4,691 research outputs found

    Approaches to measuring average and median wages in Russia and abroad: data sources and users

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    This article focuses on the measurement of average and median wages, which are closely related to indicators of living standards. We reveal the problem of insufficient discussion of current issues in labor income measurement in contemporary Russian economic literature. The aim of this article is to contribute to the scientific substantiation of the relationship between methodology, procedure, and algorithm in measuring the average and median wages of wage earners in the Russian Federation (RF). The novelty of this study lies in comparing primary indicators from the Russian Federal State Statistics Service (Rosstat) and foreign companies and formulating conclusions about significant differences between estimates from the United States, Europe, and Russia in the studied area. A comparative analysis of Rosstat’s (Russia) estimates of average and median wages shows an inverse relationship to those of US and European companies. To address the problems of significant discrepancies and unreliability of Russian figures, we propose streamlining the relationship between the concepts of “methodology,” “procedure,” and “algorithm” in measuring average and median wages in the RF. To this end, we propose meaningfully linking “methodology” with formulas for calculating average wages, “procedure” with the object of measurement, and “algorithm” with the source/subject of measurement and users of measurement results. We focus on the experiences of American and European companies as being very important, primarily in the context of users of these indicators, i.e., company employees. Informing wage earners about the results of average and median wage measurements could be an effective tool for overcoming labor income inequality in the RF

    The effect of ultraviolet radiation on water-logging resistance in Tibetan peach

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    The effect of ultraviolet (UV) radiation on the water-logging resistance of Tibetan peach (Amygdalus mira Koehne) remains unclear. In this study, Tibetan peach seedlings were subjected to 9-days of UV-B (280 - 320 nm) supplementation, water-logging or the combination of both, and the growth indicated by leaf number, net photosynthetic rate and stomatal conductance were monitored. In addition, the activities of protection enzymes SOD and POD, as well as the concentrations of hormones ABA, ZR, GAs and ZR in leaves were examined. The results show that UV-B or water-logging or the combination of both factors restrained the growth trend, net photosynthetic rate and stomatal conductance. Both UV-B and water-logging increased SOD activity, and synergistically led to a drastic increase in POD activity. Furthermore, UV-B and water-logging condition increased the concentrations of ABA, ZR and GAs, while it decreased ZR. The results suggest that the changes of hormone concentrations in Tibetan peach leaves may explain the increased activities of protection enzymes and decreased photosynthesisunder water-logging condition

    Plastic properties and microstructure evolution of 20CrMoA steel during warm deformation

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    The plastic properties and microstructure evolution of 20CrMoA steel was analyzed at 600-750 °C and strain rate of 0,01-10 s-1.The result reveals that the deformation behavior is hardening followed by softening at low strain rates(0,01 s-1 and 0,1 s-1), but hardening is dominant in the whole deformation process at high strain rates(1 s-1and 10 s-1) and low temperature(600 °C and 650 °C). The strain rate sensitivity exponent increases with the increasing deformation temperature except for 650 °C and high strain rate. The spheroidization mechanism of cementite is the mechanical fracture and the dissolution of cementite particles. At 700 °C, spheroidized particles are finer and their distribution is more uniform than that at 750 °C

    Influence of silencing the MC4R gene by lentivirusmediated RNA interference in bovine fibroblast cells

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    Melanocortin receptor 4 (MC4R) is a key element in the mechanisms used to regulate both aspects of keeping the balance between energy uptake and energy expenditure. MC4R was knocked down by lentivirus-mediated shRNA expressing plasmids, which were controlled by the U6 promoter in bovine fibroblast cells, and the expression of MC4R was examined by the real time-PCR and Western blot analysis. Real time-PCR analysis was used to characterize the expression of Leptin, POMC, AGRP, MC3R and NPY gene. The relative genes [leptin, proopiomelanocortin (POMC), agouti-related peptide (AGRP), MC3R and neuropeptide Y (NPY)] expression level seemed to be closely associated with the MC4R gene in bovine fibroblast cell lines (BFCs). The levels of both MC4R mRNA and protein were significantly reduced by RNA interference (RNAi) mediated knockdown of MC4R in BFCs cells transfected with plasmid-based MC4R-specific shRNAs. The finding of this study demonstrated that vector based siRNA expression systems were an efficient approach to the knockdown of the MC4R gene expression in bovine fibroblast cells and they provided a new molecular basis for understanding the relationship of MC4R and other genes, which were responsible for the regulation of energy homeostasis by the melanocortin system.Key words: Melanocortin receptor 4 (MC4R), RNAi, bovine fibroblast cells, energy homeostasis

    Recurrent Fusion Network for Image Captioning

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    Recently, much advance has been made in image captioning, and an encoder-decoder framework has been adopted by all the state-of-the-art models. Under this framework, an input image is encoded by a convolutional neural network (CNN) and then translated into natural language with a recurrent neural network (RNN). The existing models counting on this framework merely employ one kind of CNNs, e.g., ResNet or Inception-X, which describe image contents from only one specific view point. Thus, the semantic meaning of an input image cannot be comprehensively understood, which restricts the performance of captioning. In this paper, in order to exploit the complementary information from multiple encoders, we propose a novel Recurrent Fusion Network (RFNet) for tackling image captioning. The fusion process in our model can exploit the interactions among the outputs of the image encoders and then generate new compact yet informative representations for the decoder. Experiments on the MSCOCO dataset demonstrate the effectiveness of our proposed RFNet, which sets a new state-of-the-art for image captioning.Comment: ECCV-1
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