Multi-isotopes (δD, δ18Owater, 87Sr/86Sr, δ34S and δ18Osulfate) as indicators for groundwater salinization genesis and evolution of a large agricultural drainage lake basin in Inner Mongolia, Northwest China.

Groundwater salinization, a major eco-environmental problem in arid and semi-arid areas, can accelerate soil salinization, reducing crop productivity and imbalances in ecosystem diversity. This study classified water samples collected from the Ulansuhai Lake basin into five clusters using self-organizing maps (SOM). On this basis, multiple isotopes (δ18Owater, δD, 87Sr/86Sr, δ18Osulfate and δ34S) and isotopic models (Rayleigh fractionation and Bayesian isotope mixing models) were used to identify and quantify the genesis and evolution of groundwater salinization. The results showed that the samples were brackish or saline water, and the hydrochemical types were dominated by Na + K-Cl (SO4). It has been proved that the processes associated with groundwater salinization in the Ulansuhai Lake basin were dominated by water-rock interaction and human inputs. Among them, evaporite dissolution contributed substantially to groundwater salinity. Furthermore, salt inputs from human activities cannot be negligible. Based on the model calculations, evaporite dissolution accounted for the most significant proportion of all sources, with a mean value of 53 %. In addition, human inputs from regular agricultural activities (28 % from sewage and manure and 8 % from fertilizers) constituted another vital source of groundwater salinization associated with extensive agricultural activities in the study area. This study's results can deepen our understanding of the genesis of groundwater salinization and the evolution of the agricultural drainage lake basin. This knowledge will assist the Environmental Protection Department in developing effective policies for groundwater management in the Yellow River Basin.

A pooling convolution model for multi-classification of ECG and PCG signals.

Electrocardiogram (ECG) and phonocardiogram (PCG) signals are physiological signals generated throughout the cardiac cycle. The application of deep learning techniques to recognize ECG and PCG signals can greatly enhance the efficiency of cardiovascular disease detection. Therefore, we propose a series of straightforward and effective pooling convolutional models for the multi-classification of ECG and PCG signals. Initially, these signals undergo preprocessing. Subsequently, we design various structural blocks, including a stacked block (MCM) comprising convolutional layer and max-pooling layers, along with its variations, as well as a residual block (REC). By adjusting the number of structural blocks, these models can handle ECG and PCG data with different sampling rates. In the final tests, the models utilizing the MCM structural block achieved accuracies of 98.70 and 92.58% on the ECG and PCG fusion datasets, respectively. These accuracies surpass those of all networks utilizing its variations. Moreover, compared to the models employing the REC structural block, the accuracies are improved by 0.02 and 4.30%, respectively. Furthermore, this research has been validated through tests conducted on multiple ECG and PCG datasets, along with comparisons to other published literature. To further validate the generalizability of the model, an additional experiment involving the classification of a synchronized ECG-PCG dataset was conducted. This dataset is divided into seven different levels of fatigue based on the amount of exercise performed by each healthy subject during the testing process. The results indicate that the model using the MCM block also achieved the highest accuracy.

Simultaneous removal of fluoride, manganese and iron by manganese oxide supported activated alumina: characterization and optimization via response surface methodology.

Fluoride, iron and manganese simultaneous exceedance of standard can be observed in groundwater in northeastern China. This work aims to apply a highly efficient method combining adsorption and oxidation for the synchronous removal of the inorganic ions. An innovative adsorbent (manganese-supported activated alumina) was synthesized by the impregnation method and showed a significant adsorption capacity better than that of fresh activated alumina. The characterization (scanning electron microscope; Brunauer, Emmett and Teller; X-ray diffraction and Fourier transform infrared spectroscopy) results verified the successful introduction of MnOOH and MnO2, and the improvement of surface microstructure enhanced the removal ability. The effect of single factors, such as pH value, reaction time or dosage on the removal performance has been verified. The maximum removal efficiencies of fluoride, iron and manganese were optimized via Response surface methodology considering the independent factors in the range of MO@AA dosage (5-9 g/L), pH (4-6) and contact time (4-12 h). Noted that compared with control, MO@AA exhibited 59.4% of improved fluoride performance. At pH of 5.79, contacting time of 12 h and 8.21 g/L of MO@AA, fluoride, iron and manganese removal were found to be 91, 100 and 23%, respectively. Herein, MO@AA was distinguished as good applicability for the treatment of fluoride-, iron- and manganese-containing groundwater.