Search

A basket grinder was used as mechanochemical activation equipment to study low-grade lead–zinc oxide ores in the Lanping area, China and a mechanochemical leaching (MCL) process combining grinding with NaOH solution leaching was proposed for zinc leaching. The effects of leaching temperature, NaOH concentration, leaching time, liquid–solid ratio, and stirring speed on zinc leaching rate were investigated. Experimental results show that under the optimal conditions, the zinc leaching rate of MCL reaches 92.3%, which is 7.2% higher than that of conventional stirring leaching (CSL). And small difference in energy consumption is observed between the two processes, indicating the superiority of MCL. The strengthening mechanism of mechanochemical activation was investigated by analyzing the zinc phase of raw ore, and XRD, SEM−EDS, EPMA and particle size analyses were conducted on leaching residues from different processes under the optimal conditions.

Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This paper proposes a robust neural network structure with an improved attention module for automatic classification of heart sound wave. In the preprocessing stage, noise removal with Butterworth bandpass filter is first adopted, and then heart sound recordings are converted into time-frequency spectrum by short-time Fourier transform (STFT). The model is driven by STFT spectrum. It automatically extracts features through four down sample blocks with different filters. Subsequently, an improved attention module based on Squeeze-and-Excitation module and coordinate attention module is developed for feature fusion. Finally, the neural network will give a category for heart sound waves based on the learned features. The global average pooling layer is adopted for reducing the model's weight and avoiding overfitting, while focal loss is further introduced as the loss function to minimize the data imbalance problem. Validation experiments have been conducted on two publicly available datasets, and the results well demonstrate the effectiveness and advantages of our method.

The rapid advancements in wearable devices have enabled continuous and real-time monitoring and analysis of physiological parameters. Owing to the benefits of detecting cardiovascular diseases, arrhythmia recognition from the wearable Electrocardiogram (ECG) signals has drawn much attention. In this article, we present a novel method of using an ensemble of multiscale convolutional neural networks, the so-called EMCNet, for classifying the single-lead ECG waves acquired by wearable devices. The EMCNet includes two primary modules. Two component classifiers are first developed, which input the filtered ECG recordings and time-frequency spectrograms, and output the prediction probabilities of corresponding categories. These prediction probabilities are then sent to an improved weight matrix based on soft voting to produce the final classification result. Numerous experiments have been conducted, with results clearly demonstrating the efficacy and benefits of the EMCNet.