ALBERT

Beschreibung: Water retention is an important factor in ecosystem services, owing to its relationships with climate and land-cover change; however, quantifying the independent and combined impacts of these variables remains a challenge. We use scenario analysis and the InVEST model to assess individual or combined impacts of climate and land cover on water retention in the Upper Yangtze River Basin. Water retention decreased from 1986 to 2015 at a rate of 2.97 mm/10a in response to increasing precipitation (3.94 mm/10a) and potential evapotranspiration (16.47 mm/10a). The rate of water retention change showed regional variability (from 68 to −18 mm/a), with some eastern regions experiencing an increase and most other regions experiencing a decrease. Farmland showed the highest decrease (10,772 km2), with land mainly converted into forest (58.17%) and shrub land (21.13%) from 2000 to 2015. The impact of climate change (−12.02 mm) on water retention generally was greater than the impact of land cover change (−4.14 mm), at the basin scale. Among 22 climate zones, 77.27% primarily were impacted by climate change; 22.73% primarily were impacted by land cover change. Our results demonstrate that both individualistic and integrated approaches toward climate and vegetation management is necessary to mitigate the impacts of climate change on water resources.

Beschreibung: In order to increase the hydrogen production rate as well as ozone production at the anode side, increased voltage application and more catalyst utilization are necessary. The membrane electrode assembly (MEA) produces hydrogen/ozone via proton exchange membrane water electrolysis (PEMWE)s which gives priority to a coating method (abbreviation: ML). However, coating takes more effort and is labor-consuming. This study will present an innovative preparation method, known as flat layer (FL), and compare it with ML. FL can significantly reduce efforts and largely improve MEA production. Additionally, MEA with the FL method is potentially durable compared to ML.

Beschreibung: Root morphology is essential for plant survival. NO3− is not only a nutrient, but also a signal substance affecting root growth in plants. However, the mechanism of NO3−-mediated root growth in rice remains unclear. In this study, we investigated the effect of OsNRT2.1 on root elongation and nitrate signaling-mediated auxin transport using OsNRT2.1 overexpression lines. We observed that the overexpression of OsNRT2.1 increased the total root length in rice, including the seminal root length, total adventitious root length, and total lateral root length in seminal roots and adventitious roots under 0.5-mM NO3− conditions, but not under 0.5-mM NH4+ conditions. Compared with wild type (WT), the 15NO3− influx rate of OsNRT2.1 transgenic lines increased by 24.3%, and the expressions of auxin transporter genes (OsPIN1a/b/c and OsPIN2) also increased significantly under 0.5-mM NO3− conditions. There were no significant differences in root length, ß-glucuronidase (GUS) activity, and the expressions of OsPIN1a/b/c and OsPIN2 in the pDR5::GUS transgenic line between 0.5-mM NO3− and 0.5-mM NH4+ treatments together with N-1-naphthylphalamic acid (NPA) treatment. When exogenous NPA was added to 0.5-mM NO3− nutrient solution, there were no significant differences in the total root length and expressions of OsPIN1a/b/c and OsPIN2 between transgenic plants and WT, although the 15NO3− influx rate of OsNRT2.1 transgenic lines increased by 25.2%. These results indicated that OsNRT2.1 is involved in the pathway of nitrate-dependent root elongation by regulating auxin transport to roots; i.e., overexpressing OsNRT2.1 promotes an effect on root growth upon NO3− treatment that requires active polar auxin transport.

Beschreibung: We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined and optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme learning machine (ELM), LC-ELM and extreme learning machine based on particle optimization (PSO-ELM) that have the same network size or compact network configuration, simulation results in terms of regression and classification benchmark problems show that the proposed algorithm, which is called LC-PSO-ELM, has improved generalization performance and robustness.

Beschreibung: Precipitation strengthening is an effective approach to strengthen high-entropy alloys (HEAs) with a simple face-center-cubic (FCC) structure. In this work, CoCrFeNiMo0.2 HEAs were prepared by powder metallurgy, followed by cool rolling and subsequent heat-treatment at different temperatures. The effects of cold working and annealing on microstructure and mechanical properties have been investigated. Results show the fine and dispersed (Cr, Mo)-rich σ phase with a topologically close-packed structure precipitated in the FCC matrix after the prior cold deformation process, which enhanced the mechanical property of the CoCrFeNiMo0.2 alloy. The HEA annealed at 600 °C for 48 h had a tensile strength of 1.9 GPa but an elongation which decreased to 8%. The HEA annealed at 800 °C for 12 h exhibited a tensile strength of 1.2 GPa and an elongation of 31%. These outstanding mechanical properties can be attributed to precipitation strengthening and fine-grain strengthening.