Does ownership type matter for corporate social responsibility disclosure: Evidence from China

The evidence of the effect of ownership structure on corporate social responsibility (CSR) is relatively sparse especially in the emerging economies. This paper seeks to address this situation to comprehensively examine the link between different types of shareholders and CSR disclosure in the context of China. Our findings reveal that different owners have differential impact on the CSR. The firms controlled by the state are more likely to disclose CSR information and their CSR reports’ quality is better compared with non-SOEs. Interestingly, firms with more shares held by mutual funds, foreign investors or other corporations are significantly better at CSR disclosure. The study also discloses that firm size, profitability, and leverage affect CSR in China. Overall the study contributes to the literature on CSR practices in emerging countries and point to some policy suggestions

Production of XcscˉsˉX_{cs\bar{c}\bar{s}} in heavy ion collisions

The yields of $X_{cs\bar{c}\bar{s}}$ with its two possible configurations, i.e., the hadronic molecular state and tetraquark state, for Pb-Pb collisions at $\sqrt{s_{NN}}=5.02~\rm{TeV}$ is studied. A volume effect is found from the centrality distribution of $X_{cs\bar{c}\bar{s}}$, which could help to distinguish the inner structure of $X_{cs\bar{c}\bar{s}}$. We also show the rapidity and the transverse momentum distributions of $X_{cs\bar{c}\bar{s}}$ production as well as its elliptic flow coefficient as a function of the transverse momentum.Comment: 6 pages, 4 figure

Predicting Medication Prescription Rankings with Medication Relation Network

Medication prescription rankings and demands prediction could benefit both medication consumers and pharmaceutical companies from various aspects. Our study predicts the medication prescription rankings focusing on patients’ medication switch and combination behavior, which is an innovative genre of medication knowledge that could be learned from unstructured patient generated contents. We first construct two supervised machine learning systems for medication references identification and medication relations classification from unstructured patient’s reviews. We further map the medication switch and combination relations into directed and undirected networks respectively. An adjusted transition in and out (ATIO) system is proposed for medication prescription rankings prediction. The proposed system demonstrates the highest positive correlation with actual medication prescription amounts comparing to other network-based measures. In order to predict the prescription demand changes, we compare four predictive regression models. The model incorporated the network-based measure from ATIO system achieve the lowest mean square errors

Application of An Improved Deviation Analysis of Double Mean Data in Student’S Teaching Evaluation Data

This paper analyzes the main problems of College Students’ evaluation of teaching, and proposes a new method to analyze and process the evaluation data.In this paper, we first use the deviation analysis of double mean data method. Through numerical examples, we find an advantage of this method that it can effectively eliminate invalid data in the teaching evaluation data, but the result has a certain deviation from the original teaching evaluation data, and can not directly reflect the specific gap between different teachers or define the maximum and minimum of the teaching evaluation score. In order to objectively reflect the effects of teachers’ classroom teaching, we make a little improvement on the basis of this method in this paper, and give each student a certain weight, so as to get a more real and effective comprehensive evaluation score of each teacher. Numerical examples are given to compare the results of the two methods, and the improved method of deviation analysis of double mean data is more reasonable and effective

Digital Transformation of Enterprise Learning: A Case Study of China Telecom

This study aimed to explore the digital transformation of enterprise learning. Through the case study of China Telecom, we studied how it transformed and its transformation dimensions

Defining Next Generation Supply Chain Sustainability

The importance of understanding supply chain sustainability is being realized by increasingly more people, including corporate managers, investors, policy makers, customers and other stakeholders. A lot of practitioners and academic researchers have addressed this issue in past few years. However, most of their studies lack systematic thinking and are not quantifiable. Thus, a systematic and quantifiable model which incorporates economic, environmental and social factors is needed. In our study, a systematic and quantifiable risk assessment model based on the concept of “Triple Bottom Line” is developed in order to solve supply chain sustainability problem from risk assessment perspectiveMaster of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/110983/1/276-Defining Next Generation Supply Chain Sustainability_2015.pd

Ultrastructure of early amelogenesis in wildâ type, Amelxâ /â , and Enamâ /â mice: enamel ribbon initiation on dentin mineral and ribbon orientation by ameloblasts

IntroductionDental enamel is comprised of highly organized, oriented apatite crystals, but how they form is unclear.MethodsWe used focused ion beam (FIB) scanning electron microscopy (SEM) to investigate early enamel formation in 7â weekâ old incisors from wildâ type, Amelxâ /â , and Enamâ /â C56BL/6 mice. FIB surface imaging scans thicker samples so that the thin enamel ribbons do not pass as readily out of the plane of section, and generates serial images by a mill and view approach for computerized tomography.ResultsWe demonstrate that wildâ type enamel ribbons initiate on dentin mineral on the sides and tips of mineralized collagen fibers, and extend in clusters from dentin to the ameloblast membrane. The clustering suggested that groups of enamel ribbons were initiated and then extended by fingerâ like membrane processes as they retracted back into the ameloblast distal membrane. These findings support the conclusions that no organic nucleator is necessary for enamel ribbon initiation (although no ribbons form in the Enamâ /â mice), and that enamel ribbons elongate along the ameloblast membrane and orient in the direction of its retrograde movement. Tomographic reconstruction videos revealed a complex of ameloblast membrane processes and invaginations associated with intercellular junctions proximal to the mineralization front and also highlighted interproximal extracellular enamel matrix accumulations proximal to the interrod growth sites, which we propose are important for expanding the interrod matrix and extending interrod enamel ribbons. Amelxâ /â mice produce oriented enamel ribbons, but the ribbons fuse into fanâ like structures. The matrix does not expand sufficiently to support formation of the Tomes process or establish rod and interrod organization.ConclusionAmelogenin does not directly nucleate, shape, or orient enamel ribbons, but separates and supports the enamel ribbons, and expands the enamel matrix to accommodate continued ribbon elongation, retrograde ameloblast movement, and rod/interrod organization.This is the first report using focused ion beam microscopy to visualize enamel ribbons at high resolution as they form, which are shown to initiate on the underlying dentin crystals. This continuity between dentin and enamel mineral has been difficult to establish with conventional thin sectioning techniques and has been debated for many years. We converted serial images into movies that allowed us to better appreciate the complex infolding of cell membranes and intercellular compartmentalization that are integral to the complex mechanism of enamel biomineralization.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135052/1/mgg3253-sup-0002-FigS13-21.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135052/2/mgg3253-sup-0005-FigS41-51.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135052/3/mgg3253_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135052/4/mgg3253-sup-0003-FigS24-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135052/5/mgg3253-sup-0004-FigS29-40.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135052/6/mgg3253.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135052/7/mgg3253-sup-0001-FigS1-12.pd

Pattern formation in oscillatory complex networks consisting of excitable nodes

Oscillatory dynamics of complex networks has recently attracted great attention. In this paper we study pattern formation in oscillatory complex networks consisting of excitable nodes. We find that there exist a few center nodes and small skeletons for most oscillations. Complicated and seemingly random oscillatory patterns can be viewed as well-organized target waves propagating from center nodes along the shortest paths, and the shortest loops passing through both the center nodes and their driver nodes play the role of oscillation sources. Analyzing simple skeletons we are able to understand and predict various essential properties of the oscillations and effectively modulate the oscillations. These methods and results will give insights into pattern formation in complex networks, and provide suggestive ideas for studying and controlling oscillations in neural networks.Comment: 15 pages, 7 figures, to appear in Phys. Rev.