Optimisation of immobilisation conditions for chick pea β-galactosidase (CpGAL) to alkylamine glass using response surface methodology and its applications in lactose hydrolysis

Response surface methodology was advantageously used to optimally immobilise a β-galactosidase from chick pea onto alkylamine glass using Box–Behnken experimental design, resulting in an overall 91% immobilisation efficiency. Analysis of variance was performed to determine the adequacy and significance of the quadratic model. Immobilised enzyme showed a shift in the optimum pH; however, optimum temperature remained unaffected. Thermal denaturation kinetics demonstrated significant improvement in thermal stability of the enzyme after immobilisation. Galactose competitively inhibits the enzyme in both soluble and immobilised conditions. Lactose in milk whey was hydrolysed at comparatively higher rate than that of milk. Immobilised enzyme showed excellent reusability with retention of more than 82% enzymatic activity after 15 uses. The immobilised enzyme was found to be fairly stable in both dry and wet conditions for three months with retention of more than 80% residual activity.     

Surfactant controlled lubricity of paraffin oil-based nano-emulsion

Oil-in-water emulsions are used as metalworking fluids (MWFs). Considering the environmental issues and operator's health, the amount of oil-in-MWFs is one of the important parameters. In the present study, paraffin oil is used for emulsion formulation and possibilities to minimize the amount of paraffin oil-in-MWFs are explored. Physical characterization of emulsions is done using dynamic light scattering and goniometer. The tribological response of emulsions is recorded using the Four-ball tester. Results indicate that the amount of oil can be brought down to a level of 0.5% v/v to achieve the standard friction level. Droplet size and distribution seems to control the lubricity of emulsions, smaller droplets are desired for better lubrication.     

Multiple sourcing decisions using integrated AHP and knapsack model: a case on carton sourcing

Purchase allocation is a multi criteria decision making (MCDM) problem. Multitude of qualitative and quantitative factors is involved in the multiple sourcing decisions. Analytic hierarchy process (AHP) has widely been used to find out the relative rankings of suppliers. AHP can be combined with regular supplier quantitative audit process. In classical AHP, decision maker (DM) has to pair wise compare suppliers for each factor, whereas the proposed audit based simplified AHP will remove the complexity of comparison. Quarterly audit-based AHP rankings and supplier performance probability products can be used in place of revenues in the backward recursive resource allocation knapsack model. This combined model will decompose purchase allocation problem into different stages and combine one supplier at each stage and provide the optimum and feasible solution in the end. Solution at each stage is also a feasible option. This model is only applicable when the total order quantity and the capacity of all suppliers are integer multiples of economic or minimum order quantity. This integrated model thus provides number of orders/supplier.     

CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS

The problem of obtaining a single “consensus” clustering solution from a multitude or ensemble of clusterings of a set of objects, has attracted much interest recently because of its numerous practical applications. While a wide variety of approaches including graph partitioning, maximum likelihood, genetic algorithms, and voting-merging have been proposed so far to solve this problem, virtually all of them work on hard partitionings, i.e., where an object is a member of exactly one cluster in any individual solution. However, many clustering algorithms such as fuzzy c-means naturally output soft partitionings of data, and forcibly hardening these partitions before applying a consensus method potentially involves loss of valuable information. In this article we propose several consensus algorithms that can be applied directly to soft clusterings. Experimental results over a variety of real-life datasets are also provided to show that using soft clusterings as input does offer significant advantages, especially when dealing with vertically partitioned data.     

Novel Approach of Key Predistribution for Grid Based Sensor Networks

Wireless Personal Communications - Grid based sensor networks are significant for applications such as monitoring goods in a warehouse, studying traffic level of city streets, monitoring energy...     

First-Principles Investigation of Electronic Properties of GaAsxSb1 –x Ternary Alloys

Semiconductors - Compositional variations in GaAs based ternary alloys have exhibited wide range alterations in electronic properties. In the present paper, first-principles study of...