Digestive ripening facilitated atomic diffusion at nanosize regime: Case of AuIn2 and Ag3In intermetallic nanoparticles

Monodisperse colloidal gold-indium (AuIn2) intermetallic nanoparticles have been synthesized from Au and In colloids using the digestive ripening process. Formation of the intermetallic proceeds via digestive ripening facilitated atomic diffusion of Au and In atoms from the Au and In nanoparticles followed simultaneously by their growth in the solution. Optimization of the reaction temperature was found to be crucial for the formation of AuIn2 intermetallic from gold and indium nanoparticles. Transmission electron microscopy revealed the presence of nearly monodisperse nanoparticles of Au and AuIn2 with particle size distribution of 3.7 +/- 1.0 nm and 5.0 +/- 1.6 nm, respectively. UV-visible spectral studies brought out the absence of SPR band in pure AuIn2 intermetallic nanoparticles. Optical study and electron microscopy, in combination with powder X-ray diffraction established phase pure AuIn2 intermetallic nanoparticles unambiguously. The potential of such an unprecedented approach has been further exploited in the synthesis of Ag3In intermetallic nanoparticles with the dimension of less than 10 nm. (C) 2014 Elsevier B.V. All rights reserved.

A Polymatroid Approach to Separable Convex Optimization with Linear Ascending Constraints

We revisit a problem studied by Padakandla and Sundaresan SIAM J. Optim., August 2009] on the minimization of a separable convex function subject to linear ascending constraints. The problem arises as the core optimization in several resource allocation problems in wireless communication settings. It is also a special case of an optimization of a separable convex function over the bases of a specially structured polymatroid. We give an alternative proof of the correctness of the algorithm of Padakandla and Sundaresan. In the process we relax some of their restrictions placed on the objective function.

Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

Groundwater management involves conflicting objectives as maximization of discharge contradicts the criteria of minimum pumping cost and minimum piping cost. In addition, available data contains uncertainties such as market fluctuations, variations in water levels of wells and variations of ground water policies. A fuzzy model is to be evolved to tackle the uncertainties, and a multiobjective optimization is to be conducted to simultaneously satisfy the contradicting objectives. Towards this end, a multiobjective fuzzy optimization model is evolved. To get at the upper and lower bounds of the individual objectives, particle Swarm optimization (PSO) is adopted. The analytic element method (AEM) is employed to obtain the operating potentio metric head. In this study, a multiobjective fuzzy optimization model considering three conflicting objectives is developed using PSO and AEM methods for obtaining a sustainable groundwater management policy. The developed model is applied to a case study, and it is demonstrated that the compromise solution satisfies all the objectives with adequate levels of satisfaction. Sensitivity analysis is carried out by varying the parameters, and it is shown that the effect of any such variation is quite significant. Copyright (c) 2015 John Wiley & Sons, Ltd.

Coupling geomorphology parameters with lithology for micro-level groundwater resource assessment: a case study from semi-arid hard rock terrain in Tamil Nadu, India

The standard procedure of groundwater resource estimation in India till date is based on the specific yield parameters of each rock type (lithology) derived through pumping test analysis. Using the change in groundwater level, specific yield, and area of influence, groundwater storage change could be estimated. However, terrain conditions in the form of geomorphological variations have an important bearing on the net groundwater recharge. In this study, an attempt was made to use both lithology and geomorphology as input variables to estimate the recharge from different sources in each lithology unit influenced by the geomorphic conditions (lith-geom), season wise separately. The study provided a methodological approach for an evaluation of groundwater in a semi-arid hard rock terrain in Tirunelveli, Tamil Nadu, India. While characterizing the gneissic rock, it was found that the geomorphologic variations in the gneissic rock due to weathering and deposition behaved differently with respect to aquifer recharge. The three different geomorphic units identified in gneissic rock (pediplain shallow weathered (PPS), pediplain moderate weathered (PPM), and buried pediplain moderate (BPM)) showed a significant variation in recharge conditions among themselves. It was found from the study that Peninsular gneiss gives a net recharge value of 0.13 m/year/unit area when considered as a single unit w.r.t. lithology, whereas the same area considered with lith-geom classes gives recharge values between 0.1 and 0.41 m/year presenting a different assessment. It is also found from this study that the stage of development (SOD) for each lith-geom unit in Peninsular gneiss varies from 168 to 230 %, whereas the SOD is 223 % for the lithology as a single unit.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

A PRACTICAL PROCEDURE TO ESTIMATE THE BEARING CAPACITY OF FOOTINGS ON SAND-APPLICATION TO 87 CASE STUDIES

Following the recent work of the authors in development and numerical verification of a new kinematic approach of the limit analysis for surface footings on non-associative materials, a practical procedure is proposed to utilize the theory. It is known that both the peak friction angle and dilation angle depend on the sand density as well as the stress level, which was not the concern of the former work. In the current work, a practical procedure is established to provide a better estimate of the bearing capacity of surface footings on sand which is often non-associative. This practical procedure is based on the results obtained theoretically and requires the density index and the critical state friction angle of the sand. The proposed practical procedure is a simple iterative computational procedure which relates the density index of the sand, stress level, dilation angle, peak friction angle and eventually the bearing capacity. The procedure is described and verified among available footing load test data.