Descriptive statistics and T-test results.

Social Media is an important means of communication with audiences around the world. The purpose of this study was to explore whether GM—a famous US auto company adapts its US Cultural values to suit the prevalent cultural values of its Chinese stakeholders on Chinese social media. Content analysis was used to evaluate the cultural content of GM Company’s posts on Weibo and Twitter. Although influenced by the special features of the car industry, there is still enough evidence that the communication style of the US auto Company makes cultural adaption on Chinese social media, reflecting more Chinese prevalent cultural values.</div

Conceptual framework.

Social Media is an important means of communication with audiences around the world. The purpose of this study was to explore whether GM—a famous US auto company adapts its US Cultural values to suit the prevalent cultural values of its Chinese stakeholders on Chinese social media. Content analysis was used to evaluate the cultural content of GM Company’s posts on Weibo and Twitter. Although influenced by the special features of the car industry, there is still enough evidence that the communication style of the US auto Company makes cultural adaption on Chinese social media, reflecting more Chinese prevalent cultural values.</div

Bayesian Optimization via Exact Penalty

Constrained optimization problems pose challenges when the objective function and constraints are nonconvex and their evaluation requires expensive black-box simulations. Recently, hybrid optimization methods that integrate statistical surrogate modeling with numerical optimization algorithms have shown great promise, as they inherit the properties of global convergence from statistical surrogate modeling and fast local convergence from numerical optimization algorithms. However, the computational efficiency is not satisfied by practical needs under limited budgets and in the presence of equality constraints. In this article, we propose a novel hybrid optimization method, called exact penalty Bayesian optimization (EPBO), which employs Bayesian optimization within the exact penalty framework. We model the composite penalty function by a weighted sum of Gaussian processes, where the qualitative components of the constraint violations are smoothed by their predictive means. The proposed method features (i) closed-form acquisition functions, (ii) robustness to initial designs, (iii) the capability to start from infeasible points, and (iv) effective handling of equality constraints. We demonstrate the superiority of EPBO to state-of-the-art competitors using a suite of benchmark synthetic test problems and two real-world engineering design problems.</p

Descriptive statistics and T-test for cultural categories.

Descriptive statistics and T-test for cultural categories.</p

Cultural dimensions and sub-categories.

Social Media is an important means of communication with audiences around the world. The purpose of this study was to explore whether GM—a famous US auto company adapts its US Cultural values to suit the prevalent cultural values of its Chinese stakeholders on Chinese social media. Content analysis was used to evaluate the cultural content of GM Company’s posts on Weibo and Twitter. Although influenced by the special features of the car industry, there is still enough evidence that the communication style of the US auto Company makes cultural adaption on Chinese social media, reflecting more Chinese prevalent cultural values.</div

A New Bayesian Dose-Finding Design for Drug Combination Trials

<p>We consider the problem of finding maximum tolerated dose with targeted dose-limiting toxicity rate in drug combination trials. We propose a novel Bayesian adaptive design, which features adaptive local modeling and weighted learning along the search path. The method is robust in the sense that neither prespecifications of marginal toxicity probabilities nor subjective priors for model parameters are required. Extensive simulation studies show that the proposed method is comparable to some leading methods in terms of correct selection and superior to them when the priors are misspecified. Moreover, the proposed model can be extended to the case of trials with more than two drugs. Supplementary materials for this article are available online.</p

Combined Optimization of LID Patches and the Gray Drainage System to Control Wet Weather Discharge Pollution

Low impact development (LID) has emerged as an effective management to control urban runoff. However, for storm drainage systems located in high-density old urban areas with scarce land resources and fragmented landscapes, there are difficulties in the application of LID layouts. To solve the waterlogging and wet weather discharge pollution (WWDP), based on the spatial distribution of available land resources, runoff path, ponding area, overflow nodes, and sediments in drainages, a spatial optimized layout method of LID patches combined with gray drainages was proposed and applied to a typical storm drainage system in Shanghai, China. Only 22.9% of the ground surface and 14.0% of the roof, which accounted for the study area were reconstructed to LIDs, and the optimal LID patches combined with 0.4 m storage capacity depth (SCD) could prevent discharge below 6.5 mm rainfall. The optimal LID-gray drainages increased the reduction ratios of suspended substances (SSs) in WWDP by 37–74% compared with only LID patches in 9.1–21.8 mm rainfall. The “LID patches-gray system” could effectively control WWDP in old urban areas with high frequencies of moderate and light rain. The proposed methodology can be instructive for the sustainable reconstruction of storm drainages inappropriately connected with sewage

Additional file 1 of A dose-finding design for phase I clinical trials based on Bayesian stochastic approximation

Additional file 1: Supplementary Materials

The chronology of the SARS outbreak.

<p>From <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000266#pmed.1000266-World1" target="_blank">[39]</a>.</p

Comparison results by a jackknife methodology.

<p>Comparison results by a jackknife methodology.</p