Modified roaming optimization for multi-modal optima

This paper explains the algorithm of Modified Roaming Optimization (MRO) for capturing the multiple optima for multimodal functions. There are some similarities between the Roaming Optimization (RO) and MRO algorithms, but the MRO algorithm is created to overcome the problems facing while applying the RO to the problems possessing large number of solutions. The MRO mainly uses the concept of density to overcome the challenges posed by RO. The algorithm is tested with standard test functions and also discussions are made to improve the efficacy of the MRO algorithm. This paper also gives the results of MRO applied for solving Inverse Kinematics (IK) problem for SCARA and PUMA robots.

Improving tangential resolution with a modified delay-and-sum reconstruction algorithm in photoacoustic and thermoacoustic tomography

Spatial resolution in photoacoustic and thermoacoustic tomography is ultrasound transducer (detector) bandwidth limited. For a circular scanning geometry the axial (radial) resolution is not affected by the detector aperture, but the tangential (lateral) resolution is highly dependent on the aperture size, and it is also spatially varying (depending on the location relative to the scanning center). Several approaches have been reported to counter this problem by physically attaching a negative acoustic lens in front of the nonfocused transducer or by using virtual point detectors. Here, we have implemented a modified delay-and-sum reconstruction method, which takes into account the large aperture of the detector, leading to more than fivefold improvement in the tangential resolution in photoacoustic (and thermoacoustic) tomography. Three different types of numerical phantoms were used to validate our reconstruction method. It is also shown that we were able to preserve the shape of the reconstructed objects with the modified algorithm. (C) 2014 Optical Society of America

Cooperativity and Structural Relaxations in PVDF/PMMA Blends in the Presence of MWNTs: An Assessment through SAXS and Dielectric Spectroscopy

Intermolecular cooperativity and structural relaxations in PVDF/PMMA blends were studied in this work with respect to different surface modified (amine, similar to NH2; carboxyl acid, similar to COOH and pristine) multiwalled nanotubes (MWNTs) at 1 wt % near blend's T-g and in the vicinity of demixing using dielectric spectroscopy, SAXS, DSC, and WAXD. Intermolecular cooperativity at T-g and configurational entropy was addressed in the framework of cooperative rearranging region (CRR) at T-g. Because of specific interactions between PVDF and NH2-MWNTs, the local composition fluctuates at its average value resulting in a broad T-g. The scale of cooperativity (xi(CRR)) and the number of segments in the cooperative volume (N-CRR) is comparatively smaller in the blends with NH2-MWNTs. This clearly suggests that the number of segments cooperatively relaxing is reduced in the blends due to specific interactions leading to more heterogeneity. The configurational entropy at T-g, as derived from Vogel-Fulcher and Adam-Gibbs analysis, was reduced in the blends in presence of MWNTs manifesting in entropic penalty of the chains. The crystallite size and the amorphous miscibility was evaluated using SAXS and was observed to be strongly contingent on the surface functional groups on MWNTs. Three distinct relaxations-alpha(c) due to relaxations in the crystalline phase of PVDF, alpha(m) indicating the amorphous miscibility in PVDF/PMMA blends, and alpha beta concerning the segmental dynamics of PMMA-were observed in the blends in the temperature range T-g < T < T-c. The dynamics as well as the nature of relaxations were observed to be dependent the surface functionality on the MWNTs. The dielectric permittivity was also enhanced in presence of MWNTs, especially with NH2-MWNTs, with minimal losses. The influence of the MWNTs on the spherulite size and crystalline morphology of the blends was also confirmed by POM and SEM.

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Climate change scenarios of surface solar radiation in data sparse regions: a case study in Malaprabha River Basin, India

