Beijing Railway Institute of Mechanical & Electrical Engineering 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.

High-level computations and experiments reveal that DDQ–alkene oxidations operate on an ACET–AdE2 continuum, providing the first experimental validation of ACET and redefining electron-transfer reactivity beyond the SET paradigm.

The strong coupling between lateral and longitudinal dynamics in autonomous vehicles presents a significant challenge for trajectory tracking control, especially under high-dynamic and complex conditions. To address this, this paper proposes a real-time optimal control method driven by a Reservoir Computing (RC)-based vehicle inverse dynamics model. The approach first involves training an RC network on a comprehensive vehicle dynamics dataset, covering multiple operating conditions, to learn the inverse mapping from accelerations to control commands. Second, an online correction mechanism incorporating Proportional-Derivative (PD) feedback is designed to dynamically adjust the desired acceleration inputs based on trajectory tracking errors. Finally, these corrected accelerations are fed into the trained RC network to rapidly compute high-precision control commands, completing the closed-loop tracking. Comprehensive simulations on double-lane-change, figure-eight, and Rössler chaotic trajectories demonstrate that the proposed method achieves high-precision tracking with remarkable computational efficiency and excellent robustness against control disturbances and sensor noise. Notably, moderate sensor noise exhibits trajectory-dependent performance enhancement, with system failure boundaries under combined disturbances clearly characterized.

Wearable piezoelectric sensors have gained significant attention for real-time biomechanical monitoring applications, yet existing designs often suffer from limited sensitivity, durability, and dynamic response. To address these challenges, we develope a wearable sensor utilizing gradient-architected CNT/PMN-PT/PVDF piezoelectric composites for continuous gait monitoring during weight-bearing walking. The sensor features a dual-filler gradient configuration within a poly(vinylidene fluoride) (PVDF) matrix, in which strategically distributed carbon nanotubes (CNTs) and lead magnesium niobate-lead titanate (PMN-PT) ceramic particles synergistically enhance electromechanical coupling efficiency. The CNT-enriched surface layer boosts polarization by enhancing charge injection efficiency, while the gradient-arranged PMN-PT fillers induce stress concentration, further amplifying the sensor's piezoelectric output. As a result, the sensor exhibits exceptional performance, with a decent piezoelectric coefficient and high sensitivity (172 mV N-1). Both experimental tests and finite element simulations validate the superior performance of this gradient structure, making it highly effective for real-time kinematic monitoring during weight-bearing walking. This composite-based sensor represents a promising advancement in wearable health technology, with immediate applications in clinical gait analysis, rehabilitation monitoring and sports injury prevention.

The High Luminosity LHC project (HL-LHC) foresees the construction and installation of important new equipment to increase the performance of the LHC machine. The Hollow Electron Lens (HEL) is a promising system to control the beam halo. It improves the beam collimation system of the HL-LHC and mitigates possible equipment damage in case of failure scenarios from halo losses. The halo can store up to 30 MJ energy. The specifications for this new device are quite demanding. The source, an electron gun with an annular shaped cathode, has to deliver a current up to 5 A. This is five times higher than the current in the existing electron lenses in Fermi and Brookhaven national laboratories. This note describes the programme carried out to design and test high-perveance guns equipped with two types of high-performance scandate cathodes. The size of the final gun is now considerably smaller than the one of the first prototype, allowing a reduction of diameter and cost of the superconducting magnet system used to steer the electron beam. The tests carried out at FNAL, BVERI and BJUT demonstrated that the developed cathodes fulfil the specifications and can supply a 5 A fully Space Charge Limited (SCL) current.

With the increasing number of China's automobile production and ownership, the automobile industry faces new opportunities as well as challenges. Automobile industry, especially the environmental new energy vehicles, on one hand is facing a better policy environment and conditions; on the other hand, the competition has become more intense. OYOTA, Volkswagen and other international auto giants have settled in China, and at the same time, the domestic automobile have been rising. So many brands and companies increase the competition of the automobile market. This paper points out the problems of E company in inventory management, and puts forward the corresponding optimization strategies from improving the methods of demand forecasting and the method optimization of the inventory management.