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Many radar-gauge merging methods have been developed to produce improved rainfall data by leveraging the advantages of gauge and radar observations. Two popular merging methods, Regression Kriging and Bayesian Regression Kriging were utilized and compared in this study to produce hourly rainfall data from gauge networks and multi-source radar datasets. The authors collected, processed, and modeled the gauge and radar rainfall data (Stage IV, MRMS and RTMA radar data) of the two extreme storm events (i.e., Hurricane Harvey in 2017 and Tropical Storm Imelda in 2019) occurring in the coastal area in Southeast Texas with devastating flooding. The analysis of the modeled data on consideration of statistical metrics, physical rationality, and computational expenses, implies that while both methods can effectively improve the radar rainfall data, the Regression Kriging model demonstrates its superior performance over that of the Bayesian Regression Kriging model since the latter is found to be prone to overfitting issues due to the clustered gauge distributions. Moreover, the spatial resolution of rainfall data is found to affect the merging results significantly, where the Bayesian Regression Kriging model works unskillfully when radar rainfall data with a coarser resolution is used. The study recommends the use of high-quality radar data with properly spatial-interpolated gauge data to improve the radar-gauge merging methods. The authors believe that the findings of the study are critical for assisting hazard mitigation and future design improvement. 

In etiolated seedlings, red light (R) activates phytochrome and initiates signals that generate major changes at molecular and physiological levels. These changes include inhibition of hypocotyl growth and promotion of the growth of primary roots, apical hooks, and cotyledons. An earlier report showed that the sharp decrease in hypocotyl growth rapidly induced by R was accompanied by an equally rapid decrease in the transcript and protein levels of two closely related apyrases (APYs; nucleoside triphosphate-diphosphohydrolases) in Arabidopsis (Arabidopsis thaliana), APY1 and APY2, enzymes whose expression alters auxin transport and growth in seedlings. Here, we report that single knockouts of either APY inhibit R-induced promotion of the growth of primary roots, apical hooks, and cotyledons, and RNAi-induced suppression of APY1 expression in the background of apy2 inhibits R-induced apical hook opening. When R-irradiated primary roots and apical hook-cotyledons began to show a gradual increase in their growth relative to dark controls, they concurrently showed increased levels of APY protein, but in hook-cotyledon tissue, this occurred without parallel increases in their transcripts. In wild-type seedlings whose root growth is suppressed by the photosynthesis inhibitor 3-(3,4-dichlorophenyl)-1,1-dimethylurea, the R-induced increased APY expression in roots was also inhibited. In unirradiated plants, the constitutive expression of APY2 promoted both hook opening and changes in the transcript abundance of Small Auxin Upregulated RNA (SAUR), SAUR17 and SAUR50 that help mediate de-etiolation. These results provide evidence that the expression of APY1/APY2 is regulated by R and that APY1/APY2 participate in the signaling pathway by which phytochrome induces differential growth changes in different tissues of etiolated seedlings.

 

Thermal comfort (TC) – how comfortable or satisfied a per- son is with the temperature of her/his surroundings – is one of the key factors influencing the indoor environmental quality of schools, libraries, and offices. We conducted an experiment to explore how TC can impact students’ learning. University students (n = 25) were randomly assigned to different temperature conditions in an office environment (25◦C → 30◦C, or 30◦C → 25◦C) that were implemented using a combination of heaters and air conditioners over a 1.25 hour session. The task of the participants was to learn from tutorial videos on three different topics, and a test was given after each tutorial. The results suggest that (1) changing the room temperature by a few degrees Celsius can stat. sig. impact students’ self-reported TC; (2) the relationship between TC and learning exhibited an inverted U-curve, i.e., should be neither too uncomfortable nor too comfortable. We also explored different computer vision and sensor-based approaches to measure students’ thermal comfort automatically. We found that (3) TC can be predicted automatically either from the room temperature or from an infra-red (IR) camera of the face; however, (4) TC prediction from a normal (visible-light) web camera is highly challenging, and only limited predictive power was found in the facial expression features to predict thermal comfort. 

Nitrogen‐doped carbon nanofibers (CNFs) were synthesized using a facile electrospinning technique with the addition of urea as a nitrogen‐doping agent. The amount of urea was selectively adjusted to control the degree and effectiveness of N‐doping. The morphology of N‐doped CNFs was investigated by scanning electron microscopy, transmission electron microscopy, and X‐ray diffraction, whereas their electrochemical performance was studied using cyclic voltammetry and galvanostatic charge–discharge experiments. The nitrogen content of N‐doped CNFs increased significantly from 11.31 % to 19.06 % when the doping amount of urea increased from 0 % to 30 %. N‐doping also played an important role in improving the electrochemical performance of the CNFs by introducing more defects in the carbon structure. Results showed that N‐doped CNFs with the highest nitrogen content (19.06 %) exhibited the largest reversible capacity of 354 mAh g⁻¹under a current density of 50 mA g⁻¹; and when the current density was increased to 1 A g⁻¹, a capacity of 193 mAh g⁻¹was still maintained. It is, therefore, demonstrated that N‐doped CNFs have great potential as suitable sodium‐ion battery anode material.