Search

Developing a spatial downscaling method using machine learning techniques has emerged as a significant endeavor in meteorological forecasting. In this study, we propose a novel Residual-Pyramid-Attention Super Resolution (RPASR) model that effectively addresses the checkerboard artifacts issue prevalent in existing approaches, offering a unique perspective on spatial downscaling. Moreover, we propose a unique scaling technique during the data training to enhance the prediction performance. To validate and apply the proposed method, we utilize both low and high-resolution meteorological variables obtained from a numerical weather prediction model and observations in the Zhangbei region, China. The results demonstrate the exceptional performance of the RPASR model in spatial downscaling, exhibiting superior accuracy in meteorological prediction. Notably, the model outperforms interpolation techniques, significantly reducing deviations by 50% for variables such as 2 m temperature (T2), 2 m humidity (rh2), and 10 m wind speed (wspd10). In practical applications, our proposed model surpasses the DeepSD super-resolution model and low-resolution simulations as a whole, particularly for T2, where it enhances the T2 correlation coefficient by 1.2% compared to DeepSD. Additionally, it reduces the T2 root mean square error by 10.6% and the rh2 root mean square error by 8.6% compared to the low-resolution simulations. Overall, the RPASR model demonstrates robust validation and exhibits promising potential for advancing spatial downscaling methods in meteorological forecasting. [ABSTRACT FROM AUTHOR]

For proton exchange membranes (PEM) used in vanadium redox batteries (VRBs), doping metal-organic framework (MOF) materials to enhance the proton permeability and vanadium ion barrier property of PEM has become a research focus. In synthesizing MOFs, conventional hydrothermal method is hindered by prolonged reaction time and suboptimal material structures. This study utilizes microwave assisted heating for the preparation of UIO-66-NH2 crystals, which are then combined with Nafion to fabricate composite membranes. The results indicate that microwave heating enables a more efficient and rapid synthesis of MOF crystals (MU-NH2), with enhanced uniformity and higher yield than the conventional hydrothermal process (CU-NH2). Compared to MU-NH2/Nf-0(vanadium ion permeability: 4.4 × 10−7 cm2·min−1; ion selectivity: 5 × 104 S·min·cm−3), the vanadium ion permeability of MU-NH2/Nf-3 and CU-NH2/Nf-3 membranes exhibits a similar level of reduction, approximately 82%. Additionally, the former demonstrates an enhanced ion selectivity by 33.3%, while the latter shows an increase of 30.9%. VRBs equipped with MU-NH2/Nf membranes exhibit higher energy efficiency (83.8% at 100 mA·cm−2) and better capacity retention than those with CU-NH2/Nf membranes (82.7% at 100 mA·cm−2). Therefore, UIO-66-NH2 synthesized via the microwave method can effectively enhance the comprehensive properties of proton exchange membranes and shows promising prospects in optimizing the performance of VRBs. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; the future of AD drug development., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)