A variety of methods are available to estimate future solar radiation (SR) scenarios at spatial scales that are appropriate for local climate change impact assessment. However, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. Three methodologies to guide the estimation of SR are discussed in this study, namely: Case 1: SR is measured, Case 2: SR is measured but sparse and Case 3: SR is not measured. In Case 1, future SR scenarios are derived using several downscaling methodologies that transfer the simulated large-scale information of global climate models to a local scale ( measurements). In Case 2, the SR was first estimated at the local scale for a longer time period using sparse measured records, and then future scenarios were derived using several downscaling methodologies. In Case 3: the SR was first estimated at a regional scale for a longer time period using complete or sparse measured records of SR from which SR at the local scale was estimated. Finally, the future scenarios were derived using several downscaling methodologies. The lack of observed SR data, especially in developing countries, has hindered various climate change impact studies. Hence, this was further elaborated by applying the Case 3 methodology to a semi-arid Malaprabha reservoir catchment in southern India. A support vector machine was used in downscaling SR. Future monthly scenarios of SR were estimated from simulations of third-generation Canadian General Circulation Model (CGCM3) for various SRES emission scenarios (A1B, A2, B1, and COMMIT). Results indicated a projected decrease of 0.4 to 12.2 W m(-2) yr(-1) in SR during the period 2001-2100 across the 4 scenarios. SR was calculated using the modified Hargreaves method. The decreasing trends for the future were in agreement with the simulations of SR from the CGCM3 model directly obtained for the 4 scenarios.

Iodide Retention by Modified Kaolinite in the Context of Safe Disposal of High Level Nuclear Waste

Bentonite clay is identified as potential buffer in deep geological repositories (DGR) that store high level radioactive wastes (HLW) as the expansive clay satisfies the expected mechanical and physicochemical functions of the buffer material. In the deep geological disposal of HLW, iodine-129 is one of the significant nuclides, attributable to its long half-life (half life 1⁄4 1:7 × 107 years). However, the negative charge on the basal surface of bentonite particles precludes retention of iodide anions. To render the bentonite effective in retaining hazardous iodide species in DGR, improvement of the anion retention capacity of bentonite becomes imperative. The iodide retention capac-ity of bentonite is improved by admixing 10 and 20% Ag-kaolinite (Ag-K) with bentonite (B) on a dry mass basis. The present study produced Ag-kaolinite by heating silver nitrate-kaolinite mixes at 400°C. Marginal release of iodide retained by Ag-kaolinite occurred under extreme acidic (pH 1⁄4 2:5) and alkaline (pH 1⁄4 12:5) conditions. The swell pressure and iodide etention results of the B-Ag-K specimens bring out that mixing Ag-K with bentonite does not chemically modify the expansive clay; the mixing is physical in nature and Ag-K presence only contributes to iodide retention of the admixture. DOI: 10.1061/(ASCE)HZ.2153-5515.0000121. © 2012 American Society of Civil Engineers.

Voltammetric detection of arsenic on a bismuth modified exfoliated graphite electrode

We present the application of a bismuth modified exfoliated graphite electrode in the detection of arsenic in water. Bismuth film was electrodeposited onto an exfoliated graphite (EG) electrode at a potential of -600 mV. The modification of EG resulted in an increase in the electroactive surface area of the electrode and consequently peak current enhancement in Ru(NH3)(6)(2+/13+) redox probe. Square wave anodic stripping voltammetry was performed with the modified electrode (EG-Bi) in As (III) solutions at the optimum conditions of pH 6, deposition potential of -600 mV and pre-concentration time of 180s. The EG-Bi was able to detect As (III) to the limit of 5 mu g L-1 and was not susceptible to many interfering cations except Cu (II). The EG-Bi is low cost and easy to prepare. (C) 2013 Elsevier Ltd. All rights reserved.

A Modified Fluorimetric Method for Determination of Hydrogen Peroxide Using Homovanillic Acid Oxidation Principle

Hydrogen peroxide (H2O2) level in biological samples is used as an important index in various studies. Quantification of H2O2 level in tissue fractions in presence of H2O2 metabolizing enzymes may always provide an incorrect result. A modification is proposed for the spectrofluorimetric determination of H2O2 in homovanillic acid (HVA) oxidation method. The modification was included to precipitate biological samples with cold trichloroacetic acid (TCA, 5% w/v) followed by its neutralization with K2HPO4 before the fluorimetric estimation of H2O2 is performed. TCA was used to precipitate the protein portions contained in the tissue fractions. After employing the above modification, it was observed that H2O2 content in tissue samples was >= 2 fold higher than the content observed in unmodified method. Minimum 2 h incubation of samples in reaction mixture was required for completion of the reaction. The stability of the HVA dimer as reaction product was found to be > 12 h. The method was validated by using known concentrations of H2O2 and catalase enzyme that quenches H2O2 as substrate. This method can be used efficiently to determine more accurate tissue H2O2 level without using internal standard and multiple samples can be processed at a time with additional low cost reagents such as TCA and K2HPO4.

Titania nanotube-modified screen printed carbon electrodes enhance the sensitivity in the electrochemical detection of proteins

The use of titania nanotubes (TiO2-NT) as the working electrode provides a substantial improvement in the electrochemical detection of proteins. A biosensor designed using this strategy provided a robust method to detect protein samples at very low concentrations (C-protein ca 1 ng/mu l). Reproducible measurements on protein samples at this concentration (I-p,I-a of 80 +/- 1.2 mu A) could be achieved using a sample volume of ca 30 mu l. We demonstrate the feasibility of this strategy for the accurate detection of penicillin binding protein, PBP2a, a marker for methicillin resistant Staphylococcus aureus (MRSA). The selectivity and efficiency of this sensor were also validated using other diverse protein preparations such as a recombinant protein tyrosine phosphatase (PTP10D) and bovine serum albumin (BSA). This electrochemical method also presents a substantial improvement in the time taken (few minutes) when compared to conventional enzyme-linked immunosorbent assay (ELISA) protocols. It is envisaged that this sensor could substantially aid in the rapid diagnosis of bacterial infections in resource strapped environments. (C) 2014 Elsevier B.V. All rights reserved.

Length-scale dependent mechanical properties of Al-Cu eutectic alloy: Molecular dynamics based model and its experimental verification

This paper attempts to gain an understanding of the effect of lamellar length scale on the mechanical properties of two-phase metal-intermetallic eutectic structure. We first develop a molecular dynamics model for the in-situ grown eutectic interface followed by a model of deformation of Al-Al2Cu lamellar eutectic. Leveraging the insights obtained from the simulation on the behaviour of dislocations at different length scales of the eutectic, we present and explain the experimental results on Al-Al2Cu eutectic with various different lamellar spacing. The physics behind the mechanism is further quantified with help of atomic level energy model for different length scale as well as different strain. An atomic level energy partitioning of the lamellae and the interface regions reveals that the energy of the lamellae core are accumulated more due to dislocations irrespective of the length-scale. Whereas the energy of the interface is accumulated more due to dislocations when the length-scale is smaller, but the trend is reversed when the length-scale is large beyond a critical size of about 80 nm. (C) 2014 Author(s).

Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will change significantly due to modifications in hydro-climatic variables such as rainfall, reservoir inflows, temperature, net radiation, wind speed and humidity. An integrated regional water resources management model should capture the likely impacts of climate change on water demands and water availability along with uncertainties associated with climate change impacts and with management goals and objectives under non-stationary conditions. Uncertainties in an integrated regional water resources management model, accumulating from various stages of decision making include climate model and scenario uncertainty in the hydro-climatic impact assessment, uncertainty due to conflicting interests of the water users and uncertainty due to inherent variability of the reservoir inflows. This paper presents an integrated regional water resources management modeling approach considering uncertainties at various stages of decision making by an integration of a hydro-climatic variable projection model, a water demand quantification model, a water quantity management model and a water quality control model. Modeling tools of canonical correlation analysis, stochastic dynamic programming and fuzzy optimization are used in an integrated framework, in the approach presented here. The proposed modeling approach is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India.

Generalized scaling of misorientation angle distributions at meso-scale in deformed materials

Scaling behaviour has been observed at mesoscopic level irrespective of crystal structure, type of boundary and operative micro-mechanisms like slip and twinning. The presence of scaling at the meso-scale accompanied with that at the nano-scale clearly demonstrates the intrinsic spanning for different deformation processes and a true universal nature of scaling. The origin of a 1/2 power law in deformation of crystalline materials in terms of misorientation proportional to square root of strain is attributed to importance of interfaces in deformation processes. It is proposed that materials existing in three dimensional Euclidean spaces accommodate plastic deformation by one dimensional dislocations and their interaction with two dimensional interfaces at different length scales. This gives rise to a 1/2 power law scaling in materials. This intrinsic relationship can be incorporated in crystal plasticity models that aim to span different length and time scales to predict the deformation response of crystalline materials accurately.