Planning of an optimal product family architecture (PFA) plays a critical role in defining an organization's product platforms for product variant configuration while leveraging commonality and variety. The focus of PFA planning has been traditionally limited to the product design stage, yet with limited consideration of the downstream supply chain-related issues. Decisions of supply chain configuration have a profound impact on not only the end cost of product family fulfillment, but also how to design the architecture of module configuration within a product family. It is imperative for product family architecting to be optimized in conjunction with supply chain configuration decisions. This paper formulates joint optimization of PFA planning and supply chain configuration as a Stackelberg game. A nonlinear, mixed integer bilevel programming model is developed to deal with the leader-follower game decisions between product family architecting and supply chain configuration. The PFA decision making is represented as an upper-level optimization problem for optimal selection of the base modules and compound modules. A lower-level optimization problem copes with supply chain decisions in accordance with the upper-level decisions of product variant configuration. Consistent with the bilevel optimization model, a nested genetic algorithm is developed to derive near optimal solutions for PFA and the corresponding supply chain network. A case study of joint PFA and supply chain decisions for power transformers is reported to demonstrate the feasibility and potential of the proposed Stackelberg game theoretic joint optimization of PFA and supply chain decisions.

VES has inhibitory effects on B(a)P-induced forestomach carcinogenesis in female mice, especially by ip and it may be a potential anti-cancer agent in vivo.

This paper presents a novel approach for multi-camera calibration using spheres. We determine the projection of sphere centers first through bi-tangent lines of projection conics, which provides another solution for camera parameters on the basis of the relationship between the images of spheres and the image of the absolute conic in dual space. All parameters are refined by an optimization with the purpose of minimizing the reprojection error, which is divided into two independent parts associated with the corresponding shape parameters of the conics. Experimental results from the synthetic and real data show the feasibility and the accuracy achieved by our approach.

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Background: Anti-cancer drug response has been urgently required for individualized therapy. Measurements with wet experiments are costly and time-consuming. Artificial intelligence-based models are currently available for predicting drug response but still have challenges in prediction accuracy. Objective: Construct a model to predict drug response values for unknown cell lines and analyze drug potential association properties in sparse data. Methods: Propose a neural matrix factorization (NeuMF) framework to help predict the unknown responses of cell lines to drugs. The model uses a deep neural network to figure out latent variables for both drugs and cell lines. In NeuMF, the inputs and the parameters of the multi-layer neural network are simultaneously optimized by gradient descent to minimize the reconstruction errors between the predicted and natural values of the observed entries. Then the unknown entries can be readily recovered by propagating the latent variables to the output layer. Results: Experiments on the Cancer Cell Line Encyclopedia (CCLE) dataset and Genomics of Drug Sensitivity in Cancer (GDSC) dataset compare NeuMF with the other three state-of-the-art methods. NeuMF reduces constructing drug or cell line similarity and mines the response matrix itself for correlations in the network, avoiding the inclusion of redundant noise. NeuMF obtained drug averaged PCC_sr of 0.83 and 0.84 on both datasets. It demonstrates that NeuMF substantially improves the prediction. Some essential parameters in NeuMF, such as the strategy of global effect removal and the scales of the input layer, are also discussed. Finally, case studies have shown that NeuMF can better learn the latent characteristics of drugs, e.g., Irinotecan and Topotecan are found to act on the same pathway TOP1. The conclusions are in line with some existing biological findings. Conclusion: NeuMF achieves better prediction accuracy than existing models, and its output is biologically interpretable. NeuMF also helps in analyzing the correlations between drugs.

The corn threshing process is a complex process of contact interaction between thresher mechanical parts and corn ears. The paper analyzed this process with the discrete element method (DEM). A method was set up to calculate the contact forces between thresher mechanical parts (boundary) and corn ears. The conditions for corn kernels threshing were established as well. On this basis, we developed the corn threshing simulation software, and simulated the corn threshing process using the software. The simulation results are close to the actual situation, and the results verify the feasibility and effectiveness of the new method. Thus we have laid the foundation for using the DEM to analyze the corn threshing process and putting forward a novel method for the optimal design of the corn thresher.

Ultrasonic assisted electrical discharge machining (USEDM) is one of hybrid machining methods based on the EDM process. The effects of ultrasonic waves on EDM process were analyzed and the experimental investigation of productivity of steel induced by USEDM was reported. Results indicated that ultrasonic waves and cavitation played an important role in improving the flushing and machining efficiency during USEDM. And the material removal rate of EDM assisted by ultrasonic waves was improved greatly.

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