A New Heat Transfer Correlation for Turbulent Flow of Air With Variable Properties in Noncircular Ducts

Turbulent flow and heat transfer of air with variable properties in a set of regular polygonal ducts and circular tube have been numerically simulated. All the ducts have the same hydraulic diameter as their characteristic lengths in the Reynolds number. The flow is modeled as three-dimensional (3D) and fully elliptic by using the finite volume method and the standard k-e turbulence model. The results showed that the relatively strong secondary flow could be observed with variable properties fluid. For the regular polygonal ducts, the local heat transfer coefficient along circumferential direction is not uniform; there is an appreciable reduction in the corner region and the smaller the angle of the corner region, the more appreciable deterioration the corner region causes. The use of hydraulic diameter for regular polygonal ducts leads to unacceptably large errors in turbulent heat transfer determined from the circular tube correlations. Based on the simulation results, a correction factor is proposed to predict turbulent heat transfer in regular polygonal ducts

Y-Doped ZnO Nanorods by Hydrothermal Method and Their Acetone Gas Sensitivity

Pure and yttrium- (Y-) doped (1 at%, 3 at%, and 7 at%) ZnO nanorods were synthesized using a hydrothermal process. The crystallography and microstructure of the synthesized samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX). Comparing with pure ZnO nanorods, Y-doped ZnO exhibited improved acetone sensing properties. The response of 1 at% Y-doped ZnO nanorods to 100 ppm acetone is larger than that of pure ZnO nanorods. The response and recovery times of 1 at% Y-doped ZnO nanorods to 100 ppm acetone are about 30 s and 90 s, respectively. The gas sensor based on Y-doped ZnO nanorods showed good selectivity to acetone in the interfere gases of ammonia, benzene, formaldehyde, toluene, and methanol. The formation mechanism of the ZnO nanorods was briefly analyzed

Enhanced daytime secondary aerosol formation driven by gas-particle partitioning in downwind urban plumes

Anthropogenic emissions from city clusters can significantly enhance secondary organic aerosol (SOA) formation in the downwind regions, while the mechanism is poorly understood. To investigate the effect of pollutants within urban plumes on organic aerosol (OA) evolution, a field campaign was conducted at a downwind site of the Pearl River Delta region of China in the fall of 2019. A time-of-flight chemical ionization mass spectrometer coupled with a Filter Inlet for Gases and Aerosol (FIGAERO-CIMS) was used to probe the gas- and particle-phase molecular composition and thermograms of organic compounds. For air masses influenced by urban pollution, strong daytime SOA formation through gas-particle partitioning was observed, resulting in higher OA volatility. The obvious SOA enhancement was mainly attributed to the equilibrium partitioning of non-condensable (C * ≥ 100.5 μg m-3) organic vapors. We speculated that the elevated NOx concentration could suppress the formation of highly oxidized products, resulting in a smooth increase of condensable (C * < 100.5 μg m-3) organic vapors. Evidence showed that urban pollutants (NOx and VOCs) could enhance the oxidizing capacity, while the elevated VOCs was mainly responsible for promoting daytime SOA formation by increasing the RO2 production rate. Our results highlight the important role of urban anthropogenic pollutants in SOA control in the suburban region

Downregulation of Nuclear Protein H2B Induces Salicylic Acid Mediated Defense Against PVX Infection in Nicotiana benthamiana

Histone H2B protein is not only structurally important for chromosomal DNA packaging but is also involved in the regulation of gene expression, including the immune response of plants against pathogens. In this study, we show that the potato virus X (PVX) infection resulted in the reduced expression of H2B at both the mRNA and protein level in Nicotiana benthamiana. Tobacco rattle virus (TRV)-based virus-induced gene silencing (VIGS) was then used to down-regulate the expression of H2B in N. benthamiana and tests showed that the titre of TRV was similar in these plants to that in control treated plants. When these H2B-silenced plants were inoculated with PVX, the virus spread more slowly through the plant and there was a lower titre of PVX compared to non-silenced plants. Abnormal leaf development and stem necrosis were observed in the H2B-silenced plants, which were alleviated in H2B-silenced NahG transgenic plants suggesting the involvement of salicylic acid (SA) in the production of these symptoms. Indeed, quantitative reverse transcription (qRT)-PCR and liquid chromatography tandem mass spectroscopy (LC-MS) results showed that endogenous SA is increased in H2B-silenced N. benthamiana. Thus, downregulation of H2B induced the accumulation of endogenous SA, which was correlated with stem necrosis and a decreased accumulation of PVX in N. benthamiana

Genome-wide identification of new reference genes for RT-qPCR normalization in CGMMV-infected Lagenaria siceraria

Lagenaria siceraria is an economically important cucurbitaceous crop, but suitable reference genes (RGs) to use when the plants are infected by cucumber green mottle mosaic virus (CGMMV) have not been determined. Sixteen candidate RGs of both leaf and fruit and 18 candidate RGs mostly from separate RNA-Seq datasets of bottle gourd leaf or fruit were screened and assessed by RT-qPCR. The expression stability of these genes was determined and ranked using geNorm, NormFinder, BestKeeper and RefFinder. Comprehensive analysis resulted in the selection of LsCYP, LsH3, and LsTBP as the optimal RGs for bottle gourd leaves, and LsP4H, LsADP, and LsTBP for fruits. LsWD, LsGAPDH, and LsH3 were optimal for use in both leaves and fruits under the infection of CGMMV. Isopentenyl transferase (IPT) and DNA-directed RNA polymerase (DdRP) were used to validate the applicability of the most stable identified RGs from bottle gourd in response to CGMMV. All the candidate RGs performed in RT-qPCR consistently with the data from the transcriptome database. The results demonstrated that LsWD, LsGAPDH and LsH3 were the most suitable internal RGs for the leaf, and LsH3, LsGAPDH, LsP4H and LsCYP for the fruit

Development Prospects of Osmanthus fragrans Mixed Wine Based on Liquor and Spirits

As a plant resource, Osmanthus fragrans has both medicinal and edible effects. This paper reviews the active components and health-care functions of O. fragrans, and introduces the current research situation of O. fragrans mixed wine. In addition, combined with the development of new products of plant-based flower wine, it analyzes the development prospects of O. fragrans mixed wine based on liquor and spirits

Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province, southwestern China

Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches, enhancing forest fire warnings and emergency response capabilities, and accurately budgeting potential carbon emissions resulting from fires. However, due to the unavailability of spatial information technology, such databases are extremely difficult to build reliably and completely in the non-satellite era. This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province, southwestern China. First, the forest fire danger index (FFDI) was improved by supplementing slope and aspect information. We compared the performances of three time series models, namely, the autoregressive integrated moving average (ARIMA), Prophet and long short-term memory (LSTM) in predicting the modified forest fire danger index (MFFDI). The best-performing model was used to retrace the MFFDI of individual stations from 1941 to 1970. Following this, the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals, which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database. The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI, with a fitting determination coefficient (R2) of 0.709, mean square error (MSE) of 0.047, and validation R2 and MSE of 0.508 and 0.11, respectively. Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas, which is higher than the results determined from the original FFDI (2 out of 7). This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study