Improved Bound on Vertex Degree Version of Erd\H{o}s Matching Conjecture

For a $k$-uniform hypergraph $H$, let $\delta_1(H)$ denote the minimum vertex degree of $H$, and $\nu(H)$ denote the size of a maximum matching in $H$. In this paper, we show that for sufficiently large integer $n$ and integers $k\geq 3$ and $m\ge 1$, there is a positive $\beta=\beta(k)<1/(3^{k}2k^{5}(k!))^4$ such that if $H$ is a $n$-vertex $k$-graph with $1\leq m\leq (\frac{k}{2(k-1)}-\beta)\frac{n}{k}$ and $\delta_1(H)>{{n-1}\choose {k-1}}-{{n-m}\choose {k-1}},$ then $\nu(H)\geq m$. This improves upon earlier results of Bollob\'{a}s, Daykin and Erd\H{o}s (1976) for the range $n> 2k^3(m+1)$ and Huang and Zhao (2017) for the range $n\geq 3k^2 m$

Analysis of Heat Stress and the Indoor Climate Control Requirements for Movable Refuge Chambers

Movable refuge chambers are a new kind of rescue device for underground mining, which is believed to have a potential positive impact on reducing the rate of fatalities. It is likely to be hot and humid inside a movable refuge chamber due to the metabolism of trapped miners, heat generated by equipment and heat transferred from outside. To investigate the heat stress experienced by miners trapped in a movable refuge chamber, the predicted heat strain (PHS) model was used to simulate the heat transfer process between the person and the thermal environment. The variations of heat stress with the temperature and humidity inside the refuge chamber were analyzed. The effects of air temperature outside the refuge chamber and the overall heat transfer coefficient of the refuge chamber shell on the heat stress inside the refuge chamber was also investigated. The relationship between the limit of exposure duration and the air temperature and humidity was numerically analyzed to determine the upper limits of temperature and humidity inside a refuge chamber. Air temperature of 32 °C and relative humidity of 70% are recommended as the design standard for internal thermal environment control of movable refuge chambers

Data from: Revealing biogeographic patterns in genetic diversity of native and invasive plants and their association with soil community diversity in the Chinese coast

