As particulate organic carbon (POC) from lakes plays an important role in lake ecosystem sustainability and carbon cycle, the estimation of its concentration using satellite remote sensing is of great interest. However, the high complexity and variability of lake water composition pose major challenges to the estimation algorithm of POC concentration in Class II water. This study aimed to formulate a machine-learning algorithm to predict POC concentration and compare their modeling performance. A Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) algorithm based on spectral and time sequences was proposed to construct an estimation model using the Sentinel 2 satellite images and water surface sample data of Chaohu Lake in China. As a comparison, the performances of the Backpropagation Neural Network (BP), Generalized Regression Neural Network (GRNN), and Convolutional Neural Network (CNN) models were evaluated for remote sensing inversion of POC concentration. The results show that the CNN–LSTM model obtained higher prediction precision than the BP, GRNN, and CNN models, with a coefficient of determination (R²) of 0.88, a root mean square error (RMSE) of 3.66, and residual prediction deviation (RPD) of 3.03, which are 6.02%, 22.13%, and 28.4% better than the CNN model, respectively. This indicates that CNN–LSTM effectively combines spatial and temporal information, quickly captures time-series features, strengthens the learning ability of multi-scale features, is conducive to improving estimation precision of remote sensing models, and offers good support for carbon source monitoring and assessment in lakes.

The discrete element method (DEM) is commonly used to study various powders in motion during transportation, screening, mixing, etc.; this requires several microscopic parameters to characterize the complex mechanical behavior of the particles. Herein, a new discrete element parameter calibration method is proposed to calibrate the ultrafine agglomerated powder (recycled polyurethane powder). Optimal Latin hypercube sampling and virtual simulation experiments were conducted using the commercial DEM software; the microscopic variables included the static friction coefficient between the particles, collision recovery coefficient, Johnson–Kendall–Roberts surface energy, static friction coefficient between the particles and wall, and collision recovery coefficient. A predictive model based on genetic-algorithm-optimized feedforward neural network (back propagation) was developed to calibrate the microscopic DEM simulation parameters. The cycle search algorithm and mean-shift cluster analysis were used to confirm the input parameters’ range by comparing the mean value of the dynamic angle of repose measured via the batch accumulation test. These parameters were verified by the baffle lifting method and the rotating drum method. This calibration method, once successfully developed, will be suitable for use in a variety of fine viscous powder dynamic flow conditions.

Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R²) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R² and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture.

Hyperspectral LiDAR (HSL) has been widely discussed in recent years, which attracts increasing attention of the researchers in the field of electronic information technology. With the application of supercontinuum laser source, it is now possible to develop an HSL system, which can collect spectral and spatial information of targets simultaneously. Meanwhile, eye-safety and miniature HSL device with multiple spectral bands are given more priorities in on-site applications. In this paper, we tempt to investigate how to select spectral bands with a selection method. The proposed method consists of three steps: first, the variances among the classes based on hyperspectral feature parameters, termed inter-class variances, are calculated; second, the channels are sorted based on corresponding variances in descending order, and those with the two highest values are adopted as the initial input of classification; finally, the channels are selected successively from the rest of the sorted sequence until the classification accuracy reaches 100%. To test the performance of the proposed method, we collect 91/71-channel hyperspectral measurements of four different categories of materials with 5 nm spectral resolution using an acousto-optic tunable filter (AOTF) based HSL. Experimental results demonstrate that the proposed method could achieve higher classification accuracy than a random band selection method with different classifiers (naïve Bayes (NB) and support vector machine (SVM)) regardless of classification feature parameters (echo maximum and reflectance). To reach 100% accuracy, it demands 8–9 channels on average by echo maximum and 4–5 channels on average by reflectance based on NB classifier; these figures are 3–4 by echo maximum and 2–3 by reflectance with SVM classifier. The proposed method can complete classification task much faster than the random selection method. We further confirm the specific channels for the classification of different materials, and find that the optimal channels vary with different materials. The experimental results prove that the optimal band selection of HSL system for classification is reliable.

High-purity quartz is an emerging strategic material that has been extensively used in the semiconductor and photovoltaic fields. Taking vein quartz from southern Anhui Province as an example, raw materials were processed by ultrasonic scrubbing-desliming, magnetic separation, flotation, high-temperature calcination, water quenching, hot-press acid leaching, and deionized water cleaning to prepare high-purity quartz sand. At the same time, the microscopic structure, inclusions, phase, mineral morphology, water content in inclusions, and trace impurities of the gangue samples were analyzed using an optical microscope, a laser Raman spectrometer, an X-ray diffractometer, a scanning electron microscope, an infrared spectrum analyzer, and an inductively coupled plasma mass spectrometer. The results showed that feldspar and muscovite were the main impurities. After purification, the total amount of 13 impurities in quartz sand was reduced to 28.66 μg/g, and the contents of the main impurity elements Al, Na, and Fe were 12.81 μg/g, 12.80 μg/g, and 0.52 μg/g, respectively. The mass fraction of SiO₂ increased from 99.06% to 99.9972%. This shows that flotation, high-temperature calcination, and hot-pressing acid leaching are the keys to obtaining high-purity quartz sand. Fluoride-free flotation with the new collector XK02 can effectively realize the deep separation of quartz and mineral impurities. High-temperature calcination can form more cracks on the surface of quartz sand particles, and the mixed acid enters the open crack channels to effectively remove impurities from the inclusions. This method provides technical support for the preparation of high-purity quartz sand with high value and for the efficient utilization of quartz ore.

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of urban socio-economic activities, urban land use changes, and urban morphology, neglecting the importance of the three-dimensional spatial structure of cities. This study takes 30 cities in East China as an example. By using urban building data and carbon emission datasets, four machine learning algorithms, BP, RF, CNN, and CNN-RF, are established to build a CO₂ emission prediction model based on three-dimensional spatial structure, and the main influencing factors are further studied. The results show that the CNN-RF model performed optimally in both the testing and validation phases, with the coefficient of determination (R²), root mean square error (RMSE), and residual prediction deviation (RPD) of 0.85, 0.82; 10.60, 22.32; and 2.53, 1.92, respectively. Meanwhile, in the study unit, S, V, NHB, AN, BCR, SCD, and FAR have a greater impact on CO₂ emissions. This indicates a strong correlation between urban three-dimensional spatial structure and carbon emissions. The CNN-RF model can effectively evaluate the relationship between them, providing strategic support for spatial optimization of low-carbon cities.