Numerical Simulation on the Gas Explosion Propagation Related to Roadway

AbstractBased on the combustion, explosions and air dynamics and related theory etc, this paper describes the mathematical model of gas explosion in detail, combined with the gas explosion transmission mechanism, make a research on two wave-three area structure of gas explosion and the energy change rule of the array face of precursor wave and the array face of flame wave, with the fluid dynamics analysis Fluent software, this paper makes a numerical simulation and analysis on the overpressure transmission rule when gas explosion takes place in different types roadways. The results of the study show that: Fluent software can be used to accurately simulate gas explosion condition, when explosion wave spreads in the roadway turns, the bigger of the overpressure value in corner, the stronger of the destructive power; when tunnel has bifurcation, the overpressure will release in bifurcation, but explosions wave with flame wave will produce more powerful destruction effect. The research results can be used as a certain reference for how to prevent and control the gas explosion, and how to reduce the power of the gas explosion etc

Improving urban bus emission and fuel consumption modeling by incorporating passenger load factor for real world driving

Vehicle Specific Power (VSP) has been increasingly used as a good indicator for the instantaneous power demand on engines for real world driving in the field of vehicle emission and fuel consumption modeling. A fixed vehicle mass is normally used in VSP calculations. However, the influence of passenger load was always been neglected. The major objective of this paper is to quantify the influence of passenger load on diesel bus emissions and fuel consumption based on the real-world on-road emission data measured by the Portable Emission Measurement System (PEMS) on urban diesel buses in Nanjing, China. Meanwhile, analyses are conducted to investigate whether passenger load affected the accuracy of emission and fuel consumption estimations based on VSP. The results show that the influence of passenger load on emission and fuel consumption rates were related to vehicle's speed and acceleration. As for the distance-based factors, the influence of passenger load was not obvious when the buses were driving at a relative high speed. However the effects of passenger load were significant when the per-passenger factor was used. Per-passenger emission and fuel consumption factors decreased as the passenger load increased. It was also found that the influence of passenger load can be omitted in the emission and fuel consumption rate models at low and medium speed bins but has to be considered in the models for high speed and VSP bins. Otherwise it could lead to an error of up to 49%. The results from this research will improve the accuracy of urban bus emission and fuel consumption modeling and can be used to improve planning and management of city buses and thus achieve energy saving and emission reduction

Emergence of Fatal PRRSV Variants: Unparalleled Outbreaks of Atypical PRRS in China and Molecular Dissection of the Unique Hallmark

Porcine reproductive and respiratory syndrome (PRRS) is a severe viral disease in pigs, causing great economic losses worldwide each year. The causative agent of the disease, PRRS virus (PRRSV), is a member of the family Arteriviridae. Here we report our investigation of the unparalleled large-scale outbreaks of an originally unknown, but so-called “high fever” disease in China in 2006 with the essence of PRRS, which spread to more than 10 provinces (autonomous cities or regions) and affected over 2,000,000 pigs with about 400,000 fatal cases. Different from the typical PRRS, numerous adult sows were also infected by the “high fever” disease. This atypical PRRS pandemic was initially identified as a hog cholera-like disease manifesting neurological symptoms (e.g., shivering), high fever (40–42°C), erythematous blanching rash, etc. Autopsies combined with immunological analyses clearly showed that multiple organs were infected by highly pathogenic PRRSVs with severe pathological changes observed. Whole-genome analysis of the isolated viruses revealed that these PRRSV isolates are grouped into Type II and are highly homologous to HB-1, a Chinese strain of PRRSV (96.5% nucleotide identity). More importantly, we observed a unique molecular hallmark in these viral isolates, namely a discontinuous deletion of 30 amino acids in nonstructural protein 2 (NSP2). Taken together, this is the first comprehensive report documenting the 2006 epidemic of atypical PRRS outbreak in China and identifying the 30 amino-acid deletion in NSP2, a novel determining factor for virulence which may be implicated in the high pathogenicity of PRRSV, and will stimulate further study by using the infectious cDNA clone technique

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs), in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD) high performance liquid chromatography (HPLC) database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS), artificial neural networks (ANN), support vector machine (SVM) and random forests (RF), and feature selection techniques, including genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination based on support vector machine (SVM-RFE), for inferring PSCs from remote sensing data. Results showed that: (1) SVM-RFE worked better in selecting sensitive features; (2) RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3) machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4) sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5) the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing

De novo assembly and characterization of the complete chloroplast genome of Elymus magellanicus (É.Desv.) Á.Löve, 1984 (Poaceae, Pooideae)

Elymus magellanicus (É.Desv.) Á.Löve is a foliage accent plant that originated in South America. In this study, the complete chloroplast genome of E. magellanicus is reported. It was found to have a total size of 133,249 bp. The chloroplast genome was found to consist of two inverted repeats (IRA and IRB) of 21,421 bp each, a small single-copy region of 12,709 bp, and a large single-copy region (77,698 bp). The annotation results show the GC content of the chloroplast genome to be 38.47%, including 40 tRNA genes, 82 protein-coding genes, and 8 rRNA genes. Phylogenetic analysis of 29 species revealed that E. magellanicus is closely related to E. arenarius

Application of Sentinel 2 MSI Images to Retrieve Suspended Particulate Matter Concentrations in Poyang Lake