Within-species genetic diversity is shaped by multiple evolutionary forces within the confines of geography, and has cascading effects on the biodiversity of other taxa and levels. Invasive species are often initially limited in genetic diversity but still respond rapidly to their new range, possibly through 'pre-adapted' genotypes or multiple sources of genetic diversity, but little is known about how their genetic structure differs from that of native species and how it alters the genetic-species diversity relationship. Here, we selected a widespread native species (Phragmites australis) and its co-occurring invasive competitor (Spartina alterniflora) as our model plant species. We investigated the genetic structure of P. australis using two chloroplast fragments and ten nuclear microsatellites in 13 populations along the Chinese coastal wetlands. We discovered a distinct geographical differentiation, showing that the northern and southern populations harbored unique genotypes. We also found a significant increase in genetic diversity (allelic richness and expected heterozygosity) from south to north. Combined with previous studies of S. alterniflora, the Mantel tests revealed a significant correlation of genetic distances between P. australis and S. alterniflora even when controlling for geographic distance, suggesting that the invasive species S. alterniflora might exhibit a phylogeographic pattern similar to that of the native species to some extent. Furthermore, our results suggest that the S. alterniflora invasion has altered the relationship between the genetic diversity of the dominant native plant and the associated species richness of soil nematodes. The reason for the alteration of genetic-species diversity relationship might be that the biological invasion weakens the environmental impact on both levels of biodiversity. Our findings contribute to understanding the latitudinal patterns of intraspecific genetic diversity in widespread species. This work on the genetic diversity analysis of native species also provides significant implications for the invasion stage and ecological consequences of biological invasions.Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 32100304Funding provided by: Ministry of Natural Resources of the People's Republic of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/100015809Award Number: 2022101Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: U22A20558Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 32171661Sampling, genotyping and sequencing of P. australis We collected 194 individuals of P. australis from 13 sites along the Chinese coast. To avoid collecting the same clone and to get enough genetic variation, we selected five P. australis reed populations within each site, with a distance of at least 1 km between stands. We collected three individuals from each population. All individuals were transplanted with rhizomes in a common garden at the Jiangwan Campus of Fudan University in Shanghai (31.28°N, 121.48°E). After the plants regrew, we collected and dried young leaves from all individuals, and stored them in zip-lock plastic bags with silica gel at room temperature until DNA isolation. We extracted total DNA from the dried leaves according to a modified cetyltrimethylammonium bromide (CTAB) method. We examined the quality and quantity of extracted DNA with 1% agarose gels and a microscope spectrophotometer, and stored DNA at -20℃ until later genotyping and sequencing. To measure genetic variation, we used 10 microsatellite primer pairs previously designed for P. australis (Saltonstall 2003, Yu et al. 2013). Forward primers were labeled at the 5' end with the fluorescent dyes FAM, HEX or TAMRA. We performed polymerase chain reaction (PCR) as described by Liu et al. (2022), and separated the PCR products by capillary electrophoresis using an ABI 3730XL DNA capillary sequencer (Applied Biosystems, Foster City, California, USA) after confirming the PCR product on a 2% agarose gel. We scored fragment profiles and carefully check the stutter peaks and the low-frequency alleles with GeneMarker 2.2.0 to reduce the potential effect of null allele. We did not discover the null alleles with the Hardy-Weinberg equilibrium-based method, since there is no reliable approach to elimination of allele dosage for our polyploid data. The same clones were detected by the function assignClones in R package polysat (Clark and Jasieniuk 2011). The duplicated genotypes were removed for further genetic estimates. To determine the haplotype, we amplified two non-coding chloroplast regions by PCR in one sample of each stand, using the primer pairs [trnT (UGU) "a"-trnL (UAA) "b" and rbcL-psaI] as described previously (Saltonstall 2002). We sequenced the PCR products in both directions on an ABI 3730XL DNA sequencer (Applied Biosystems). We assembled and checked the sequencing with SeqMan 7.7.0 (Lasergene, Santa Clara, USA) and identified haplotypes to the naming scheme of P. australis described by Saltonstall (Saltonstall 2016). Data analysis of genetic diversity and structure of P. australis To estimate the genetic diversity level of P. australis, we calculated the number of alleles per locus or allelic richness (Na), and the expected heterozygosity (He) with R package polysat (Clark and Jasieniuk 2011). We assessed the relationship between genetic diversity and latitude using linear regression. To assess the genetic structure of P. australis, we calculated genetic differentiation (Fst) with the R package polysat. We also calculated Pairwise Bruvo distances based on microsatellite variation, and used the genetic distance matrix for principal coordinates analysis (PCoA) and hierarchical cluster analysis using the unweighted pair-group method with arithmetic means (UPGMA). We applied Bayesian clustering with Structure 2.3.4 (Pritchard et al. 2000) to detect the genetic structure of P. australis. We performed 20 replicates of the clustering analysis at each value of K from 1 to 10 under the admixture model with 50,000 burn-in steps and 500,000 Markov Chain Monte Carlo repeats. We calculated Delta K using the online program Structure Harvest (Earl and vonHoldt 2012) to determine the most likely cluster number (K value) for our genetic data, grouped replicates in CLUMPP 1.1.2b (Jakobsson and Rosenberg 2007) and visualized in DISTRUCT 1.1 (Ramasamy et al. 2014). Correlation analysis between geographical and genetic distances of P. australis and S. alterniflora We used the previously published nuclear microsatellites and chloroplast sequences data of S. alterniflora in China (Qiao et al. 2019, Shang et al. 2019, Xia et al. 2020). We extracted the geographical coordinates and the diversity indices (i.e., Allele number, Na; Expected heterozygosity, He) of surveyed populations of S. alterniflora from Shang et al. (2019) and Xia et al. (2020) for further comparisons. For genetic analysis of S. alterniflora, we used the raw data of 11 nuclear microsatellites from Qiao et al. (2019). We removed three loci from the raw dataset because there were many missing values or null alleles in loci 5, 7 and 9. We calculated geographic distances using the function distm in R package geosphere and used pairwise Fst for genetic distance. We used Mantel test and multiple matrix regression with randomization (MMRR) (Wang 2013) to examine relationships between geographic and genetic distance matrices at the site level. We ran correlation analyses between geographical and genetic distance matrices using the function mantel in R package vegan, and regression analyses using the function MMRR written by Wang (2013) with genetic distance as the dependent matrix and geographical distances as the independent (predictor) matrices with 9,999 permutations. The correlation of genetic distances between the two species were also performed with partial Mantel test while controlling the geographical distance for seven common sites. Correlation between genetic variation and nematode community We used the geographic records of nematode genera from a published work (Zhang et al. 2019). These nematode data were investigated to reveal the biotic homogenization of nematode communities by exotic S. alterniflora in China. This study found a clear latitudinal cline (nematode diversity increased with increasing latitude) and a strong correlation of nematode diversity to environmental variables in soils for P. australis, but weak for S. alterniflora (Zhang et al. 2019). Because the TJ and TS sites were located within a very short distance (approximately 54 km) around the same bay, we considered them as one site when comparing the variation in geography, genetics, and community. Thus, we had seven common sites with both genetic and nematode information. We estimated the Jaccard distances between nematode communities using the function dist with a method binary parameter. We used these Jaccard distances to perform principal coordinates analysis (PCoA) of nematodes. Matrix correlation analyses between geographic, genetic and nematode distance matrices for seven common sites were performed using both Mantel test using the function mantel in R package vegan and MMRR using the function MMRR written by Wang (2013) with nematode distance matrices as the dependent variable using 9,999 permutations

