Antimicrobial Properties of Nanostructured TiO2 Plus Fe Additive Thin Films Synthesized by a Cost-Effective Sol-Gel Process

We prepared thin films composed of pure TiO2 or TiO2 with an Fe additive (at concentrations of 0.2-0.8 wt%) via a simple and cost effective sol gel process, and tested their antifungal properties (against Candida albicans (MTCC-1637), Candida tropicalis (MTCC-184), Candida parapsilosis (MTCC-2509), and Candida glabrata (MTCC-3019) and antibacterial properties (against Staphylococcus faecalis (NCIM-2604) Staphylococcus epidermidis (NCIM-2493), Staphylococcus aureus (NCIL-2122), and Bacillus subtilis (NCIM-2549)). The films were deposited on glass and Si substrates and subjected to annealing at 400 degrees C for 3 h in ambient air. The film structural and morphological properties were investigated by X-ray photoelectron spectroscopy profilometry and scanning electron microscopy, respectively. Antifungal and antibacterial tests were conducted using the drop test method. Among the species examined, Candida albicans (MTCC-1637), and Staphylococcus aureus (NCIL-2122) showed complete colony formation inhibition after exposure for 4 h for the TiO2 loaded with 0.8 wt% Fe thin films. These results indicate that increasing the Fe concentration increased the antimicrobial activity, with complete inhibition of colony formation after 4 h exposure.

Prediction of extension test results of soft cemented clays with a simple constitutive model

In the present paper, the constitutive model is proposed for cemented soils, in which the cementation component and frictional component are treated separately and then added together to get overall response. The modified Cam clay is used to predict the frictional resistance and an elasto-plastic strain softening model is proposed for the cementation component. The rectangular isotropic yield curve proposed by Vatsala (1995) for the bond component has been modified in order to account for the anisotropy generally observed in the case of natural soft cemented soils. In this paper, the model proposed is used to predict the experimental results of extension tests on the soft cemented soils whereas compression test results are presented elsewhere. The model predictions compare quite satisfactorily with the observed response. A few input parameters are required which are well defined and easily determinable and the model uses associated flow rule.

Optical, electrical and structural characterization of ZnO:Al thin films prepared by a low cost sol-gel method

ZnO:Al thin films were prepared on glass and silicon substrates by the sol-gel spin coating method. The x-ray diffraction (XRD) results showed that a polycrystalline phase with a hexagonal structure appeared after annealing at 400 degrees C for 1 h. The transmittance increased from 91 to about 93% from pure ZnO films to ZnO film doped with 1 wt% Al and then decreased for 2 wt% Al. The optical band gap energy increased as the doping concentration was increased from 0.5 wt% to 1 wt% Al. The metal oxide semiconductor (MOS) capacitors were fabricated using ZnO films deposited on silicon (100) substrates and electrical properties such as current versus voltage (I-V) and capacitance versus voltage (C-V) characteristics were studied. The electrical resistivity decreased and the leakage current increased with an increase of annealing temperature. The dielectric constant was found to be 3.12 measured at 1 MHz. The dissipation value for the film annealed at 300 degrees C was found to be 3.1 at 5 V. (C) 2011 Elsevier Ltd. All rights reserved.

Low cost calibrated mechanical noisemaker for hearing screening of neonates in resource constrained settings

Background & objectives: There is a need to develop an affordable and reliable tool for hearing screening of neonates in resource constrained, medically underserved areas of developing nations. This study valuates a strategy of health worker based screening of neonates using a low cost mechanical calibrated noisemaker followed up with parental monitoring of age appropriate auditory milestones for detecting severe-profound hearing impairment in infants by 6 months of age. Methods: A trained health worker under the supervision of a qualified audiologist screened 425 neonates of whom 20 had confirmed severe-profound hearing impairment. Mechanical calibrated noisemakers of 50, 60, 70 and 80 dB (A) were used to elicit the behavioural responses. The parents of screened neonates were instructed to monitor the normal language and auditory milestones till 6 months of age. This strategy was validated against the reference standard consisting of a battery of tests - namely, auditory brain stem response (ABR), otoacoustic emissions (OAE) and behavioural assessment at 2 years of age. Bayesian prevalence weighted measures of screening were calculated. Results: The sensitivity and specificity was high with least false positive referrals for. 70 and 80 dB (A) noisemakers. All the noisemakers had 100 per cent negative predictive value. 70 and 80 dB (A) noisemakers had high positive likelihood ratios of 19 and 34, respectively. The probability differences for pre- and post- test positive was 43 and 58 for 70 and 80 dB (A) noisemakers, respectively. Interpretation & conclusions: In a controlled setting, health workers with primary education can be trained to use a mechanical calibrated noisemaker made of locally available material to reliably screen for severe-profound hearing loss in neonates. The monitoring of auditory responses could be done by informed parents. Multi-centre field trials of this strategy need to be carried out to examine the feasibility of community health care workers using it in resource constrained settings of developing nations to implement an effective national neonatal hearing screening programme.

Prediction of inter-laminar stresses in composite honeycomb sandwich panels under mechanical loading using Variational Asymptotic Method

This work focuses on the formulation of an asymptotically correct theory for symmetric composite honeycomb sandwich plate structures. In these panels, transverse stresses tremendously influence design. The conventional 2-D finite elements cannot predict the thickness-wise distributions of transverse shear or normal stresses and 3-D displacements. Unfortunately, the use of the more accurate three-dimensional finite elements is computationally prohibitive. The development of the present theory is based on the Variational Asymptotic Method (VAM). Its unique features are the identification and utilization of additional small parameters associated with the anisotropy and non-homogeneity of composite sandwich plate structures. These parameters are ratios of smallness of the thickness of both facial layers to that of the core and smallness of 3-D stiffness coefficients of the core to that of the face sheets. Finally, anisotropy in the core and face sheets is addressed by the small parameters within the 3-D stiffness matrices. Numerical results are illustrated for several sample problems. The 3-D responses recovered using VAM-based model are obtained in a much more computationally efficient manner than, and are in agreement with, those of available 3-D elasticity solutions and 3-D FE solutions of MSC NASTRAN. (c) 2012 Elsevier Ltd. All rights reserved.

Northeast monsoon over India: variability and prediction

South peninsular India experiences a large portion of the annual rainfall during the northeast monsoon season (October to December). In this study, the facets of diurnal, intra-seasonal and inter-annual variability of the northeast monsoon rainfall (the NEMR) over India have been examined. The analysis of satellite derived hourly rainfall reveals that there are distinct features of diurnal variation over the land and oceans during the season. Over the land, rainfall peaks during the late afternoon/evening, while over the oceans an early morning peak is observed. The harmonic analysis of hourly data reveals that the amplitude and variance are the largest over south peninsular India. The NEMR also exhibits significant intra-seasonal variability on a 20-40 day time scale. Analysis also shows significant northward propagation of the maximum cloud zone from south of equator to the south peninsula during the season. The NEMR exhibits large inter-annual variability with the co-efficient of variation (CV) of 25%. The positive phases of ENSO and the Indian Ocean Dipole (IOD) are conducive for normal to above normal rainfall activity during the northeast monsoon. There are multi-decadal variations in the statistical relationship between ENSO and the NEMR. During the period 2001-2010 the statistical relationship between ENSO and the NEMR has significantly weakened. The analysis of seasonal rainfall hindcasts for the period 1960-2005 produced by the state-of-the-art coupled climate models, ENSEMBLES, reveals that the coupled models have very poor skill in predicting the inter-annual variability of the NEMR. This is mainly due to the inability of the ENSEMBLES models to simulate the positive relationship between ENSO and the NEMR correctly. Copyright (C) 2012 Royal Meteorological Society

Influence of Sn doping on structural, optical and electrical properties of ZnO thin films prepared by cost effective sol-gel process

Tin (Sn) doped zinc oxide (ZnO) thin films were synthesized by sol-gel spin coating method using zinc acetate di-hydrate and tin chloride di-hydrate as the precursor materials. The films were deposited on glass and silicon substrates and annealed at different temperatures in air ambient. The agglomeration of grains was observed by the addition of Sn in ZnO film with an average grain size of 60 nm. The optical properties of the films were studied using UV-VIS-NIR spectrophotometer. The optical band gap energies were estimated at different concentrations of Sn. The MOS capacitors were fabricated using Sn doped ZnO films. The capacitance-voltage (C-V), dissipation vs. voltage (D-V) and current-voltage (I-V) characteristics were studied and the electrical resistivity and dielectric constant were estimated. The porosity and surface area of the films were increased with the doping of Sn which makes these films suitable for opto-electronic applications. (C) 2012 Elsevier B.V. All rights reserved.

Polymer thin film structures for ultra-low cost biosensing

Reflectance change due to binding of molecules on thin film structures has been exploited for bio-molecular sensing by several groups due to its potential in the development of sensitive, low cost, easy to fabricate, large area sensors with high multiplexing capabilities. However, all of these sensing platforms have been developed using traditional semiconductor materials and processing techniques, which are expensive. This article presents a method to fabricate disposable thin film reflectance biosensors using polymers, such as polycarbonate, which are 2-3 orders of magnitude cheaper than conventional semiconductor and dielectric materials and can be processed by alternate low cost methods, leading to significant reduction in consumable costs associated with diagnostic biosensing. (C) 2011 Elsevier GmbH. All rights reserved.

Rationalization and prediction of drug resistant mutations in targets for clinical anti-tubercular drugs

Resistance to therapy limits the effectiveness of drug treatment in many diseases. Drug resistance can be considered as a successful outcome of the bacterial struggle to survive in the hostile environment of a drug-exposed cell. An important mechanism by which bacteria acquire drug resistance is through mutations in the drug target. Drug resistant strains (multi-drug resistant and extensively drug resistant) of Mycobacterium tuberculosis are being identified at alarming rates, increasing the global burden of tuberculosis. An understanding of the nature of mutations in different drug targets and how they achieve resistance is therefore important. An objective of this study is to first decipher sequence as well as structural bases for the observed resistance in known drug resistant mutants and then to predict positions in each target that are more prone to acquiring drug resistant mutations. A curated database containing hundreds of mutations in the 38 drug targets of nine major clinical drugs, associated with resistance is studied here. Mutations have been classified into those that occur in the binding site itself, those that occur in residues interacting with the binding site and those that occur in outer zones. Structural models of the wild type and mutant forms of the target proteins have been analysed to seek explanations for reduction in drug binding. Stability analysis of an entire array of 19 mutations at each of the residues for each target has been computed using structural models. Conservation indices of individual residues, binding sites and whole proteins are computed based on sequence conservation analysis of the target proteins. The analyses lead to insights about which positions in the polypeptide chain have a higher propensity to acquire drug resistant mutations. Thus critical insights can be obtained about the effect of mutations on drug binding, in terms of which amino acid positions and therefore which interactions should not be heavily relied upon, which in turn can be translated into guidelines for modifying the existing drugs as well as for designing new drugs. The methodology can serve as a general framework to study drug resistant mutants in other micro-organisms as well.

Prediction of Shallow Landslide prone regions in Undulating Terrains

Genetic Algorithm for Rule-set Prediction (GARP) and Support Vector Machine (SVM) with free and open source software (FOSS) - Open Modeller were used to model the probable landslide occurrence points. Environmental layers such as aspect, digital elevation, flow accumulation, flow direction, slope, land cover, compound topographic index and precipitation have been used in modeling. Simulated output of these techniques is validated with the actual landslide occurrence points, which showed 92% (GARP) and 96% (SVM) accuracy considering precipitation in the wettest month and 91% and 94% accuracy considering precipitation in the wettest quarter of the year.

HD resolution intra prediction architecture for H.264 decoder

High performance video standards use prediction techniques to achieve high picture quality at low bit rates. The type of prediction decides the bit rates and the image quality. Intra Prediction achieves high video quality with significant reduction in bit rate. This paper presents novel area optimized architecture for Intra prediction of H.264 decoding at HDTV resolution. The architecture has been validated on a Xilinx Virtex-5 FPGA based platform and achieved a frame rate of 64 fps. The architecture is based on multi-level memory hierarchy to reduce latency and ensure optimum resources utilization. It removes redundancy by reusing same functional blocks across different modes. The proposed architecture uses only 13% of the total LUTs available on the Xilinx FPGA XC5VLX50T.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Position control of DC motors with experience mapping based prediction controller

The paper presents a new controller inspired by the human experience based, voluntary body action control (dubbed motor control) learning mechanism. The controller is called Experience Mapping based Prediction Controller (EMPC). EMPC is designed with auto-learning features without the need for the plant model. The core of the controller is formed around the motor action prediction-control mechanism of humans based on past experiential learning with the ability to adapt to environmental changes intelligently. EMPC is utilized for high precision position control of DC motors. The simulation results are presented to show that accurate position control is achieved using EMPC for step and dynamic demands. The performance of EMPC is compared with conventional PD controller and MRAC based position controller under different system conditions. Position Control using EMPC is practically implemented and the results are presented.