Mechanical behaviors and probabilistic multiphase network model of polyvinyl alcohol hydrogel after being immersed in sodium hydroxide solution.

Because of the advantages of a uniform distribution of reinforcing particles and in situ preparation, in situ precipitation has become an important way to prepare magnetic and other smart hydrogels. An important step in this process is to immerse hydrogels in alkaline solution to implant magnetic particles. Previous studies generally have ignored the effect of this process on the network structure and mechanical properties of hydrogels. In this study, we immersed polyvinyl alcohol (PVA) hydrogel samples in sodium hydroxide solutions of different concentrations to study changes in mechanical properties, such as stress-strain relationship, self-recovery, and fracture failure. The results showed that after the immersion process, the hydrogel's tensile and compressive properties changed significantly, and the failure behavior changed from brittle fracture to ductile fracture. Through a microscopic mechanism, the alkaline solution caused a high degree of phase separation and crystallization within the polymer network, thereby changing the PVA hydrogel network from a single phase to a multiphase. Hence, we used a continuous multiphase network model with a certain probability distribution to describe this tensile behavior. This model well described the stress-strain relationship of the hydrogel from stretching to fracture and revealed that the macroscopic failure corresponded to the peak of fracture distribution. Studies have shown that attention should be paid to the influence of the in situ precipitation on the mechanical properties, and the probabilistic multiphase network model can be used to predict the mechanical behavior of hydrogels with multiple phase separation.

Tracking Based Multi-Orientation Scene Text Detection: A Unified Framework With Dynamic Programming.

There are a variety of grand challenges for multi-orientation text detection in scene videos, where the typical issues include skew distortion, low contrast, and arbitrary motion. Most conventional video text detection methods using individual frames have limited performance. In this paper, we propose a novel tracking based multi-orientation scene text detection method using multiple frames within a unified framework via dynamic programming. First, a multi-information fusion-based multi-orientation text detection method in each frame is proposed to extensively locate possible character candidates and extract text regions with multiple channels and scales. Second, an optimal tracking trajectory is learned and linked globally over consecutive frames by dynamic programming to finally refine the detection results with all detection, recognition, and prediction information. Moreover, the effectiveness of our proposed system is evaluated with the state-of-the-art performances on several public data sets of multi-orientation scene text images and videos, including MSRA-TD500, USTB-SV1K, and ICDAR 2015 Scene Videos.