Calabi-Yau/Landau-Ginzburg Correspondence for Weil-Peterson Metrics and tt∗tt^* Structures

The aim of this paper is to rigorously establish the Calabi-Yau/Landau-Ginzburg (CY/LG) correspondence for the $tt^*$ geometry structure--a generalized version of variation of Hodge structures. Although it is well-known that there exists a map between Hodge structures on the LG and CY's sides that preserves the Hodge filtration and bilinear form, it remains unclear whether the real structures are also preserved. In our paper, we conduct a detailed analysis of two period integrals on the LG's side. Based on this analysis, we modify the real structure proposed by Cecotti on LG's side, and show that the aforementioned map is also preserved under the modified real structure. As a result, we establish full CY/LG correspondence for $tt^*$ structures.Comment: 34 page

Experiment on MICP-solidified calcareous sand with different rubber particle contents and sizes

Microbially induced calcite precipitation (MICP) is a new environmentally friendly technology, with the ability to improve the mechanical properties of calcareous sand. Rubber is a high-compressibility material with a higher damping ratio than that of calcareous sand. In this study, calcareous sand was replaced by equal volume contents (0%, 1%, 3%, 5%, 7%, and 9%) and different sizes (0–1, 1–2, and 2–3 mm) of rubber, and a series of water absorption and unconfined compressive strength (UCS) tests were conducted on MICP-solidified rubber–calcareous sand (MRS). The results showed that the water absorption is reduced when the rubber content is larger. The UCS of 0–1-mm MRS decreased with the increase in rubber content. For 1–2-mm and 2–3-mm MRS, the UCS was improved by 11.30% and 15.69%, respectively, compared with the clean sand. Adding rubber promoted the formation of calcium carbonate, but the strength and stiffness of rubber particles were lower than those of the calcareous sand. Therefore, higher rubber content weakened the sand frame bearing system, and the UCS decreased when the rubber content was more than 5%. Moreover, a large amount of 0–1-mm rubber led to the increase in transverse deformation of the samples, which caused the acceleration of the destruction of the sand structure. The water absorption of 0–1-mm MRS was higher than that of 1–2-mm and 2–3-mm MRS, but the UCS of 0–1-mm MRS was lower. The best rubber size is 1–2 mm and 2–3 mm, and the best rubber content is 3%–5%. The outcome of this study may, in the authors’ view, prove beneficial in improving the strength of calcareous sand when it is reinforced by MICP-combined rubber

An intelligent decision support approach for quantified assessment of innovation ability via an improved BP neural network

In today's competitive and changing social environment, innovation and entrepreneurial ability have become important factors for the successful development of college students. However, relying solely on traditional evaluation methods and indicators cannot comprehensively and accurately evaluate the innovation and entrepreneurial potential and ability of college students. Therefore, developing a comprehensive evaluation model is urgently needed. To address this issue, this article introduces machine learning methods to explore the learning ability of subjective evaluation processes and proposes an intelligent decision support method for quantitatively evaluating innovation capabilities using an improved BP (Back Propagation) neural network. This article first introduces the current research status of evaluating the innovation and entrepreneurship ability of college students, and based on previous research, it has been found that inconsistent evaluation standards are one of the important issues at present. Then, based on different BP models and combined with the actual situation of college student innovation and entrepreneurship evaluation, we selected an appropriate input layer setting for the BP neural network and improved the setting of the middle layer (hidden layer). The identification of output nodes was also optimized by combining the current situation. Subsequently, the conversion function, initial value and threshold were determined. Finally, evaluation indicators were determined and an improved BP model was established which was validated using examples. The research results indicate that the improved BP neural network model has a low error rate, strong generalization ability and ideal prediction effect which can be effectively used to analyze problems related to intelligent evaluation of innovation ability

Increased concentrations of soluble B7-H3 and interleukin 36 in bronchoalveolar lavage fluid of Children with Mycoplasma pneumoniae pneumonia

Supporting data. (XLS 58 kb

Genome-wide association study reveals genetic loci and candidate genes for meat quality traits in a four-way crossbred pig population

Meat quality traits (MQTs) have gained more attention from breeders due to their increasing economic value in the commercial pig industry. In this genome-wide association study (GWAS), 223 four-way intercross pigs were genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) and phenotyped for PH at 45 min post mortem (PH45), meat color score (MC), marbling score (MA), water loss rate (WL), drip loss (DL) in the longissimus muscle, and cooking loss (CL) in the psoas major muscle. A total of 227, 921 filtered single nucleotide polymorphisms (SNPs) evenly distributed across the entire genome were detected to perform GWAS. A total of 64 SNPs were identified for six meat quality traits using the mixed linear model (MLM), of which 24 SNPs were located in previously reported QTL regions. The phenotypic variation explained (PVE) by the significant SNPs was from 2.43% to 16.32%. The genomic heritability estimates based on SNP for six meat-quality traits were low to moderate (0.07–0.47) being the lowest for CL and the highest for DL. A total of 30 genes located within 10 kb upstream or downstream of these significant SNPs were found. Furthermore, several candidate genes for MQTs were detected, including pH45 (GRM8), MC (ANKRD6), MA (MACROD2 and ABCG1), WL (TMEM50A), CL (PIP4K2A) and DL (CDYL2, CHL1, ABCA4, ZAG and SLC1A2). This study provided substantial new evidence for several candidate genes to participate in different pork quality traits. The identification of these SNPs and candidate genes provided a basis for molecular marker-assisted breeding and improvement of pork quality traits

Genome-wide analysis for the melatonin trait associated genes and SNPs in dairy goat (Capra hircus) as the molecular breeding markers

Previous studies have reported that the endogenous melatonin level is positively associated with the quality and yield of milk of cows. In the current study, a total of 34,921 SNPs involving 1,177 genes were identified in dairy goats by using the whole genome resequencing bulked segregant analysis (BSA) analysis. These SNPs have been used to match the melatonin levels of the dairy goats. Among them, 3 SNPs has been identified to significantly correlate with melatonin levels. These 3 SNPs include CC genotype 147316, GG genotype 147379 and CC genotype 1389193 which all locate in the exon regions of ASMT and MT2 genes. Dairy goats with these SNPs have approximately 5-fold-higher melatonin levels in milk and serum than the average melatonin level detected in the current goat population. If the melatonin level impacts the milk production in goats as in cows, the results strongly suggest that these 3 SNPs can serve as the molecular markers to select the goats having the improved milk quality and yield. This is a goal of our future study