Exploring the Role of the Spatial Characteristics of Visible and Near-Infrared Reflectance in Predicting Soil Organic Carbon Density

Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a classical and highly commonly used model in constructing soil spectral models and predicting soil properties. Nevertheless, using PLSR alone may not consider soil as characterized by strong spatial heterogeneity and dependence. However, considering the spatial characteristics of soil can offer valuable spatial information to guarantee the prediction accuracy of soil spectral models. Thus, this study aims to construct a rapid and accurate soil spectral model in predicting soil organic carbon density (SOCD) with the aid of the spatial autocorrelation of soil spectral reflectance. A total of 231 topsoil samples (0–30 cm) were collected from the Jianghan Plain, Wuhan, China. The spectral reflectance (350–2500 nm) was used as auxiliary variable. A geographically-weighted regression (GWR) model was used to evaluate the potential improvement of SOCD prediction when the spatial information of the spectral features was considered. Results showed that: (1) The principal components extracted from PLSR have a strong relationship with the regression coefficients at the average sampling distance (300 m) based on the Moran’s I values. (2) The eigenvectors of the principal components exhibited strong relationships with the absorption spectral features, and the regression coefficients of GWR varied with the geographical locations. (3) GWR displayed a higher accuracy than that of PLSR in predicting the SOCD by VNIRS. This study aimed to help people realize the importance of the spatial characteristics of soil properties and their spectra. This work also introduced guidelines for the application of GWR in predicting soil properties by VNIRS

Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes

The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r2Pre = 0.66, RMSE = 2.78 g∙kg−1, residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350–800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350–1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard–Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content

Molecular Modeling of Ammonia Gas Adsorption onto the Kaolinite Surface with DFT Study

With high porosity and being one of the most abundant clay minerals, dried kaolinite may be an excellent adsorbent to remove ammonia gas (NH3). Here, the plane wave pseudopotential method based on density functional theory (DFT) was used to explore the mechanism of ammonia gas adsorption on the dried kaolinite, the Mulliken electric charge, and the partial density of states of atoms of the NH3/kaolinite (001) system. NH3 adsorption on kaolinite can happen in three different type adsorption positions: “top”, “bridge” and “hollow”. The “hollow” position is enclosed by two "upright" hydroxyl groups perpendicular to the (001) surface of kaolinite and a "lying" hydroxyl group parallel to the surface. At this position, the adsorption is the most stable and has the highest adsorption energy. The nitrogen atom of the NH3 molecule bonds with the hydrogen atom in the "upright" hydroxyl group on the (001) surface and its hydrogen atom forms HN…O hydrogen bond with oxygen atom in the "lying" hydroxyl group, which leads to the NH3 stably adsorbed on kaolinite (001) surface. A small part of electrons transfer between NH3 molecules and kaolinite creates weakly electrostatic adsorption between them

An Adaptive Parallel EI Infilling Strategy Extended by Non-Parametric PMC Sampling Scheme for Efficient Global Optimization

This paper presents a novel adaptive parallel Expected Improvement (EI) infilling strategy for Efficient Global Optimization (EGO) by introducing a two-staged Non-parametric Population Monte Carlo Sampling (NPMS) scheme. The samples are uniformly generated from EI function in the first stage and converge to sub-domains of high EI values thresholded by a non-parametric sampling selection method in Population Monte Carlo (PMC) iterative succession. In the second stage, learning from potential information, Density-Based Spatial Clustering (DBSCAN) method is used to cluster samples and converge to candidate points. Compared to the original EI strategy, NPMS improves the minimum result by 14.6&#x0025; and reduces the number of candidate points by 15.8&#x0025; on our benchmark cases of EGO. Furthermore, 13 test functions involving different input space sizes, difficulties, and dimensions are conducted on six strategies including NPMS, and the results showed that NPMS achieves the highest ranking in terms of result finding and cost savings but slightly decreases optimization efficiency. Benefiting from broad sampling and dynamic clustering, especially in large input space size cases, NPMS not only guarantees high result accuracy but also reduces optimization costs by up to 34.9&#x0025; compared to other parallel methods. Finally, our proposed NPMS-extended EI strategy has successfully reduced the number of candidate points, which is expected to provide a cost-practical approach to more complex problems

The development of cladding materials for the accident tolerant fuel system from the Materials Genome Initiative

The 2011 Fukushima Daiichi disaster raises the requirement of accident tolerant fuel (ATF) cladding. As promising candidates, the FeCrAl ternary alloy and SiC fiber reinforced SiC matrix (SiC/SiC) composites are discussed with details for recent development, as well as the issues of their in-core application and fabrication. Herein, the design of materials based on the Materials Genome Initiative (MGI) is introduced as the effective scheme in accelerating the development for ATF claddings. Correspondingly, the potential technical routes are provided and some examples of preliminary modelling studies as the preparation steps for MGI are presented. (C) 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved