Preparation and Characterization of Biomass Tannin-Based Flexible Foam Insoles for Athletes.

The exploitation of bio-based foams implies an increase in the use of renewable biological resources to reduce the rapid consumption of petroleum-derived resources. Both tannins and furfuryl alcohol are derived from forestry resources and are, therefore, considered attractive precursors for the preparation of tannin-furanic foams. In addition, toughening modification of tannin-furanic foams using polyvinyl alcohol (PVOH) results in a more flexible network-like structure, which imparts excellent flexibility to the foams, whose relative properties are even close to those of polyurethane foams, which are the most used for fabrication of insoles for athletes. In addition, the addition of PVOH does not affect the thermal insulation properties of the foams by testing the thermal conductivity, resilience, and elongation at break, while reducing the brittleness of the samples and improving the mechanical properties. Also, the observation of the morphology of the foam shows that the compatibility between PVOH and tannin-furanic resin is good, and the cured foam does not show fragmentation and collapse, while the bubble pore structure is uniform. The developed flexible foam derived from biomass resources endows the foam with good thermal insulation properties and high mechanical properties, and the samples exhibit suitable physical parameters to be used as flexible insoles for athletes.

Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment.

With the wide adoption of health and sport concepts in human society, how to effectively analyze the personalized sports preferences of students based on past sports training records has become a crucial and emergent task with positive research significance. However, the past sports training records of students are often accumulated with time and stored in a central cloud platform and therefore, the data volume is too large to be processed with quick response. In addition, the past sports training records of students often contain certain sensitive information, which probably discloses partial user privacy if we cannot protect the data well. Considering these two challenges, a privacy-aware and efficient student clustering approach, named PESC is proposed, which is based on a hash technique and deployed on a central cloud platform connecting multiple local servers. Concretely, in the cloud platform, each student is firstly assigned an index based on the past sports training records stored in a local server, through a uniform hash mapping operation. Then similar students are clustered and registered in the cloud platform based on the students' respective sport indexes. At last, we infer the personalized sport preferences of each student based on their belonged clusters. To prove the feasibility of PESC, we provide a case study and a set of experiments deployed on a time-aware dataset.