Does mandatory CSR disclosure affect enterprise total factor productivity?

Corporate social responsibility (CSR) reports are important carriers of enterprises non-financial information disclosure, which are inextricably related to the production efficiency and performance of enterprises. The objective of this paper is discovering the causal effect of the CSR mandatory disclosure policy and the total factor productivity (TFP) of enterprises. This paper uses the sharp regression discontinuity design based on the micro data of the enterprises to study the impact by taking China’s mandatory disclosure policy in 2008 as a quasi-natural experiment. This paper makes some contribution to the impact of mandatory CSR disclosure on enterprise TFP and the mechanism and heterogeneity of this impact. The research draws the following conclusions: First, the CSR mandatory disclosure can significantly improve the TFP of enterprises on the whole, and this effect has the characteristics of long-term and dynamic decline. Second, the mechanism of mandatory disclosure of CSR on TFP is through the mediating effect of R&D and innovation expenditures. Third, the heterogeneity of the impact of CSR mandatory disclosure on TFP is reflected in two aspects: industry and equity nature differences. These conclusions are strongly correlated with the contingent decision-making behaviour of enterprises and give some ideas to the policy makers

AutoAMG(θ\theta): An Auto-tuned AMG Method Based on Deep Learning for Strong Threshold

Algebraic Multigrid (AMG) is one of the most used iterative algorithms for solving large sparse linear equations $Ax=b$. In AMG, the coarse grid is a key component that affects the efficiency of the algorithm, the construction of which relies on the strong threshold parameter $\theta$. This parameter is generally chosen empirically, with a default value in many current AMG solvers of 0.25 for 2D problems and 0.5 for 3D problems. However, for many practical problems, the quality of the coarse grid and the efficiency of the AMG algorithm are sensitive to $\theta$; the default value is rarely optimal, and sometimes is far from it. Therefore, how to choose a better $\theta$ is an important question. In this paper, we propose a deep learning based auto-tuning method, AutoAMG($\theta$) for multiscale sparse linear equations, which are widely used in practical problems. The method uses Graph Neural Networks (GNNs) to extract matrix features, and a Multilayer Perceptron (MLP) to build the mapping between matrix features and the optimal $\theta$, which can adaptively output $\theta$ values for different matrices. Numerical experiments show that AutoAMG($\theta$) can achieve significant speedup compared to the default $\theta$ value

Synthesis of Flower-Like Cu 2

Flower-like Cu2ZnSnS4 (CZTS) nanoflakes were synthesized by a facile and fast one-pot solution reaction using copper(II) acetate monohydrate, zinc acetate dihydrate, tin(IV) chloride pentahydrate, and thiourea as starting materials. The as-synthesized samples were characterized by X-ray diffraction (XRD), Raman scattering analysis, field emission scanning electron microscopy (FESEM) equipped with an energy dispersion X-ray spectrometer (EDS), transmission electron microscopy (TEM), and UV-Vis absorption spectra. The XRD patterns shown that the as-synthesized particles were kesterite CZTS and Raman scattering analysis and EDS confirmed that kesterite CZTS was the only phase of product. The results of FESEM and TEM show that the as-synthesized particles were flower-like morphology with the average size of 1~2 μm which are composed of 50 nm thick nanoflakes. UV-Vis absorption spectrum revealed CZTS nanoflakes with a direct band gap of 1.52 eV

Comparison of Evaluation Tests for Compressive Strength of Structural Concrete

The concrete strength of existing structures is an important index in the aspects of safety insurance, evaluation, and strengthening of existing structures. However, different testing methods are used to evaluate the concrete strength, which provide information with different reliability, and the results are thus difficult to be unified. This paper investigated the evaluation tests for compressive strength of structural concrete. The compressive strength of field-cured, standard-cured and core samples, and the rebound method calculated strength of structural concrete were obtained. The results showed that the compressive strength of field-cured and core specimens, and the rebound method calculated strength cannot reach that of standard-cured specimens at the equivalent age of 28 days. The compressive strength of standard-cured and field-cured specimens can be used to represent that of cores for evaluating the quality of structural concrete. All four strength indexes increased in a logarithmic trend with the increasing equivalent age

Intelectin contributes to allergen-induced IL-25, IL-33, and TSLP expression and type 2 response in asthma and atopic dermatitis.

The epithelial and epidermal innate cytokines IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) have pivotal roles in the initiation of allergic inflammation in asthma and atopic dermatitis (AD). However, the mechanism by which the expression of these innate cytokines is regulated remains unclear. Intelectin (ITLN) is expressed in airway epithelial cells and promotes allergic airway inflammation. We hypothesized that ITLN is required for allergen-induced IL-25, IL-33, and TSLP expression. In two asthma models, Itln knockdown reduced allergen-induced increases in Il-25, Il-33, and Tslp and development of type 2 response, eosinophilic inflammation, mucus overproduction, and airway hyperresponsiveness. Itln knockdown also inhibited house dust mite (HDM)-induced early upregulation of Il-25, Il-33, and Tslp in a model solely inducing airway sensitization. Using human airway epithelial cells, we demonstrated that HDM-induced increases in ITLN led to phosphorylation of epidermal growth factor receptor and extracellular-signal regulated kinase, which were required for induction of IL-25, IL-33, and TSLP expression. In two AD models, Itln knockdown suppressed expression of Il-33, Tslp, and Th2 cytokines and eosinophilic inflammation. In humans, ITLN1 expression was significantly increased in asthmatic airways and in lesional skin of AD. We conclude that ITLN contributes to allergen-induced Il-25, Il-33, and Tslp expression in asthma and AD

Genome-wide identification of rubber tree (Hevea brasiliensis Muell. Arg.) aquaporin genes and their response to ethephon stimulation in the laticifer, a rubber-producing tissue

Expression profiles of the 51 HbAQP genes in the laticifer of rubber tree clone RRIM928. (PDF 36 kb