BiTCAN: A emotion recognition network based on saliency in brain cognition.

In recent years, with the continuous development of artificial intelligence and brain-computer interfaces, emotion recognition based on electroencephalogram (EEG) signals has become a prosperous research direction. Due to saliency in brain cognition, we construct a new spatio-temporal convolutional attention network for emotion recognition named BiTCAN. First, in the proposed method, the original EEG signals are de-baselined, and the two-dimensional mapping matrix sequence of EEG signals is constructed by combining the electrode position. Second, on the basis of the two-dimensional mapping matrix sequence, the features of saliency in brain cognition are extracted by using the Bi-hemisphere discrepancy module, and the spatio-temporal features of EEG signals are captured by using the 3-D convolution module. Finally, the saliency features and spatio-temporal features are fused into the attention module to further obtain the internal spatial relationships between brain regions, and which are input into the classifier for emotion recognition. Many experiments on DEAP and SEED (two public datasets) show that the accuracies of the proposed algorithm on both are higher than 97%, which is superior to most existing emotion recognition algorithms.

Major ion compositions, sources and risk assessment of karst stream under the influence of anthropogenic activities, Guizhou Province, Southwest China.

To explore the influence of different types of anthropogenic activity on the rivers, we investigate the major ion composition, sources and risk assessment of the karst stream (Youyu stream and Jinzhong stream), which are heavily influenced by mining activities and urban sewage, respectively. The chemical compositions of the Youyu stream water, which is heavily influenced by mining activities, are dominated by Ca2+ and SO42-. However, the chemical compositions of the Jinzhong stream water, which is heavily influenced by urban sewage, are dominated by Ca2+ and HCO3-. The Ca2+, Mg2+ and HCO3- in Jinzhong stream are mainly derived from rock weathering, while the Youyu stream is affected by acid mine drainage, and sulfuric acid is involved in the weathering process. Ion sources analysis indicates that the Na+, K+, NO3-, and Cl- in the Jinzhong stream mainly derive from urban sewage discharge; but NO3- and Cl- of the Youyu stream mainly derive from agricultural activities, and Na+, K+ are mainly from natural sources. The element ratios analysis indicates the ratio of SO42-/Mg2+ in Youyu stream (4.61) polluted by coal mine is much higher than that in Jinzhong stream (1.29), and the ratio of (Na++K++Cl-)/Mg2+ in Jinzhong stream (1.81) polluted by urban sewage is higher than Youyu stream (0.64). Moreover, the ratios of NO3-/Na+, NO3-/K+, and NO3-/Cl- in the agriculturally polluted Youyu stream were higher than those in the Jinzhong stream. We can identify the impact of human activities on streams by ion ratios (SO42-/Mg2+, (Na++K++Cl-)/Mg2+, NO3-/Na+, NO3-/K+, and NO3-/Cl-). The health risk assessment shows the HQT and HQN for children and adults are higher in Jinzhong stream than in Youyu stream and the total HQ value (HQT) of children was higher than one at J1 in the Jinzhong stream, which shows that children in Jinzhong stream basin are threatened by non-carcinogenic pollutants. Each HQ value of F- and NO3- for children was higher than 0.1 in the tributaries into Aha Lake, indicating that the children may also be potentially endangered.

Assessment of heavy metal pollution in surface sediments of the Chishui River Basin, China.

Accumulated heavy metals in surface sediments are released into the aquatic environment, causing secondary contamination of the hydrosphere, and increasing the risks to human health. To evaluate the pollution characteristics of heavy metals in the sediments of the Chishui River Basin, in the present study, the concentrations of five heavy metals in surface sediments of the Chishui River Basin in China were investigated using the geo-accumulation index, pollution load index, and potential ecological risk indexes. These indexes evaluated the degree of contamination and the influence of human activities on heavy metal levels in the basin. Cu, Zn, Cd, Hg, and As were found at concentrations of 5.12-120.40, 36.01-219.31, 0.03-1.28, 0.01-1.18, and 1.56-11.59 mg kg-1, respectively, with mean values of 37.43, 91.92, 0.25, 0.07, and 5.16 mg kg-1, respectively, in the order Zn > Cu > As > Cd > Hg. The contamination indices revealed Hg as the principal pollutant based on the spatial distribution, while Pearson's correlation coefficients suggested that Cu, Zn, and As originated from a similar source. Hg had a different source from the other metals, whereas Cd originated from a different source compared with that of Zn, As, and Hg. This paper showed a Hg and Cd contamination in the Chishui River Basin.

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

Anthropogenic Rare Earth Elements: Gadolinium in a Small Catchment in Guizhou Province, Southwest China.

