Study on Long-term Mechanism for Government to Encourage Enterprises on Low-carbon Development——Analysis based on Enterprises’ Capacity Variance and Task Difficulty

AbstractBased on reference to Lazear and Rosen (1981) rank-order tournaments mechanism model, this essay introduces the two variables, namely capacity variance and task difficulty, to establish a long-term mechanism model for the government to encourage enterprises for low-carbon development. It is included through model analysis that, the optimal effort level during enterprises’ low-carbon development is directly proportional to bonus difference (L-l) set by the government, and inversely proportional to task difficulty, capacity variance and “performance” appraisal error. When enterprises’ capacities are identical, their optimal effort level will be the highest and equal. Besides, the government should take task difficulty and capacity into comprehensive consideration to set reasonable bonus difference. If task difficulty is relatively large, the government should enlarge bonus difference; if task difficulty is small, the government should correspondingly shorten bonus difference

Products of Generalized Stochastic Sarymsakov Matrices

In the set of stochastic, indecomposable, aperiodic (SIA) matrices, the class of stochastic Sarymsakov matrices is the largest known subset (i) that is closed under matrix multiplication and (ii) the infinitely long left-product of the elements from a compact subset converges to a rank-one matrix. In this paper, we show that a larger subset with these two properties can be derived by generalizing the standard definition for Sarymsakov matrices. The generalization is achieved either by introducing an "SIA index", whose value is one for Sarymsakov matrices, and then looking at those stochastic matrices with larger SIA indices, or by considering matrices that are not even SIA. Besides constructing a larger set, we give sufficient conditions for generalized Sarymsakov matrices so that their products converge to rank-one matrices. The new insight gained through studying generalized Sarymsakov matrices and their products has led to a new understanding of the existing results on consensus algorithms and will be helpful for the design of network coordination algorithms

Language Policy and Language Planning: A Comparative Perspective of Economics and Linguistics

Language planning refers to a kind of humanly-conscious intervention within certain limits in the process of language selection. It has not only something to do with the language itself, but is far more involved in such issues as the adjustments of the relations among people or between people and society through language problems. The traditional analysis on language planning is mainly based on sociolinguistic theories, which tends to emphasize the basic concepts and categories in this area so that, at the macro level of public policy, neither practical nor reasonable measures have been able to brought up. The economic rational-choice theory and the cost-benefit analytical method, however, can effectively compensate for the weaknesses of the traditional studies of language planning, and greatly enrich the development of language planning. This paper reviews the connotation and denotation of language policies and language planning in details, discusses the significance and feasibility of conducting economic analysis and research on these two issues, and makes a comparison between the traditional sociolinguistic analysis and the new-rising economic analysis on language planning.language policy; language planning; economics of language

A Knowledge Adoption Model Based Framework for Finding Helpful User-Generated Contents in Online Communities

Many online communities allow their members to provide information helpfulness judgments that can be used to guide other users to useful contents quickly. However, it is a serious challenge to solicit enough user participation in providing feedbacks in online communities. Existing studies on assessing the helpfulness of user-generated contents are mainly based on heuristics and lack of a unifying theoretical framework. In this article we propose a text classification framework for finding helpful user-generated contents in online knowledge-sharing communities. The objective of our framework is to help a knowledge seeker find helpful information that can be potentially adopted. The framework is built on the Knowledge Adoption Model that considers both content-based argument quality and information source credibility. We identify 6 argument quality dimensions and 3 source credibility dimensions based on information quality and psychological theories. Using data extracted from a popular online community, our empirical evaluations show that all the dimensions improve the performance over a traditional text classification technique that considers word-based lexical features only

Compound-specific delta D and its hydrological and environmental implication in the lakes on the Tibetan Plateau

The hydrogen isotopic composition (delta D) of n-alkanes in lacustrine sediments is widely used in palaeoenvironmental studies, but the heterogeneous origins and relative contributions of these lipids provide challenges for the interpretation of the increasing dataset as an environment and climatic proxy. We systematically investigated n-alkane delta D values from 51 submerged plants (39 Potamogeton, 1 Myriophyllum, and 11 Ruppia), 13 algae (5 Chara, 3 Cladophora, and 5 Spirogyra) and 20 terrestrial plants (10 grasses and 10 shrubs) in and around 15 lakes on the Tibetan Plateau. Our results demonstrate that delta D values of C-29 n-alkane are correlated significantly with the lake water delta D values both for algae (R (2)=0.85, p < 0.01, n=9) and submerged plants (R (2)=0.90, p < 0.01, n=25), indicating that delta D values of these algae and submerged plants reflect the delta D variation of lake water. We find that apparent hydrogen isotope fractionation factors between individual n-alkanes and water (epsilon (a/w)) are not constant among different algae and submerged plants, as well as in a single genus under different liminological conditions, indicating that the biosynthesis or environmental conditions (e.g. salinity) may affect their delta D values. The delta D values of submerged plant Ruppia in the Xiligou Lake (a closed lake) are significant enriched in D than those of terrestrial grasses around the lake (one-way ANOVA, p < 0.01), but the algae Chara in the Keluke Lake (an open lake) display similar delta D values with grasses around the lake (one-way ANOVA, p=0.826 > 0.05), suggesting that the n-alkane delta D values of the algae and submerged plants record the signal of D enrichment in lake water relative to precipitation only in closed lakes in arid and semi-arid area. For each algae and submerged plant sample, we find uniformed delta D values of different chain length n-alkanes, implying that, in combination with other proxies such as Paq and Average Chain Length, the offset between the delta D values of different chain length n-alkanes can help determine the source of sedimentary n-alkanes as well as inferring the hydrological characteristics of an ancient lake basin (open vs closed lake)

Incentive Mechanism of Enterprises Energy-saving and Emission Reduction Based on Rank Order Tournaments

AbstractThe article establishes the analytical framework of incentive mechanism and the rank order tournaments model that based on the relative performance for developing energy-saving and emission reduction by using the theory of principle-agent and the Malcomson model. We systematically analyzes the model, and proposes the corresponding policy suggestions

Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.Comment: Submitted to ROBIO 201