Discovery of plasmon resonance and negative permittivity in carbon allotropes at much lower frequencies than those of metals has evoked interest to develop random metacomposites by suitable means of addition of these dispersoids in an overall dielectric matrix. Random metacomposites have always the advantage for their easy preparation techniques over those of their regular arrayed artificial counterpart. However, thermal management during the heat generation by electromagnetic attenuation in metamaterials is not yet studied well. The present communication discusses the dielectric permittivities and loss parameters of aluminum nitride−single-wall carbon nanotube (AlN−SWCNT) composites considering high thermal conductivities of both materials. The composites are dense and have been prepared by a standard powder technological method using hot pressing at 1850 °C under a nitrogen atmosphere. Increase in the negative permittivity value with SWCNT concentration (1, 3, and 6 vol %) in the composites had been observed at low frequencies. Characterization of the materials with Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and microstructure analysis by scanning and transmission electron microscopy (TEM) revealed the survivability of the SWCNTs and the nature of the matrix−filler interface. Plasmonic resonance following Drude's law could be observed at much lower plasma frequencies than that of pure SWCNT and for very little SWCNT addition. Exhibition of the negative permittivity has been explained with relation to the microstructure of the composites observed from field emission scanning electron micrographs (FESEM), TEM images, and the equivalent circuit model. High energy conversion efficiency is expected in these composites due to the possession of dual functionalities like high thermal conductivity as well as high negative permittivity, which should ensure the application of these materials in wave filter, cloaking device, supercapacitors, and wireless communication.

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In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

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