Forecasting the multifactorial interval grey number sequences using grey relational model and GM (1, N) model based on effective information transformation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the context of data eruption, the data often shows a short-term pattern and changes rapidly which makes it difficult to use a single real value to express. For this kind of small-sample and interval data, how to analyze and predict muti-factor sequences efficiently becomes a problem. By this means, grey system theory (GST) is developed in which the interval grey numbers, as a typical object of GST, characterize the range of data and the grey relational and prediction models analyze the relations of multiple grey numbers and forecast the future. However, traditional grey relative relational model has some limitations: the results obtained always show low resolution and there are no extractions for the interval feature information from the interval grey number sequence. In this paper, the grey relational analysis model (GRA) based on effective information transformation of interval grey numbers is established, which contains comprehensive information of area differences and slope variances and optimizes the resolution of traditional grey degree. Then, according to the relational results, the multivariable GM model (GM(1,N)) is proposed to forecast the interval grey number sequence. To verify the effectiveness of this novel model, it is established to analyze the relationship between the degree of traffic congestion and its relevant factors in the Yangtze River Delta of China and predict the development of urban traffic congestion degrees in this area over the next five years. In addition, some traditional statistical methods (principal component analysis, multiple linear regression models and curve regression models) are established for comparisons. The results show high performances of the novel GRA model and GM(1,N) model, which means the models proposed in this paper are suitable for interval grey numbers from regional data. The strengths which recommend the use of this novel method lie in its high recognition mechanism and muti-angle information transformation for interval grey numbers as well as its characteristic of timeliness in information processing

A New 95 GHz Methanol Maser Catalog: I. Data

The Purple Mountain Observatory 13.7 m radio telescope has been used to search for 95 GHz (8$_0$--7$_1$A$^+$) class I methanol masers towards 1020 Bolocam Galactic Plane Survey (BGPS) sources, leading to 213 detections. We have compared the line width of the methanol and HCO$^+$ thermal emission in all of the methanol detections and on that basis we find 205 of the 213 detections are very likely to be masers. This corresponds to an overall detection rate of 95 GHz methanol masers towards our BGPS sample of 20%. Of the 205 detected masers 144 (70%) are new discoveries. Combining our results with those of previous 95 GHz methanol masers searches, a total of four hundred and eighty-one 95 GHz methanol masers are now known, we have compiled a catalog listing the locations and properties of all known 95 GHz methanol masers.Comment: 18 pages, 7 figures, 8 tables, accepted for publication in ApJ

Should chronic hepatitis B mothers breastfeed? a meta analysis

<p>Abstract</p> <p>Background</p> <p>Hepatitis B virus (HBV) exists in the breast milk of chronic hepatitis B (CHB) mothers. The authors use a meta-analytic technique to quantify the evidence of an association between breastfeeding and risk of CHB infection among the infants vaccinated against HBV.</p> <p>Methods</p> <p>Literature search is performed up to 2010 on the relationship between infantile CHB infection within one-year follow up after immunization with the third-dose hepatitis B vaccine and breastfeeding. Two reviewers independently extract the data and evaluate the methodological quality. A random-effects model is employed to systematically combine the results of all included studies.</p> <p>Results</p> <p>Based on data from 32 studies, 4.32% (244/5650) of infants born of CHB mothers develop CHB infection. The difference in risk of the infection between breastfed and formula-fed infants (RD) is -0.8%, (95% confidence interval [CI]: -1.6%, 0.1%). Analysis of the data from 16 of the studies finds that RD for mothers who are positive for the HBeAg and/or the HBV DNA, 0.7% (95%CI: -2.0%, 3.5%), is similar to that for those who are negative for these infectivity markers, -0.5% (95%CI: -1.7%, 0.6%).</p> <p>Conclusions</p> <p>Breast milk is infectious; yet, breastfeeding, even by mothers with high infectivity, is not associated with demonstrable risk of infantile CHB infection, provided that the infants have been vaccinated against HBV at birth.</p

Enhancing firms’ innovation persistence in the circular economy through government-supported green supply chain demonstrations: cost leadership or differentiation?

Government support is pivotal in guiding firms towards adopting green supply chain (GSC) practices aligned with the circular economy. Our study addressed this critical issue through a quasi-natural experiment of GSC demonstration in China. We conducted a difference-in-differences estimation to assess the variation in the persistent innovation capabilities between the treatment and control groups. The results indicate that the GSC demonstration, as an external policy change, stimulated the development of firms’ persistent innovation capabilities. These capabilities have positive effects on both innovation input and output dimensions. Additionally, we explored the interaction between firms’ competitive strategies and government support. Findings indicate that differentiation strategies have a stronger positive impact on innovation persistence, while cost leadership strategies weaken this link. These results emphasize the government's critical role in fostering GSC adoption, offering implications for effective government-business collaboration towards a circular economy and sustainable planning across social, environmental, and technological innovation factors