TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals

Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms of recognition. Electroencephalography (EEG) signals have significant potential for use in the measurement of learning style. This study uses EEG signals to design a deep-learning-based model of recognition to recognize people’s learning styles with EEG features by using a non-overlapping sliding window, one-dimensional spatio-temporal convolutions, multi-scale feature extraction, global average pooling, and the group voting mechanism; this model is named the TSMG model (Temporal-Spatial-Multiscale-Global model). It solves the problem of processing EEG data of variable length, and improves the accuracy of recognition of the learning style by nearly 5% compared with prevalent methods, while reducing the cost of calculation by 41.93%. The proposed TSMG model can also recognize variable-length data in other fields. The authors also formulated a dataset of EEG signals (called the LSEEG dataset) containing features of the learning style processing dimension that can be used to test and compare models of recognition. This dataset is also conducive to the application and further development of EEG technology to recognize people’s learning styles

Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery

Soil texture is a key physical property that affects the soil’s ability to retain moisture and nutrients. As a result, it is of extreme importance to conduct remote sensing monitoring of soil texture. Songnen Plain is located in the black soil belt of Northeast China. The development of satellite imagery in remote sensing technology enables the rapid monitoring of large areas. This study aimed to map the surface soil texture of cultivated land in Songnen Plain using Sentinel-2 images and Random Forest (RF) algorithm. We conducted this study by collecting 354 topsoil (0–20 cm) samples in Songnen Plain and evaluating the effectiveness of the bands and spectral indices of Sentinel-2 images and RF algorithm in predicting soil texture (sand, silt, and clay fractions). The results demonstrated that the 16 covariates were moderately and highly correlated with soil texture. And, Band11 of Sentinel-2 images could be used as the corresponding band of soil texture. For sand fraction, the Sentinel-2 images and RF algorithm’s Coefficient of Determination (R2) and Root Mean Square Error (RMSE) were 0.77 and 10.48%, respectively, and for silt fraction, they were 0.75 and 9.38%. Sand fraction decreased from southwest to northeast in Songnen Plain, while silt and clay fractions increased. We found that the Songnen Plain was affected by water erosion and wind erosion, in the northeast and southwest, respectively, providing reference for the implementation of Conservation Tillage policies. The outcome of the study can provide reference for future soil texture mapping with a high resolution

High-Performance A-Site Deficient Perovskite Electrocatalyst for Rechargeable Zn–Air Battery

Zinc–air batteries are one of the most excellent of the next generation energy devices. However, their application is greatly hampered by the slow kinetics of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) of air electrode. It is of great importance to develop good oxygen electrocatalysts with long durability as well as low cost. Here, A-site deficient (SmSr)0.95Co0.9Pt0.1O3 perovskites have been studied as potential OER electrocatalysts prepared by EDTA–citrate acid complexing method. OER electrocatalytic performance of (SmSr)0.95Co0.9Pt0.1O3 was also evaluated. (SmSr)0.95Co0.9Pt0.1O3 electrocatalysts exhibited good OER activities in 0.1 M KOH with onset potential and Tafel slope of 1.50 V and 87 mV dec−1, similar to that of Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF-5582). Assembled rechargeable Zn–air batteries exhibited good discharge potential and charge potential with high stability, respectively. Overall, all results illustrated that (SmSr)0.95Co0.9Pt0.1O3 is an excellent OER electrocatalyst for zinc–air batteries. Additionally, this work opens a good way to synthesize highly efficient electrocatalysts from A-site deficient perovskites

Moderate grazing has little effect on global warming potential in the temperate steppes of northern China

Grazing has been reported to significantly affect the flux of three greenhouse gases (GHGs: CO2, CH4 and N2O) in grasslands, but its effect on total global warming potential (GWP) is still unclear. To assess the effect of grazing on GWP, we simultaneously measured the flux of these three GHGs using static chambers in meadow, typical, and desert steppes under no grazing (NG) and summer grazing (SG) conditions during the 2012-14 growing seasons. We aimed to examine the impact of grazing on total GWP across different steppes and to assess the relative contribution of different environmental factors to changes in GWP. Our results showed that total GWP values were almost entirely negative in all steppe environments and displayed high spatio-temporal variability. Net ecosystem exchange was the most important predictor of total GWP in all three steppes, and the positive GWP induced by N2O emission was approximately equal to the negative GWP induced by CH4 uptake. Steppe type and sampling year-but not grazing treatment-were found to affect GWP. Air temperature and precipitation were the major factors driving total GWP change under the no grazing treatment. In contrast, soil temperature, soil moisture, and precipitation explained a significant percentage of variation in total GWP under the summer grazing treatment. Our study suggests that moderate grazing does not change the role of temperate steppe's function in mitigating climate change; however, multi-year GWP data are necessary for extrapolation to a regional scale

