Enhanced Potentiometric Hydrogen Sensing Response Based on the BaSrCoFeO Electrode with Unusual Polarity.

In this work, unusual potentiometric hydrogen sensing of mixed conducting BaSrCoFeO was reported. Inspired by the unusual polarity, a dual sensing electrode (SE) potentiometric hydrogen sensor was fabricated by pairing BaSrCoFeO with electronic conducting ZnO to enhance the hydrogen response. Hydrogen sensing measurements suggested that significantly higher response, larger sensitivity, and lower limit of detection (LOD) were achieved by the dual SE sensor when compared with the single SE sensor based on BaSrCoFeO or ZnO.

Seasonal succession of microbial community co-occurrence patterns and community assembly mechanism in coal mining subsidence lakes.

Coal mining subsidence lakes are classic hydrologic characteristics created by underground coal mining and represent severe anthropogenic disturbances and environmental challenges. However, the assembly mechanisms and diversity of microbial communities shaped by such environments are poorly understood yet. In this study, we explored aquatic bacterial community diversity and ecological assembly processes in subsidence lakes during winter and summer using 16S rRNA gene sequencing.

Phytoplankton-Zooplankton Community Structure in Coal Mining Subsidence Lake.

Land subsidence from coal mining has shaped new artificial aquatic ecosystems, these subsidence lakes are known for their restricted ecological system, water pollution, and extreme habitat conditions. However, knowledge concerning the community structure of plankton in these types of water bodies is still limited. Therefore, both phytoplankton and zooplankton communities' abundance, distribution, and diversity, as well as relations of these communities to physicochemical water quality variables were analyzed, alongside the interaction between phytoplankton and zooplankton groups.

Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data.

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat.

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images.

Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (H) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and H were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques.