Crowd-powered database systems can leverage the crowd's ability to address machine-hard problems, e.g., data integration. Existing crowdsourcing systems adopt the traditional tree model to select a good query plan. However, the tree model can optimize the I/O cost but cannot optimize the monetary cost, latency and quality, which are three important optimization goals in crowdsourcing. To address this limitation, we demonstrate CDB, a crowd-powered database system. CDB proposes a new graph-based model that adopts a fine-grained tuple-level optimization model which significantly outperforms existing coarse-grained tree-based optimization models. Moreover, CDB provides a unified framework to simultaneously optimize the monetary cost, quality and latency. We have deployed CDB on well-known crowd-sourcing platforms and users can easily use our system to deploy their applications. We will demonstrate how to use CDB to address real-world applications, including web table integration and entity collection.

Shape memory behavior of thermo-induced shape memory polyurethane (TSMPU) under proportionally multiaxial loading is investigated experimentally. The shape memory effect is discussed under different axial and torsional loading angles. By comparing the equivalent stress-equivalent strain curves under different loading angles, it is found that the difference in thermo-mechanical behavior is mainly reflected in the shape memory effect. The equivalent characteristic quantities describing the shape memory effect are extracted from the experimental results. By analyzing their evolutions in the strain and stress spaces, it is found that the shape memory effect shows obvious anisotropy under different loading angles, i.e. the equivalent recovery rate of TSMPU increases with the increase of the loading angles. These results are helpful to establish a thermo-mechanical constitutive model for describing the shape memory effect under multiaxial loading.

The cyclic shape memory effect of thermo-induced shape memory polymers (TSMPs) is a typical thermo-mechanical process that can be affected by thermo-mechanical loading histories. During the deformation stage of the cyclic shape memory effect, polymer chains exhibit the initial dissociation of sub-entanglements, slipping and orientation with the increase in the deformation. The strain can be recovered with the internal rotation of the dihedral angle due to the enhancing motion of polymer chains during the reheating. Based on the rheological theory, a thermo-viscoelastic model is proposed to capture the cyclic shape memory effect of TSMPs. A temperature-dependent stress threshold value is introduced to reflect the slipping of polymer chains. In order to take into account how orientation affects the cyclic shape memory effect, a relationship between the strain and orientation is constructed and introduced into the evolution equations of elastic modulus, viscosity and irrecoverable strain. By comparing the experimental and simulated results at different loading levels and numbers of cycles, the proposed model is verified. The results show that the proposed model can reasonably predict the cyclic shape memory effect of TSMPs.