System Equipment (China)

To address the intermittency of renewable energy supply in the production of renewable methanol, an optimal design method of hybrid production systems (HPS) for renewable methanol is proposed, which incorporates the economic merit of scaled production systems (SPS) and the operational flexibility of modular production systems (MPS) to alleviate the adverse impacts of fluctuating renewable energy supply. For the optimal design of the HPS, the strategies of frequency-domain decomposition and power allocation are established by using the cutoff amplitude method in spectrum analysis. A mathematical programming model is developed to simultaneously optimize the system design and operational strategy of the HPS for renewable methanol. The case study demonstrates that the HPS achieves a favorable trade-off between operational stability and economic performance by leveraging the complementary strengths of the SPS and MPS. The results show that when the economic performance is prioritized as the objective, SPS is more effective in accommodating renewable energy supply with high power amplitude and low variability, whereas MPS shows superior adaptability under intensive fluctuation of renewable energy supply. Compared to the standalone SPS and MPS, the HPS not only enhances economic performance while maintaining operational robustness but also significantly improves operational stability and equipment utilization efficiency. This work provides a methodology for the optimal design and operation of renewable methanol production systems under complex application scenarios.

In the digital age, software security is essential for the stability of information systems and data protection, yet increasing complexity in software systems has made vulnerabilities a significant cybersecurity threat, leading to data breaches, system crashes, and service disruptions. Traditional vulnerability assessments usually analyze vulnerabilities in isolation, ignoring their time relations and the risk of attackers exploiting multiple vulnerabilities simultaneously, known as co-exploitation. This paper proposes an innovative time-aware cybersecurity knowledge graph (TCG) reasoning method called TCGFormer, which is designed to address these challenges. TCGFormer comprises four modules: (1) an entity encoding module that adjusts attention based on positional information and interaction frequency, (2) a novel attention mechanism for encoding relational topology graphs, (3) a joint sequence encoding module for extracting temporal representations and node relations from historical interactions, and (4) a parameter learning module for predicting entities and relations. Extensive experiments on three public temporal datasets demonstrate that TCGFormer significantly outperforms existing baseline methods, and validation on a cybersecurity knowledge graph dataset—including NVD, CVE details, CWE database, and EDB—further confirms its efficacy in identifying co-exploitation behaviors.

novel wideband antenna with five reconfigurable polarization states is presented for intelligent IoT applications, where polarization agility can be exploited by a higher-layer controller to enhance link reliability in dynamic multipath environments. To achieve wideband performance across all polarization states, coupling mechanisms are strategically introduced into the radiation structure, feeding structure, and feeding network simultaneously. Firstly, through equivalent circuit analysis, shorting pins, metal strips, and slots are integrated into the radiation structure. The coupling mechanisms excite first- and second-order frequency responses, resulting in significant gain improvement and overlapping bandwidth enhancement. Secondly, a co-planar capacitive feeding structure is implemented into the feeding network to improve impedance matching. Subsequently, weakly coupled transmission lines are deployed within both differential and full-polarization feeding networks. Such a configuration ensures wide overlapping bandwidth performance throughout all five polarization states by suppressing phase shift errors. Finally, by controlling the ON/OFF states of PIN diodes, the feeding network generates desired phase differences, enabling the proposed antenna to be reconfigured between five polarization states, i.e., linearly polarization (LP) with three orientations, left-hand circularly polarization (LHCP), and right-hand circularly polarization (RHCP). Experimental results demonstrate that the antenna achieves an operating bandwidth of 8–13 GHz (47.6%), with axial ratio (AR) < 3.0 dB and in-band gain fluctuation < 3.0 dB. Characterized by wide bandwidth, full-polarization agility, and standard fabrication, the design presents a viable solution for demanding multi-polarization applications in industrial Internet of Things (IoT) scenarios.

Current Language-Guided 3D Scene Generation methods struggle to generate complex 3D composite scenes, such as cases where chair is placed interleaved under a table. In these scenarios, the bounding boxes of the table and chair overlap, revealing a limitation of existing algorithms. To address this, we propose a Language-Guided Compositional 3D Scene Generation Framework that decomposes generation into semantic planning, 3D asset creation, and layout assembly. Central to our approach is a Fine-Grained 3D Representation. Unlike conventional coarse bounding boxes, we utilize a Part-Level Decomposition module (powered by Hunyuan3D-Part) to parse generated 3D assets into semantically meaningful sub-volumes. This empowers Large Language Models (LLMs) to function as Common-Sense Geometric Solvers, utilizing intra-object functional voids, such as the accessible space between table legs for precise spatial reasoning. By bridging the modality gap with serialized geometric constraints, our framework enables LLMs to optimize layouts that are semantically faithful and physically valid. Experiments demonstrate that our method significantly outperforms baselines, producing complex scenes with superior semantic coherence and collision-free geometry.

This study proposes a fiber optic sensing and multimodal decision-making based security system to enhance nuclear facility perimeter protection in extreme environments. The system integrates a specially designed optical cable into a metal fence structure, combining physical barrier and intrusion detection. To address environmental interference, a Multimodal Decision Algorithm with Dynamic Deformation Threshold (MDTA) is developed, implementing a three-level processing mechanism: adaptive Kalman filtering for wind vibration noise reduction, dual-wavelength detection for temperature compensation, and health assessment matrix generation. A gradient-weighted fusion strategy dynamically optimizes thresholds for various intrusion types. Experimental results demonstrate outstanding performance: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8. 7 \%}$</tex> intrusion recognition accuracy, 0.03 false alarms/year under level 12 winds, and 0.8 -second response time (37 % improvement over industry standards). The system maintains 2.4 % detection interference in heavy rain and achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 1.5 ~\mathrm{m}$</tex> positioning accuracy across full environmental conditions (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 0} \boldsymbol{\sim} \mathbf{8 0} \boldsymbol{\circ}^{\circ} \mathrm{C} \boldsymbol{,} \mathbf{1 0} \boldsymbol{\sim} \mathbf{9 5 \%} \mathbf{R H} \boldsymbol{)}$</tex>. Supported by domestic hardware, this solution provides highly reliable, robust protection for nuclear facilities.