Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual parts’ interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance

Design representation for performance evaluation of 3D shapes in structure-aware generative design

Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs

Quantitative assessment of the associations between XRCC1 polymorphisms and bladder cancer risk

BACKGROUND: The XRCC1 polymorphisms have been implicated in bladder cancer risk, but individually published studies show inconsistent results. The aim of our study was to clarify the effects of XRCC1 variants on bladder cancer risk. METHODS: A systematic literature search up to September 13, 2012 was carried out in PubMed, EMBASE and Wanfang databases, and the references of retrieved articles were screened. Crude odds ratios with 95% confidence intervals were used to assess the associations between XRCC1 Arg194Trp and Arg399Gln polymorphisms and bladder cancer risk. Heterogeneity and publication bias were also evaluated. RESULTS: A total of 14 and 18 studies were eligible for meta-analyses of Arg194Trp and Arg399Gln, respectively. Regrouping was adopted in accordance with the most probable appropriate genetic models. No obvious heterogeneity between studies was found. For overall bladder cancer, the pooled odds ratios for Arg194Trp and Arg399Gln were 1.69 (95% confidence interval: 1.25 to 2.28; P = 0.001) and 1.10 (95% confidence interval: 1.03 to 1.19; P = 0.008), respectively. After excluding the studies that were not in Hardy–Weinberg equilibrium, the estimated pooled odds ratio still did not change at all. CONCLUSIONS: The meta-analysis results suggest that XRCC1 Arg194Trp and Arg399Gln polymorphisms may be associated with elevated bladder cancer risk

The progress of pulmonary artery denervation

Pulmonary arterial hypertension (PAH) is a chronic pulmonary vascular disease characterized by increased pulmonary arterial pressure and pulmonary arterioles remodeling. Some studies have discovered the relationship between sympathetic nerves (SNs) and pathogenesis of PAH. This review is aimed to illustrate the location and components of SNs in the pulmonary artery, along with different methods and effects of pulmonary artery denervation (PADN). Studies have shown that the SNs distributed mainly around the main pulmonary artery (MPA) and pulmonary artery (PA) bifurcation. And the SNs could be destroyed by three ways: the chemical way, the surgical way and the catheter-based way. PADN can significantly decrease pulmonary arterial pressure rapidly, improve hemodynamic varieties, and then palliate PAH. PADN has been recognized as a prospective and effective therapy for PAH patients, especially for those with medication-refractory PAH. However, further enlarged clinical studies are needed to confirm accurate distribution of SNs in the pulmonary artery and the efficacy of PADN