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This review summarizes the unique advantages and opportunities offered by the mechanochemical process to generate MOF composites.

The melting behaviour of metal-organic frameworks (MOFs) has aroused significant research interest in the areas of materials science, condensed matter physics and chemical engineering. This work first introduces a novel method to fabricate a bimetallic MOF glass, through melt-quenching of the cobalt-based zeolitic imidazolate framework (ZIF) [ZIF-62(Co)] with an adsorbed ferric coordination complex. The high-temperature chemically reactive ZIF-62(Co) liquid facilitates the formation of coordinative bonds between Fe and imidazolate ligands, incorporating Fe nodes into the framework after quenching. The resultant Co-Fe bimetallic MOF glass therefore shows a significantly enhanced oxygen evolution reaction performance. The novel bimetallic MOF glass, when combined with the facile and scalable mechanochemical synthesis technique for both discrete powders and surface coatings on flexible substrates, enables significant opportunities for catalytic device assembly.

The transformer architecture has shown great capability in learning long-term dependency and works well in multiple domains. However, transformer has been less considered in audio-visual speech enhancement (AVSE) research, partly due to the convention that treats speech enhancement as a short-time signal processing task. In this paper, we challenge this common belief and show that an audio-visual transformer can significantly improve AVSE performance, by learning the long-term dependency of both intra-modality and inter-modality. We test this new transformer-based AVSE model on the GRID and AVSpeech datasets, and show that it beats several state-of-the-art models by a large margin.

Most existing audio-visual speech enhancement (AVSE) methods work well in conditions with strong noise, however when applied to conditions with a medium SNR, serious performance degradations are often observed. These degradations can be partly attributed to the feature-fusion(early fusion etc.) architecture that tightly couples the audio information that is very strong and the visual information that is relatively weak. In this paper, we present a safe AVSE approach that can make the visual stream contribute to audio speech enhancment(ASE) safely in conditions of various SNRs by late fusion.The key novelty is two-fold: Firstly, we define power binary masks (PBMs) as a rough representation of speech signals. This rough representation admits the weakness of the visual information and so can be easily predicted from the visual stream. Secondly, we design a posterior augmentation architecture that integrate the visual-derived PBMs to the audio-derived masks via a gating network. By this architecture, the entire performance is lower-bounded by the audio-based component. Our experiments on the Grid dataset demonstrated that this new approach consistently outperforms the audio-based system in all noise conditions, confirming that it is a safe way to incorporate visual knowledge in speech enhancement.

Impulse interference is one of the major interferences in power system, and the existed algorithms mainly focus on narrow-band interference when using wireless communication, which have little effect on impulse interference. Aiming at this problem, this paper proposes an impulse interference suppression algorithm combined with adaptive gain control (AGC) in Electric Energy Data Acquire System, which uses correlation of the interference to suppress the interference. Simulation results show that: On the condition of Gaussian noise plus impulse interference, when the interference to signal ratio (ISR) is 25 dB, compared with AGC, the algorithm can have a 1.2~5.1 dB promotion of the bit error ratio (BER).