Search

The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking-continuous casting sections (SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation (process matching) degrees of SCCSs were obtained as 0.638 (0.610) and 1.000 (0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied, the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

Intelligent iron/steel manufacturing has been drawing increasing attention in recent years. Slagging is of importance for clean steel production in the ladle furnace (LF) refining process. In this ...

Objective To explore the morbidity of chronic obstructive pulmonary disease (COPD) related complications and mortality of the COPD patients, and analyze the risk factors for complications. Methods 1829 COPD patients were collected from Department of Respiratory and Critical Care Medicine Center of the Second Affiliated Hospital of Army Medical University and Department of Respiratory of the People's Hospital of Fengdu County from January 2018 to December 2018. Then, the morbidity and mortality of the complication were observed, meanwhile, the risk factors were analyzed by multifactor logistic regression. Results 89.2%(1631/1829) of COPD patients complicated with other diseases, the mortality rate was 2.5%(46/1829). The complications main included respiratory and cardiovascular system diseases, the mortality rates were 68.8% and 34.4%, respectively. Then, the data indicated that the disease with the highest mortality rate was nervous system diseases, which was 9.3%, especially pulmonary encephalopathy, the mortality rate was 21.7%. Furthermore, logistic regression analysis showed that age over 80 years, smoking long than 20 years, smoking more than 700 cigarettes per year, drinking, C-reactive protein (CRP) more than 50 mg/L, hyperglycemia and hypoproteinemia were the risk factors of COPD with complications (OR=3.488, 4.085, 4.086, 2.464, 2.208, 2.066, 11.804). Conclusion The incidence of COPD complications is relatively high, and the mortality also increase obviously. Therefore, more attention should be payed to the treatment and prevention for complications of COPD. DOI: 10.11855/j.issn.0577-7402.2020.08.08

Desulphurization is essential in the steelmaking process for high-quality steel production, and sulphide capacity has proven to be an effective index to evaluate the desulphurization ability of mol...

Common burden tests have different statistical performance in genetic association studies of rare variants. Here, we compare the statistical performance of burden tests, such as CMC, WST, SUM and extension methods, using the computer-simulated datasets of rare variants with different parameters of sample sizes, linkage disequilibrium (LD), and different numbers of mixed non-associated variants. The simulation results showed that the type I error for all methods is near 0.05. When the rare variants had the same direction of effect, the higher LD and the less non-associated variants, the higher the power of these method, except the data adaptive SUM test. When the direction was different, the power was significantly reduced for all methods. The methods that consider the direction yielded larger statistical power than those methods without considering the effect direction, except the strong LD condition. And the larger the sample size, the larger the power. The statistical performance of burden tests is affected by a variety of factors, including the sample size, effect direction of variants, non-associated variants, and LD. Therefore, when choosing the method and setting the collection unit and weight, the prior biological information of genetic variation should be integrated to improve study efficiency.

Salinity and alkalinity seriously threaten global rice production. As a consequence, elucidation of the genetic basis of salinity and alkalinity tolerance is crucial for rice breeding. To identify genetic marker loci/QTLs associated with salinity and alkalinity tolerance, we performed association mapping based on different statistical models using 347 rice accessions from all over the world. Salinity tolerance for all traits was evaluated on saline land along the seashore of Hebei, while alkalinity tolerance at the seedling stage was assessed in the alkaline soil of paddy fields in Jilin. A total of 148 SSRs were used for genotyping. Within the entire population, 64.1 % of SSR locus pairs were in significant linkage disequilibrium (LD) (P

Cold tolerance at booting stage is one of the major determinants for a stable yield of rice (Oryza sativa L.) in many high elevation or high latitude regions. Understanding the genetic basis of cold tolerance is crucial for the improvement of cold tolerance through breeding. In this study, association mapping was performed in 347 rice accessions worldwide with different statistical models in order to identify the genetic marker loci/QTL associated with cold tolerance traits at the booting stage. The evaluation of cold tolerance for all the traits was conducted under natural low temperature in Yunnan and cold water irrigation in Jilin. The 148 SSRs were used for the genotyping. Population structure analysis identified three main subpopulations for the accessions that corresponded to major geographic origins. The relative kinship analysis revealed a weak or no relationship for most of the individual pairs. Model comparisons indicated that the Q+K model controlling both population structure (Q) and the relative kinship (K) was performed better than other models in association mapping. In total, 24 markers were identified that were significantly associated with cold tolerance, including five markers in Yunnan and 19 markers in Jilin. Moreover, RM282, RM252, RM335 and RM6824 were identified in multiple environments or years. Many of these identified markers were located either in or nearby the regions where the QTLs have been reported for cold tolerance at booting stage. These results highlighted the targeted regions for future studies and might be subsequently used in breeding programs to trace and select the useful alleles by MAS.

