A novel ultra-low temperature and wide temperature window NOx elimination strategy is proposed to boost denitrification performance. In this work, MnMoOx catalysts are composited utilizing the Mo-doped sucrose reduction method. Notably, the MnMoOx-0.05 catalyst exhibits remarkable denitrification performance at 40–240 °C, with NO conversion exceeding 95
The development of efficient and cost-effective electrocatalysts for the hydrogen evolution reaction (HER) is crucial for sustainable hydrogen production. An integrated Ni-foam-Ni–Co–P electrode was designed and synthesized via a facile two-step method involving electrodeposition of Ni/Co hydroxide nanosheet arrays on Ni-foam as the precursors followed by a low-temperature phosphorization. The morphology analysis confirmed that the density of the nanosheet array and the thickness of the individual nanosheets were effectively tuned by adjusting the Ni/Co ratio. The electrode with a Ni/Co ratio of 2:1 exhibited optimal hydrogen-evolution activity with a low overpotential of 110 mV (η10) at a current density of 10 mA cm− 2. During prolonged operation, the electrode underwent activation, and after 18 h of continuous hydrogen evolution, the η10 decreased to 84 mV. In addition, the integrated electrode demonstrated excellent stability, with its activity remaining almost unchanged even after 36 h of continuous testing. The excellent hydrogen-evolution performance is attributed to the dual active sites favorable for hydrogen evolution and the three-dimensional porous structure of Ni–Co–P nanosheet array for rapid mass transport. This work presents an effective strategy for constructing high-performance, binder-free HER electrodes and highlights the significance of composition control in engineering catalyst nanostructures.
The vibration signals of rolling bearing exhibit strong nonstationary characteristics under time-varying speed conditions, render traditional signal processing methods incapable of effectively revealing fault information. In recent years, methods such as variational nonlinear chirp mode decomposition (VNCMD) has been established as pivotal tools for nonlinear signal analysis. However, mode mixing and limited capability in instantaneous feature extraction are still encountered when multicomponent chirp signals are processed. To address the aforementioned limitations, this study aims to develop a more precisely signal decomposition algorithm which can be applied in bearing fault diagnosis to extract instantaneous characteristics. An improved signal decomposition framework, termed dynamic chirp mode tracking extraction (DCMTE), is proposed in this study. Specifically, a time-varying frequency error weighting term is novelly incorporated into the objective function of DCMTE to balance the global reconstruction accuracy of the signal with the concentration ability of the target component and mitigate mode mixing. Furthermore, to make the bandwidth of the filter bank dynamically adjustable, a bandwidth parameter updating formula is derived. In addition, a new algorithmic restart strategy is designed in DCMTE to enhance robustness and noise immunity. Several experiments, including both simulated and physical signals, are presented to verify the performance of DCMTE. Compared with other existing methods, the proposed approach shows improved capability in extracting transient features from noisy signals and alleviates the spectral mixing issue among adjacent modes. The findings have provided a theoretical algorithm for vibration signal processing, which enables accurate characterization of time-varying information, and contributes to fault diagnosis of bearings.
Continuous mining and continuous backfilling (CMCB) provides an effective approach to coordinating resource extraction and environmental protection. However, as CMCB progresses, the load-bearing structure of the backfill–surrounding rock system evolves continuously, while the stage-dependent deformation and failure mechanisms of the roof remain unclear. Taking a representative panel as the engineering background, this study systematically investigates the stage-dependent mechanical response of the roof through theoretical analysis, numerical simulation, and field validation. An elastic foundation beam model is established to derive the roof deflection equation and identify the key controlling factors. The second invariant of the stress deviator ( J_2 ) and roof displacement are employed to characterize plastic failure and deformation instability. The results indicate that a uniform stress field is most favorable for maintaining roof stability. In contrast, a vertically dominated stress field promotes J_2 concentration and rock failure, whereas a horizontally dominated stress field is more likely to induce large-deformation instability. The influence of backfill on J_2 evolution and deformation control is more pronounced under horizontally dominated stress conditions. Furthermore, the backfill degree governs the onset of roof support, with higher values enabling earlier load transfer and more effective suppression of roof deformation. Although increasing backfill strength enhances load-bearing capacity, its contribution becomes limited once a critical threshold is exceeded. Based on these findings, targeted optimization measures are proposed for the backfilled roadway, providing guidance for the design and application of CMCB.
Empathetic Question Generation (EQG), an essential component of emotional support dialogue systems. Existing EQG models face two significant issues. First, the perception of long-distance emotional clues suffers from attenuation. Second, semantic focus tends to be diluted across multi-perspective knowledge representations. To address these challenges, the Empathetic Question Generation via Syntactic-guided Multi-perspective Heterogeneous Knowledge Adaptive Coupling framework (Emp-MHKC) is proposed. Specifically, syntactic dependency representations are constructed using an Empathy-Oriented Global Hierarchical Syntactic Dependency Representation method. A global syntactic dependency tree is constructed. Tree-structured positional encoding is then employed to capture hierarchical syntactic dependency features and represent long-distance dependencies across sentences. A Context-aware Multi-perspective Heterogeneous Knowledge Coupling mechanism is designed. It constructs novel multi-perspective heterogeneous knowledge for emotional cognition, contextual inference, and psychological motivation. Through context-aware semantic modulation factors, the mechanism adaptively couples heterogeneous knowledge from multiple perspectives. The coupling promotes focused semantic representation in EQG tasks. Experimental results demonstrate that Emp-MHKC achieves strong performance on both automatic and human evaluations, demonstrating robust contextual alignment and practical empathy guidance.
