Correlation and prediction of mixing thermodynamic properties of ester-containing systems Ester+alkane and ester+ester binary systems and the ternary dodecane+ethyl pentonoate+ethyl ethanoate

[EN]Excess thermodynamic properties VE m and HE m, have been measured for the ternary mixture dodecane + ethyl pentanoate + ethyl ethanoate and for the corresponding binaries dodecane + ethyl pentanoate, dodecane + ethyl ethanoate, ethyl pentanoate + ethyl ethanoate at 298.15 K. All mixtures show endothermic and expansive effects. Experimental results are correlated with a suitable equation whose final form for the excess ternary quantity ME contains the particular contributions of the three binaries (i–j) and a last term corresponding to the ternary, all of them obtained considering fourth-order interactions.

Mixing properties of ester-containing solutions: a study on the ternary (methyl alkanoate (pentanoate and methanoate)+methanol and the corresponding binaries. New contributions to the (ester+ester) interactions

[EN]This work studies the volumetric (VE m) and energetic (HE m) properties resulting from the mixing processes of binary systems and the corresponding ternary of two methyl esters (methanoate and pentanoate) with methanol. The three binaries produce net endothermic mixing effects, with important energetic interactions, with maximum values of HE m ffi 400 J mol 1 , for the (ester + ester) system. This produces expansive effects VE m > 0, but the binaries of the (methyl esters + methanol) give rise to contractions VE m < 0, due to the formation of molecular aggregates.

A low complexity system based on multiple weighted decision trees for indoor localization

[EN] Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity.