Greening Supply Chains in China: Practical Lessons From China-Based Suppliers in Achieving Environmental Performance

Presents case studies of how five China-based suppliers are meeting international buyers' environmental requirements. Examines management processes; effective low-cost ways to reduce water pollution; and the roles of multistakeholders and third parties

Evaluation of Development of Agricultural Modernization in Central China

AbstractBased on multiple-index comprehensive evaluation, the evaluation system of agricultural modernization was constructed, and the level of agricultural modernization in central China was evaluated. As results showed, central China as a whole is still in the beginning stages of the modernization of agriculture, lagging behind the eastern region significantly. To promote agricultural modernization of central China, differently local resource endowments and levels of development on current situation must be considered, and measures should be taken in a line with the local condition and step-by-step, exerting their comparative advantages and selecting the dominant mode, strategic focus and implementation path of agricultural modernization

Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score

Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in epidemiology and other related areas of population science. In this paper, we study statistical inference in the increasingly popular two-sample summary-data MR design. We show a linear model for the observed associations approximately holds in a wide variety of settings when all the genetic variants satisfy the exclusion restriction assumption, or in genetic terms, when there is no pleiotropy. In this scenario, we derive a maximum profile likelihood estimator with provable consistency and asymptotic normality. However, through analyzing real datasets, we find strong evidence of both systematic and idiosyncratic pleiotropy in MR, echoing the omnigenic model of complex traits that is recently proposed in genetics. We model the systematic pleiotropy by a random effects model, where no genetic variant satisfies the exclusion restriction condition exactly. In this case we propose a consistent and asymptotically normal estimator by adjusting the profile score. We then tackle the idiosyncratic pleiotropy by robustifying the adjusted profile score. We demonstrate the robustness and efficiency of the proposed methods using several simulated and real datasets.Comment: 59 pages, 5 figures, 6 table

DC Algorithm for Sample Average Approximation of Chance Constrained Programming: Convergence and Numerical Results

Chance constrained programming refers to an optimization problem with uncertain constraints that must be satisfied with at least a prescribed probability level. In this work, we study a class of structured chance constrained programs in the data-driven setting, where the objective function is a difference-of-convex (DC) function and the functions in the chance constraint are all convex. By exploiting the structure, we reformulate it into a DC constrained DC program. Then, we propose a proximal DC algorithm for solving the reformulation. Moreover, we prove the convergence of the proposed algorithm based on the Kurdyka-\L ojasiewicz property and derive the iteration complexity for finding an approximate KKT point. We point out that the proposed pDCA and its associated analysis apply to general DC constrained DC programs, which may be of independent interests. To support and complement our theoretical development, we show via numerical experiments that our proposed approach is competitive with a host of existing approaches.Comment: 31 pages, 3 table