Pattern Recognition of Cognitive Load Using EEG and ECG Signals

The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system

Bioassessment of Macroinvertebrate Communities Influenced by Gradients of Human Activities

This study explores the impact of anthropogenic land use changes on the macroinvertebrate community structure in the streams of the Cangshan Mountains. Through field collections of macroinvertebrates, measurement of water environments, and delineation of riparian zone land use in eight streams, we analyzed the relationship between land use types, stream water environments, and macroinvertebrate diversities. The results demonstrate urban land use type and water temperature are the key environmental factors driving the differences in macroinvertebrate communities up-, mid-, and downstream. The disturbed streams had lower aquatic biodiversity than those in their natural state, showing a decrease in disturbance-sensitive aquatic insect taxa and a more similar community structure. In the natural woodland area, species distributions may be constrained by watershed segmentation and present more complex community characteristics

Irrigation-facilitated low-density polyethylene microplastic vertical transport along soil profile : An empirical model developed by column experiment

The emerging issue of microplastic pollution of agricultural soils derives from the intensive utilization of plastic mulching film. Although surface runoff may transport microplastic off-site, infiltration may also facilitate microplastic transport from surface soil to deeper depths. Microplastic comprises a relatively new category of soil contaminants, whose transport in the soil has not yet been widely studied. In this study, we investigated microplastic transport from contaminated surface soil (50 g kg-1) driven by irrigation, from permanent wilting point to saturation, and developed an empirical model to characterize the resulting accumulation of microplastic along soil profile. A soil column experiment was conducted under various treatments: the control, 1, 2 and 4 runs of irrigation. Soil samples were collected from inside and outside of soil cracks (if present) in each soil layer (0–2 cm (source layer), 2–5 cm, 5–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, 40–50 cm). The results showed that with increasing irrigation runs, microplastic in the source soil layer decreased, while microplastic contents in deeper soil depths increased significantly (p 0.92). Further research is needed to develop an physical-based model in order to assess microplastic migration risks driven by irrigation and other agricultural management practices

The Joint Contributions of Environmental Filtering and Spatial Processes to Macroinvertebrate Metacommunity Dynamics in the Alpine Stream Environment of Baima Snow Mountain, Southwest China

While macroinvertebrates are extensively investigated in many river ecosystems, meta-community ecology perspectives in alpine streams are very limited. We assessed the role of ecological factors and temporal dynamics in the macroinvertebrate meta-community assembly of an alpine stream situated in a dry-hot valley of Baima Snow Mountain, China. We found that spatial structuring and environmental filtering jointly drive the structure of macroinvertebrate meta-community, with relative contributions to the variance in community composition changing over time. RDA ordination and variation partitioning indicate that environmental variables are the most important predictors of community organization in most scenarios, whereas spatial determinants also play a significant role. Moreover, the explanatory power, identity, and the relative significance of ecological factors change over time. Particularly, in the years 2018 and 2019, stronger environmental filtering was found shaping community assembly, suggesting that deterministic mechanisms predominated in driving community dynamics. However, spatial factors had a stronger predictive power on meta-community structures in 2017, implying conspicuous dispersal mechanisms which may be owing to increased connectivity amongst sites. Thereby, we inferred that the alpine stream macroinvertebrate metacommunity composition can be regulated by the interaction of both spatial processes and environmental filtering, with relative contributions varying over time. Based on these findings, we suggest that community ecology studies in aquatic systems should be designed beyond single snapshot investigations