Suspended particulate matter (SPM) is one of the dominant water constituents in inland and coastal waters, and SPM concnetration (CSPM) is a key parameter describing water quality. This study, using in-situ spectral and CSPM measurements as well as Sentinel 2 Multispectral Imager (MSI) images, aimed to develop CSPM retrieval models and further to estimate the CSPM values of Poyang Lake, China. Sixty-eight in-situ hyperspectral measurements and relative spectral response function were applied to simulate Sentinel 2 MIS spectra. Thirty-four samples were used to calibrate and the left samples were used to validate CSPM retrieval models, respectively. The developed models were then applied to two Sentinel 2 MSI images captured in wet and dry seasons, and the derived CSPM values were compared with those derived from MODIS B1 (λ = 645 nm). Results showed that the Sentinel 2 MSI B4–B8b models achieved acceptable to high fitting accuracies, which explained 81–93% of the variation of CSPM. The validation results also showed the reliability of these six models, and the estimated CSPM explained 77–93% of the variation of measured CSPM with the mean absolute percentage error (MAPE) ranging from 36.87% to 21.54%. Among those, a model based on B7 (λ = 783 nm) appeared to be the most accurate one. The Sentinel 2 MSI-derived CSPM values were generally consistent in spatial distribution and magnitude with those derived from MODIS. The CSPM derived from Sentinel 2 MSI B7 showed the highest consistency with MODIS on 15 August 2016, while the Sentinel 2 MSI B4 (λ = 665 nm) produced the highest consistency with MODIS on 2 April 2017. Overall, this study demonstrated the applicability of Sentinel 2 MSI for CSPM retrieval in Poyang Lake, and the Sentinel 2 MSI B4 and B7 are recommended for low and high loadings of SPM, respectively

Improving Stem Lodging Resistance, Yield, and Water Efficiency of Wheat by Adjusting Supplemental Irrigation Frequency

Optimizing supplemental irrigation (SI) measures and enhancing stem lodging resistance can be the keys to achieving a high and stable yield and high efficiency in wheat. The experiment was established as a two-factor field trial in 2018–2020. We used four SI combinations at different stages: rain-fed (T0), SI at jointing (T1), SI at jointing + anthesis (T2), and SI at regreening + jointing + anthesis (T3) with ‘Bainong4199’ (BN4199) and ‘Zhoumai18’ (ZM18) as experimental materials. We researched the effects of different SI combinations on the stem characteristics, stem vigor, grain filling, and yield of winter wheat. The results suggest that the basal internode at the anthesis stage grew with the increase in SI amount, but the stem fracture resistance of T1 and T2 was higher than that of T0 and T3. As grain filling continued, the lodging index increased and stem vigor decreased. In comparison with T3, the average stem lodging index of T2 decreased by 21.92% for ‘BN4199’ and 36.63% for ‘ZM18’, but the WUE increased by 29.76% and 14.92%, respectively. The grain yield increased with the increase in irrigation times during the growth period; there was no significant difference between T2 and T3 in 2018–2019. In a biennial comparison, the grain yield of all treatments in 2019–2020 was significantly lower than those in 2018–2019, and the grain yield of ‘ZM 18’ was lower than that of ‘BN 4199’. Correlation analysis displayed that there were significant positive correlations between post-anthesis stem vigor and the dry matter contribution rate of post-anthesis to grains and between the grain filling rate at 21–28 days after anthesis (DAA) and stem strength at 30 DAA. In summary, selecting a high-yield lodging-resistant wheat variety with SI at jointing and anthesis was beneficial for forming strong stems and maintaining higher stem vigor at the later growth stage for grain filling, which reduced lodging risk and ensured high yield and high WUE

Sequencing and characterization of the complete mitochondrial genome of Thinopyrum obtusiflorum (DC.) Banfi, 2018 (Poaceae)

In this study, the mitochondrial genome of Thinopyrum obtusiflorum was sequenced, assembled, and annotated. The complete circular mitogenome of Th. obtusiflorum is 390,725 bp in length and the overall A + T content of mitogenome is 55.61%. It harbors 33 protein-coding genes (PCGs), 21 transfer RNA genes (tRNAs), six ribosomal RNA genes (rRNAs), and 20 simple sequence repeats (SSRs). Phylogenetic analysis indicates that Th. obtusiflorum is a sister to the clade including Aegilops speltoides, Triticum aestivum, and Triticum aestivum cultivar Chinese Yumai in the Triticeae

Genome-Wide Association Study on Seedling Phenotypic Traits of Wheat under Different Nitrogen Conditions

Nitrogen fertilizer input is the main determinant of wheat yield, and heavy nitrogen fertilizer application causes serious environmental pollution. It is important to understand the genetic response mechanism of wheat to nitrogen and select wheat germplasm with high nitrogen efficiency. In this study, 204 wheat species were used to conduct genome-wide association analysis. Nine phenotypic characteristics were obtained at the seedling stage in hydroponic cultures under low-, normal, and high-nitrogen conditions. A total of 765 significant loci were detected, including 438, 261, and 408 single nucleotide polymorphisms (SNPs) associated with high-, normal, and low-nitrogen conditions, respectively. Among these, 14 SNPs were identified under three conditions, for example, AX-10887638 and AX-94875830, which control shoot length and root–shoot ratio on chromosomes 6A and 6D, respectively. Additionally, 39 SNPs were pleiotropic for multiple traits. Further functional analysis of the genes near the 39 SNPs shows that some candidate genes play key roles in encoding proteins/enzymes, such as transporters, hydrolases, peroxidases, glycosyltransferases, oxidoreductases, acyltransferases, disease-resistant proteins, ubiquitin ligases, and sucrose synthetases. Our results can potentially be used to develop low-nitrogen-tolerant species using marker-assisted selection and provide a theoretical basis for breeding efficient nitrogen-using wheat species