Rare earth elements (REEs), known as "industrial vitamins", are widely used in medical treatment, industry, agriculture, etc. However, with the increasing demand for REEs, excess REEs, such as gadolinium (Gd), are considered micropollutants in the environment. In this paper, the distributions of dissolved REEs were analyzed in three small streams, in order to determine the extent and occurrence of Gd anomalies. The shale-normalized REE patterns in the three streams were less smooth with heavy REEs higher than light REEs, for a weak reaction of the heavy REE complexes. A negative Ce (cerium) anomaly and positive samarium (Sm) and europium (Eu) anomalies were observed in the three streams and the negative Ce anomaly was affected by the pH of the alkaline rivers. However, a positive Gd anomaly was found in only a typical urban small stream, Jinzhong. With a population of approximately 60,000, Jinzhong runs by a hospital and through wastewater treatment plants (WWTPs). The concentrations of Gd in Jinzhong ranged from 1.54 to 86.65 ng/L with high anthropogenic Gd proportions (63.64%-98.07%). Anthropogenic Gd showed significant seasonal variations and distinct spatial disparities from upstream to downstream, and it was associated with certain ions such as Cl-. Anthropogenic Gd could be attributed to gadopentetic acid (Gd-DTPA), which is used in magnetic resonance imaging (MRI) in hospitals. This type of Gd was shown to be correlated with municipal wastewater. Due to the high stability and low particulate reactivity in water, anthropogenic Gd has great potential to serve as a tracer to prove the presence of medical wastewater.

[Rock Weathering Characteristics and the Atmospheric Carbon Sink in the Chemical Weathering Processes of Qingshuijiang River Basin].

Carbon sink produced during rock weathering is critical to global carbon cycles. In this work, the major ion chemistry and ion sources of Qingshuijiang River Basin were investigated. The principal component analysis, mass balance approach and deduction method were applied for estimating the weathering rate and atmospheric CO2 consumption via the chemical weathering of rocks. The results demonstrated that the chemical weathering of carbonate and silicate rocks within the drainage basin was the main source of the dissolved chemical substances in the Qingshuijiang River Basin, prior to carbonate rock weathering. Some 58.28% of the total dissolved chemical substances were derived from the chemical weathering of carbonate rock, 17.38% from the dissolution of silicate rock, and 17.74% from atmospheric CO2 contribution rates. The chemical weathering rate of this catchment was estimated to be 109.97t·(km2·a)-1, which was comparable to Wujiang River Basin, but higher than the average of global rivers. Furthermore, the atmospheric CO2 consumption rate was estimated to be 7.25×105 mol·(km2·a)-1. The CO2 flux consumed by the rock chemical processes within this catchment was 12.45×109 mol·a-1, of which about 63.13%(7.86×109 mol·a-1) was resulted from carbonate weathering and 36.87%(4.59×109 mol·a-1) from silicate weathering. The CO2 consumed by rock chemical weathering in the Qingshuijiang River reduced the atmospheric CO2 level and constituted a significant part of the global carbon budget. Correlation and spatial distribution analysis of SO42-, F-, NO3- showed that anthropogenic activities contributed remarkably to dissolved solutes and associated CO2 consumption worldwide, and anthropogenic inputs probably contributed some 4.87% to the dissolved solutes in the Qingshuijiang River.

[Hydrochemical Characteristics and Sources of Qingshuijiang River Basin at Wet Season in Guizhou Province].

According to the ion content and chemical characteristic analysis of Qingshuijiang River water during wet season, the result shows that the chemical composition of the river water is dominated by Ca2+, HCO(3-); Mg2+, and SO4(2-). The TDS concentration (213.96 mg · L(-1)) is significantly higher than the average value of rivers worldwide. Seawater correction approach (Cl(-) normalized seawater ratios) was applied to estimate the contribution proportions of local precipitation to the solutes, and it is found that the contribution ratio of precipitation (2.23%) is lower than the average value (3%) of global catchments. Furthermore, Gibbs graph combining major ion element ratio analysis indicates that the catchment hydrochemistry is mainly originated from carbonate rock weathering, which becomes increasingly distinct as the river goes downstream. Both carbonic acid and sulfuric acid play crucial roles in the chemical weathering. Ion source analysis demonstrates that Ca2+, Mg2+, HCO3- are mainly derived from chemical weathering of carbonate mineral (Dolomite and Calcite); Na+, K+ and Cl- are primarily contributed by silicate mineral weathering; SO4(2-) and NO3- stemmed are mainly from acid atmospheric deposition and from urban sewage input. Anthropogenic analysis suggests that the chemical composition of Qingshuijiang River is greatly impacted by the upriver industrial and mining enterprises activities.