Disturbance in Thermo-Microstretch Elastic Medium With Internal Heat Source

The present investigation is aimed at studying the deformation in thermo-microstretch elastic medium occuping the region y ? 0 with a stable internal heat source. A nonviscous fluid layer of height h is overlying the thermoelastic medium and a mechanical source of constant magnitude is applied along the fluid/solid interface. The normal mode analysis is used to obtain the exact expressions for displacement components, force stress, and temperature distribution. The numerical results are given and presented graphically for the Green–Lindsay theory of thermoelasticity. The variations of the considered variables through the horizontal distance are illustrated graphically. Comparisons are made with the results in the presence and absence of microstretch and microrotation parameters.     

A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification

In multi-label classification problems, every instance is associated with multiple labels at the same time. Binary classification, multi-class classification and ordinal regression problems can be seen as unique cases of multi-label classification where each instance is assigned only one label. Text classification is the main application area of multi-label classification techniques. However, relevant works are found in areas like bioinformatics, medical diagnosis, scene classification and music categorization. There are two approaches to do multi-label classification: The first is an algorithm-independent approach or problem transformation in which multi-label problem is dealt by transforming the original problem into a set of single-label problems, and the second approach is algorithm adaptation, where specific algorithms have been proposed to solve multi-label classification problem. Through our work, we not only investigate various research works that have been conducted under algorithm adaptation for multi-label classification but also perform comparative study of two proposed algorithms. The first proposed algorithm is named as fuzzy PSO-based ML-RBF, which is the hybridization of fuzzy PSO and ML-RBF. The second proposed algorithm is named as FSVD-MLRBF that hybridizes fuzzy c-means clustering along with singular value decomposition. Both the proposed algorithms are applied to real-world datasets, i.e., yeast and scene dataset. The experimental results show that both the proposed algorithms meet or beat ML-RBF and ML-KNN when applied on the test datasets.

     

Design,Synthesis, Drug-Likeness and In Silico Prediction of Polycyclic Aromatic Schiff base Tethered Organosilatranes

Silicon - The present article includes the generation of aromatic Schiff base tethered organosilatranes via two step synthetic pathways starting from anthracene-10-carbaldehyde and...