and model visual social behavioural cues with increasing accuracy. However, translating these cues into socially meaningful interpretations remains a significant challenge, often resulting in unnatural interactions and limited empathic responsiveness in HAI systems. In this paper, we coined the concept of computational social vision (CSV) spanning the domain of both psychology and computing science to understand social signals. We summarize visual-cognitive computing techniques for this purpose with an emphasis on how social vision and artificial intelligence technologies support the inference of latent social states from dynamic behavioural evidence. To structure the field, we organise representative methods according to the semantic roles of visual behaviours, including both head-centric and body-centric social vision approaches. Finally, we discuss socially interactive embodied systems, analyzing how agents, such as social robots and virtual humans, adaptively perceive their environments and interact with humans as well as the future trend.

Physical artificial intelligence (AI) is becoming an important paradigm for embodied intelligence, where agents must reason, imagine, and act under real-world physical constraints. However, current physical reasoning model (PRM) and physical generation model (PGM) may still produce physically inconsistent judgments, scenes, and motions, exposing the lack of a verifiable truth anchor for reliable physical intelligence. Existing simulation-based methods can provide local physical supervision, but their limited coverage makes them insufficient for open and evolving physical environments. To address this bottleneck, this letter proposes parallel physical intelligence (PPI), a truth-anchored co-evolution framework based on parallel intelligence and the artificial systems, computational experiments, and parallel execution (ACP) methodology. PPI introduces the physical fidelity anchor (PFA) to calibrate both PRM and PGM, while enabling PRM and PGM to co-evolve through mutual data augmentation and physical plausibility feedback. During parallel execution, embodied agents and disembodied agents interact through a virtual-real closed loop, where real-world failures and novel physical phenomena update the PFA and trigger subsequent co-evolution. This provides a scalable and adaptive pathway for physically grounded embodied intelligence in complex environments.