BOE Technology Group (China)

This study examines the perceptions and expectations of diverse stakeholders in society with respect to contemporary fire management. By tracing the evolutionary trajectory of fire management practices in recent years, the paper proposes a progressive model that transitions from experience-based approaches to evaluation-driven upgrading, and ultimately toward comprehensive refinement, decomposition, and systematic implementation. In parallel, the study synthesizes the roles, responsibilities, and obligations undertaken by relevant parties during the design and construction phases [1], alongside practical insights derived from fire management during the operational phase. It is posited that the advancement of fire safety management across various organizational units necessarily depends on the adoption of a comprehensively refined and decomposed management model. This paradigm not only enhances operational precision but also serves as a foundational pathway for institutionalizing fire safety performance.

Liquid metals (LMs) characterized with high conductivity and inherent deformability are potential materials for stretchable electronics and circuits. However, technological challenges associated with facile and efficient patterning of LMs currently impede their widespread implementation. Here, we present an environmentally friendly approach for the facile fabrication of stretchable conductors and circuits through the self-assembly of aqueous LM inks. This process leverages the anisotropic surface characteristics that drive the movement of the ink from hydrophobic to hydrophilic regions. Simultaneously, the ink with a stabilizer mitigated the premature deposition of LM particles. This culminated in the precise deposition of LM particles in accordance with the desired patterns on the substrate. The resulting LM patterns exhibit high resolution (<100 μm line width), high conductivity (2.0 × 10⁵ S m−1), and excellent electromechanical durability. Further demonstrated applications including stretchable displays, three-dimensional touch sensors and soft actuators showcase the versatility of this fabrication for stretchable electronics.

In this paper, we investigate the grant-free (GF) accessing for cyber–physical systems (CPSs) over space–air–ground integrated networks (SAGINs) by jointly considering system stability and power consumption. The problem of GF access for CPSs over SAGINs is modeled as a Markov decision process where preamble sequences are chosen to minimize power consumption while guaranteeing system stability. To solve this problem, a distributed multi-agent deep reinforcement learning framework based on factorization technology is proposed. In addition, a local network based on hierarchical reinforcement learning is designed to prevent the explosion of the dimension of the action space, in turn reducing the computational complexity of the proposed algorithm. Finally, the simulation results validate the performance superiority of the proposed scheme in terms of convergence, power consumption and stability compared with the baseline schemes.

Accurately predicting the remaining useful life of a bearing is essential for predictive maintenance of industrial apparatus. Conventional Transformer models struggle to capture the local temporal relationships and essential time-step information present in vibration signals. This research introduces the PEC-GAT Transformer model for predicting the remaining useful life of bearings, which combines multi-scale positional encoding convolutions with a gated attention mechanism to tackle these issues. Multi-scale 1D convolutions capture local temporal data across diverse ranges, while trainable position embeddings and global scaling factors provide adaptive position encoding. A dual-gate attention mechanism amalgamates fundamental traits with overarching contextual information, dynamically modifying attention weights. Experiments demonstrate that the PEC-GAT method significantly improves the accuracy of predicting the remaining useful life of bearings and effectively overcomes the limitations of conventional Transformer models. This methodology offers a comprehensive and efficient intelligent prediction system for the control of industrial equipment health.