Removal of As(V) by adsorption onto mixed rare earth oxides

Arsenic pollution of water is a major problem faced worldwide. Arsenic is a suspected carcinogen in human beings and is harmful to other living beings. In the present study, a novel adsorbent was used to remove arsenate [As(V)] from synthetic solutions. The adsorbent, which is a mixture of rare earth oxides, was found to adsorb As(V) rapidly and effectively. The effect of various parameters such as contact time, initial concentration, pH, and adsorbent dose on adsorption efficiency was investigated. More than 90% of the adsorption occurred within the first 10 min and the kinetic rate constant was found to be about 3.5 mg min(-1). Adsorption efficiency was found to be dependent on the initial As(V) concentration, and the adsorption behavior followed the Langmuir adsorption model. The optimum pH was found to be 6.5. The presence of other ions such as nitrate, phosphate, sulphate, and silicate decreased the adsorption of As(V) by about 20-30%. The adsorbed As(V) could be desorbed easily by washing the adsorbent with pH 12 solution. This study demonstrates the applicability of naturally occurring rare earth oxides as selective adsorbents for As(V) from solutions.

Unsteady mixed convection flow from a rotating vertical cone with a magnetic field

An analysis is developed to study the unsteady mixed convection flow over a vertical cone rotating in an ambient fluid with a time-dependent angular velocity in the presence of a magnetic field. The coupled nonlinear partial differential equations governing the flow have been solved numerically using an implicit finite-difference scheme. The local skin friction coefficients in the tangential and azimuthal directions and the local Nusselt number increase with the time when the angular velocity of the-cone increases, but the reverse trend is observed for decreasing angular velocity. However, these are not mirror reflection of each other. The magnetic field reduces the skin friction coefficient in the tangential direction and also the Nusselt number, but it increases the skin friction coefficient in the azimuthal direction. The skin friction coefficients and the Nusselt number increase with the buoyancy force.

Integer Sequence window based Reconfigurable FIR filters

Conventional hardware implementation techniques for FIR filters require the computation of filter coefficients in software and have them stored in memory. This approach is static in the sense that any further fine tuning of the filter requires computation of new coefficients in software. In this paper, we propose an alternate technique for implementing FIR filters in hardware. We store a considerably large number of impulse response coefficients of the ideal filter (having box type frequency response) in memory. We then do the windowing process, on these coefficients, in hardware using integer sequences as window functions. The integer sequences are also generated in hardware. This approach offers the flexibility in fine tuning the filter, like varying the transition bandwidth around a particular cutoff frequency.

Computationally Efficient Sub-optimal Mid Course Guidance Using Model Predictive Static Programming (MPSP)

For a homing interceptor, suitable initial condition must be achieved by mid course guidance scheme for its maximum effectiveness. To achieve desired end goal of any mid course guidance scheme, two point boundary value problem must be solved online with all realistic constrain. A Newly developed computationally efficient technique named as MPSP (Model Predictive Static Programming) is utilized in this paper for obtaining suboptimal solution of optimal mid course guidance. Time to go uncertainty is avoided in this formulation by making use of desired position where midcourse guidance terminate and terminal guidance takes over. A suitable approach angle towards desired point also can be specified in this guidance law formulation. This feature makes this law particularly attractive because warhead effectiveness issue can be indirectly solved in mid course phase.

Fault-Tolerant Average Execution Time Optimization for General Purpose Multi-Processor System-on Chips

Fault-tolerance is due to the semiconductor technology development important, not only for safety-critical systems but also for general-purpose (non-safety critical) systems. However, instead of guaranteeing that deadlines always are met, it is for general-purpose systems important to minimize the average execution time (AET) while ensuring fault-tolerance. For a given job and a soft (transient) error probability, we define mathematical formulas for AET that includes bus communication overhead for both voting (active replication) and rollback-recovery with checkpointing (RRC). And, for a given multi-processor system-on-chip (MPSoC), we define integer linear programming (ILP) models that minimize AET including bus communication overhead when: (1) selecting the number of checkpoints when using RRC, (2) finding the number of processors and job-to-processor assignment when using voting, and (3) defining fault-tolerance scheme (voting or RRC) per job and defining its usage for each job. Experiments demonstrate significant savings in AET.

A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels

Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

Electrochemical performance of mixed crystallographic phase nanotubes and nanosheets of titania and titania-carbon/silver composites for lithium-ion batteries

The role of homogeneity in ex situ grown conductive coatings and dimensionality in the lithium storage properties of TiO(2) is discussed here. TiO(2) nanotube and nanosheet comprising of mixed crystallographic phases of anatase and TiO(2) (B) have been synthesized by an optimized hydrothermal method. Surface modifications of TiO(2) nanotube are realized via coating the nanotube with Ag nanoparticles and amorphous carbon. The first discharge cycle capacity (at current rate = 10 mA g(-1)) for TiO(2) nanotube and nanosheet were 355 mAh g(-1) and 225 mAhg(-1), respectively. The conductive surface coating stabilized the titania crystallographic structure during lithium insertion-deinsertion processes via reduction in the accessibility of lithium ions to the trapping sites. The irreversible capacity is beneficially minimized from 110 mAh g(-1) for TiO(2) nanotubes to 96 mAh g(-1) and 57 mAhg(-1) respectively for Ag and carbon modified TiO(2) nanotubes. The homogeneously coated amorphous carbon over TiO(2) renders better lithium battery performance than randomly distributed Ag nanoparticles coated TiO(2) due to efficient hopping of electrons. (C) 2011 Elsevier B.V. All rights reserved.

Minimization of Constraint Violation in Fuzzy Multiobjective Programming

Fuzzy multiobjective programming for a deterministic case involves maximizing the minimum goal satisfaction level among conflicting goals of different stakeholders using Max-min approach. Uncertainty due to randomness in a fuzzy multiobjective programming may be addressed by modifying the constraints using probabilistic inequality (e.g., Chebyshev’s inequality) or by addition of new constraints using statistical moments (e.g., skewness). Such modifications may result in the reduction of the optimal value of the system performance. In the present study, a methodology is developed to allow some violation in the newly added and modified constraints, and then minimizing the violation of those constraints with the objective of maximizing the minimum goal satisfaction level. Fuzzy goal programming is used to solve the multiobjective model. The proposed methodology is demonstrated with an application in the field of Waste Load Allocation (WLA) in a river system.