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Accurate identification and monitoring of aerosol properties is crucial for understanding their sources and impacts on human health and the environment. Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. In this study, an experimental platform was utilized to gather light-scattering signals from particles with varying characteristics. The signals are then processed using the EMD-ICA noise reduction technique. Then, the wavelet scattering network is used to realize the adaptive extraction of the characteristics of the particle light-scattering signal, and the Bayesian Optimization model is used to optimize the hyperparameters of the LSTM neural network. The extracted scattering coefficient matrix is input into the LSTM for iterative training. Finally, the SoftMax layer's probability classification method is applied to the identification of particle attributes. The results show that the multi-angle particle light-scattering signal detection system designed and built in this study performs well and is capable of effectively collecting particle light-scattering signals. At the same time, the proposed new method for particle property recognition demonstrates good classification performance for six different types of particles with a particle size of 2 μm, achieving a classification accuracy of 98.83%. This proves its effectiveness in recognizing particle properties and provides a solid foundation for particle identification. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) are rapidly evolving experimental platforms that play an important role in remote sensing. In this study, we investigated a machine learning method for monitoring soil nutrient content using UAV hyperspectral remote sensing. We employed machine learning techniques for feature extraction and soil hyperspectral information modelling. In contrast to traditional mathematical transformation methods, we adopted a combination of random forest and differential evolution algorithms to rank the weights of individual hyperspectral data, thereby obtaining a series of spectral feature subsets for soil organic matter, total nitrogen and available phosphorus and potassium. Furthermore, the analytic hierarchy process was used for weight analysis, and the characteristic bands of the four soil nutrients were successfully extracted. Next, a quantitative inversion model based on a back-propagation (BP) neural network was established to estimate soil nutrient content, with determination coefficients higher than 0.7 and 0.6 for the modelling and verification sets, respectively. The relative percent difference values were greater than 2, among which the highest was for available potassium, with determination coefficients of 0.95 and 0.84 for the modelling and verification sets, respectively. In addition, visualization distribution maps of soil nutrients were obtained by combining the BP model and original reflectance hyperspectral images, and the comparisons of content histograms showed a relatively consistent distribution between the sampling and inversion points. The results verified the effectiveness of the combined machine learning method for large-scale and high-precision monitoring and visualization of soil nutrient contents. [ABSTRACT FROM AUTHOR]

With the rapid development of biotechnology, the importance of microfluidic bioseparation and bioassay in biomedicine, clinical diagnosis, and other fields has become increasingly prominent. Microfluidic technology, with its significant advantages of high throughput, automated operation, and low sample consumption, has brought new breakthroughs in the field of biological separation and bioassay. In this paper, the latest research progress in microfluidic technology in the field of bioseparation and bioassay is reviewed. Then, we focus on the methods of bioseparation including active separation, passive separation, and hybrid separation. At the same time, the latest research results of our group in particle separation are introduced. Finally, some application examples or methods for bioassay after particle separation are listed, and the current challenges and future prospects of bioseparation and bioassay are discussed. [ABSTRACT FROM AUTHOR]

This study proposed an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. The matching characteristics between aerosol and cloud particle size distributions and gamma distributions were studied using aircraft observation data. The feasibility of the retrieval of the particle effective radius from lidar ratios and backscatter ratios was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using the backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmospheric particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 µm. For cloud droplets, it is 1.0–10 µm. An atmospheric-observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud layer was captured. The microphysical parameters of aerosol and clouds during this process were retrieved. The results clearly demonstrate the growth of the effective radius and number concentration. [ABSTRACT FROM AUTHOR]