Solid-State Emissive meso-Aryl/Alkyl-Substituted and Heteroatom-Mixed BOPPY Dyes: Syntheses, Structures, Physicochemical, Redox Properties, and Computational Analysis.

A series of solid-state emissive meso-aryl/alkyl-substituted and heteroatom-mixed bisBF2-anchoring fluorophore incorporating pyrrolyl-pyridylhydrazone (BOPPY) dyes have been developed by a one-pot condensation of ketonized or formylated pyrroles and 2-heterocyclohydrazine as well as the subsequent borylation coordination. Interestingly, the BOPPY dyes with meso-alkyl-substituted groups or oxygen-substituted pyridine moieties exhibit high fluorescence quantum yields (QYs) of up to 79%, the highest solid QY of 74%, and long lifetimes independent of polarity in the available BOPPYs. On the other hand, the BOPPYs with meso-aryl or N-substituted moieties display a high solution QY of up to 93% and slight emission wavelength maxima. However, the S-substituted BOPPY dye exhibited weak fluorescence in all studied solvents, which was attributed to the structural flexibility of the N-C-S bond and different from those BOPPYs with O or N substitution, indicated by quantum calculations. And the significant excited-state structural rearrangement in a polar solvent is further confirmed by femtosecond time-resolved transient absorption spectroscopy. More importantly, those novel and barely fluorescent BOPPYs in acetonitrile show advantageous aggregation-induced enhanced emission and viscosity-dependent activities. These advancements in the photophysical and electrochemical properties of BOPPY dyes offer valuable insights into their further development and potential applications.

Ship Radiated Noise Recognition Technology Based on ML-DS Decision Fusion.

Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster-Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification.