Magnetically Modified Waste Bark of Aegle marmelos Tree as a Promising Biosorbent for Uranium(VI) Sorption

Radiochemistry - Magnetically modified waste bark of the Aegle marmelos tree was prepared by using green synthesis method and was used in a batch system for U(VI) removal from aqueous solution. The...     

A metaheuristic analysis of heat transfer rates through porous fins of tapered and step profiles: a comparative study

Neural Computing and Applications - The advanced high-end gadgets and sophisticated machines require effective mechanism of transferring heat away from the sources. In a large number of...     

Nonlinear model predictive control with polytopic invariant sets

Ellipsoidal invariant sets have been widely used as target sets in model predictive control (MPC). These sets can be computed by constructing appropriate linear difference inclusions together with additional constraints to ensure that the ellipsoid lies within a given inclusion polytope. The choice of inclusion polytope has a significant effect on the size of the target ellipsoid, but the optimal inclusion polytope cannot in general be computed systematically. This paper shows that use of polytopic invariant sets overcomes this difficulty, allowing larger stabilizable sets without loss of performance. In the interests of online efficiency, consideration is focused on interpolation-based MPC.     

Effect of temperature on the optical and electro-optical properties of poly(methyl methacrylate)/E7 polymer-dispersed liquid crystal composites

Experiments on the effect of temperature on the optical and electro-optical behaviors of polymer-dispersed liquid crystals (PDLCs) are considered. Composite films composed of poly(methyl methacrylate) (PMMA) and the nematic-type liquid crystal (LC) E7 were prepared by solvent casting in chloroform. The PDLC film contained droplets of E7 from 10 to 80 wt % in a PMMA matrix. Morphological studies illustrated the formation of isolated droplets of LC due to phase separation, and their homogeneous distribution increased with increasing E7 content. Thermo-optical studies showed an increase in the nematic–isotropic transition temperature of composites, which indicated preferential solvation during the phase-separation process. The electro-optical characteristics were studied under the conditions of an externally applied square wave electric field with a He–Ne laser (λ = 632.8 nm) as a light source. The responses improved as the E7 content in PMMA increased. Semipermanent memory effects were noticed in composites at higher temperatures. Changes in the transmittance due to thermal variations provided the possibility of using such a device as a temperature sensor. The results obtained indicate that under these experimental conditions, the output can be controlled to the desired level by the selection of a suitable loading of LC to prepare PDLC electro-optically active composite films with a response time on the order of only a few milliseconds. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Synthesis of polymeric pour point depressants for Nada crude oil (Gujarat,India) and its impact on oil rheology

Five flow improvers have been synthesized to study rheological properties of Nada crude oil (Gujarat, India). Anhydride copolymers were prepared making use of the copolymerization of acrylates of different alkyls with maleic anhydride and the Poly (n-alkyl acrylates-co-N-hexadecylmaleimide) were prepared by the reaction of copolymer with hexadecylamine. The additives were purified and characterized by FTIR, GPC. The prepared polymeric additives shows dual function both as wax dispersants and flow improvers and all of them acts as good pour point depressants. Yield stress and the viscosity of the crude oil at different temperatures and concentrations of additives were evaluated by zero friction advanced rheometer AR-500 of TA instrument. Comparison of morphologies and structures of wax crystals or aggregates in waxy crude oils beneficiated with and without a PPD was also done by micro photographic studies which show the modification in wax crystal morphology due to additives.     

Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach

Water Resources Management - Reference evapotranspiration (ET0) is a crucial element for deriving irrigation scheduling of major crops. Thus, precise projection of ET0 is essential for better...     

FMS scheduling with knowledge based genetic algorithm approach

In this paper a complex scheduling problem in flexible manufacturing system (FMS) has been addressed with a novel approach called knowledge based genetic algorithm (KBGA). The literature review indicates that meta-heuristics may be used for combinatorial decision-making problem in FMS and simple genetic algorithm (SGA) is one of the meta-heuristics that has attracted many researchers. This novel approach combines KB (which uses the power of tacit and implicit expert knowledge) and inherent quality of SGA for searching the optima simultaneously. In this novel approach, the knowledge has been used on four different stages of SGA: initialization, selection, crossover, and mutation. Two objective functions known as throughput and mean flow time, have been taken to measure the performance of the FMS. The usefulness of the algorithm has been measured on the basis of number of generations used for achieving better results than SGA. To show the efficacy of the proposed algorithm, a numerical example of scheduling data set has been tested. The KBGA was also tested on 10 different moderate size of data set to show its robustness for large sized problems involving flexibility (that offers multiple options) in FMS.     

Search Strategies for Multiple UAV Search and Destroy Missions

Multiple UAVs are deployed to carry out a search and destroy mission in a bounded region. The UAVs have limited sensor range and can carry limited resources which reduce with use. The UAVs perform a search task to detect targets. When a target is detected which requires different type and quantities of resources to completely destroy, then a team of UAVs called as a coalition is formed to attack the target. The coalition members have to modify their route to attack the target, in the process, the search task is affected, as search and destroy tasks are coupled. The performance of the mission is a function of the search and the task allocation strategies. Therefore, for a given task allocation strategy, we need to devise search strategies that are efficient. In this paper, we propose three different search strategies namely; random search strategy, lanes based search strategy and grid based search strategy and analyze their performance through Monte-Carlo simulations. The results show that the grid based search strategy performs the best but with high information overhead.     

Multiple UAV Coalitions for a Search and Prosecute Mission

Unmanned aerial vehicles (UAVs) have the potential to carry resources in support of search and prosecute operations. Often to completely prosecute a target, UAVs may have to simultaneously attack the target with various resources with different capacities. However, the UAVs are capable of carrying only limited resources in small quantities, hence, a group of UAVs (coalition) needs to be assigned that satisfies the target resource requirement. The assigned coalition must be such that it minimizes the target prosecution delay and the size of the coalition. The problem of forming coalitions is computationally intensive due to the combinatorial nature of the problem, but for real-time applications computationally cheap solutions are required. In this paper, we propose decentralized sub-optimal (polynomial time) and decentralized optimal coalition formation algorithms that generate coalitions for a single target with low computational complexity. We compare the performance of the proposed algorithms to that of a global optimal solution for which we need to solve a centralized combinatorial optimization problem. This problem is computationally intensive because the solution has to (a) provide a coalition for each target, (b) design a sequence in which targets need to be prosecuted, and (c) take into account reduction of UAV resources with usage. To solve this problem we use the Particle Swarm Optimization (PSO) technique. Through simulations, we study the performance of the proposed algorithms in terms of mission performance, complexity of the algorithms and the time taken to form the coalition. The simulation results show that the solution provided by the proposed algorithms is close to the global optimal solution and requires far less computational resources.