Superhydrophobic surfaces with silane‐treated diatomaceous earth/resin systems

Superhydrophobic coatings were prepared using fluorosilane‐treated diatomaceous earth (DE) with either polyurethane or epoxy binders. The surface wettability and morphology of the films were analyzed using contact angle measurements and scanning electron microscopy (SEM), respectively. The water contact angles were studied as a function of the fluorocarbon fraction on DE and the particle loadings of treated DE in the coating. The contact angles exceeded 150° for coatings with at least 0.02 fluorocarbon fraction (mass of fluorosilane/mass of particle) on the DE and with 0.2 particle loadings (mass of treated particles/mass of coating). The water contact angles of the surfaces were dependent on the nature of the binder below 0.2 particle loadings of the superhydrophobic DE particles, but were independent of the binder type after attaining superhydrophobicity. The results were consistent with the superhydrophobicity resulting from the migration of the superhydrophobic DE moving to and covering the surfaces completely. It was also shown that the treatment with fluorosilanes restricted the pores in DE and reduces the specific surface area of the material. However, these changes had effectively no effect on the superhydrophobicity of the coatings. The results of this work clearly identify some important considerations relative to producing superhydrophobic coatings from inexpensive diatomaceous earth. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44072.     

Covulcanization of LLDPE/EMA blends using dicumyl peroxide

The effect of dicumyl peroxide (DCP) content on the gel fraction, mechanical, dynamic mechanical, and thermal properties of linear low‐density polyethylene (LLDPE)/ethylene‐co‐methyl acrylate (EMA) blends were studied. Gel content of the blends increases with increasing DCP content, and EMA is more prone to crosslinking than LLDPE. Wide‐angle X‐ray diffraction (WAXD) and differential scanning calorimetry (DSC) were used to study the effect of DCP crosslinking on percent crystallinity and crystalline structure of the blends and individual components. At lower level of DCP loading, crosslinking process does not have significant effect on the crystalline structure of the LLDPE, which was confirmed from the percent crystallinity and lattice distance value. However, at higher DCP content, percent crystallinity decreases significantly. At lower EMA concentration (<50%), percent crystallinity and lattice distance remain unchanged up to 2 wt % of DCP. For EMA contents of more than 50 wt %, increasing DCP content reduces the crystallinity of the blends and increases the lattice distance. The highest level of mechanical and dynamic mechanical properties was observed for 60/40 LLDPE/EMA blends at 2 wt % DCP. Addition of LLDPE‐g‐MA (3 wt %) as a compatibilizer enhances the properties of the vulcanizates. Blends crosslinked with DCP up to 0.3 wt % can easily be reprocessed. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Variance-penalized Markov decision processes: dynamic programming and reinforcement learning techniques

In control systems theory, the Markov decision process (MDP) is a widely used optimization model involving selection of the optimal action in each state visited by a discrete-event system driven by Markov chains. The classical MDP model is suitable for an agent/decision-maker interested in maximizing expected revenues, but does not account for minimizing variability in the revenues. An MDP model in which the agent can maximize the revenues while simultaneously controlling the variance in the revenues is proposed. This work is rooted in machine learning/neural network concepts, where updating is based on system feedback and step sizes. First, a Bellman equation for the problem is proposed. Thereafter, convergent dynamic programming and reinforcement learning techniques for solving the MDP are provided along with encouraging numerical results on a small MDP and a preventive maintenance problem.     

Machining feature-based similarity assessment algorithms for prismatic machined parts

This paper presents algorithms for identifying machined parts in a database that are similar to a given query part based on machining features. In this paper we only consider parts that are machined on 3-axis machining centers. We utilize reduced feature vectors consisting of machining feature access directions, feature types, feature volumes, feature dimensional tolerances and feature group cardinality as a basis for assessing shape similarity. We have defined a distance function between two sets of reduced feature vectors to assess the similarity between them from the machining effort point of view. To assess the similarity between the two parts, one set of reduced feature vectors is transformed in space using rigid body transformations with respect to the other set such that the distance between them is minimized. The distance between the two sets of aligned reduced feature vectors is used as a measure of similarity between the two parts. The existing machined parts are rank ordered based on the value of the distance with respect to the query part. The cost of previously machined parts that have a very small distance from the query part can be used as a basis for estimating the cost of machining the new part.     

Closed-loop identification using direct approach and high order ARX/GOBF-ARX models

Model accuracy plays a key role in the performance of advanced, model predictive control algorithms. Model fidelity is usually affected by routine operating condition changes, which necessitate reidentification. From several theoretical and practical considerations, it is recommended that such re-identification be performed under closed-loop conditions. The direct approach for closed-loop identification, owing to its simplicity, is better suited for MPC. In order to yield unbiased and consistent parameter estimates, however, this approach requires the noise model to be sufficiently parameterized. Towards this objective, high order ARX models are the most suitable candidates from the viewpoint of ease of parameter estimation. For multivariable systems, however, the identification of high order ARX models would require longer experiments to be performed. This being undesirable from a practical viewpoint, there is a need for a parsimonious parameterization that would retain the benefits of high order ARX models. In this work, we propose to use generalized orthonormal basis filters (GOBFs) to achieve this parsimonous parameterization. Further, we propose an approach to obtain reduced order models by emphasizing important frequencies so as to suitably shape the bias. We also show that the choice of the GOBF parameterization has another important merit, viz. their ability to perform well even with minimal perturbation data or short experiment times. The efficacy of the proposed approach is demonstrated via simulations on the benchmark Shell Control Problem and a laboratory quadruple tank setup.     

A multiple-criteria decision problem for optimal management of farm resources under uncertainty - a case study

An attempt is made to minimize the total operational cost and specific energy for rice cultivation, when the constraints relating to the availability of power sources and crop yield are supposed to be stochastic in nature. The Sequential Linear Goal Programming algorithm has been used to solve the resulting multiple objective optimization problem.     

III–V super-lattice SPST/SPMT pin switches for THz communication: theoretical reliability and experimental feasibility studies

Microsystem Technologies - A physics-based Quantum-Modified CLassical Drift–Diffusion (QMCLDD) non-linear mathematical model has been developed for design and characterisation of GaN/AlGaN...     

Cooperative adaptive control for synchronization of second‐order systems with unknown nonlinearities

This paper studies synchronization to a desired trajectory for multi‐agent systems with second‐order integrator dynamics and unknown nonlinearities and disturbances. The agents can have different dynamics and the treatment is for directed graphs with fixed communication topologies. The command generator or leader node dynamics is also nonlinear and unknown. Cooperative tracking adaptive controllers are designed based on each node maintaining a neural network parametric approximator and suitably tuning it to guarantee stability and performance. A Lyapunov‐based proof shows the ultimate boundedness of the tracking error. A simulation example with nodes having second‐order Lagrangian dynamics verifies the performance of the cooperative tracking adaptive controller. Copyright © 2010 John Wiley & Sons, Ltd.     

Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information

Applied Intelligence - The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active...     

Neo Arithmetic and Ranking Techniques for Trapezoidal Generalized Interval Valued Fuzzy Numbers: Their Applications in Decision Making for Medical Investigation

Neural Processing Letters - The uncertainty caused mainly by the deficiency of precision and data, artificial/human-made errors, information accessed from expert opinions or very miniature size of...