Formation of acrylamide in microwave-roasted sorghum and associated dietary risk

The effect of microwave roasting parameters (300, 450 and 600 W; 5, 10 and 15 min) on acrylamide content in sorghum grain was determined using High Pressure Liquid Chromatography (HPLC)-photo diode array (PDA) detector coupled with C-18 column. Samples roasted at 300 and 450 W did not possess acrylamide, whereas 600 W (15 min) favoured formation of 2740.19 µg/kg of acrylamide, levels far exceeding the defined European Union (EU) limits. The chronic daily intake (CDI) for acrylamide through consumption of such grain flour was 3.25–9.5-fold higher to Joint FAO/WHO Expert Committee on Food Additives (JECFA) defined high exposure limits. The margin of exposure (MOE) values ranged from 4.3 to 12.76 and from 11.07 to 32.27 for neoplastic and neurological effects, respectively, demonstrating high exposure and serious health concerns associated with dietary intake of this toxicant. This study assesses the risk for the Indian population and highlights the importance of optimising process parameters for food product to minimise such exposure risks.     

Neurocomputing strategies in structural design — on analysing weights of feedforward neural networks

The sequel of two papers explores the applicability of selected neurocomputing strategies in the optimization of structural systems. The present paper describes the use of interconnection weights of a multilayer, feedforward neural network to extract information pertinent to a design space modelled by such a network. It is shown that aweights analysis provides a technique to assess the effect of all input quantities on a given output. Such dependencies are expressed in the form of atransition matrix, and their evaluation is reduced to the inspection of elements of a matrix row. Explicit formulae are derived for networks with one and two hidden layers and can easily be generalized to networks with an arbitrary number of hidden layers. In addition to its use as a tool to partition design spaces, the weights analysis may be employed to assist in determining the size of hidden layers and an adequate number of training patterns (input-output pairs). Several numerical examples from the field of structural analysis are provided, and the paper underscores the utility of the present technique in decomposition driven optimal design; such optimization is treated in full in the companion paper.     

Thermal/structural dynamic analysis via approximate analytical approach

The present paper uses a classical Galerkin weighted residual formulation to obtain the approximate analytical solution of a thermally loaded beam executing free flexural vibrations. The approach used is one where the time variable is considered in the same manner as the spatial variable, and is included in the basis functions. The basis functions used in the approach are polynomials obtained from the terms of a power series, with the condition of nullity on the boundary. This choice simplifies the algebraic manipulations considerably and yields close form expressions for components of the system matrix. The latter also simplifies the numerical computation of coefficients of the approximating polynomial. The approach provides benefits in terms of increased accuracy and lower computational costs.     

Scientific basis for establishing country greenhouse gas estimates for rice-based agriculture: An Indian case study

A comprehensive scientific assessment of CH₄ budget estimation for Indian rice paddies, based on a decade of measurements in India, is presented. Indian paddy cultivation areas contain soils that have low to medium levels of soil organic carbon. The average seasonally integrated CH₄ flux (E _sif) values calculated from these measurements were 15.3 ± 2.6 g m^–2 for continuously flooded (CF), 6.9 ± 4.3 g m^–2 for intermittently flooded (IF) single aeration (SA) and 2.2 ± 1.5 g m^–2 for IF multiple aeration (MA) rice ecosystems. For CF and IF (MA) rice ecosystems having high soil organic carbon, without organic amendments, the CH₄ flux (E _sif) may be increased by 1.7 times relative to low soil organic carbon, whereas it may enhance by 5.3 for CF if amended organically. Organic amendment and high soil organic carbon paddy areas do not alter the methane budget estimates for India (3.6±1.4 TgY^–1) much, due to their small paddy harvested area. Methane estimated using average emission factors (E _sif) for all paddy water regimes, which include harvested areas having soils with high organic carbon and organic amendments, may give a budget of 5 TgY^–1 for India.     

Optimization of process parameters affecting surface roughness in magnetic abrasive finishing process

Development in manufacturing technology enhances the mechanical behavior of machined parts and improves the surface finish with high precision, which conveys the progressive importance of magnetic abrasive finishing (MAF) process. In current research work, magnetic abrasive particles were used as finishing tools during the MAF process. However, these magnetic abrasives are fabricated by special techniques, i.e., the adhesive bonding-based method, the sintering method, the plasma-based method and so on. The present study explores the basic finishing characteristics of the magnetic abrasive produced by the sintering process. After the sintering process, improved quality of magnetic abrasives was obtained, where the abrasive particle sticks on the base metal matrix. The abrasive particle used is alumina powder and the magnetic particle is iron powder. Experiments were performed on Stainless Steel 202 to inspect the sound effects of several process parameters such as rotational speed, electromagnet voltage, machining gap and abrasive particle size on machining performance. Apart from that, surface roughness was also measured, which revealed the influence of the abrasive particle on the machined surface in terms of surface finish. It is observed from this study that appropriate size of magnetic abrasive particle optimizes the surface finish.     

On the use of energy minimization for CA based analysis in elasticity

There has been recent interest in exploring alternative computational models for structural analysis that are better suited for a design environment requiring repetitive analysis. The need for such models is brought about by significant increases in computer processing speeds, realized primarily through parallel processing. To take full advantage of such parallel machines, however, the computational approach itself must be revisited from a totally different perspective; parallelization of inherently serial paradigms is subject to limitations introduced by a requirement of information coordination. The cellular automata (CA) model of decentralized computations provides one such approach which is ideally tailored for parallel computers. The proposed paper examines the applicability of the cellular automata model in problems of 2-D elasticity. The focus of the paper is in the use of a genetic algorithm based optimization process to derive the rules for local interaction required in evolving the cellular automata. Received August 28, 2000     

Decomposition-based design optimization method using genetic co-evolution

The paper examines new strategies for the implementation of genetic algorithms in a decomposition-based approach for design. In this approach, the design problem is decomposed into smaller-sized sub-problems, the solutions for which are obtained through co-evolution. The emphasis resides in evaluating methods for exchanging design information relevant to coordinating solutions of temporarily decoupled sub-problems. Methods based on modification of genetic makeup through experiential inheritance (exposure to another species), and through inter-species migration are investigated in this work. Different forms of design problem coupling are investigated, ranging from coupling through constraints only, to coupled objective and constraint functions. The proposed strategies are validated through implementation in representative algebraic and structural design problems.     

Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.