An Efficient Electrocatalyst (PtCo/C) for the Oxygen Reduction Reaction

The oxygen reduction reaction (ORR) is paid much more attention because of the high overpotential required for driving the four-electron process in the field of storage and sustainable energy conversion, including fuel cell applications. In this paper, PtCo nanoparticles encapsulated on carbon supports were prepared by a simple modified polyol method with ethylene glycol. Structural as well as electrochemical characterizations illustrated that the PtCo/C electrocatalysts had a minimum particle size of 4.8 nm, which is close to the commercial 40 wt% Pt/JM. Moreover, the electrochemical measurements indicated that ORR activity was competitive with the commercial 40 wt% Pt/JM catalyst. The synthesis method is a critical way to produce PtCo/C catalysts for use in polymer electrolyte membranes in fuel cells (PEMFCs)

Enhancement on PrBa0.5Sr0.5Co1.5Fe0.5O5 Electrocatalyst Performance in the Application of Zn-Air Battery

Due to the insufficient stability and expensive price of commercial precious metal catalysts like Pt/C and IrO2, it is critical to study efficiently, stable oxygen reduction reaction as well as oxygen evolution reaction (ORR/OER) electrocatalysts of rechargeable Zn-air batteries. PrBa0.5Sr0.5Co1.5Fe0.5O5 (PBSCF) double perovskite was adopted due to its flexible electronic structure as well as higher electro catalytic activity. In this study, PBSCF was prepared by the citrate-EDTA method and the optimized amount of PBSCF-Pt/C composite was used as a potential ORR/OER bifunctional electrocatalyst in 0.1 M KOH. The optimized composite exhibited excellent OER intrinsic activity with an onset potential of 1.6 V and Tafel slope of 76 mV/dec under O2-saturated 0.1 M KOH. It also exhibited relatively competitive ORR activity with an onset potential of 0.9 V and half-wave potential of 0.78 V. Additionally, Zn–air battery with PBSCF composite catalyst showed relatively good stability. All these results illustrate that PBSCF-Pt/C composite is a promising bifunctional electrocatalyst for rechargeable Zn-air batteries

Enhancement on PrBa_0.5Sr_0.5Co_1.5Fe_0.5O₅ Electrocatalyst Performance in the Application of Zn-Air Battery

Due to the insufficient stability and expensive price of commercial precious metal catalysts like Pt/C and IrO2, it is critical to study efficiently, stable oxygen reduction reaction as well as oxygen evolution reaction (ORR/OER) electrocatalysts of rechargeable Zn-air batteries. PrBa0.5Sr0.5Co1.5Fe0.5O5 (PBSCF) double perovskite was adopted due to its flexible electronic structure as well as higher electro catalytic activity. In this study, PBSCF was prepared by the citrate-EDTA method and the optimized amount of PBSCF-Pt/C composite was used as a potential ORR/OER bifunctional electrocatalyst in 0.1 M KOH. The optimized composite exhibited excellent OER intrinsic activity with an onset potential of 1.6 V and Tafel slope of 76 mV/dec under O2-saturated 0.1 M KOH. It also exhibited relatively competitive ORR activity with an onset potential of 0.9 V and half-wave potential of 0.78 V. Additionally, Zn–air battery with PBSCF composite catalyst showed relatively good stability. All these results illustrate that PBSCF-Pt/C composite is a promising bifunctional electrocatalyst for rechargeable Zn-air batteries

Different Responses and Links of N:P Ratio Among Ecosystem Components Under Nutrient Addition in a Temperate Forest

Nitrogen (N) and phosphorus (P) deposition have increased rapidly during the past decades, which likely changes soil N and P availability. These soil resource variations will further affect N, P concentration and N:P ratio in different ecosystem pools (i.e., soil, leaf, litter, root, and microbe). Various pools may show different stoichiometric responses to nutrient enrichment, with a further influence on ecosystem nutrient cycling. However, few studies have been conducted to fully examine the stoichiometric responses of different pools and their nutrient relationships in a given ecosystem. Here we established a 2-year experiment of N (10 g m(-2) year(-1)), P (10 g m(-2) year(-1)), and combined N + P addition in a temperate forest of Changbai Mountain. We found significantly different N:P stoichiometric responses among various ecosystem components under P addition, with the leaves showing a higher response than litter and root while microbe behaving the lowest response. The responses of N:P ratio to N + P addition were similar with those under P addition in all pools. In most cases, N addition did not significantly affect N:P ratio. These results indicate that N:P ratio response was mainly determined by changes in P rather than N concentration in this temperate forest ecosystem. Moreover, we found tighter N:P stoichiometric correlations than elements among diverse ecosystem components under nutrient addition. Overall, our research reveals different responses and tight links of element stoichiometric variations among various ecosystem components in face of nutrient enrichment. It calls our attention to considering stoichiometric changes in the whole ecosystem beyond individual plant organ or microbial component