Designing an experimental rig for developing a fire severity model using numerical simulation

In this study, an experimental rig representing a deep enclosure was designed to be used to validate a CFD‐based fire model in predicting the outcome. The model then can be used for further study to investigate physical phenomenon within a deep enclosure and to develop an engineering fire severity (heat release rate, HRR, vs time vs position [1]) model. Two empirical models (the VU model [1] and Kawagoe model [2]) were used along with Fire Dynamics Simulator (FDS) in designing the experimental rig. For a specific‐sized enclosure, when the HRR was prescribed to the FDS as input from the VU model, it was accurately reproduced, while the HRR from the Kawagoe was used as the input, the FDS calculated much lower value. The experimental rig of that specific size was then built, and various parameters were measured from the tests with liquid fuel fire within this experimental rig. The measured HRR was prescribed into the FDS, and the FDS could reproduce HRR values well. However, the predicted temperature and radiation flux was not as good, especially when the flames were near the opening. This may be due to the tendency of flames over‐projecting outside the opening in FDS simulations.     

State estimation for short‐time switched linear systems under asynchronous switching

In this note, the state estimation problem for a class of short‐time switched linear systems is investigated. By incorporating the concept of finite‐time stability, an observer is designed to ensure the error dynamics finite‐time stable in the short‐time switching interval under the general asynchronous switching whereas synchronous switching mode is its special case. A numerical example is provided to illustrate the effectiveness of our study in this paper. Copyright © 2013 John Wiley & Sons, Ltd.     

Facile hydrothermal synthesis of 3D flower‐like La‐MoS2 nanostructure for photocatalytic hydrogen energy production

Three‐dimensional (3D) flower‐like MoS₂ nanostructures were prepared via facile and cost‐effective hydrothermal method by varying hydrothermal temperature (180°C, 200°C, and 220°C) and reaction time (6, 12, 24, and 36 hours). The results demonstrated that the sample prepared at 200°C for 24 hours have 3D flower‐like MoS₂ nanostructure (SEM) with hexagonal phase structure (XRD). Moreover, this novel photocatalyst was also modified by lanthanum element (La³⁺) with varying La³⁺ atomic ratio (0.5%, 1%, 2%, 3%, and 4%). Interestingly, the La³⁺ incorporation into MoS₂ has good effect on the specific surface area and optical properties of MoS₂ photocatalyst. Furthermore, the flower‐like 3%LaMoS₂ nanostructure photocatalyst exhibited 5.2‐times higher efficiency for H₂ evolution via water splitting as compared with pure MoS₂ under the same conditions. This superior efficiency of the photocatalyst for H₂ production arises from the positive synergistic effect between MoS₂ and lanthanum in the composite photocatalyst due to higher surface area, enhanced light absorption, and inhibited electron‐holes pair recombination. This study presents an expensive photocatalyst for energy production via water spitting.     

Processing and characterization of A-site deficient [(Ca,Sr) x (La,Nd)2/3−2x/3]TiO3 dielectric ceramics

A-site deficient perovskite ceramics in the [(Ca, Sr) _x (La, Nd)_2/3?2x/3]TiO₃ (x = 0.1–0.9) system were prepared by a mixed-oxide solid state sintering route. Synchrotron X-ray diffraction and Raman spectroscopy revealed a tetragonal (I4/mcm) symmetry for the compositions with x = 0.1–0.7 and orthorhombic (Pbnm) symmetry for the x = 0.8 and 0.9 compositions. The grain size increased with an increase in x up to 0.7 and then decreased upon further increase in x to 0.8 and 0.9. The optimum microwave dielectric properties (ε_r ~ 48, Q × f = 3,664 GHz and τ _f  ~ +61 GHz) were recorded for the compositions with x = 0.1.     

Watermarking as a service (WaaS) with anonymity

Multimedia Tools and Applications - With rapid increase in Internet usage, it has become quite easy to produce illegal digital contents. As a result, copyright protection and authentication...     

Performing competitive analysis in QFD studies using state multipole moments and bootstrap sampling

QFD often makes use of Likert scales to convert opinions into data for analysis. Likert scales are prone to bias in relation to their labeling, interpretation, and distributional assumptions. Analysis is often based on means, without regard to variation or distribution shape, making inferential statistical procedures unreliable. We present a method for extracting increased information from Likert data using State Multipole Moments, and use bootstrap sampling to enable the moments to be statistically compared. We demonstrate application using a published QFD competitor analysis, showing how this method can enable practitioners to perform statistical comparisons resulting in improved decision making.     

A probabilistic approach to quantum Bayesian games of incomplete information

A Bayesian game is a game of incomplete information in which the rules of the game are not fully known to all players. We consider the Bayesian game of Battle of Sexes that has several Bayesian Nash equilibria and investigate its outcome when the underlying probability set is obtained from generalized Einstein–Podolsky–Rosen experiments. We find that this probability set, which may become non-factorizable, results in a unique Bayesian Nash equilibrium of the game.     

Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou's general PseAAC and Support Vector Machine

Proteins control all biological functions in living species. Protein structure is comprised of four major classes including all-α class, all-β class, α+β, and α/β. Each class performs different function according to their nature. Owing to the large exploration of protein sequences in the databanks, the identification of protein structure classes is difficult through conventional methods with respect to cost and time. Looking at the importance of protein structure classes, it is thus highly desirable to develop a computational model for discriminating protein structure classes with high accuracy. For this purpose, we propose a silco method by incorporating Pseudo Average Chemical Shift and Support Vector Machine. Two feature extraction schemes namely Pseudo Amino Acid Composition and Pseudo Average Chemical Shift are used to explore valuable information from protein sequences. The performance of the proposed model is assessed using four benchmark datasets 25PDB, 1189, 640 and 399 employing jackknife test. The success rates of the proposed model are 84.2%, 85.0%, 86.4%, and 89.2%, respectively on the four datasets. The empirical results reveal that the performance of our proposed model compared to existing models is promising in the literature so far and might be useful for future research.