The FPS1 gene coding for the Fps1p aquaglyceroporin protein of an industrial strain of Saccharomyces cerevisiae was disrupted by inserting CUP1 gene. Wild-type strain, CE25, could only grow on YPD medium containing less than 0.45% (v/v) acetic acid, while recombinant strain T12 with FPS1 disruption could grow on YPD medium with 0.6% (v/v) acetic acid. Under 0.4% (v/v) acetic acid stress (pH 4.26), ethanol production and cell growth rates of T12 were 1.7 ± 0.1 and 0.061 ± 0.003 g/l h, while those of CE25 were 1.2 ± 0.1 and 0.048 ± 0.003 g/l h, respectively. FPS1 gene disruption in an industrial ethanologenic yeast thus increases cell growth and ethanol yield under acetic acid stress, which suggests the potential utility of FPS1 gene disruption for bioethanol production from renewable resources such as lignocelluloses.

Using an F 2:3 population with 200 lines derived from a cross between the japonica rice ( Oryza sativa L.) varieties Gaochan 106 and Changbai 9, quantitative trait loci (QTLs) for root number, root length, seedling height, and their relative alkaline damage rates at early seedling stage were identified under 0.15% Na 2 CO 3 alkaline stress. A total of 26 QTLs associated with these traits were detected on chromosomes 1, 5, 6, 7, 8, 9, and 11, consisting of 4 QTLs for root number, 5 QTLs for relative alkaline damage rate of root number, 6 QTLs for root length, 2 QTLs for relative alkaline damage rate of root length, 5 QTLs for seedling height, and 4 QTLs for relative alkaline damage rate of seedling height. QTLs qRN6-1 , qRN11 , qRRN11-2 , qRRL11-2 , qSH1 , qSH11-2 , qRSH5 , and qRSH6-2 accounted for 29.91, 13.42, 23.86, 21.06, 15.81, 16.53, 29.89, and 34.63% of the phenotypic variations, respectively. These QTLs, except for qRN6-1 with the marker flanking distance of 19.0 cM, were located with relatively large marker flanking distances over 26.3 cM. The positive alleles at 13 loci originated from the tolerant parent Changbai 9, and the positive alleles at other loci were from the sensitive parent Gaochan 106. Partial dominance and over dominance were the main effects at these loci.

This paper aims to achieve Bootstraping in hierarchical data and to provide a method for the estimation on confidence interval(CI) of intraclass correlation coefficient(ICC).First, we utilize the mixed-effects model to estimate data from ICC of repeated measurement and from the two-stage sampling. Then, we use Bootstrap method to estimate CI from related ICCs. Finally, the influences of different Bootstraping strategies to ICC's CIs are compared. The repeated measurement instance show that the CI of cluster Bootsraping containing the true ICC value. However, when ignoring the hierarchy characteristics of data, the random Bootsraping method shows that it has the invalid CI. Result from the two-stage instance shows that bias observed between cluster Bootstraping's ICC means while the ICC of the original sample is the smallest, but with wide CI. It is necessary to consider the structure of data as important, when hierarchical data is being resampled. Bootstrapping seems to be better on the higher than that on lower levels.

The quantitative trait loci (QTLs) for the dead leaf rate (DLR) and the dead seedling rate (DSR) at the different rice growing periods after transplanting under alkaline stress were identified using an F(2:3) population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F(3) lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent "Gaochan106". The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.

In this study, we analyze spring precipitation from 92 meteorological stations spanning between 1961 and 2012 to understand temporal–spatial variability and change of spring precipitation over Southwest China. Various analysis methods are used for different purposes, including empirical orthogonal function (EOF) analysis and rotated EOF (REOF) for analyzing spatial structure change of precipitation anomaly, and the Mann–Kendall testing method to determine whether there were abrupt changes during the analyzed time span. We find that the first spatial mode of the precipitation has a domain uniform structure; the second is dominated by a spatial dipole; and the third contains five variability centers. The 2000s is the decade with the largest amount of precipitation while the 1990s is the decade with the smallest amount of precipitation. The year-to-year difference of that region is large: the amount of the largest precipitation year doubles that of the smallest precipitation year. We also find that spring precipitation in Southwest China experienced a few abrupt changes: a sudden increase at 1966, a sudden decrease at 1979, and a sudden increase at 1995. We speculate that the spring precipitation will increase gradually in the next two decades.