Stabilization of 6H-Hexagonal SrMnO3 Polymorph by Al2O3 insertion

We demonstrate the structural evolution of polymorphic phases in Al₂O₃-inserted SrMnO₃ ceramics synthesized by solid state reaction. While the 4H-hexagonal phase is predominant in pure SrMnO₃ ceramics, a small amount of 6H-hexagonal polymorph is identified in addition to the primary 4H-hexagonal SrMnO₃ and the secondary hexagonal SrAl₂O₄ phases in the as-sintered ceramics, evidenced by x-ray diffraction and subsequent Rietveld refinement analyses. The existence of the 6H-hexagonal SrMnO₃ phase is corroborated using Raman spectroscopy. The chemical compositions and electronic structures of the Al₂O₃-inserted SrMnO₃ compounds are also examined using energy dispersive spectroscopy and x-ray photoelectron spectroscopy, respectively. The first-principles calculations reveal that there is no clear difference between the total energies of 4H- and 6H-hexagonal polymorphs regardless of the presence/absence of Sr and oxygen vacancies. Possible origins are discussed with the estimation of actual strain based on the refined lattice parameter of 6H SrMnO₃.     

Compressive strength and wear properties of SiC/Al6061 composites reinforced with high contents of SiC fabricated by pressure-assisted infiltration

Pressure-assisted infiltration was used to synthesize SiC/Al 6061 composites containing high weight percentages of SiC. A combination of PEG and glass water was used to fabricate SiC preforms and the effect of the presence of glass water on the microstructure and mechanical properties of the preforms was evaluated by performing compression tests on the preforms. Also, the compressive strength and the hardness of the SiC/Al composites were investigated. The results revealed that the glass water improved the compressive strength of the preforms by about five times. The microstructural characterization of the composites showed that the penetration of the aluminum melt into the preforms was completed and almost no porosity could be seen in the microstructures of the composites. Moreover, the composite containing 75 wt% SiC exhibited the highest compressive strength as well as the maximum hardness. The results of the wear tests showed that increasing the SiC content reduces the wear rate so that the Al-75 wt% SiC composite has a lower wear rate and a lower coefficient of friction than those of Al-67 wt% SiC composite. This indicated higher wear resistance in these composites than the Al alloy due to the formation of a tribological layer on the surface of the composites.     

Correction to: Cloud-based vehicular networks: a taxonomy,survey, and conceptual hybrid architecture

Wireless Networks - The original version of this article unfortunately contained a mistake in the title. The correct title has been published with this erratum.     

A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

Theoretical kinetic study of the reaction between dimethyl disulfide and OH radicals

The potential energy profile of the reaction between dimethyl disulfide and OH^? radicals is explored by utilizing ab initio and hybrid meta density functional theory methods. Having the energies and structural data of the stationary points, statistical rate theories, such as transition state theory and variable reaction coordinate-transition state theory, are employed to compute the overall rate constants, and discuss the mechanism and product channels. On the basis of the calculations, the overall rate coefficient is predicted to be 2.49?×?10^?10?cm³?molecule^?1?s^?1 at 298?K. It is found that in the most favorable pathway, the reaction proceeds via formation of the relatively unstable intermediate CH₃S^?(OH)SCH₃ decomposing rapidly to yield CH₃S^?+CH₃SOH.     

A knowledge construction methodology to automate case-based learning using clinical documents

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.     

Ni-Rhodanine Complex Supported on FSM-16 as Mesoporous Silica Support: Synthesis,Characterization and Application in Synthesis of Tri and Tetrasubstituted Imidazoles and 3,4-Dihydropyrimidine-2-(1H)-Ones

A new, efficient and recoverable heterogeneous catalyst was successfully synthesized by functionalization of mesoporous silica FSM-16. The FSM-16/CPTMS-Rh-     

A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection

Neural Computing and Applications - To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related...     

ACTNET: End-to-End Learning of Feature Activations and Multi-stream Aggregation for Effective Instance Image Retrieval

International Journal of Computer Vision - We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable...