NP [CGG], Ratio 10-19

Although the future Internet paradigm especially Information-Centric Networking (ICN) has been recognized and accepted in recent years, the end-to-end IP network structure always shows the overwhelming development tendency due to the deployment difficulty of ICN on the real and large-scale network environment. However, each coin has two sides, that is to say, ICN also has its own unique value, that is, in-network caching feature which is not included in the current IP network. In this letter, we propose an In-network caching enabled IP network paradigm, which is a novel concept different from both ICN and the traditional IP network. Based on such paradigm, we present a light content delivery scheme depending on identity, with the chain-based fine-grained caching support. Given the NP-hardness of delivery scheme in mesh topology, we make a case study in Metropolitan Area Network (MAN) to address the large-scale content delivery scenario. The experimental results show that the proposal in this letter can effectively relieve the load pressure of origin server, and can also greatly improve the delivery efficiency.

Nanofluid refers to the mixture of fluid and solid nanoparticles. If this mixture contains more than one NP or fluid, it is called "hybrid nanofluid"; further, if HN contains more than one NP and also more than one fluid, it is called "dihybrid nanofluid." In this research, first, titanium dioxide NP was dispersed in the water-ethylene glycol basefluid and formed an HN. Then, thermal conductivity of HN was measured. After that, MWCNT NP was added to the HN and formed a DHN. Further, TC of DHN measured. Both HN and DHN TCs were compared, and the results revealed that by adding MWCNT, thermal conductivity enhanced about 30.83% (from 25.65% of HN to 56.48% of DHN). On the other hand, to analyze the phase structure, and to observe the microstructure, X-ray diffraction analysis, energy-dispersive X-ray analysis, and field emission scanning electron microscopy were examined. The measured TC for both samples was at volume fractions up to 1.0% and temperatures up to 50 degrees C. After an experimental study, two novel correlations were calculated by the curve-fitting method for HN and DHN, individually. In the end, to predict the other Vf and temperature, an artificial neural network has been modeled for both HN and DHN.