No abstract

Purpose Thermoplastic polymer fabrics are normally heat set to make them dimensionally stable. These fabrics in garment panel form may again be exposed to heat during the processes such as bonding, sublimation printing and cause to change their dimensions. The purpose of this paper is to investigate the response of polyester yarns in knitted fabrics to heat setting and post-heat treatments. Design/methodology/approach In this study, the thermal shrinkage behaviour of heat set polyester knitted fabrics when subjected to post-heat treatment processes are analyzed using differential scanning calorimetry (DSC) and analysis of fabric shrinkage. DSC is a thermo-analytical technique that measures the difference in the amount of heat needed to increase the temperature of the sample and the reference. A heat flux versus temperature curve is one of the results of a DSC experiment. The polymer structure and morphology of polyester heat-treated and post-heat–treated fabrics were determined by examining the DSC thermograms. Findings Heat setting and post-heat setting causes the effective temperature of polyester to change. Effective temperature occurred around 160°C for fabrics heat set at low temperatures and increases as the heat setting temperature increases. Post-heat treatments cause to elevate the effective temperature. Shrinkage of fabrics below the effective temperature is not statistically significant while the shrinkage at higher temperatures is significant. Effective temperature is the main determinant of thermal shrinkage behaviour of polyester. Originality/value The study reveals the significance of the effective temperature of polyester on heat treatments and post-heat treatments. The study revealed that heat-setting temperature is a primary determinant of the thermal stability of polyester fabric that are subjected to heat treatments.

Modern day systems are facing an avalanche of data, and they are being forced to handle more and more data intensive use cases. These data comes in many forms and shapes: Sensors (RFID, Near Field Communication, Weather Sensors), transaction logs, Web, social networks etc. As an example, weather sensors across the world generate a large amount of data throughout the year. Handling these and similar data require scalable, efficient, reliable and very large storages with support for efficient metadata based searching. This paper present Mahasen, a highly scalable storage for high volume data intensive applications built on top of a peer-to-peer layer. In addition to scalable storage, Mahasen also supports efficient searching, built on top of the Distributed Hash table (DHT)