Buckypapers of polyvinyl chloride/poly(styrene-co-maleic anhydride) blend intercalated graphene oxide-carbon nanotube nanobifiller: Physical property exploration

Novel buckypaper of polyvinyl chloride (PVC) and poly(styrene-co-maleic anhydride) (PSMA) intercalated with carbon nanotube (CNT) and graphene oxide-CNT (GO-CNT) nanobifiller was prepared using resin infiltration technique. Two series of buckypaper were prepared with varying CNT and GO-CNT contents. According to field emission scanning electron microscopy, PVC/SMA/CNT 0.1 showed porous morphology while PVC/PSMA/GO-CNT 0.1 revealed unique sort of island-nodule morphology. T_max of PVC/PSMA/GO-CNT 0.1 was 561°C while PVC/PSMA/CNT 0.1 depicted relatively lower value (552°C). T_g of PVC/PSMA/GO-CNT 0.1 was also increased to 294°C. Peak heat release rate of poly(methyl methacrylate-co-methacrylic acid)/polyamide 6/montmorillonite-modified GO was decreased from 322 to 209 kW/m² (65%) with 0.03–0.3 g GO-CNT loading.     

M2 macrophage-targeted iron oxide nanoparticles for magnetic resonance image-guided magnetic hyperthermia therapy

Tumor-associated macrophages (TAMs) play an important role in tumor development and progression.In particular,M2 TAMs can promote tumor growth by facilitating tumor progression and malignant behav-iors.Selectively targeted elimination of M2 TAMs to inhibit tumor progression is of great significance for cancer treatment.Iron oxide nanoparticles based magnetic hyperthermia therapy (MHT) is a classical approach to destroy tumor tissue with deep penetration depth.In this study,we developed a typical M2 macrophage-targeted peptide (M2pep) functionalized superparamagnetic iron oxide nanoparticle(SPIO) for magnetic resonance imaging (MRI)-guided MHT in an orthotopic breast cancer mouse model,The obtained multifunctional SPIO-M2pep with a hydrodynamic diameter of 20 nm showed efficient targeting capability,high transverse relaxivity (149 mM-1 s-1) and satisfactory magnetic hyperthermia performance in vitro.In vivo studies demonstrated that the SPIO-M2pep based MRI can monitor the distri-bution of nanoparticles in tumor and indicate the suitable timing for MHT.The M2 macrophage-targeted MHT significantly reduced the tumor volume and the population of pro-tumoral M2 TAMs in tumor.In addition,the SPIO-M2pep based MHT can remodel the tumor immune microenvironment (TIME).The multifunctional SPIO-M2pep with M2 macrophage-targeting ability,high magnetic hyperthermia effi-ciency,MR imaging capability and effective role in remodeling the TIME hold great potential to improve clinical cancer therapy outcomes.     

A survey on search results diversification techniques

The quantity of information placed on the web has been greater than before and is increasing rapidly day by day. Searching through the huge amount of data and finding the most relevant and useful result set involves searching, ranking, and presenting the results. Most of the users probe into the top few results and neglect the rest. In order to increase user’s satisfaction, the presented result set should not only be relevant to the search topic, but should also present a variety of perspectives, that is, the results should be different from one another. The effectiveness of web search and the satisfaction of users can be enhanced through providing various results of a search query in a certain order of relevance and concern. The technique used to avoid presenting similar, though relevant, results to the user is known as a diversification of search results. This article presents a survey of the approaches used for search result diversification. To this end, this article not only provides a technical survey of existing diversification techniques, but also presents a taxonomy of diversification algorithms with respect to the types of search queries.     

Adaptive transfer learning in deep neural networks: Wind power prediction using knowledge transfer from region to region and between different task domains

Transfer learning (TL) in deep neural networks is gaining importance because, in most of the applications, the labeling of data is costly and time consuming. Additionally, TL also provides an effective weight initialization strategy for deep neural networks. This paper introduces the idea of adaptive TL in deep neural networks (ATL‐DNN) for wind power prediction. Specifically, we show in case of wind power prediction that adaptive TL of the deep neural networks system can be adaptively modified as regards training on a different wind farm is concerned. The proposed ATL‐DNN technique is tested for short‐term wind power prediction, where continuously arriving information has to be exploited. Adaptive TL not only helps in providing good weight initialization, but also in utilizing the incoming data for effective learning. Additionally, the proposed ATL‐DNN technique is shown to transfer knowledge between different task domains (wind power to wind speed prediction) and from one region to another region. The simulation results show that the proposed ATL‐DNN technique achieves average values of 0.0637, 0.0986, and 0.0984 for the mean absolute error, root mean squared error, and standard deviation error, respectively.     

Novel Organometallic Aromatic Azo Polyesters Based on Ferrocene

Some ferrocene containing aromatic azo polyesters were prepared by condensing 1,1-ferrocenedicarbonyl chloride with different azo groups containing aromatic diols, which were synthesized by diazotization coupling reaction from aromatic diamines. The polymeric products were separated as two portions on the basis of their solubility and subsequently characterized by their elemental analysis and FTIR spectral studies. Thermal properties of the products were measured by differential scanning calorimetry and thermogravimetry and the activation energies of pyrolysis were calculated using Horowitz and Metzger's method. The molecular weights of the soluble portions were determined by gel permeation chromatography (GPC) and these portions were also characterized by their inherent viscosities and UV-Vis spectroscopic studies.     

Novel Thermally Conductive Thermoplastic/Ceramic Composite Foams

Multifunctional materials that are lightweight and thermally conductive but electrically insulating are important for modern electronics, computer, and telecommunication technologies. Here, a novel foam structure of a polymer/matrix composite filled with ceramic platelets with improved thermal conductivity is reported. Such improvement is caused by the stress‐induced alignment of thermally conductive fillers in the cell wall of the plastic foam. The foam structure is very promising for use as a lightweight electronic packaging material owing to its light weight, thermal conduction ability, electrical insulation, and good processability.