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Use of Human activity recognition is increasing day by day for smart home, eldercare, remote health monitoring and surveillance purpose. Serving these purposes better, needs detailed recognition of activities, viz. sitting on chair or floor, slow or brisk walk, running with load, etc. Very few works aim to distinguish between intense activities (such as walk carrying weight) from its counterpart (walk) which is essential for effective health monitoring of elder adults and patients recovering from surgery. In this work, solution has been proposed for this purpose with the help of wearable and smartphone-embedded sensors. Accordingly, the contribution of this work is to present a framework for detailing in identification for both static and dynamic activities, as well as their intense counterparts by designing an ensemble of classifiers. The ensemble is designed that applies weighted majority voting for classification of test instances. Weights of the base classifiers are determined by feeding their output performance for training dataset in a neural network. We observed that our work achieves above 94% recognition accuracy.

We apply our fluid dynamic model with chemical kinetics of dusty comet comae to P/Tempel 2. A brief summary of results concerning gas/dust dynamics and chemistry is given.

Emission features of C2, C3, CN, and dust in Comets Thiele (1985m), Hartley-Good (1985l), and Giacobini-Zinner (1984e) have been analyzed and their mass loss rates of about 0.5, 1.1, and 0.8 Mg s−1 have been determined.

The r-centroids for 36 bands of NbO (A2Δ → X2Δ), BiO (B 4 ∑ - ← X 2 1 ∏ 1 2 ) , PO (A2∑ → X2∏r and InF (B3∏1 → X1∑+) systems have been computed by the α-average quadratic equation and graphical methods. The agreement between the r v′,v″ values obtained by the two methods is satisfactory. Vibrational transition probabilities have been evaluated by an approximate analytical method for the NbO (A2Δ → X2Δ) and InF (B3∏1 - X1∑+) systems.

Franck-Condon factors and r-centroids for some bands of the D2Σ+ ↔ X2Σ+ system of SrH are presented and briefly discussed.

The potential-energy curves for Au-Be interactions, corresponding to the X2Σ+, A2Σ+ and BΠ 1 2 states of AuBe, have been computed by the Rydberg-Klein-Rees-Vanderslice (RKRV) method. The ground state dissociation energy has been estimated by fitting the Lippincott three-parameter potential functions to the RKRV curves. The dissociation products, as well as the dissociation energies, have been correlated for these states by using the Wigner-Whitmer rules.